ANALISIS TRIWULANAN: Perkembangan Moneter, Perbankan dan Sistem Pembayaran, Triwulan II - 2007

SUSUNAN PENGURUS BULETIN EKONOMI MONETER DAN PERBANKAN Departemen Riset Kebanksentralan Bank Indonesia Pelindung Dewan Gubernur Bank Indonesia Dewan Editor Prof. Dr. Anwar Nasution Prof. Dr. Miranda S. Goeltom Prof. Dr. Insukindro Prof. Dr. Iwan Jaya Azis Prof. Iftekhar Hasan Prof. Dr. Masaaki Komatsu Dr. M. Syamsuddin Dr. Perry Warjiyo Dr. Iskandar Simorangkir Dr. Solikin M. Juhro Dr. Haris Munandar Dr. M. Edhie Purnawan Dr. Burhanuddin Abdullah Dr. Andi M. Alfian Parewangi Pimpinan Editorial Dr. Perry Warjiyo Editor Pelaksana Dr. Solikin M. Juhro Dr. Andi M. Alfian Parewangi Sekretariat Ir. Triatmo Doriyanto, M.S Nurhemi, S.E., M.A

Buletin ini diterbitkan oleh Bank Indonesia, Departemen Riset Kebanksentralan. Isi dan hasil penelitian dalam tulisan-tulisan di buletin ini sepenuhnya tanggungjawab para penulis dan bukan merupakan pandangan resmi Bank Indonesia. Kami mengundang semua pihak untuk menulis pada buletin ini paper dikirimkan dalam bentuk file ke Departemen Riset Kebanksentralan, Bank Indonesia, Menara Sjafruddin Prawiranegara Lt. 21; Jl. M.H. Thamrin No. 2, Jakarta Pusat, email : [email protected] Buletin ini diterbitkan secara triwulan pada bulan April, Juli, Oktober dan Januari, bagi yang ingin memperoleh terbitan ini dapat menghubungi Unit Diseminasi – Divisi Diseminasi Statistik dan Manajemen Intern, Departemen Statistik, Bank Indonesia, Menara Sjafruddin Prawiranegara Lt. 2; Jl. M.H. Thamrin No. 2, Jakarta Pusat, telp. (021) 2981-8206. Untuk permohonan berlangganan: telp. (021) 2981-6571, fax. (021) 3501912.

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BULETIN EKONOMI MONETER DAN PERBANKAN

Volume 19, Nomor 4, April 2017

QUARTERLY OUTLOOK ON: Monetary, Banking, And Payment System In Indonesia: Quarter I, 2017 Bambang Pramono, Syachman Perdymer, Handri Adiwilaga, Nurkholisoh Ibnu Aman, Rio Khasananda, Saraswati, Illinia A. Riyadi Financial Intermediation Sector In Indonesia’s Production Pyramid Martin P.H. Panggabean

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385

Tri-Cycles Analysis On Bank Performance: Panel Var Approach Denny Irawan and Febrio Kacarib

403

What Protect Emerging Markets From Developed Countries Unconventional Monetary Policy Spillover? Eko Sumando

443

Indonesia’s Fdi – Exports – Gdp Growth Nexus: Trade Or Investment - Driven? Panky Tri Febiyansah

467

Halaman ini sengaja dikosongkan

QUARTERLY OUTLOOK ON : Monetary, Banking, and Payment System In Indonesia: Quarter I, 2017

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QUARTERLY OUTLOOK ON MONETARY, BANKING, AND PAYMENT SYSTEM IN INDONESIA: QUARTER I, 2017 Bambang Pramono, Syachman Perdymer, Handri Adiwilaga, Nurkholisoh Ibnu Aman, Rio Khasananda, Saraswati, Illinia A. Riyadi1 Abstract Globally, the economy continues to recover. The economic growth in AS increases supported by solid consumption and increase on non-residential investment, as well as the economy of Tiongkok, supported by private investment and better export performance. European economy also better off with stronger consumption and export, and the reduction of geopolitical risk post the presidential election in France. The economy in Japan also increases supported by stronger domestic and export demand. This global trend supports the growth in Indonesia that rises to the level of 5,01% (yoy), with the pillars of export performance, better global demand and commodity prices, as well as higher government expenditure – particularly on investment – and the household consumption. Spatially, the national growth was mainly from Java and Kalimantan due to their better export performance. Inflation increases slightly particularly related to price regulation implemented in early 2017. Spatially, inflation occurs in most area except Sumatera who recorded deflation. The balance of payment recorded a surplus arisen from financial and capital surplus of 7.9 milliard dolar AS. However, the current account recorded deficit due to the deficit of oil trade balance and primary income. The reserve increases to 121.8 miliar dolar AS, accompanied with stronger Rupiah with lower volatility relative to peer countries. Following the monetary ease on previous Quarter IV, 2016, the monetary transmission is better yet not optimal due to the prudent practice of the bank on allocating credit. The interest rate decreases reflected on daily PUAB O/N reduction by 7 point to 4.23%. The deposit rate also decreases as well as the lending rate with larger decrease. Looking forward, the growth in 2017 will be higher than 2016 on the range of 5.0 – 5.4%, while inflation will be around the target of 4 + 1%. We need to anticipate the impact of Fed Fund Rate increase, the lower of FED balance, and the trade and fiscal US policy, as well as the geopolitical dynamics across regions particularly in Korean Bay. Bank Indonesia will keep strengthening his policy mix and macroprudential, and his coordination with the government to ensure the inflation control, greater stimulus for growth, and the implementation of structural reform run on the right track, and hence preserve the sustainable economic development. Keywords: Macroeconomy, monetary, economic outlook. JEL Classification: C53, E66, F01, F41 1 Authors are researcher on Monetary and Economic Policy Department (DKEM). Bambang Pramono ([email protected]); Syachman Perdymer ([email protected]); Handri Adiwilaga ([email protected]); Nurkholisoh Ibnu Aman ([email protected]); Rio Khasananda ([email protected]); Saraswati ([email protected]); Illinia Ayudhia Riyadi ([email protected]).

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I. PERKEMBANGAN GLOBAL Pertumbuhan ekonomi dunia diperkirakan membaik, meskipun beberapa risiko tetap perlu dicermati. Peningkatan prospek ekonomi dunia ditopang oleh meningkatnya pertumbuhan ekonomi di AS, Tiongkok, Eropa dan Jepang. Perekonomian di AS didukung oleh konsumsi yang solid serta peningkatan investasi nonresidensial. Di Tiongkok, perekonomian tumbuh lebih baik dengan meningkatnya kegiatan investasi swasta dan perbaikan ekspor. Di Eropa, pertumbuhan ekonomi didorong oleh meningkatnya kinerja sektor manufaktur sejalan dengan perbaikan konsumsi dan ekspor, serta telah menurunnya risiko geopolitik pasca Pemilihan Presiden di Perancis. Di Jepang, kenaikan permintaan domestik dan ekspor telah mendorong perbaikan pertumbuhan ekonomi di negara tersebut. Sejalan dengan perbaikan pertumbuhan ekonomi dunia tersebut, volume perdagangan dunia dan harga komoditas non migas mengalami peningkatan. Ke depan, sejumlah risiko terhadap perekonomian global tetap perlu diwaspadai, antara lain kenaikan Fed Fund Rate, kebijakan fiskal dan perdagangan serta penurunan besaran neraca bank sentral AS, dan perkembangan geopolitik di beberapa kawasan, khususnya di Semenanjung Korea. Meningkatnya kinerja perekonomian di AS didukung oleh konsumsi yang solid serta peningkatan investasi nonresidensial. Konsumsi AS yang solid tercermin dari pertumbuhan konsumsi pada triwulan I 2017 yang masih cukup kuat yaitu sebesar 2,8% (yoy) (Grafik 1). Pertumbuhan konsumsi melanjutkan kinerja positif selama setahun terakhir yang tumbuh di kisaran 2,7%-3,1% (yoy). Pertumbuhan konsumsi yang solid tersebut didukung oleh kondisi ketenagakerjaan yang membaik, tercermin dari menurunnya tingkat pengangguran dan meningkatnya pertumbuhan upah.

% (SAAR)

% (yoy, SA)

%

5

4,5

4,5

4,0

4

3,5

3,5 3

2,5 2,0

2

0,43

8

0

0,27

4

-0,07

1,5

1,5

1,0

1 0,5

12

0,76

3,0

2,5

%

2

3,6

3,7

4,6

2,4

2,0

2,7

2,3

1,6

4,3

3,0

3,5

0,3

0

0,5 0,0

-2

Mei Jul Sep Nov Jan Mar Mei Jul Sep Nov Jan Mar Mei Jul Sep Nov Jan Mar

2014

2015

2016

2017

Konsumsi Riil (qtq, SAAR)

Konsumsi Riil (yoy, skala kanan)

Penjualan Ritel Riil (yoy, skala kanan)

Pendapatan Riil Rata-rata (yoy, RHS)

0

Investasi Nonresidensial (Kontribusi Tahunan) Investasi Nonresidensial (Kontribusi) Investasi Nonresidensial (yoy) I

‘13 ‘14 ‘15 ‘16

II

III IV

2013

I

II

III IV

2014

I

II

III IV

2015

-4 I

II

2016

Sumber: BEA, FRED, Bloomberg, diolah

Sumber: BEA, FRED, Bloomberg, diolah

Grafik 1. Konsumsi, Penjualan Ritel, dan Pendapatan

III IV

Grafik 2. Investasi Nonresidensial AS

I

2017

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357

Selain itu, investasi nonresidensial AS juga meningkat, antara lain tercermin dari pertumbuhan positif belanja konstruksi nonresidensial (7,5%, yoy), dan pertumbuhan pengiriman barang modal yang cukup solid (2,5%) dan melanjutkan tren positif (Grafik 2). Investasi nonresidensial diperkirakan tetap kuat mendukung investasi pada 2017, terutama dari sektor energi seiring dengan harga minyak yang diperkirakan masih tinggi. Perekonomian Tiongkok tumbuh lebih baik dengan meningkatnya investasi swasta dan perbaikan ekspor. Perekonomian Tiongkok pada triwulan I 2017 tumbuh sebesar 6,9% (yoy), lebih baik dibanding ekspektasi dan pertumbuhan pada triwulan sebelumnya yang tercatat 6,8% (yoy). Pada triwulan I 2017, kinerja investasi membaik, didorong oleh investasi swasta yang berkontribusi sebesar 70% terhadap investasi agregat. Investasi swasta melanjutkan pemulihan pada triwulan I 2017 dengan pertumbuhan sebesar 7,7% (yoy), mengimbangi perlambatan investasi Pemerintah (Grafik 3). Meningkatnya pertumbuhan ekonomi Tiongkok juga didukung oleh membaiknya kinerja sektor eksternal, tercermin dari ekspor dan impor triwulan I 2017 yang tumbuh pesat sejalan dengan pulihnya permintaan global dan reflasi harga komoditas (Grafik 4). Pada triwulan I 2017, pertumbuhan ekspor Tiongkok tercatat sebesar 22,3% (yoy), lebih tinggi dibandingkan pertumbuhan pada triwulan IV 2016 yang hanya sebesar 0,6% (yoy). Pertumbuhan ekspor tersebut sejalan dengan pulihnya permintaan global. Ke depan, pulihnya permintaan global diperkirakan mendukung kinerja perdagangan Tiongkok.

%

Miliar Dolar AS

30

80

25

60

%, yoy 100

Neraca Perdagangan Ekspor yoy (rhs) Impor yoy (rhs) 50

40

20 13.600 15

13.800

10

7,7

5

6,9

Investasi Swasta, YTD yoy Investasi Pemerintah, YTD yoy

23,92

20

16,4 0

20,3

-20 -50

-40

0 Jul Okt Jan Apr Jul Okt Jan Apr Jul Okt Jan Apr Jul Okt Jan Apr Jul Okt Jan Apr

2012

2013

2014

2015

2016

2017

Sumber: Bloomberg

Grafik 3. Perkembangan Investasi Pemerintah dan Swasta Tiongkok

0

DesMarJun Sep DesMarJun Sep DesMarJun Sep DesMarJun Sep DesMarJun Sep DesMar Jun Sep Des Mar

2010 2011

2012

2013

2014

2015

2016 2017

Sumber: Bloomberg

Grafik 4. Perkembangan Kinerja Perdagangan Tiongkok

Sementara itu, pertumbuhan ekonomi Eropa didukung oleh meningkatnya kinerja sektor manufaktur sejalan dengan perbaikan konsumsi dan ekspor, serta telah menurunnya risiko geopolitik pasca Pemilihan Presiden di Perancis. Sektor industri dan manufaktur Eropa pada triwulan I 2017 menunjukkan optimisme, tercermin dari perkembangan indeks PMI manufaktur

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Buletin Ekonomi Moneter dan Perbankan, Volume 19, Nomor 4, April 2017

yang masih melanjutkan periode ekspansinya dan mencapai level tertinggi sejak 2011 pada April 2017, yakni sebesar 56,8 (Grafik 5). Sejalan dengan hal tersebut, aktivitas konsumsi juga meningkat, didukung oleh tren kenaikan Indeks Keyakinan Ekonomi (IKE) (Grafik 6). Selain itu, kinerja ekspor juga terus meningkat pada triwulan I 2017 sehingga mendorong neraca perdagangan Eropa terus mengalami surplus (Grafik 7). Ekspor tumbuh sebesar 8,3% (yoy) pada triwulan I 2017 dan mencapai nilai 500,6 miliar euro. Peningkatan ekspor terjadi sejak Oktober 2016, ditopang oleh naiknya permintaan global dan Euro yang mengalami depresiasi terhadap dolar AS sampai dengan akhir tahun 2016. Selain itu, risiko geopolitik di Eropa terus menurun seiring dengan kekalahan kelompok populis pada Pemilu di Perancis, setelah sebelumnya kelompok populis juga mengalami kekalahan dalam Pemilu di Belanda. Hal ini tercermin dari meningkatnya rata-rata Sentix Investor Confidence dan rata-rata Zew Survey Expectation (Grafik 8).

Indeks

Indeks 109

58 Markit Eurozone Manufacturing PMI Markit Eurozone Services PMI Markit Eurozone Composite PMI

57 56

107

Indeks 20

Indeks Keyakinan Konsumen (Skala Kanan) Indeks Keyakinan Industri (Skala Kanan) Indeks Keyakinan Sektor Jasa (Skala Kanan) Economic Confidence

15

55

105

54

103

5

52

101

0

51

99

-5

97

-10

53

50 49

10

95

48 Jun

Sep

Des

Mar

2014

Jun

Sep

Des

Mar

Jun

2015

Sep

Des

Mar

2016

Indeks

Indeks

% 12 10

500

8 Neraca Perdagangan Nilai Impor Impor (yoy)

6

Nilai Ekspor Ekspor (yoy)

4 2 0

200

-2

100

-4

0

-6 II

III

2015

2016

2017

Grafik 6. Perkembangan Indeks Keyakinan Ekonomi Eropa

600

I

2015

Sumber: Bloomberg, diolah

Grafik 5. PMI Manufaktur Eropa

300

2014

2017

Sumber: Bloomberg, diolah

400

-15 Feb Apr Jun Ags Okt Des Feb Apr Jun Ags Okt Des Feb Apr Jun Ags Okt Des Feb Apr

IV

I

II

III

2016

Sumber: Bloomberg, diolah

Grafik 7. Kinerja Perdagangan Eropa

IV

I

2017

30

Indeks Sentix Investor Confidence ZEW Survey Expectations (skala kanan)

20

23,9

80 60

10

40 26,3

0

20

-10

0

-20 -30

-20 Jan Mar Mei Jul Sep Nov Jan Mar Mei Jul Sep Nov Jan Mar Mei Jul Sep Nov Jan Mar

2014

2015

2016

2017

Sumber: Bloomberg, diolah

Grafik 8. Tingkat Keyakinan Investor di Kawasan Euro

QUARTERLY OUTLOOK ON : Monetary, Banking, and Payment System In Indonesia: Quarter I, 2017

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Perekonomian Jepang ditopang oleh permintaan domestik dan ekspor mengalami pertumbuhan meningkat. Kenaikan permintaan domestik di Jepang mendorong peningkatan indeks Industrial Production (IP) Jepang (Grafik 9). Rata-rata pertumbuhan indeks IP pada triwulan I 2017 sebesar 4,0% (yoy), lebih tinggi dibandingkan triwulan I 2016 yang justru mengalami kontraksi sebesar 1,4% (yoy) dan triwulan IV 2016 yang tumbuh lebih lambat sebesar 2,1% (yoy). Selain itu, ekspor juga melanjutkan perbaikan pada triwulan I 2017, tercermin dari pertumbuhannya yang tercatat sebesar 8,2% (yoy), lebih tinggi dibandingkan pertumbuhan pada triwulan IV 2016 sebesar 1,8% (Grafik 10). Meningkatnya ekspor Jepang dipengaruhi oleh pelemahan yen terhadap mata uang peers (sejak November 2016 s.d. Februari 2017), meskipun dalam beberapa waktu terakhir terdapat kecenderungan penguatan.

%

% 40

Industrial Production yoy Industrial Production mom 3 per. Mov. Avg. (Industrial Production yoy)

20

3

Milyar JPY Neraca Perdagangan (Skala kanan) Ekspor (yoy) Impor (yoy)

1.500

0 -500 -20

-2.500

-40

-7 Jan Mar Mei Jul Sep Nov Jan Mar Mei Jul Sep Nov Jan Mar Mei Jul Sep Nov Jan

2014

2015

2016

Sumber: Bloomberg, diolah

Grafik 9. Industrial Production Jepang

2017

Jan Apr Jul Okt Jan Apr Jul Okt Jan Apr Jul Okt Jan Apr Jul Okt Jan Apr Jul Okt Jan

2012

2013

2014

2015

2016

2017

Sumber: Bloomberg, diolah

Grafik 10. Ekspor, Impor, dan Neraca Perdagangan Jepang

Sejalan dengan perbaikan pertumbuhan ekonomi dunia tersebut, volume perdagangan dunia dan harga komoditas non migas diprakirakan lebih tinggi dari prakiraan sebelumnya. Volume perdagangan dunia pada 2017 dan 2018 diperkirakan lebih tinggi seiring dengan koreksi ke atas realisasi pertumbuhan volume perdagangan dunia tahun 2016, didukung membaiknya perdagangan Tiongkok dan PDB dunia. Negara emerging markets, terutama Tiongkok sebagai negara dengan volume perdagangan terbesar di dunia, menjadi sumber utama perbaikan volume perdagangan dunia (Grafik 11).

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Buletin Ekonomi Moneter dan Perbankan, Volume 19, Nomor 4, April 2017

%, yoy 80

%, yoy Ekspor Tiongkok WTV (RHS)

60

20

Impor Tiongkok

15 10

40

5

20

0 -5

0

-10 -20

Korelasi WTV-Ekspor : 0,82 Korelasi WTC-Impor : 0,76

-15

-40 Mar - 00 Des - 00 Sep - 01 Jun - 02 Mar - 03 Des - 03 Sep - 04 Jun - 05 Mar - 06 Des - 06 Sep - 07 Jun - 08 Mar - 09 Des - 09 Sep - 10 Jun - 11 Mar - 12 Des - 12 Sep - 13 Jun - 14 Mar - 15 Des - 15 Sep - 16

-20

Sumber : Bloomberg, CPB

Grafik 11. Volume Perdagangan Dunia dan Ekspor Impor Tiongkok

II. DINAMIKA MAKROEKONOMI INDONESIA 2.1. Pertumbuhan Ekonomi Perekonomian Indonesia pada triwulan I 2017 tumbuh membaik sebesar 5,01% (yoy), lebih tinggi dibandingkan triwulan sebelumnya sebesar 4,94% (yoy). Pertumbuhan yang tinggi tercatat pada ekspor dan belanja pemerintah (Tabel 1). Perbaikan kinerja ekspor terutama dipengaruhi oleh membaiknya harga komoditas global, seperti batubara dan karet, serta meningkatnya pertumbuhan ekonomi dunia. Sementara itu, meningkatnya konsumsi pemerintah didorong oleh belanja barang dan modal sehingga dapat memperbaiki kinerja investasi terutama investasi bangunan.

Tabel 1. Pertumbuhan Ekonomi Sisi Pengeluaran Persen, yoy

Komponen Konsumsi Rumah Tangga Konsumsi LNPRT Konsumsi Pemerintah Investasi Investasi Bangunan Investasi NonBangunan Ekspor Barang dan Jasa Impor Barang dan Jasa PDB Sumber : BPS (diolah)

2015 I

II

III

IV

5,01 -8,06 2,91 4,60 5,71

4,97 -7,98 2,61 4,01 4,72

4,95 6,57 7,09 4,93 6,11

4,93 8,33 7,12 6,43 7,78

2015

2016 I

II

III

IV

4,96 -0,62 5,32 5,01 6,11

4,97 6,40 3,43 4,67 6,78

5,07 6,71 6,23 4,18 5,07

5,01 6,64 -2,95 4,24 4,96

4,99 6,72 -4,05 4,80 4,07

2016 5,01 6,62 -0,15 4,48 5,18

1,62

2,05

1,65

2,47

1,95

-1,20

1,70

2,16

7,07

2,45

-0,68 -2,63 4,82

-0,26 -7,37 4,74

-0,95 -6,65 4,77

-6,38 -8,75 5,17

-2,12 -6,41 4,88

-3,29 -5,14 4,92

-2,18 -3,20 5,18

-5,65 -3,67 5,01

4,24 2,82 4,94

-1,74 -2,27 5,02

2017 I 4,93 8,02 2,71 4,81 5,90 1,55 8,04 5,02 5,01

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Sementara itu, pertumbuhan konsumsi rumah tangga tetap kuat. Secara spasial, perbaikan PDB triwulan I 2017 ditopang oleh pertumbuhan ekonomi di Jawa terkait investasi dan di Kalimantan karena ekspor. Di sisi lain, perlambatan ekonomi terjadi di Sumatera karena penurunan investasi dan perdagangan antar daerah, serta di Sulampua dan Balinusra karena menurunnya ekspor bahan tambang. Ekspor tercatat mengalami kenaikan pertumbuhan yang signifikan pada triwulan I 2017 sejalan dengan membaiknya permintaan maupun harga komoditas global. Pertumbuhan ekspor pada triwulan I 2017 mencapai 8,04% (yoy), lebih tinggi dibandingkan triwulan sebelumnya yang tercatat 4,24% (yoy). Perbaikan ini terutama ditopang oleh perbaikan ekspor komoditas primer sejalan dengan berlanjutnya perbaikan harga komoditas global seperti batubara, minyak kelapa sawit serta karet (Grafik 12). Faktor lain yang turut mendorong positifnya kinerja ekspor nonmigas pada periode ini adalah berlanjutnya perbaikan ekonomi negara mitra dagang utama seperti Amerika Serikat, China, India dan Jepang. Selain komoditas primer, perbaikan ekspor juga didorong oleh masih positifnya kinerja ekspor manufaktur unggulan antara lain produk bahan kimia, kendaraan bermotor serta alat dan perlengkapan listrik. Dari sisi impor, pertumbuhan impor juga mengalami peningkatan pada triwulan I 2017 menjadi 5,02% (yoy), dibandingkan triwulan IV 2016 yang sebesar 2,82% (yoy). Peningkatan impor terutama dipengaruhi oleh impor migas, sedangkan impor nonmigas tumbuh melambat pada triwulan I 2017 dipengaruhi oleh melambatnya impor barang konsumsi dan bahan baku (Grafik 13).

%, yoy

%, yoy

25

30,0

Total Pertanian Pertambangan Manufaktur PDB Ekspor

20 15 10 5

20,0 10,0

0

0,0

-5

-10,0

-10 -15

-20,0

-20 -25

Total

-30,0 I

II

III

2014

IV

I

II

III

2015

IV

I

II

III

2016

Sumber: Bank Indonesia

Grafik 12. Pertumbuhan Ekspor Nonmigas Riil

IV

I

I

Konsumsi II

2017

III

2015

IV

Bahan Baku I

II

Barang Modal III

IV

2016

Sumber: Bank Indonesia

Grafik 13. Pertumbuhan Impor Nonmigas Riil

I

2017

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Buletin Ekonomi Moneter dan Perbankan, Volume 19, Nomor 4, April 2017

Konsumsi Pemerintah pada triwulan I 2017 tumbuh lebih tinggi dibandingkan triwulan sebelumnya. Konsumsi pemerintah tercatat tumbuh 2,71% (yoy) pada triwulan I 2017, meningkat dari triwulan sebelumnya yang mengalami kontraksi pertumbuhan sebesar -4,05% (yoy). Peningkatan kinerja konsumsi pemerintah tersebut terutama didorong oleh meningkatnya belanja pegawai dan belanja barang. Meski demikian, kenaikan pertumbuhan konsumsi pemerintah tersebut lebih rendah dibandingkan periode sama tahun sebelumnya yang sebesar 3,43% (yoy). Hal ini dipengaruhi oleh transfer ke daerah yang tumbuh negatif sejalan dengan perbaikan formula alokasi dan reformasi sistem penyaluran transfer ke daerah, sebagaimana tercermin dari lebih rendahnya realisasi Dana Alokasi Khusus (DAK) Non Fisik dan belum adanya realisasi penyaluran dana desa pada triwulan I 2017. Meningkatnya pertumbuhan konsumsi pemerintah diikuti oleh membaiknya kinerja investasi, terutama investasi bangunan. Pertumbuhan investasi pada triwulan I 2017 sebesar 4,81% (yoy) relatif stabil dibandingkan triwulan sebelumnya (4,80% yoy). Perbaikan kinerja investasi terutama didukung oleh investasi bangunan sejalan dengan berlanjutnya proyek infrastruktur pemerintah dan mulai meningkatnya pembangunan konstruksi sektor swasta (Grafik 14). Meningkatnya aktivitas konstruksi tercermin dari penjualan semen yang meningkat secara signifikan. Sementara itu, kinerja investasi nonbangunan ditopang oleh penjualan mesin dan perlengkapan, serta kendaraan yang membaik. Berlanjutnya perbaikan harga komoditas dan pertumbuhan ekonomi global menjadi salah satu faktor pendorong positifnya investasi kendaraan yang tercermin pada perbaikan penjualan alat berat, terutama pada kelompok

%, yoy

%, yoy

10,0

8,58

%, yoy, cma

50

40 27,4

8,0 6,0

5,90

4,0

4,81

2,0

1,55

0,0

25 7,3

0 -5,0

10

5,2

0

-0,2

-10

-6,2

-25

-4,0 PMTB NonBangunan excl. Haki & CBR

-6,0 -8,0 I

II

III

2014

IV

I

II

III

IV

I

2015

Bangunan NonBangunan

II

III

2016

IV

I

-20 -30

-50

-40 I

II

III

2015

2017

IV

I

II

III

2016

IV

I

2017

Investasi Nonbangunan (alat angkut) Penjualan Mobil Niaga (skala kanan) Impor Suku Cadang dan Peralatan untuk Alat Angkut (skala kanan) Impor Alat Angkut Industri (skala kanan)

Sumber: Bank Indonesia

Sumber: BPS, CEIC

Grafik 14. Pertumbuhan Investasi

30 20

14,8 6,8

-2,0

25,4

Grafik 15. Impor Kendaraan dan Suku Cadang

QUARTERLY OUTLOOK ON : Monetary, Banking, and Payment System In Indonesia: Quarter I, 2017

363

pertambangan dan pertanian (Grafik 15). Data realisasi investasi yang dirilis BKPM pada triwulan I 2017 juga menunjukkan hal yang sama. Pertumbuhan investasi tertinggi dicatatkan oleh subsektor pertambangan dan transportasi serta listrik, gas dan air bersih. Sementara itu, pertumbuhan konsumsi rumah tangga (RT) tetap kuat didukung oleh optimisme konsumen. Konsumsi RT pada triwulan I 2017 tumbuh sebesar 4,93% (yoy), relatif stabil dibandingkan triwulan sebelumnya (4,99%, yoy). Tetap kuatnya konsumsi RT sejalan dengan keyakinan konsumen yang meningkat baik ekspektasi ke depan maupun terhadap kondisi ekonomi saat ini (Grafik 16). Namun, pertumbuhan konsumsi RT tersebut sedikit melambat dibandingkan triwulan sebelumnya sebagai pengaruh dari penyesuaian tarif listrik dan harga bahan bakar minyak yang berimbas ke daya beli masyarakat. Hal tersebut terindikasi dari penjualan ritel yang melambat pada seluruh komponen (Grafik 17). Penjualan mobil dan motor juga mengalami penurunan pada triwulan I-2017 dibandingkan triwulan sebelumnya.

Indeks 140

%, yoy

Indeks Keyakinan Konsumen Indeks Kayakinan Saat Ini

130

Indeks Ekspektasi Konsumen

30 20 10

120

0

110

-10 100

-20

90

Penjualan Ritel Penjualan Mobil Penjualan Motor

-30

80

-40 I

II

III

2015

IV

I

II

III

2016

Sumber: Bank Indonesia

Grafik 16. Indeks Keyakinan Konsumen

IV

I

2017

I

II

III

2014

IV

I

II

III

2015

IV

I

II

III

IV

2016

I

2017

Sumber: Bank Indonesia

Grafik 17. Penjualan Ritel dan Kendaraan Bermotor

Dari sisi Lapangan Usaha (LU), pertumbuhan ekonomi pada triwulan I 2017 ditopang baik oleh membaiknya kinerja sektor tradable maupun nontradable. Membaiknya permintaan global menjadi salah satu faktor utama yang mendorong perbaikan sektor tradable khususnya pada lapangan usaha industri. Meski demikian, pada sisi lain lapangan usaha pertambangan justru mengalami perlambatan sebagai pengaruh dari penurunan produksi dan negosiasi ijin ekspor (Tabel 2). Sementara itu, lapangan usaha Pertanian tumbuh lebih tinggi bersumber dari peningkatan produksi di subsektor tanaman pangan dan perkebunan, seiring dengan panen raya yang berlangsung hampir serentak serta membaiknya harga komoditas Crude Palm Oil (CPO). Dari sektor nontradable, peningkatan kinerja yang signifikan terjadi pada lapangan usaha

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Perdagangan, Hotel, dan Restoran (PHR) sebagai pengaruh dari perdagangan ekspor-impor. Demikian halnya dengan kinerja lapangan usaha Konstruksi yang meningkat, seiring dengan semakin membaiknya kinerja investasi bangunan. Sejalan dengan transaksi ekspor-impor yang meningkat, sektor PHR khususnya pada subsektor perdagangan mengalami peningkatan di tengah masih relatif lemahnya perdagangan ritel domestik.

Tabel 1.2 Pertumbuhan Ekonomi Sisi Lapangan Usaha Persen, yoy

Sektor

2015 I

II

III

IV

Pertanian,Peternakan,Kehutanan,& Perikanan

3,76

6,54

2,88

1,64

Pertambangan & Penggalian

0,58

-3,59

-4,41

Industri Pengolahan

4,07

4,20

4,60

Listrik, Gas, Air Bersih, dan Pengadaan Air*

1,97

1,22

Konstruksi

6,03

Perdagangan dan Penyediaan Akomodasi dan Mamin**

2015

2016

2016

2017

I

II

II

IV

3,77

1,47

3,44

3,03

5,31

3,25

7,12

I

-6,03

-3,42

1,20

1,15

0,29

1,60

1,06

-0,49

4,43

4,33

4,68

4,63

4,52

3,36

4,29

4,21

1,12

1,02

1,32

7,35

6,09

4,69

3,11

5,26

1,80

5,35

6,82

7,13

6,36

6,76

5,12

4,95

4,21

5,22

6,26

3,70

1,95

1,97

4,03

2,90

4,43

4,25

3,79

4,01

4,11

4,76

Transportasi, Pergudangan, Informasi dan Komunikasi***

7,88

7,72

9,08

8,51

8,31

7,73

8,24

8,64

8,79

8,36

8,45

Jasa Keuangan, Real Estat, dan Jasa Perusahaan****

6,88

4,19

7,57

8,56

6,81

7,52

9,25

6,87

4,51

6,99

5,23

Jasa-jasa Lainnya*****

5,79

8,60

5,03

6,14

6,37

5,67

5,35

3,94

2,92

4,42

3,87

PDB

4,82

4,74

4,77

5,17

4,88

4,92

5,18

5,01

4,94

5,02

5,01

Sumber : BPS ^ Proyeksi Bank Indonesia * Penggabungan 2 lap. usaha: (i) Pengadaan Listrik dan Gas dan (ii) Pengadaan Air ** Penggabungan 2 lap. usaha: (i) Perdagangan Besar dan Eceran, Reparasi Mobil dan Motor, serta (ii) Penyediaan akomodasi dan makan minum *** Penggabungan 2 lap. usaha: (i) Transportasi dan Pergudangan serta (ii) Informasi dan Komunikasi **** Penggabungan 3 lap. usaha: (i) Jasa Keuangan, (ii) Real Estate, dan (iii) Jasa Perusahaan ***** Penggabungan 4 lap. usaha: (i) Adm. Pemerintahan, Pertahanan, Jaminan Sosial Wajib, (ii) Jasa Pendidikan, (iii) Jasa Kesehatan dan Kegiatan Lainnya, dan (iv) Jasa Lainnya

Secara spasial, pertumbuhan ekonomi nasional didukung oleh meningkatnya pertumbuhan ekonomi kawasan Jawa terkait investasi dan Kalimantan karena ekspor (Gambar 1). Pertumbuhan ekonomi Jawa pada triwulan I 2017 tumbuh meningkat sebesar 5,66% (yoy) dari sebelumnya 5,45% (yoy), didorong oleh membaiknya konsumsi pemerintah sejalan dengan pelaksanaan Pilkada yang dilaksanakan serentak pada Februari 2017, serta meningkatnya investasi baik oleh pemerintah terkait proyek infrastruktur serta oleh swasta terutama industri manufaktur. Perekonomian Kalimantan tumbuh meningkat dari 2,22% (yoy) pada triwulan IV 2016 menjadi 4,92% (yoy) pada triwulan I 2017 didorong oleh perbaikan kinerja ekspor. Sementara itu, ekonomi Sumatera tumbuh melambat dari 4,49% (yoy) pada triwulan sebelumnya menjadi 4,05% (yoy), disebabkan oleh perlambatan investasi khususnya terjadi untuk investasi non-bangunan. Meski demikian, meningkatnya kinerja ekspor khususnya ekspor komoditas dan konsumsi rumah tangga, serta membaiknya konsumsi pemerintah mampu menopang pertumbuhan ekonomi Sumatera di kisaran 4% (yoy). Di sisi lain, terdapat tekanan kinerja ekspor khususnya di wilayah Balinusra dan Sulampua terutama terkait dengan menurunnya ekspor mineral tambang dari NTB dan Papua.

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SUMATERA (22%)

JAWA (58%)

4,19 4,47 4,03 4,49 4,05

I

5,38

I

II III IV I 2016 2017

5,82 5,70

5,45

KALIMANTAN (8,5%)

5,66

1,97 1,62 2,21 2,22

I

II III IV I 2016 2017

BALINUSRA (3%) 6,74 6,83 5,22

4,92

II III IV I 2016 2017

I

SULAMPUA (8,5%) 6,02 5,56

4,87 3,26

II III IV I 2016 2017

I

8,72 9,21

KTI (20%)

6,08

4,33 4,03 5,39

II III IV I 2016 2017

I

ACEH 2,9

5,54 5,01

II III IV I 2016 2017

Nasional SUMUT 4,5

4,92

KEP. RIAU 2 RIAU 2,8

KALBAR 4,8

KALTARA 6,17 SULTENG 3,9

JAMBI 4,3 SUMSEL 5,1 KEP. BABEL 6,4

SUMBAR 4,9

LAMPUNG 5,1 BANTEN 5,9

PDRB ≥ 7,0%

JABAR 5,2

6,0% ≤ PDRB < 7,0%

MALUT 7,5

I

4,94 5,01

II III IV I 2016 2017

PAPBAR 3,7

KALTIM 3,9

KALTENG 9,5 DKI KALSEL JAKARTA 5,3 6,5 JATENG 5,2

BENGKULU 5,6

SULUT 6,4

5,18 5,01

DIY 5,1

SULBAR 7,4

GORONTALO 7,3

SULSEL 7,5 BALI 5,7

JATIM 5,4

PAPUA 3,4

NTT 5

SULTRA 8,4

MALUKU 6,2

NTB -4,18

5,0% ≤ PDRB < 6,0%

4,0% ≤ PDRB < 5,0%

0% ≤ PDRB < 4,0%

PDRB < 0%

Sumber: BPS, diolah

Gambar 1. Peta Pertumbuhan Ekonomi Daerah Triwulan I 2017 (%yoy)

2.2. Neraca Pembayaran Indonesia Neraca Pembayaran Indonesia (NPI) triwulan I 2017 kembali mencatat surplus, didukung oleh surplus transaksi modal dan finansial. Surplus NPI tercatat 4,5 miliar dolar AS, relatif sama dengan surplus pada triwulan sebelumnya, tetapi jauh lebih baik dibandingkan triwulan I 2016 yang mengalami defisit 0,3 miliar dolar AS. Surplus NPI yang berlanjut pada triwulan I 2017 ditopang oleh tingginya surplus transaksi modal dan finansial yang melampaui defisit transaksi berjalan (Grafik 18). Meningkatnya surplus transaksi modal dan finansial sejalan dengan membaiknya pertumbuhan ekonomi dan persepsi positif terhadap prospek perekonomian. Surplus transaksi modal dan finansial pada triwulan I 2017 mencapai 7,9 miliar dolar AS, lebih besar dibandingkan dengan surplus pada triwulan IV 2016 yang sebesar 7,6 miliar dolar AS maupun surplus pada triwulan I 2016 yang sebesar 4,2 miliar dolar AS (Grafik 19). Peningkatan ini terutama didorong oleh derasnya aliran masuk modal investasi portofolio pada instrumen berdenominasi rupiah (SUN, SPN, dan saham) dan adanya penerbitan sukuk global pemerintah. Peningkatan surplus transaksi modal dan finansial lebih lanjut tertahan oleh penurunan surplus investasi langsung, terutama karena outflow investasi langsung sektor migas, dan defisit investasi lainnya khususnya karena penempatan aset sektor swasta di luar negeri.

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Miliar Dolar AS

Miliar dolar AS 15

15

10

10

5

5

0

0

-5

-5

-10

Investasi Portofolio Investasi Langsung Investasi Lainnya Transaksi Modal Finansial

-10 Transaksi Modal dan Finansial Transaksi Berjalan Neraca Keseluruhan

-15 -20 I

-15 -20

II III IV I

II III IV I

II III IV I

2011

2012

2013

II III IV I

2014

II III IV I* II* III* IV* I**

2015

I

2016 2017

Sumber: Bank Indonesia *angka sementara **angka sangat sementara

II III IV I

2011

II III IV I

2012

Sumber: Bank Indonesia

Grafik 18. Neraca Pembayaran Indonesia

II III IV I

II III IV I

II III IV I* II* III* IV* I**

2013

2014

2015

* angka sementara

2016 2017

** angka sangat sementara

Grafik 19. Transaksi Modal dan Finansial

Sementara itu, defisit transaksi berjalan meningkat didorong oleh meningkatnya defisit neraca perdagangan migas dan pendapatan primer (Grafik 20). Defisit transaksi berjalan pada triwulan I 2017 tercatat 2,4 miliar dolar AS (1,0% PDB), meningkat dari 2,1 miliar dolar AS (0,9% PDB) pada triwulan IV 2016, namun jauh lebih rendah jika dibandingkan dengan defisit pada triwulan I 2016 yang sebesar 4,7 miliar dolar AS (2,1% PDB). Peningkatan defisit transaksi berjalan pada triwulan I 2017 terutama akibat naiknya defisit neraca perdagangan migas dan pendapatan primer. Peningkatan defisit neraca perdagangan migas dipengaruhi oleh naiknya harga minyak dunia di tengah penurunan lifting minyak, sementara kenaikan defisit neraca pendapatan primer mengikuti jadwal pembayaran bunga surat utang pemerintah yang lebih tinggi dan meningkatnya pembayaran pendapatan investasi langsung (Grafik 21). Peningkatan

Miliar dolar AS 14

%

Neraca Pendapatan Sekunder Neraca Pendapatan Primer Neraca Perdagangan

10

Neraca Jasa Transaksi Berjalan CA/GDP (%) (rhs)

6

Miliar Dolar AS 3 1 -1 -3

2

-5

-2

11

7 5 3

-7

1

-9

-1

-10

-11

-3

-14

-13

-5

-6

I II III IV I II III IV I II III IV I II III IV I II III IV I* II* III* IV* I**

2011

2012

Sumber: Bank Indonesia

2013

* angka sementara

2014

2015

** angka sangat sementara

Grafik 20. Neraca Transaksi Berjalan

2016 2017

Neraca Nonmigas Neraca Migas Neraca Perdagangan

9

I

II III IV I

II III IV I

II III IV I

II III IV I

II III IV I* II* III* IV* I**

2011

2012

2013

2014

2015

Sumber: Bank Indonesia

* angka sementara

** angka sangat sementara

Grafik 21. Neraca Perdagangan

2016 2017

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367

defisit transaksi berjalan lebih lanjut tertahan oleh kenaikan surplus neraca perdagangan nonmigas yang ditopang meningkatnya ekspor nonmigas sejalan dengan berlanjutnya kenaikan harga komoditas dan menurunnya defisit neraca jasa terutama karena naiknya surplus jasa perjalanan. Posisi cadangan devisa pada akhir triwulan I 2017 tercatat 121,8 miliar dolar AS, meningkat dari periode akhir triwulan sebelumnya yang sebesar 116,4 miliar dolar AS (Grafik 22). Peningkatan tersebut terutama dipengaruhi oleh penerimaan devisa, antara lain berasal dari penerimaan pajak dan devisa ekspor migas bagian pemerintah serta hasil lelang Surat Berharga Bank Indonesia (SBBI) valas. Penerimaan devisa tersebut melampaui kebutuhan devisa untuk pembayaran utang luar negeri pemerintah dan SBBI valas jatuh tempo. Posisi cadangan devisa per akhir Maret 2017 tersebut cukup untuk membiayai 8,9 bulan impor atau 8,6 bulan impor dan pembayaran utang luar negeri pemerintah, serta berada di atas standar kecukupan internasional sekitar 3 bulan impor. Bank Indonesia menilai cadangan devisa tersebut mampu mendukung ketahanan sektor eksternal dan menjaga kesinambungan pertumbuhan ekonomi Indonesia ke depan.

Miliar Dolar AS

Bulan

120

9

100

8

80

7

60 6

40

5

20

4

0 1 2 3 4 5 6 7 8 9 101112 1 2 3 4 5 6 7 8 9 101112 1 2 3 4 5 6 7 8 9 101112 1 2 3

2014

2015

2016

2017

Bulan Impor dan Pembayaran Utang Pemerintah (Skala Kanan) Sumber: Bank Indonesia, diolah

Grafik 22. Perkembangan Cadangan Devisa

2.3. Nilai Tukar Rupiah Nilai tukar rupiah bergerak menguat sepanjang triwulan I 2017 disertai volatilitas yang lebih rendah dibanding negara peers. Pada triwulan I 2017, secara point to point rupiah menguat sebesar 1,10% menjadi Rp13.326 per dolar AS (Grafik 23). Penguatan rupiah pada triwulan I 2017 didukung oleh kondisi domestik yang cukup solid ditengah sentimen negatif terhadap US dolar dan membaiknya risiko pasar keuangan global. Volatilitas Rupiah pada triwulan I 2017

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Buletin Ekonomi Moneter dan Perbankan, Volume 19, Nomor 4, April 2017

tercatat paling rendah dibandingkan negara peers yaitu Lira (Turki), Rand (Afrika Selatan), Real (Brazil), Won (Korea Selatan), Bath (Thailand), Rupee (India), Singapore Dollar (Singapura), Peso (Filipina) dan Ringgit (Malaysia) (Grafik 24). Pada April 2017 nilai tukar rupiah bergerak relatif stabil. Hingga akhir April 2017, rupiah menguat sebesar 1,07% (ytd) menjadi Rp13.329 per dolar AS (Grafik 25). Di sisi eksternal, berlanjutnya penguatan rupiah dipengaruhi oleh meningkatnya sentimen negatif terhadap arah kebijakan perekonomian AS dan kenaikan pertumbuhan Tiongkok yang mendorong berlanjutnya aliran modal asing masuk ke negara EM termasuk Indonesia. Sementara di sisi domestik, penguatan rupiah didukung oleh meningkatnya cadangan devisa pada akhir Maret 2017 sejalan dengan perbaikan outlook sovereign rating dan sentimen positif terhadap kondisi dan prospek perekonomian Indonesia. Berlanjutnya penguatan rupiah pada April 2017 juga disertai volatilitas yang tetap rendah dibanding negara peers (Grafik 26).

Rupiah per Dolar AS

TW.IV 2016 vs TW.I 2017 TRY PHP CNY IDR EUR MYR ZAR BRL THB INR KRW

13.600

-3,10 -1,20

Data s.d 28 Apr-17

13.500 13.412

0,84

13.400

1,10

13.338

13.315

1,27

13.300

13.000

4,74 7,81

0

13.329

13.100

4,31

-5

13.304

13.247

13.200 4,27

13.304

13.361 13.348

1,37 2,43

13.345

-5

IDR/USD Rata-rata Bulanan Rata-rata Triwulanan

13.018

12.900 10 %

3 8 13182328 2 7 12172227 2 7 12172227 1 6 11162126 31 5 10152025 2 7 12172227 1 6 11162126

Okt

Nov

Des

Jan

Feb

Mar

Apr

Sumber: Reuters, Bloomberg, diolah

Sumber: Reuters, Bloomberg

Grafik 23. Nilai Tukar Kawasan

Grafik 24. Nilai Tukar Rupiah

%

%

30

TW IV-2016

30

TW I-2017

25

25

20

20

15

15

10

10

5

5

2016 YTD 2017 Rata-rata YTD 2017

Data s.d. 28 Apr-17

-

0 TRY

ZAR

BRL

KRW

THB

INR

SGD

Sumber: Reuters, diolah

PHP

MYR

IDR

TRY

ZAR

BRL

KRW

THB

INR

SGD

Sumber: Reuters, Bloomberg, diolah

Grafik 25. Volatilitas Triwulanan

Grafik 26. Volatilitas Bulanan

PHP

MYR

IDR

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369

2.4. Inflasi Inflasi pada triwulan I 2017 tetap terkendali dalam kisaran sasaran inflasi 2017 yaitu 4+1%, meski sedikit lebih tinggi dibanding periode triwulan sebelumnya. Pada akhir triwulan I 2017, realisasi inflasi IHK tercatat sebesar 3,61% (yoy) atau lebih tinggi dibanding periode akhir triwulan IV 2016 yang sebesar 3,02% (yoy). Tekanan kenaikan inflasi di periode triwulan ini bersumber dari kelompok administered prices, terutama terkait penerapan sejumlah kebijakan tarif di awal tahun 2017. Sementara itu, inflasi inti relatif stabil dan inflasi pangan bergejolak (volatile foods) tercatat lebih rendah. Secara bulanan, sepanjang periode triwulan I 2017 tekanan inflasi yang cukup tinggi hanya terjadi pada bulan Januari yakni sebesar 0,97% (mtm) karena didorong oleh penerapan kenaikan tarif perpanjangan STNK dan tarif listrik. Tekanan inflasi kembali menurun pada Februari 2017 menjadi sebesar 0,23% (mtm) dan pada Maret 2017 bahkan mengalami deflasi 0,02% (mtm) seiring dengan melimpahnya pasokan pangan. Memasuki awal triwulan II 2017, IHK kembali mengalami inflasi namun masih berada pada level yang cukup rendah yakni sebesar 0,09% (mtm) pada April 2017. Realisasi inflasi IHK pada awal triwulan II 2017 dipengaruhi oleh kenaikan inflasi pada kelompok administered price (AP), sementara inflasi inti tercatat cukup rendah, sedangkan volatile foods bahkan masih tercatat mengalami deflasi. Secara tahunan, inflasi IHK pada April 2017 tercatat sebesar 4,17% (yoy), masih sejalan dengan kisaran sasaran inflasi 2017 (Grafik 27).

%, yoy 20

IHK

Inti

Volatile Food

Administered Prices

16 12 8,68

8 3,28

4

4,17 2,66

0 -4

1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4

2015

2016

2017

Sumber: BPS, diolah

Grafik 27. Perkembangan Inflasi Tahunan

Inflasi kelompok administered prices pada April 2017 tercatat 1,27% (mtm), meningkat dibanding bulan sebelumnya yang sebesar 0,37% (mtm). Tekanan pada inflasi administered prices terutama didorong oleh penerapan penyesuaian tarif listrik tahap dua untuk pelanggan pascabayar daya 900 VA nonsubsidi dan kenaikan tarif angkutan udara karena meningkatnya

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Buletin Ekonomi Moneter dan Perbankan, Volume 19, Nomor 4, April 2017

permintaan akibat libur panjang (Tabel 3). Selain itu, kenaikan harga bahan bakar khusus (BBK) seperti Pertalite, Pertamax, Pertamax Turbo, Dexlite dan Pertamina Dex masing-masing sebesar Rp100/liter turut mendorong kenaikan inflasi kelompok administered prices pada bulan April 2017. Demikian pula dengan kenaikan cukai rokok yang turut berkontribusi pada naiknya inflasi kelompok administered prices di periode awal triwulan II 2017. Secara tahunan, inflasi administered prices pada April 2017 tercatat berada pada level yang cukup tinggi yakni sebesar 8,68% (yoy) atau berada di atas realisasi bulan sebelumnya yang sebesar 5,50% (yoy) (Grafik 28).

%

%

10

Administered Prices (%, mtm) Administered Prices (%, yoy) - skala kanan

8

15 10

6 4

5

2

0

0 -5

-2 -4

-10 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4

2015

2016

Sumber: BPS, diolah

2017

Tabel 3. Penyumbang Inflasi Administered Prices No.

Komoditas

Inflasi/Deflasi Sumbangan (% mtm) (%)

INFLASI 1

Tarif listrik

5,29

0,20

2

Angkutan udara

1,39

0,02

3

Bensin

0,39

0,01

4

Rokok kretek filter

0,46

0,01

5

Rokok kretek

0,73

0,01

Sumber: BPS, diolah

Grafik 28. Inflasi Administered prices

Sementara itu, inflasi inti pada April 2017 cukup terkendali sejalan dengan masih terbatasnya permintaan domestik, terjaganya ekspektasi inflasi, dan menguatnya nilai tukar rupiah. Inflasi Inti tercatat 0,13% (mtm) sedikit lebih tinggi dari bulan Maret 2017 yang sebesar 0,10% (mtm), namun masih lebih rendah dibandingkan pola historis 2010-2012 (Grafik 29). Inflasi kelompok inti pada bulan April 2017 dipengaruhi oleh tekanan pada komponen inti nontraded terutama karena naiknya tarif pulsa ponsel dan tarif sewa rumah. Sementara itu, komponen inti traded mengalami perlambatan terutama karena turunnya harga komoditas gula pasir seiring menguatnya rupiah dan turunnya harga gula di pasar global. Tekanan permintaan domestik diperkirakan masih relatif terbatas yang tercermin dari pertumbuhan M2 dan kredit konsumsi yang masih relatif rendah. Di sisi lain, hasil survey mengindikasikan mulai adanya peningkatan ekspektasi inflasi, baik di tingkat konsumen maupun di tingkat pedagang eceran, terutama dipengaruhi oleh jatuhnya Ramadhan pada Juni 2017 (Grafik 30 dan Grafik 31). Secara tahunan, inflasi inti pada April 2017 tercatat 3,28% (yoy), relatif stabil dibanding realisasi inflasi inti di bulan sebelumnya yang sebesar 3,30% (yoy).

QUARTERLY OUTLOOK ON : Monetary, Banking, and Payment System In Indonesia: Quarter I, 2017

371

%, mtm 1,0

Inti

Inti Traded

Inti Non-Traded

0,8 0,6 0,4 0,2 0,0 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4

2015

2016

2017

Sumber: BPS, diolah

Grafik 29. Inflasi Inti

Indeks

%, yoy

200 180

Indeks 20

Inflasi IHK aktual (skala kanan) Indeks Ekspektasi Harga Pedagang 3 bln yad Indeks Ekspektasi Harga Pedagang 6 bln yad

%, yoy

200

Inflasi IHK aktual (skala kanan) Indeks Ekspektasi Harga Konsumen 3 bln yad Indeks Ekspektasi Harga Konsumen 6 bln yad

190 15

180

20

15

170

160 10 140

10

160 150

5

120

5

140 130

100

0 1 2 3 4 5 6 7 8 9 101112 1 2 3 4 5 6 7 8 9 101112 1 2 3 4 5 6 7 8 9

2015

2016

2017

Grafik 30. Ekspektasi Inflasi Pedagang Eceran

0

120 1 2 3 4 5 6 7 8 9 101112 1 2 3 4 5 6 7 8 9 101112 1 2 3 4 5 6 7 8 9

2015

2016

2017

Grafik 31. Ekspektasi Inflasi Konsumen

Inflasi kelompok volatile food pada April 2017 tercatat mengalami deflasi sebesar 1,26% (mtm), melanjutkan deflasi pada bulan sebelumnya yang sebesar 0,77% (mtm) (Grafik 32). Deflasi terutama bersumber dari komoditas cabai merah, cabai rawit, bawang merah, beras, daging sapi, ikan segar, telur ayam ras, dan minyak goreng. Penurunan harga pangan terjadi seiring dengan melimpahnya pasokan karena panen raya di berbagai daerah sentra produksi. Deflasi lebih lanjut tertahan oleh kenaikan harga bawang putih dan daging ayam ras yang didorong oleh mulai terbatasnya pasokan di tengah permintaan yang mulai meningkat (Tabel 4). Secara tahunan, inflasi volatile food mencapai sebesar 2,66% (yoy), sedikit lebih rendah dibanding bulan sebelumnya yang sebesar 2,89% (yoy).

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Tabel 4. Penyumbang Inflasi Volatile Food

%, mtm 4

No No.

Inflasi VF 2015 Inflasi VF 2016 Inflasi VF 2017 Rata-rata 2010-2012

Inflasi/Deflasi (% mtm)

Komoditas

Sumbangan (%)

INFLASI

2

1

Bawang Putih

8,38

0,03

2

Daging Ayam Ras

1,54

0,02

3

Tomat Sayur

10,46

0,02

DEFLASI

0

-2 Jan

Feb Mar Apr

Mei

Jun

Jul

Ags

Sep

Okt Nov Des

Sumber: BPS, diolah

1 2 3

Cabai Merah Cabai Rawit Bawang Merah

-14,77 -24,34 -11,71

-0,09 -0,09 -0,08

4 5 6

Beras Daging Sapi Ikan Segar Telur Ayam Ras Minyak Goreng

-0,43 -0,97 -0,30

-0,02 -0,01 -0,01

-0,79 -1,20

-0,01 -0,01

7 8

Grafik 32. Inflasi Volatile food

Sumber: BPS, diolah

Secara spasial, tekanan inflasi terjadi hampir di seluruh wilayah kecuali Sumatera yang tercatat mengalami deflasi (Gambar 2). Secara berurutan, inflasi tertinggi terjadi di KTI (0,12%; mtm), kemudian Jawa (0,12%; mtm). Inflasi KTI terutama disumbang oleh Kalimantan yang dpengaruh oleh kenaikan tarif listrik, bensin dan tomat sayur. Sementara peningkatan tekanan inflasi di Jawa, disumbang oleh inflasi yang berlangsung di hampir seluruh provinsi di Jawa, kecuali DKI Jakarta, terutama bersumber dari kenaikan tarif angkutan udara, harga rokok dan bawang putih serta daging ayam ras Sejumlah daerah dengan inflasi tertinggi adalah Kepulauan ACEH -0,33 SUMUT -0,43

Inflasi Nasional: 0,09%, mtm

KEP. RIAU 0,38 RIAU 0,19

KALBAR 0,28

KALTARA 0,27 SULTENG 0,46

JAMBI 0,57 SUMSEL -0,05 KEP. BABEL 0,99

SUMBAR -0,3

LAMPUNG -0,2 BANTEN 0,06

Inf > 3,0%

JABAR 0,17

2,0% < Inf < 3,0%

PAPBAR -0,26

KALTIM 0,13

KALTENG 0,18 DKI KALSEL JAKARTA -0,02 JATENG 0,26 0,15

BENGKULU -0,3

SULUT -0,02

MALUT 0,36

DIY 0,28

SULBAR 0,06

GORONTALO -0,12

SULSEL 0,33 BALI -0,14

JATIM 0,29

NTT 0,24

SULTRA -0,28

MALUKU -0,68

NTB 0,03

1,0% < Inf < 2,0%

0,5% < Inf < 1,0%

0% < Inf < 0,5%

Sumber: BPS, diolah

Gambar 2. Peta Inflasi Regional, April 2017 (%, mtm)

Inf < 0%

PAPUA 0,42

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Bangka Belitung (0,99%; mtm), Jambi (0,57%; mtm), Sulawesi Tengah (0,46%; mtm), Papua (0,42%; mtm), dan Kepulauan Riau (0,38%; mtm). Secara tahunan (yoy), sebagian besar daerah masih mencatatkan inflasi di dalam rentang sasaran 4+1%, kecuali beberapa provinsi yaitu Kepulauan Bangka Belitung (8,37%; yoy), Bengkulu (6,60%; yoy), Riau (6,40%; yoy), Kalimantan Barat (5,79%; yoy) dan Sulawesi Tengah (5,09%; yoy).

III. PERKEMBANGAN MONETER, PERBANKAN, DAN SISTEM PEMBAYARAN 3.1. Moneter Transmisi pelonggaran kebijakan moneter membaik meski belum optimal sejalan dengan kehatihatian bank dalam mengelola risiko kredit. Pertumbuhan kredit dan Dana Pihak Ketiga (DPK) menunjukkan peningkatan di tengah berlanjutnya tren penurunan suku bunga DPK maupun kredit dengan level penurunan yang lebih terbatas. Kondisi tersebut didukung oleh likuiditas yang masih memadai. Pada sisi lain, pertumbuhan pembiayaan pada pasar keuangan non bank juga mulai searah dengan pertumbuhan kredit dan mencatatkan level tertinggi setidaknya dalam 7 tahun terakhir. Kondisi PUAB yang stabil disertai dengan berlanjutnya tren penurunan suku bunga mengindikasikan likuiditas perbankan masih memadai. Rata-rata harian suku bunga PUAB O/N pada triwulan I 2017 tercatat 4,23% atau turun 7 bps dibandingkan triwulan sebelumnya (4,30%). Secara umum, suku bunga PUAB O/N sepanjang triwulan I 2017 semakin konsisten berada di dalam range koridor suku bunga yaitu 4,25% s.d. 5,25% (Grafik 33). Volatilitas suku bunga PUAB O/N juga jauh lebih terjaga, tercermin dari rata-rata harian spread min-max PUAB O/N yang berada pada angka 12 bps, lebih rendah dari triwulan sebelumnya pada 33 bps. Selain penurunan rata-rata harian suku bunga, volume transaksi PUAB O/N juga meningkat, memperkuat indikasi masih terjaganya likuiditas perbankan (Grafik 34).

%

Rp T rPUAB O/N 7 Days RR (Stlh 19/08/16) DF Rate LF Rate BB Koridor Skb Ops BA Koridor Skb Ops BI Rate

bps 200

20,0

150

16,0

100

12,0

50 -

8,0

(50) 4,0

(100)

1-Jan-16 15-Jan-16 29-Jan-16 12-Feb-16 26-Feb-16 11-Mar-16 25-Mar-16 8-Apr-16 22-Apr-16 6-Mei-16 20-Mei-16 3-Jun-16 17-Jun-16 1-Jul-16 15-Jul-16 29-Jul-16 12-Ags-16 26-Ags-16 9-Sep-16 23-Sep-16 7-Okt-16 21-Okt-16 4-Nov-16 18-Nov-16 2-Des-16 16-Des-16 30-Des-16 13-Jan-17 27-Jan-17 10-Feb-17 24-Feb-17 10-Mar-17 24-Mar-17 7-Apr-17 21-Apr-17

8,25 8,00 7,75 7,50 7,25 7,00 6,75 6,50 6,25 6,00 5,75 5,50 5,25 5,00 4,75 4,50 4,25 4,00 3,75

Sumber: LHBU

Grafik 33. Perkembangan Suku Bunga PUAB O/N

(150) 1 18 7 24 10 27 14 31 17 4 21 7 24 10 27 14 31 17 4 21 7 24 10 27 15 1 18 5 22 8 25 12 29 15 1 18 5 22 8 25 12 29 1 15 18 7 24 10 27

Feb Mar Apr MeiJun Jul Ags Sep OktNovDes Jan FebMarApr Mei Jun Jul AgsSep Okt Nov Des JanFebMar Apr

2015 Vol PUAB Pagi

Vol PUAB Sore

2016 2017 Spread Skb PUAB ON Pagi-Sore

Sumber: Bank Indonesia

Grafik 34. Volume PUAB O/N

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Buletin Ekonomi Moneter dan Perbankan, Volume 19, Nomor 4, April 2017

Penurunan suku bunga deposito masih berlanjut pada triwulan I 2017. Rata-rata tertimbang suku bunga deposito pada triwulan I 2017 turun 11 bps (qtq) menjadi 6,61%. Dengan demikian, sejak awal penurunan suku bunga kebijakan pada Desember 2015 penurunan suku bunga deposito sudah mencapai 133 bps. Selain suku bunga kebijakan yang menurun, membaiknya likuiditas menjadi faktor utama penurunan suku bunga deposito. Penurunan suku bunga deposito terjadi pada tenor panjang yaitu 12 bulan dan 24 bulan yang turun masingmasing 21 bps (qtq) dan 32 bps (qtq) dibanding Desember 2016. Suku bunga kredit terus menurun dengan penurunan yang lebih besar dari suku bunga deposito. Pada triwulan I 2017, suku bunga kredit turun 14 bps (qtq), lebih dalam dari penurun suku deposito pada periode yang sama sebesar 11 bps. Sejak awal penurunan suku bunga kebijakan pada Desember 2015, suku bunga kredit telah menurun mencapai 93 bps. Penurunan suku bunga kredit terjadi pada semua jenis kredit berdasarkan penggunaan, dengan penurunan terbesar pada jenis kredit produktif yaitu kredit modal kerja (KMK) yang turun 17 bps (qtq), diikuti suku bunga kredit investasi (KI) sebesar 16 bps (qtq), dan suku bunga kredit konsumsi (KK) sebesar 11 bps (qtq) (Grafik 35). Sementara itu, spread suku bunga relatif stabil pada triwulan 1 2017. Spread suku bunga pada triwulan I 2017 sedikit menyempit didorong oleh lebih dalamnya penurunan suku bunga kredit dibandingkan dengan suku bunga deposito. Secara bulanan, spread suku bunga deposito dan kredit turun sebesar 3 bps yaitu dari 532 bps menjadi 529 bps (Grafik 36).

%

%

14,5

rKMK

rKI

rKK

%

13,5

RRT Sb Kredit

7,0

12,5

14,0

Spread Kredit -Depo (rhs) BI Rate RRT Sb Depo

11,5

13,5

13,48

10,5

13,0

9,5

12,5

8,5

7 Days LF Rate RRT Sb Kredit

11,90

11,5

11,19

11,0

11,05 MarMei Jul Sep Nov Jan Mar Mei Jul Sep Nov Jan Mar Mei Jul Sep Nov Jan Mar Mei Jul Sep Nov Jan Mar

2013

2014

2015

2016

Grafik 35. Suku Bunga Kredit: KMK, KI dan KK

4,0 3,0

6,61

6,5

2,0 1,0

5,5 4,5

0,0 Jan MarMei Jul SepNov Jan MarMei Jul SepNov Jan MarMei Jul SepNov Jan Mar Mei Jul SepNov Jan Mar

2017

Sumber: LBU

6,0 5,0

Selisih rKredit - rDepo: 529 bps

7,5

12,0

11,90

2013

2014

2015

2016

2017

Sumber: LBU

Grafik 36. Spread Suku Bunga Perbankan

Di sisi likuiditas, pertumbuhan likuiditas perekonomian M2 (uang beredar dalam arti luas) tumbuh meningkat. Pertumbuhan M2 pada Triwulan I 2017 sebesar 10,0% (yoy), lebih tinggi dibandingkan pertumbuhan pada Triwulan I 2016 sebesar 7,4% (yoy). Berdasarkan komponen pembentuknya, peningkatan M2 pada Triwulan I 2017 terutama disumbang oleh pertumbuhan

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QUARTERLY OUTLOOK ON : Monetary, Banking, and Payment System In Indonesia: Quarter I, 2017

uang kuasi. Pertumbuhan komponen uang kuasi tercatat sebesar 8,6% (yoy), lebih tinggi dibandingkan periode yang sama pada tahun sebelumnya sebesar 6,3% (yoy) (Grafik 37). Sementara itu, M1 tumbuh 14,2% (yoy), lebih tinggi dibandingkan Triwulan I 2016 sebesar 11,2% (yoy). Pertumbuhan M1 disumbang oleh dua komponen pembentuknya, uang kartal dan simpanan giro Rupiah (Grafik 38). Berdasarkan faktor yang memengaruhinya, peningkatan M2 disumbang oleh kenaikan NFA yang tercatat tumbuh sebesar 17,6% (yoy), jauh lebih tinggi dibandingkan Triwulan I 2016 yang mengalami kontraksi sebesar 0,2% (yoy) (Grafik 39).

25 Kuasi

M1

M2

20 15 14,19 10,00

10

8,62

5 0

Jun Sep Des Mar Jun Sep Des Mar Jun Sep Des Mar Jun Sep Des Mar Jun Sep Des Mar

2012

2013

2014

2015

2016

2017

Sumber: Bank Indonesia

Grafik 37. Pertumbuhan M2 dan Komponennya % 25 Giro Rp

Uang Kartal

20

M1

NFA

NDA

M2

20 15 15 10

10

5 5 0 -5

0 Jun Sep Des Mar Jun Sep Des Mar Jun Sep Des Mar Jun Sep Des Mar Jun Sep Des Mar

2012

2013

2014

2015

2016

Sumber: Bank Indonesia

Grafik 38. Pertumbuhan M1 dan Komponennya

2017

Jan

Apr

Jul

2014

Okt

Jan

Apr

Jul

2015

Okt Jan

Apr

Jul

2016

Okt

Jan

2017

Sumber: Bank Indonesia

Grafik 39. Pertumbuhan M2 dan Faktor-faktor yang Mempengaruhinya

3.2. Industri Perbankan Ketahanan industri perbankan tetap kuat didukung oleh tingginya rasio kecukupan modal di tengah risiko kredit yang cenderung tinggi. Ketahanan permodalan industri perbankan masih berada pada level yang cukup kuat dan jauh diatas threshold-nya seiring dengan terjaganya

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Buletin Ekonomi Moneter dan Perbankan, Volume 19, Nomor 4, April 2017

profitabilitas perbankan. Permodalan perbankan atau Capital Adequacy Ratio (CAR) mencapai 22,7% pada akhir Triwulan I 2017. Level kecukupan permodalan perbankan yang terus meningkat dibandingkan dengan tahun-tahun sebelumnya diperkirakan mampu untuk menahan dampak negatif dari peningkatan risiko kredit dan mengantisipasi kebutuhan pemenuhan Capital Surcharge serta Countercyclical Capital Buffer (Grafik 1.40). Sementara itu, risiko kredit yang tercermin dari rasio Non Performing Loan (NPL) masih terjaga meski mengalami sedikit peningkatan. NPL berada pada level 3,04% pada akhir triwulan I 2017, sedikit lebih tinggi dari 2,93% pada akhir tahun 2016.

%

22

%

Rp Triliun

24

22,7%

Modal - Skala kanan ATMR - Skala kanan CAR

7.000

40

6.000

35

5.000

20

4.000

18

3.000 2.000

16 14 3 6 9 12 3 6 9 12 3 6 9 12 3 6 9 12 3 6 9 12 3

2012

2013

2014

2015

2016

Sumber: Bank Indonesia

Grafik 40. Permodalan Industri Perbankan

2017

Kredit

KMK

KI

KK

30 25 20 15

9,2%

10

1.000

5

0

0 Sep

Des Mar Jun

2013

2014

Sep

Des Mar Jun

2015

Sep

Des Mar Jun

2016

Sep

Des Mar

2017

Sumber: Bank Indonesia

Grafik 41. Pertumbuhan Kredit Menurut Penggunaan

Pertumbuhan kredit pada triwulan I 2017 meningkat. Kredit tumbuh 9,2% (yoy) pada triwulan I 2017 didorong oleh pertumbuhan kredit investasi (KI) dan kredit konsumsi (KK). Sementara, itu, pertumbuhan kredit modal kerja (KMK) mencapai 8,6% (yoy), yang merupakan jenis kredit dengan pangsa terbesar, masih berada di bawah pertumbuhan kredit total (Grafik 41). Berdasarkan sektor ekonomi, pertumbuhan kredit triwulan I 2017 meningkat terjadi di hampir seluruh setor, dengan kenaikan tertinggi pada kredit ke sektor perdagangan (yang memiliki porsi kredit terbesar) yakni sebesar 7,3% (yoy) dari 6,4% (yoy) di triwulan IV 2016. Penyaluran kredit ke sektor industri yang memiliki porsi kredit cukup besar meningkat menjadi sebesar 3,7% (yoy), terakselerasi dibandingkan triwulan sebelumnya (2,8% yoy). Kredit sektor konstruksi juga meningkat sebesar 26,4% (yoy), naik dibandingkan triwulan sebelumnya yang sebesar 24,2% (yoy). Sementara, penyaluran kredit pada sektor Pertambangan terus melanjutkan tren pertumbuhan positif menjadi 3,3% (yoy), meningkat pesat dibandingkan triwulan IV 2016 yang mengalami kontraksi 6,61% (yoy) (Grafik 42). Dana Pihak Ketiga (DPK) pada triwulan I 2017 tumbuh sebesar 10,0% (yoy), meningkat dibandingkan triwulan sebelumnya sebesar 9,6% (yoy) (Grafik 43). Berdasarkan jenisnya,

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QUARTERLY OUTLOOK ON : Monetary, Banking, and Payment System In Indonesia: Quarter I, 2017

pertumbuhan DPK pada triwulan I 2017 terutama bersumber dari kenaikan pertumbuhan deposito dan tabungan, sementara pertumbuhan giro masih cenderung stabil.

%

Pertambangan Jasa Sosial

3,34 (6,61)

17,49

1,27

14,75 15,59 12,27 11,18

Pertanian Konstruksi

(10)

-

10

gTabungan

gDeposito

15 10,0%

15

10

10 Mar-17 Des-16

25 20

20 26,41 24,18

8,41 8,27 7,29 6,40

Lain-lain

gGiro

25

3,72 2,65

Perdagangan

% gDPK (skala kanan)

30

Jasa Dunia Usaha

(2,75) Pengangkutan (3,24) Industri

35

40,15 36,21

Listrik

5

5

0 -5 20

30

Sumber: Bank Indonesia

Grafik 42. Pertumbuhan Kredit Sektoral

40

50

0 Des Mar Jun Sep Des Mar Jun Sep Des Mar Jun Sep Des Mar Jun Sep Des Mar

2012

2013

2014

2015

2016

2017

Sumber: Bank Indonesia

Grafik 43. Pertumbuhan DPK

3.3. Pasar Saham dan Pasar Surat Berharga Negara Pasar saham domestik pada triwulan I 2017 dan hingga April 2017 berada dalam tren yang menguat, terutama dipengaruhi oleh sentimen domestik. IHSG pada akhir triwulan I 2017 tumbuh positif dengan ditutup pada level 5.568,11, naik 5,12% (qtq) dari posisi akhir tahun 2016 pada level 5.296,71. Pada penutupan akhir April 2017, IHSG berada di level 5.685,30 atau naik 117 poin (2,10%, mtm). IHSG sempat beberapa kali menembus rekor tertingginya (all time high) hingga sempat menyentuh rekor tertinggi di level 5.726,53 (26 April 2017). Kinerja positif IHSG ditengah sentimen global yang masih mixed terutama dipengaruhi oleh fundamental ekonomi domestik yang solid sebagaimana tercermin dari beberapa indikator ekonomi domestik yang positif. Indikator positif tersebut antara lain data inflasi Maret yang lebih baik dibandingkan ekspektasi pasar, inflasi April yang diperkirakan masih terjaga, data cadangan devisa yang naik dan laporan keuangan triwulan I 2017 dari beberapa emiten berkapitalisasi besar seperti WIKA, WSKT, dan TLKM yang lebih baik dari perkiraan. Meski diwarnai aksi profit taking seiring concern valuasi IHSG yang dianggap cukup tinggi dan situasi politik dalam negeri, namun optimisme investor terhadap fundamental ekonomi RI menahan pelemahan lebih lanjut. Kinerja saham domestik sejalan dengan pergerakan bursa saham global yang juga tumbuh positif. Secara umum, bursa global bergerak positif pada triwulan I 2017. Bursa global kembali bergerak positif dengan pertumbuhan 1,1% (mtm) pada April 2017 (Grafik 44). Dibandingkan dengan bursa regional, kinerja IHSG pada April 2017 masih tercatat cukup baik dengan pertumbuhan di bawah bursa Filipina yang tumbuh paling tinggi sebesar 4,8% disusul

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Buletin Ekonomi Moneter dan Perbankan, Volume 19, Nomor 4, April 2017

Indonesia dan Hong Kong yang masing-masing tumbuh 2,10%. Penguatan bursa regional diantaranya didorong oleh rilis data manufaktur Jepang yang positif dan optimisme investor terhadap pertemuan Trump-Ji Xinping yang cukup positif. Menguatnya IHSG tercermin dari kinerja positif sebagian besar indeks sektoral. Kenaikan terutama terjadi pada sektor perdangan yang tumbuh 3,9% dan disusul sektor infrastruktur yang tumbuh 3,8% (Grafik 45). Kinerja positif saham sektor perdagangan sejalan dengan kenaikan harga batubara dunia akibat terganggunya produksi dan pengiriman batubara yang disebabkan oleh gangguan cuaca yang mempengaruhi produksi Australia. Sementara penguatan sektor infrastruktur didorong oleh rilis data emiten sektor yang membaik. Kenaikan harga saham berbasis konstruksi seperti Wijaya Karya dan Waskita Karya dipengaruhi oleh sentimen positif rilis laporan keuangan triwulan I 2017 yang sesuai ekspektasi pasar. Sedangkan koreksi pada saham di sektor pertanian sebesar -2,3% dipicu oleh futures CPO yang melemah akibat naiknya output Malaysia sehingga menyebabkan tertekannya saham pertanian. Kepemilikan saham oleh nonresiden meningkat. Investor non residen tercatat melakukan net beli sebesar Rp13,97 triliun pada April 2017, naik dibandingkan bulan sebelumnya dengan net beli sebesar Rp10,12 triliun sehingga secara total dana asing asing yang tercatat masuk ke pasar saham domestik sebesar Rp22,32 triliun. Dengan perkembangan tersebut, porsi investor nonresiden di pasar saham tercatat meningkat menjadi sebesar 39,5% (mtm).

World EM ASIA US (Dow Jones) Japan (Nikkei) England (FTSE) India (SENSEX) Hong Kong (Hang Seng) Shanghai (SHCOMP) Strait Times (STI) Kuala Lumpur (KLCI) Philippine Thailand (SET) Vietnam Indonesia (IHSG)

Properti

1,1 2,2

-2,3%

Perdagangan

-1,6

3,9%

Konsumsi

1,0

1,2%

Aneka Industri

2,1 -2,1

2,9%

Industri Dasar 0,0

0,3%

Infrastruktur

0

1

2

3,8%

IHSG

2,10

-1

2,3%

Pertambangan

4,8 -0,6 -0,6

-2

3,5%

Keuangan

1,6

-3

-0,9%

Pertanian

1,3 1,5

3

4

5

6 %

2,10% -5%

0%

5%

Sumber: Bloomberg

Sumber: Bloomberg

Grafik 44. HSG dan Indeks Bursa Global (mtm)

Grafik 45. Perkembangan Indeks Sektoral Triwulan I 2017

Sejalan dengan kinerja pasar saham, kinerja pasar SBN juga tercatat positif sepanjang triwulan I 2017 dan April 2017. Yield SBN masih melanjutkan tren penurunan meski dengan magnitude yang lebih kecil (Grafik 46). Secara keseluruhan, yield pada April 2017 tercatat turun sebesar 8 bps dari 7,10% menjadi 7,02% (mtm). Pada periode yang sama, yield jangka pendek, menengah dan panjang masing-masing turun sebesar 14 bps, 7 bps dan 1 bps menjadi

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6,54%, 7,00% dan 7,72%. Sementara itu, yield benchmark 10 tahun relatif stabil di level 7,05%. Tren positif di pasar SBN dipengaruhi oleh membaiknya kinerja ekonomi domestik yang tercermin dari rilis data makroekonomi seperti inflasi, cadangan devisa, serta didukung oleh tren kenaikan nilai tukar. Investor non residen tercatat melakukan net beli. Investor non-residen tercatat melakukan net beli sebesar sebesar Rp22,58 triliun di pasar SBN pada April 2017, atau turun dibandingkan aliran dana masuk pada bulan sebelumnya yang mencapai Rp31,32 triliun. Penurunan ini dipengaruhi oleh meningkatnya tensi geopolitik sehingga investor cenderung berhati-hati (cautious). Investor juga cenderung melakukan aksi tunggu (wait and see) terhadap perkembangan kondisi global terutama terkait perkembangan Pemilu Perancis, pertemuan pimpinan Eurozone, perkembangan tax reform AS, maupun kondisi domestik seperti keputusan S&P terkait dengan rating RI. Di samping itu, investor juga melakukan aksi ambil untung (profit taking) seiring tren penguatan SBN yang terus berlanjut. Dengan perkembangan tersebut, kepemilikan investor non residen di pasar SBN pada April 2017 tercatat naik menjadi 38,23% dari sebelumnya 37,39% (Grafik 47).

T Rp 10 9

%

T Rp 45

Net Beli Jual Asing - skala kanan 10YR

35

%

2.500

38,23

35 2.000

30

25

8

15

7

5

6

25

1.500

20 1.000

15

-5

5

-15

4

-25 Feb Apr Jun Ags Okt Des Feb Apr Jun Ags Okt Des Feb Apr Jun Ags Okt Des Feb Apr Jun Ags Okt Des Feb Apr

2013

2014

2015

2016

2017

Sumber: Bloomberg

40

10

500

5 0

0 Feb Apr Jun AgsOkt Des Feb Apr Jun AgsOkt Des Feb Apr Jun AgsOkt Des Feb Apr Jun AgsOkt Des Feb Apr

2013 2014 Pangsa Asing - skala kanan

2015 Total Asing

2016 2017 Total SBN

Sumber: Bloomberg, BI

Grafik 46. Yield SBN dan Net Jual/Beli Asing Triwulanan

Grafik 47. Perubahan Kepemilikan SBN Asing(mtm)

3.4. Perkembangan Sistem Pembayaran Posisi Uang Kartal yang Diedarkan (UYD) menurun. Posisi UYD pada akhir triwulan I 2017 tercatat sebesar Rp562,7 triliun, turun sebesar Rp49,8 triliun atau 8,1% (qtq) dibandingkan posisi akhir triwulan sebelumnya yang mencapai Rp612,5 triliun. Menurunnya posisi UYD tersebut seiring dengan arus balik uang dari masyarakat ke Bank Indonesia paska perayaan Hari Raya Natal dan Tahun Baru 2017 (seasonal factor). Secara tahunan, posisi UYD pada periode laporan tercatat Rp508,5 triliun atau tumbuh 10,7% (yoy) (Grafik 48). Peningkatan UYD tersebut sejalan dengan perkembangan perekonomian nasional yang tetap tumbuh positif.

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T Rp 700 600 500

% 30

UYD % ∆UYD yoy (skala kanan) % ∆UYD qtq (skala kanan)

25 20 15

400

10

300

0

200

-5

5

-10

100

-15 -20

0 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1

2013

2014

2015

2016

2017

Sumber: Bank Indonesia

Grafik 48. Perkembangan Uang Kartal yang Diedarkan (UYD)

Untuk mendukung penyediaan uang kartal di seluruh wilayah NKRI, Bank Indonesia terus memperluas jaringan distribusi uang nasional melalui pembukaan kas titipan pada perbankan yang menjangkau wilayah-wilayah terpencil atau terluar. Bank Indonesia telah membangun roadmap coverage jaringan distribusi uang sekaligus coverage layanan kas yang dapat menjangkau seluruh wilayah NKRI. Selama triwulan I 2017, terdapat penambahan 6 (enam) kas titipan yaitu di Liwa (Provinsi Lampung), Baturaja (Provinsi Sumatera Selatan), Buntok (Provinsi Kalimantan Tengah), Sumbawa (Provinsi Nusa Tenggara Barat), Labuha (Provinsi Maluku Utara), dan Polewali Mandar (Provinsi Sulawesi Barat). Dengan perkembangan tersebut, sampai dengan akhir triwulan I 2017 terdapat total 68 wilayah kas titipan dengan jumlah peserta 560 kantor bank. Penambahan kas titipan tersebut berkontribusi pada tingginya penarikan uang kartal oleh bank pengelola kas titipan dari Bank Indonesia. Selama triwulan I 2017, terjadi penarikan sebesar Rp13,9 triliun, tumbuh lebih tinggi secara signifikan sebesar 97,5% (yoy) dibandingkan triwulan yang sama tahun sebelumnya yaitu sebesar Rp7,1 triliun. Secara umum, sistem pembayaran yang diselenggarakan oleh Bank Indonesia dan industri berjalan dengan aman, lancar, efisien dan handal. Nominal transaksi Sistem Pembayaran Non Tunai oleh Bank Indonesia (SPBI) pada triwulan I 2017 mencapai Rp44.169,10 triliun atau turun 7,40% (qtq) dibanding triwulan sebelumnya yang tercatat sebesar Rp47.700,08 triliun. Penurunan nominal transaksi tersebut disebabkan oleh menurunnya nominal transaksi pada seluruh layanan SPBI (Tabel 5).

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Tabel 5. Perkembangan Nilai Transaksi Sistem Pembayaran Non Tunai Volume (Ribu Transaksi)

2016

Transaksi Sistem Pembayaran Nontunai

Q-I

Q-II

Q-III

BI-RTGS - Pengelolaan Moneter - Pemerintah - Masyarakat - Pasar Modal - Valas - PUAB - Lain-lain BI-SSSS SKNBI Debet - Cek - Bilyet Giro - Warkat Debet Lainnya Kredit Total

26.739,53 11.960,33 1.159,52 4.603,10 1.431,28 1.856,29 1.584,27 4.144,73 12.994,90 1.110,34 371,00 51,50 319,41 0,09 739,35 40.844,77

27.117,76 10.975,31 1.043,66 5.232,32 1.623,57 2.098,90 1.746,17 4.397,85 11.777,14 1.199,35 372,81 50,77 321,94 0,10 826,54 40.094,25

26.926,33 11.008,30 1.257,81 5.304,77 1.846,98 1.902,99 1.609,17 3.996,31 12.082,03 891,98 340,12 46,35 293,68 0,09 551,86 39.900,34

Q-IV

Total 2016

31.043,73 111.827,35 14.630,02 48.573,96 1.270,44 4.731,43 5.991,29 21.131,48 1.693,98 6.595,81 1.840,63 7.698,80 1.409,69 6.349,29 4.207,70 16.746,58 15.693,96 52.548,02 962,39 4.164,07 359,48 1.443,41 54,82 203,43 304,57 1.239,61 0,09 0,37 602,91 2.720,66 47.700,08 168.539,45

2017 Q-I

Naik (turun) QtQ

28.924,76 (2.118,98) 13.265,57 (1.364,44) 1.240,04 (30,40) 5.464,49 (526,79) 1.643,13 (50,85) 1.887,00 46,38 1.541,75 132,06 3.882,76 (324,93) 14.352,91 (1.341,05) 891,44 (70,95) 327,21 (32,27) 45,64 (9,17) 281,47 (23,10) 0,10 0,00 564,23 (38,68) 44.169,10 (3.530,98)

YoY 2.185,22 1.305,24 80,52 861,39 211,85 30,71 (42,52) (261,97) 1.358,01 (218,90) (43,79) (5,85) (37,95) 0,01 (175,11) 3.324,33

% Naik (turun) QtQ -6,83% -9,33% -2,39% -8,79% -3,00% 2,52% 9,37% -7,72% -8,55% -7,37% -8,98% -16,73% -7,59% 2,49% -6,42% -7,40%

YoY 8,17% 10,91% 6,94% 18,71% 14,80% 1,65% -2,68% -6,32% 10,45% -19,72% -11,80% -11,37% -11,88% 12,49% -23,68% 8,14%

Sumber: Bank Indonesia

Transaksi melalui BI-RTGS selama triwulan I 2017 tercatat turun, baik secara nominal maupun volume. Di sisi nominal, transaksi melalui BI-RTGS pada triwulan I 2017 tercatat Rp28.924,76 triliun, turun 6,83% (qtq) dibanding triwulan sebelumnya sebesar Rp31.043,73. Kondisi ini selaras dengan penurunan di sisi volume transaksi, yaitu turun sebesar 6,38% (qtq) (Tabel 6). Namun, secara tahunan, nominal dan volume transaksi melalui Sistem BI-RTGS pada triwulan I 2017 meningkat sebesar masing-masing 8,17% (yoy) dan 67,27% (yoy). Transaksi BI-SSSS turun secara nominal. Nominal transaksi BI-SSSS pada triwulan I 2017 mencapai Rp14.352,91 triliun atau menurun 8,55% (qtq) dibandingkan triwulan sebelumnya yaitu sebesar Rp15.693,96 triliun. Secara volume, transaksi BI-SSSS meningkat sebesar 1,38% (qtq) dari 72,31 ribu transaksi menjadi 73,30 ribu transaksi. Secara tahunan, nominal dan volume transaksi meningkat sebesar masing-masing 10,45% (yoy) dan 6,37% (yoy). Transaksi melalui SKNBI juga mengalami turun, baik secara nominal maupun volume. Nominal transaksi melalui SKNBI juga menurun yaitu sebesar 7,37% (qtq), dari Rp962,39 triliun menjadi Rp891,44 triliun. Sejalan dengan penurunan nominal transaksi, volume transaksi juga tercatat menurun sebesar 5,76% (qtq), yaitu dari 33.269,01 ribu transaksi menjadi 31.352,96 ribu transaksi. Adapun nominal transaksi kliring kredit pada periode laporan mengalami penurunan sebesar 6,42% (qtq), yaitu dari periode sebelumnya sebesar Rp602,91 triliun menjadi sebesar Rp564,23 triliun. Secara tahunan, nominal transaksi melalui SKNBI pada triwulan I 2017 turun sebesar 19,72% (yoy), sedangkan secara volume transaksi terjadi peningkatan sebesar 6,74% (yoy).

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Tabel 6. Perkembangan Volume Transaksi Sistem Pembayaran Non Tunai Volume (Ribu Transaksi)

2016

Transaksi Sistem Pembayaran Non Tunai

Q-I

Q-II

Q-III

BI-RTGS - Pengelolaan Moneter - Pemerintah - Masyarakat - Pasar Modal - Valas - PUAB - Lain-lain BI-SSSS SKNBI Debet - Cek - Bilyet Giro - Warkat Debet Lainnya Kredit Total

1.436,25 26,93 77,45 979,47 48,47 37,36 20,52 246,05 68,91 29.372,08 8.664,63 759,68 7.785,64 119,32 20.707,45 30.877,25

1.523,86 28,19 50,29 1.050,57 62,09 37,27 22,10 273,34 80,46 32.271,09 8.695,86 763,60 7.826,68 105,58 23.575,23 33.875,40

2.131,25 27,40 23,56 1.699,33 63,93 33,68 20,21 263,15 67,46 29.617,04 7.728,27 687,54 6.950,83 89,90 21.888,77 31.815,75

Q-IV

Total 2016

7.657,45 2.566,09 115,40 32,88 170,94 19,65 5.814,47 2.085,10 250,81 76,32 143,15 34,85 81,34 18,52 1.081,33 298,79 289,14 72,31 33.269,01 124.529,22 8.125,02 33.213,78 2.942,42 731,60 7.319,79 29.882,94 388,42 73,62 25.143,99 91.315,44 35.907,41 132.475,80

2017 Q-I

Naik (turun) QtQ

YoY

966,17 (163,67) 2.402,42 6,95 1,00 33,88 (64,38) (6,58) 13,07 967,02 (138,61) 1.946,49 27,31 (0,53) 75,78 (5,10) (2,59) 32,26 (0,95) 1,05 19,57 35,32 (17,42) 281,37 4,39 1,00 73,30 1.980,88 31.352,96 (1.916,05) (581,94) (1.121,55) 7.543,08 (104,18) (76,10) 655,50 (950,10) (484,26) 6.835,53 (67,28) (21,58) 52,04 3.102,43 23.809,88 (1.334,11) 2.951,44 33.828,68 (2.078,72)

% Naik (turun) QtQ -6,38% 3,04% -33,48% -6,65% -0,70% -7,42% 5,68% -5,83% 1,38% -5,76% -7,16% -10,40% -6,62% -29,31% -5,31% -5,79%

YoY 67,27% 25,79% -83,13% 98,73% 56,34% -13,65% -4,64% 14,35% 6,37% 6,74% -12,94% -13,71% -12,20% -56,38% 14,98% 9,56%

Sumber: Bank Indonesia

Sistem pembayaran yang diselenggarakan oleh industri juga mengalami penurunan. Pada triwulan I 2017, nominal transaksi ritel masyarakat yang menggunakan instrumen Alat Pembayaran dengan Menggunakan Kartu (APMK) mengalami penurunan sebesar 4,10% (qtq) menjadi Rp1,495 triliun. Dari sisi volume, transaksi juga menurun 3,74% (qtq) menjadi 1.395,5 juta transaksi. Namun, secara tahunan transaksi menggunakan APMK masih mengalami peningkatan, baik secara nominal maupun volume yakni masing-masing sebesar 9,25% (yoy) dan 7,86% (yoy). Pada jenis uang elektronik, nominal transaksi meningkat 7,86% (qtq), sedangkan volume transaksi menurun 12,98% (qtq). Namun, secara tahunan nominal transaksi maupun volume transaksi uang elektronik masih meningkat, yaitu masing-masing sebesar 59.01% (yoy) dan 29.08% (yoy).

IV. PROSPEK PEREKONOMIAN Pertumbuhan ekonomi Indonesia pada tahun 2017 masih sesuai dengan prakiraan sebelumnya. Bank Indonesia memperkirakan pertumbuhan ekonomi pada tahun 2017 lebih tinggi dari tahun 2016 yaitu pada kisaran 5,0 – 5,4%. Kinerja perekonomian domestik yang meningkat diperkirakan bersumber dari peningkatan investasi dan perbaikan ekspor. Investasi diperkirakan meningkat sejalan dengan berlanjutnya pembangunan infrastruktur dan mulai membaiknya investasi swasta nonbangunan. Sementara itu, perbaikan kinerja ekspor akan ditopang

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oleh kenaikan harga komoditas. Dari sisi lapangan usaha (LU), meningkatnya pertumbuhan ekonomi akan ditopang oleh membaiknya kinerja industri pengolahan, serta pengangkutan dan komunikasi sejalan dengan pemulihan ekonomi global dan perbaikan ekonomi domestik. Dalam periode yang sama, inflasi diperkirakan tetap terkendali dalam kisaran sasarannya. Inflasi tahun 2017 diprakirakan lebih tinggi dibandingkan tahun sebelumnya namun tetap terkendali dalam kisaran targetnya sebesar 4 + 1%. Tekanan inflasi 2017 diperkirakan bersumber dari beberapa kebijakan terkait administered prices seperti terkait tarif tenaga listrik, harga BBM non subsidi (selain premium dan solar), dan biaya perpanjangan STNK. Sementara itu, tekanan inflasi inti tahun 2017 diprakirakan moderat dan tekanan inflasi volatile food diperkirakan terkendali. Bank Indonesia akan terus mencermati beberapa risiko yang membayangi perekonomian Indonesia ke depan. Dari sisi global, risiko tersebut antara lain berkaitan dengan dampak dari kenaikan Fed Fund Rate, kebijakan fiskal dan perdagangan serta penurunan besaran neraca bank sentral AS, dan perkembangan geopolitik di beberapa kawasan, khususnya di Semenanjung Korea. Dari sisi domestik, beberapa risiko yang tetap perlu diwaspadai adalah terkait dampak penyesuaian administered prices terhadap inflasi, serta berlanjutnya konsolidasi korporasi dan perbankan. Untuk memitigasi berbagai risiko tersebut, Bank Indonesia terus memperkuat bauran kebijakan moneter, makroprudensial, dan sistem pembayaran guna menjaga stabilitas makroekonomi dan sistem keuangan. Bank Indonesia juga terus mempererat koordinasi bersama Pemerintah dalam rangka pengendalian inflasi agar tetap berada pada kisaran sasaran dan mendorong kelanjutan reformasi struktural agar dapat mendukung pertumbuhan ekonomi yang berkesinambungan.

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Halaman ini sengaja dikosongkan

Financial Intermediation Sector in Indonesia’s Production Pyramid

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FINANCIAL INTERMEDIATION SECTOR IN INDONESIA’S PRODUCTION PYRAMID Martin P.H. Panggabean1

Abstract

This paper investigates the importance of financial intermediation sector in the inter-industry context, using input-output tables from 1995, 2000, 2005, and 2010. Known as matrix triangulation problem, the problem was mathematically categorized as NP-Hard where exact solution to real-world data cannot be ascertained. The algorithm used in this paper was proposed by Chanas-Kobylanski. The computation results confirm that the financial intermediation sector is consistently among the most important sector in the production structure of the Indonesian economy by serving non-negligible input to most sectors in the economy. This paper shows that the sector has mixed record toward small-scale businesses. Financial intermediation sector supports directly and indirectly retail trade, agricultural and food-beverages sectors. The relatively large share of input from financial sector implies the high interest rate charged by banks to the retail trade sector, which in turn reflects high risk associated with Retail Trade (and SMEs in general). Thus tightening and improving efficiency between financial intermediation and retail trade sector will not only increase SMEs participation in the economy but also improve the economic activities in the agricultural and food-beverages sectors which combined contributes to around 19 percent of Indonesia’s GDP.

Keywords: Financial sector; Input-output table; Indonesia; Triangulation; NP-Hard; Chenery-Watanabe; Chanas-Kobylanski; Meta-heuristic; JEL Classification: D57; C67; F43; G21;

1 Lecturer, School of Business and Management, Institut Teknologi Bandung ([email protected]). The author wishes to acknowledge technical and computational assistance provided by Daniel Allan Juvito and Stefan Batara Panggabean.

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I. INTRODUCTION Financial sector is deemed critical to the functioning of a modern economy (Laeven and Valencia 2008). Indeed, the 1997-1998 financial crises in Indonesia (Radelet and Sachs 1998), near economic meltdowns both in the US (2008) and in Europe (2010) all serve to further reemphasize the importance of the financial sector in an economy (Laeven and Valencia 2008; Brunnermeier 2008). The attention afforded to the importance of the financial sector is exemplified by the action of the Basel Committee on Banking Supervision (BCBS) which put forward increasingly stringent global banking regulations in the belief that banks’ failure may cause systemic risk which can exacerbate or even lead to the collapse of economies at the global level (BlundellWignall and Atkinson 2010). In Indonesia, following the 1997-1998 near-collapse of the economy, financial intermediation sector (especially banks) has undergone rapid changes. Some of those changes include a reduction in the number of banks, a relaxation of foreign ownership in domestically owned banks, a rapid implementations of Basel regulations, and an adoption of a host of regulations in the insurance industry strengthening capital base. However, the most radical change implemented recently is the separation of banking supervision task from the central bank (Bank Indonesia) (Iyer and Lane 2013). Beginning 2014, an independent Otoritas Jasa Keuangan (OJK, Financial Services Authority) that combined the banking supervision part of Bank Indonesia with the non-banking supervision part of the Ministry of Finance (Bapepam) became effectively operational. Despite the assumed importance of the financial intermediation sector in an economy, especially the dominance of the banking sub-sector in Indonesia, several questions remain unanswered. First, how should the importance of a sector to the economy be defined? Economic importance as measured by GDP share of a sector does not seem to be adequate since GDP share of a sector is measured from the final demand perspective, thereby ignoring the importance of financial sector services in the entire production (including inter-industry transactions) process. An inter-dependent nature of a modern economy implies that the rest of the economy is using financial intermediation sector services in its production process. Thus an effort must be made to disentangle the inter-dependences among sectors to arrive at a coherent measure of the importance of financial intermediation sector to the economy. To the best our knowledge, this approach (the Chanas-Kobylanski approach) has never been used in the context of the Indonesian economy, thus this paper breaks new ground in terms of methodology. Second, once a measure of relative importance of a sector can be proposed, this paper empirically calculate the importance of the financial intermediation sector. Emprical section will address questions such as: is financial intermediation sector more important than agricultural sector? How has the importance of the financial intermediation sector changed over time?

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This paper measures and ranks the importance of financial intermediation sector in the Indonesian economy in selected years between 1995 - 2010 period using national input-output data. The findings on the structure of the Indonesia economy will then be used to analyze several government development strategies, especially with respect to its effort in increasing the contribution of small-to-medium scale enterprises to the Indonesia’s economy. The structure of the paper is as follows. After this introductory part, the second part of the paper outlines the theoretical and methodological frameworks used in the paper. Section three explain the computational algorithm, including the framework introduced more than 50 years ago by Chenery-Watanabe (1958), where the empirical and computational framework used at that time was deemed inadequate since it did not reflect the mathematical complexity in finding the optimal solution. Since then various methodological has been and is still being developed. A brief survey of available methods (including the preferred method) is also given in this section, including the outlines the data used for analysis. The fourth part presents computational results of the paper, including its policy implications and recommendation regarding developments of the small and medium enterprises in Indonesia. The final section summarizes and concludes.

II. THEORY The use of an Input-Output table as the basic data for analysis directly assumes that production function of an economy is associated with a specific functional class known as the Leontief function (Varian 1992). Given the L-shaped isoquant of the Leontief production function, substitution among production factors is not possible. Thus, all production factors needed must enter in fixed proportions. The unavailability of certain input (as supplied by other sectors) directly inhibits production activity since factor substitution is not possible. The direct implication of the Leontief function is that for production to take place then all input must be available at a certain proportion (Varian 1992). Shortage in the availability of certain factors will limit output in all directly related production activities. In this sense, the most important and critical sectors of an economy are the one whose output is needed as an input by all (or nearly all) production process. To create an order of importance, a matrix triangulation is needed. However, the published Input-Output table does not directly conform to the required triangle form. Instead, the original ordering of the input-output Table in Indonesia follows the classification of Indonesia’s GDP. For example, at the nine-sectors aggregation level, all the primary and extractive sectors (such as agriculture and mining) are listed first. Secondary sectors such as manufacturing and energy production are reported next. Tertiary sectors (services such as retail and wholesale trade, transportation, and hotels) are placed close to the bottom of the reported table. As a result, the input-output table follows the GDP’s structure. i.e., ordering of sectors does not reflect the importance of the sector in supporting general production activities.

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The ordering of sectors using primary-secondary-tertiary sector’s ordering also fail to take into accounts the issue of connectedness (or interrelatedness) in an economy (Hewings and Jensen 1987). The interrelatedness can be found by rearranging the sectors in such a way so that the skeleton of, and hierarchy in, production system can be seen. Such hierarchy shows a linear dependence and unidirectional flow in a production sector of an economy. Hence an important sector, regardless of its primary-secondary-tertiary distinction, lies at the bottom of the triangle and provides a roughly unidirectional flow to other sectors above it. Once such matrix is obtained, this paper is concerned about the location (hence the role) of the financial intermediation sector in Indonesia’s triangulated Input-Output table. While there are conflicting evidences of whether global financial integrations positively affects growth (Bernini 2011), there is evidence that financial development is linked to future growth (Demirgüç-Kunt and Detragiache 1998). Also, (King and Levine 1993) using data for 80 economies in 30-years period, found that financial development has positive correlation with future rates of economic growth. Yet another research (Christopoulos and Tsionas 2004) concluded that causality runs from financial development to growth. Finally, (Fisman and Love 2004) conducted research on industry level growth. The most pertinent result of this study is that financial development benefits disproportionally those industries with strong growth opportunities, regardless their reliance on external finance. Hence empirical evidence favours the positive role of financial intermediation on the growth of other sectors. The role of the financial intermediation on the growth of the small-scale enterprises is also a concern to policy-makers as well the public in Indonesia. Financial development leads to sector’s growth, but the magnitude of the positive response differs according to the firm size distribution and industry-specific research and development intensity (Beck et al. 2008). All else being equal, Beck et al (2008) shows that industries composed by a greater proportion of small firms grow faster than the others during the process of financial development, Finally, the role of interest rates is also an important feature in financial development process. In macroeconomics, interest rates play an important role in the inter-temporal allocations of resources (Romer 1996). In inter-industry analysis, the role of interest rates takes additional dimension. For financial intermediaries, translating the macro policy rate into lending rates must take into accounts several factors: the cost of funds (which is highly correlated to policy rate), operating expenses, loan losses and provisions, and finally profits needed to expand its capital base in preparation for planned future growth (Fernando 2006). Specifically, loan losses and loan provisions must be adjusted to represent risk factors of loans. The risk factors differ among sectors, resulting in some sectors receive more favourable interest rates compared to other sectors. The difference in interest rates for loan, in turn, translated into faster growth. Hence it is important that the relatively important sectors received lower interest rates that can then be translated into faster growth and an improved ability to support other sectors.

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III. METHODOLOGY 3.1. Computational Algorithm Chenery-Watanabe (1958) proposed to show the structure of an economy by simultaneously permuting the rows and columns of an input-output matrix of the economy in such a way to maximize the sum of either lower/upper-triangular matrix. For upper triangular matrix, such an arrangement places sector that serves the most sectors in the economy at the top of the (rearranged) matrix, followed by sectors that tend to deliver less output to inter-industry transaction and more toward end users. The bottom of the inverted pyramid will be occupied by sectors that deliver output mostly for final demand. Triangulation problem is mathematically stated as follows. Let matrix E = Eij be an n x n arbitrary square matrix. Each element Eij represents monetary value of output of sector i delivered as input to sector j. Then the sum of the upper triangular of matrix E is defined as (Campos, Laguna, and Martí 1999; Chanas and Kobylański 1996):

C p p  pm

m

m

i 

j i

¦ ¦e

pi p j

Where p denotes position of the i-th row and i-th column in matrix E. Presented this way, triangulation can be seen as a combinatorial optimization achieved by simultaneous permutation of rows and columns of the matrix E to maximize the sum of upper triangular matrix E (Chang et al. 2013). Exact solution to this optimization of small-sized matrix E is trivially achieved through an exhaustive permutation of all possible rows combination. In contrast, a square matrix with 10 rows will already have 3,628,800 possible (factorial of 10) rearrangements of rows (and columns), and hence exhaustive permutation impractical since computation time will very large (Martí and Reinelt 2011). As such, the large-sized triangulation problem falls under the category of NP-Hard (Hartmanis 1982). Chenery-Watanabe (Chenery and Watanabe 1958) put row with the biggest sum at the top of the matrix, followed by row with the next largest sum. The lower-most row will have the smallest row sum. Hence leading to a inverted-pyramid-shaped upper-triangular matrix. This algorithm have been superseded by other algorithms through various improvements. Broad overview of the developments along with the performance of the major algorithms in the available algorithms can be found in two articles written by Marti, Reinelt, Duarte (2012) and Garcia et al. (2006) (Martí, Reinelt, and Duarte 2012; Garcia et al. 2006). This paper chose to use the Chanas and Kobylanski (Chanas and Kobylański 1996) multistart method (CK, henceforth) as the method to triangulate the Indonesian input-output matrix. Comparisons made among several methods (Martí, Reinelt, and Duarte 2012; Campos et al.

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2001) shows that the CK approach is able to find a high quality solution similar to other metaheuristic approaches, with only negligible differences with the best solution found. In terms of computation time, while not the fastest, the CK method is also quite fast. In this paper, the solutions were arrived at in less than one second for each input-output tables being analyzed. The algorithm was coded in C++ and run on a notebook PC with an i3 processor. The algorithm of CK was described by Chanas-Kobylanski (1996) as:

The pseudo-code of the algorithm is the following: function sort(p) for i = 1 to length(p)-1 if cost(p) < cost(swap(p, i, i+1)) p = swap(p, i, i+1) endif endfor return p end function reverse(p) for i = 1 to floor(length(p)/2) p = swap(p, i, length(p)-i+1); endfor end function chanas-kobylanski(p) //==== SORT* ==== p = sort(p) while cost(p) < cost(sort(p)) p = sort(p) endwhile //=============== //==== (SORT*oReverse)* ==== p = sort(reverse(p)) pnew = p while cost(pnew) < cost(sort(pnew)) pnew = sort(pnew) endwhile while cost(p) < cost(pnew) p = sort(reverse(pnew)) pnew = p while cost(pnew) < cost(sort(pnew)) pnew = sort(pnew) endwhile endwhile //============================ end

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The outline of the algorithm works as follows. Let the initial permutation be P1. Based on this permutation, the objective function value C(P1) is calculated. The algorithm then proceed through P1, and compare the value of C(P1) with the value C(P1new) where P1new is P1 where two adjacent elements m and m-1 (which are the last and second-to-last elements of P1) are swapped. If C(P1new) is larger than C(P1), then set P1 as P1new. Using the new P1 ordering, this comparison process is continued as P1(m-1) is then compared against P1(m-2), and so on. This is the so-called SORT part that results in permutation P1final. Set P1 as P1final, the SORT process is repeated until no further improvement is possible. This is the SORT* stage of the CK algorithm. The critical part of the CK algorithm appears when the SORT* is followed by the REVERSE stage. The optimal solution P1final from the SORT* is reversed such that the first element in P1final become the last element, and vice versa. The new, reversed P1final become the new permutation P2, and the SORT* algorithm begins again. Once the optimal P2 is obtained then it is reversed again, and the algorithm continued until no further increase in objective value is possible. Three things are important to note. First, the CK method is based on the symmetry property of the LOP (Chanas and Kobylański 1996). If a permutation (p1, p2, …pm) is an optimal solution to the maximization problem, then the permutation (pm, pm-1, …, p1) is an optimal solution to the minimization problem. Second, the re-starting from a reversed local optimum is expected to induce a diversification component over the search. Third, it is possible to iterate the entire process multiple times and choose which final permutation gives the best possible objective function value.

3.2. Data Data for this paper is based on the World Input-Output Table (WIOT) described in detail in Timmer (Timmer et al. 2015). This data set contains linked Input-output tables for 40 countries comprising 85 percent of the 2008 world GDP. Since inter-linkage among countries is not the focus of this paper, only the Indonesia’s part of the WIOT table are being used. The choice of the WIOT data source has both positive and negative implications. On the negative side, the input-output (IO, henceforth) tables are limited to only 35 sectors, mostly at two-digit ISIC rev. 3 groups. In other words, the Indonesia’s economy is divided into 35 sectors. In Timmer (2015) the choice to divide the economy into 35 sectors was dictated by the necessity to impose the same number of sectors across the 40 countries included in the database. As a comparison, the 2010 (latest) Indonesia’s IO table contains 185 sectors. However, some other countries included in the WIOT database may have reliable data for only at most 35 sectors. Thus Timmer’s (2015) choice for 35 sectors was dictated by the lowest common denominator. In practice, however, this paper only uses 33 sectors. Sector 19 (“Sale and repair

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of motor vehicles and motorcycles; retail sale of fuel “) and sector 35 (“Private households with employed persons”) have empty values for Indonesia’s data. These two sectors are dropped from further analysis. The complete list of the 33 sectors included in the database is given in appendix A. On the positive side, it must be emphasized that annual time-series data for Indonesia’s IO table (and for most countries in the world) is non-existent. Thus, despite the limited number of sectors available for analysis, the construction and availability of annual IO database from the WIOT becomes very important for analyzing structural changes conducted in this paper. Actual survey-based constructions of IO table for Indonesia were conducted for the years 1995, 2000, 2005, and 2010 (B. P. Statistik 2007, 2000; Statistika 1995; B. Statistik 2015). Input-output tables between the intervening years where actual data were not available were constructed using bi-proportionate adjustment, also called RAS adjustment (Miller and Blair 2009; Parikh 1979). Hence, IO tables for the years 2001, 2002, 2003, 2004 were based on IO table in 2000 but the IO coefficients are adjusted (through RAS method) to match the actual GDP by expenditures and GDP by sector’s value-added. The reader is also referred to Timmer (2015) for further detail regarding the adjustment methodology. The major motivations for new construction of the IO table in 1995, 2000, 2005, and 2010 are to incorporate structural changes occurring the economy, as well as to fine-tune definition of sectors as new data becomes available. This paper uses the input-output tables for the year of 1995, 2000, 2005 and 2010. The IO tables used in this paper are expressed in current US Dollars. Conversion from Indonesia’s Rupiah to US Dollars used market exchange rates. The IO tables are derived from transactions quoted in basic price, defined as producers’ price excluding transportation cost (price at fob, free-on-board, excluding trade margin as well as excluding taxes/subsidies). The use of basic price is following international best practice espoused by the United Nations (Dietzenbacher et al. 2013). There are three distinct ways that the IO tables can be analyzed. The most direct approach is to use the 33-by-33 inter-industry in its raw form, i.e. in nominal amount as given by the WIOT database. This is the approach that was used in Chenery-Watanabe (1958) and will also be used in this paper. The second approach (Kondo 2010, 2014) is to convert a raw IO table into its corresponding Leontief matrix. A Leontief coefficient of a particular column in the IO table is calculated as the proportion of that column with respect to the sum of that particular column (i.e. total input, including primary inputs such as labor and capital). This results in a matrix whose elements are fractions whose values lies between zero and one. The third approach (Kondo 2014) is to start from Leontief matrix obtained in the second approach. Any element of the Leontief matrix smaller than 1/n (where n is the row of the IO matrix used in the analysis) is converted to zero.

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In all three approaches, the main diagonal of the matrices is zeroed. This is to preclude the possibility that a sector can become important only because it is a major buyer of its own product. As stated in the theoretical section, the importance of a sector is defined through its contribution in the production of other sectors.

IV. RESULTS AND ANALYSIS In part two it is proposed that, along the Chenery Watanabe (1958) line, Linear Ordering ranking (matrix triangulation) can be used to measure sector’s importance in terms of interindustry transactions. It was also established in part two that triangulation of a matrix is an NP-Hard problem, and hence a meta-heuristic model with a multi-start approach should be used. For a relatively small-sized matrix, the Chanas-Kobylanski (CK, henceforth) approach is not disadvantaged compared to other, more recent, approaches. Hence the CK approach is used in this paper. After preliminary data analysis, the empirical importance and rankings of Financial sector between 1995 and 2011 is reported.

4.1. Preliminary Data Analysis Analysis of the raw data from Timmer’s (2015) database suggests a declining importance of the financial intermediation sector. Two initial evidences are provided: from the GDP perspective and from the perspective of inter-industry total transaction table. From the GDP perspective, using 2010 data as a case in point, financial intermediation sector accounts for only 1.65 percent of Indonesia’s GDP. This is 0.52 percentage points lower compared to the 2005 GDP contribution of the sector. Thus there is a steady decline in the contribution of financial intermediation sector to the GDP. In contrast, during the same period the share of construction sector has increased by 8.09 percentage points (from 15.81 percent in 2005 to 23.90 percent share of GDP in 2011). Also the contribution of the foodbeverage-tobacco sector has increased by 1.42 percentage points in the same period (with GDP contribution of 10.63 in 2005 to become 12.05 percent GDP share in 2010). From the inter-industry transaction perspective, a similar picture of declining contribution can also be seen. In 2005, financial intermediation sector provided 3.48 percent of all inputs used in the production. By 2010, contributions of the sector to the inter-industry transactions were down to 2.84 percent (reduced by 0.64 percentage points). Inter-industry transactions in 2010 were dominated by agricultural sector (15.09 percent share of total inter-industry transaction) and mining-quarrying sector (11.65 percent share of total inter-industry transaction). While both perspectives provide a similar picture of a small and declining share of the financial intermediation sector in the Indonesian economy, the results are valid if it is assumed that GDP share and total inter-industry input share are good indicators of the importance of an

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economic sector. If this assumption is accepted, then financial intermediation sector is ranked 18th in terms of importance from the perspective of GDP share, and ranked 12th in terms of importance from the perspective of contribution to total inter-industry input.

4.2. Rank of Importance This section provides importance ranking on the entire 33 sectors available in the IO tables of the Indonesian economy used in this analysis. As a reminder, part two of this paper proposed that calculation of the ranking uses direct raw data. All elements of the main diagonal of the matrix are set to zero to further emphasize the role of inter-industry transaction in determining rank of importance. Using the CK approach set forth in the second part of this paper, the relative rankings of financial sector in Indonesia are computed at four points in time: 1995, 2000, 2005, and 2010. Computation time for each of the years took less than one second on an i3 notebook, confirming that CK method is not disadvantaged in terms of speed. Table 1 shows improvements that are made to the initial value of the upper-triangular matrix after the triangulation procedures take place. In general, Table 1 shows that triangulations have succeeded in performing its task. Table 1. Improvement in Linearity Measures Due to Triangulation 1995

2000

2005

2010

Initial Linearity (%)

59.5704

63.4284

59.1612

59.7026

Final Linearity (%)

86.1119

87.6751

82.4739

82.6626

Change (Final–Initial)

26.5415

24.2466

23.3127

22.9600

In 1995, for example, index of linearity of the published data (the ratio between value of the upper-triangular matrix to the total value of the matrix) was 59.57 percent. After the triangulation (rearranging of rows and columns) takes place, the index of linearity becomes 86.11 percent, an improvement of 26.54 percentage points. This substantial improvement over the initial (published) arrangement shows that better rearrangements (in the sense of improved triangulation) of rows and columns have taken place. In general, note that the CK method improved linearity by at least 22 percent (in 2010). However, there is a tendency for improvements linearity index to decline over time. The trend is to be expected because the Indonesian economy (and inter-industry transactions) grows more complex over time.

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Table 2 contains results that show the top five most important sectors on years under consideration. The first part of Table 2 presents sector’s rankings based on direct IO data. Financial intermediation sector ranked fifth in 1995, ranked second in 2000, and ranked first in 2005 and 2010 in terms of importance to the inter-industry transactions, implying that the sector provides substantial amount of inputs to many sectors in the Indonesian economy. Table 2. Top Five Highest-Ranked Sectors After Triangulation Rank

2000

2005

2010

1

Electricity (17)

1995

Financial Intermediati (27)

Financial Intermediation (27)

Financial Intermediation (27)

2

Real Estate (28)

Renting of Machinery (29)

Machinery (13)

Real Estate (28)

3

Pulp, Paper (7)

Mining and Quarrying (2)

Real Estate (28)

Other supporting transport activities (25)

4

Post and telecommunication (26)

Real Estate (28)

Mining and Quarrying (2)

Post and telecommunication (26)

5

Financial intermediation (27)

Transport equipment (15)

Coke, refined petroleum and nuclear fuel (8)

Air transport (24)

This result is in direct contrast with the simple use of GDP share and total inter-industry share as a measure of importance: while the agricultural sector and the mining-quarrying sector account for 15.09 percent and 11.65 percent share of total inter-industry input, these sectors’ importance were eclipsed by financial intermediation sector even though the finance intermediation sector only contribute 2.84 percent to total inter-industry in 2010. The Chenery-Watanabe (1958) theory proposed rearranging sectors not only in terms of magnitude a sector’s contribution to other sectors, but also whether it directly serves many or only few sectors in the economy. Table 3 shows that indeed financial sector serves many sectors. Table 3. Support To Other Production Activities Sector

Description

GDP Share (%)

Inter-industry Share (%)

Number of Sectors Directly Served

1

Agriculture, hunting, forestry and fishing

6.95

15.09

22

2

Mining and quarrying

5.40

11.65

19

23

Food, beverages and tobacco

12.05

8.27

21

27

Financial Intermediation

1.65

2.84

31

28

Real estate activities

0.47

5.27

32

25

Other supporting transport activities

0.45

5.19

24

5

Leather, leather products and footwear

0.47

0.14

9

30 29

Public administration and defense Renting of machinery & equipment

5.11 2.11

0.39 0.04

18 10

* Note: negligible amounts of inter-industry transactions (cell with values less than USD 9 millions) have been omitted from the calculations of the last columns.

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The construction of Table 3 is based on inter-industry table for the year 2010, and serves as an example. Thus the financial intermediation sector output is being used by 31 other sectors in the economy (out of the possible maximum of 32 sectors). The sector that received only negligible input from the financial intermediation sector is the “coke, refined petroleum and nuclear fuel” sector. In contrast, the agricultural sector with 15.09 percent share in total inter-industry transactions serves only 22 other sectors and hence ranked lower in terms of inter-industry importance. At the other end of the spectrum, those sectors that contribute little to other sectors are leather and leather product sector (0.14 percent inter-industry share and directly supplies to nine other sectors), public administration and defense sector (0.39 percent inter-industry share and directly supplies to 18 other sectors, mostly with small nominal values), and renting of machinery and equipment sector (0.04 percent inter-industry share and directly supplies to 10 sectors). Public administration sector as well as leather and leather-product sectors serves final demand, and contribute less to inter-industry transactions. While it is not the main goal of this paper, an important observation will be made regarding the role of the manufacturing sector. Throughout the years under analysis, the manufacturing sector’s best performance was when it ranked 23rd in 2005. Thus the role of manufacturing itself has never been important to date. Given that economic progress is often symbolized by the activity in the manufacturing sector, further research should be done regarding the root causes of this low ranking in inter-industry performance.

4.3. Financial Intermediation Linkages with SME Sectors Despite the importance of the financial intermediation sector, not all sectors was provided enough input from the financial intermediation sector. Thus this subsection will show economic sectors that rely on financial intermediation sector. Table 4 shows share of input obtained from financial intermediation sector in five sectors. Table 4. Share of Input Obtained from Financial Intermediation Sector Sector Real estate activities Retail trade and repair, except of motor vehicles and motorcycles;

Share of Intermediate Input (%) 12.01 11.48

Wholesale trade, except of motor vehicles and motorcycles

11.48

Agriculture, hunting, forestry and fishing

0.73

Food, beverages and tobacco

0.17

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Three sectors that use the most input from financial intermediation sector are real estate, wholesale trade, and retail trade. Each of these sectors obtained more than 10 percent of their total intermediate input from financial intermediation. (Intermediate input is defined here as excluding imported input and primary input such as labor and capital). At this point we need to compare three sectors are populated by micro- and small-scale enterprises (Mourougane 2012). On one hand, retail trade uses substantial amount of input from financial intermediation sector. In contrast, despite having many micro- and small-scale enterprises, agricultural sector utilizes input from financial intermediation only in a minor fashion. A further research on this area is needed as the Indonesian government pushes its financial inclusion efforts forward. Nevertheless, this paper provides a partial explanation to the contrasting situation between retail trade and agricultural sectors. Further analysis of the 2010 input-output table reveals that retail trade sector directly uses output from agricultural sector (12.86 percent) and foodbeverage sector (17.69 percent) as its major intermediate input. Hence, while only having weak direct linkages, financial intermediation sector indirectly drive growth in both agricultural and food-beverages sectors through its strong involvement in the retail sector. Thus retail sector provides a good entry point for the government policies (through banks) to reach SMEs both directly and directly. However, the role of the financial intermediation sector toward the retail sector is not uniformly supportive. The rather large (11.48 percent) input of retail trade obtained from the financial intermediation sector can be seen in two different, and competing, perspectives. First, as has been discussed, the large input share confirms the importance of the financial intermediation sector to the retail trade sector’s cost structure. Second, and more importantly going forward, a competing perspective suggests that such a large share of the input from financial intermediation sector may be unwarranted because it can be too high. Data from Bank Indonesia shows that indeed retail trade sector have a large pool of non-performing loans coming from SMEs (Keuangan 2015). Hence banks charge much higher interest rate for loans to SMEs (which corresponds to retail trade sector). Anecdotal evidence suggests that while large corporations were charged around 11 percent interest rate per annum, SME pays interest rate in excess of 20 percent. Thus large input share from financial intermediation sector in the trade and retail sector is a not only a reflection of importance of the financial intermediation sector but also a reflection of risk in the retail trade sector. What exactly need to be done, and what policies need to be in place, will be an exciting field of future research. What this paper has asserted and showed is that financial intermediation sector is one of the most important sector in the Indonesian economy which, directly and indirectly, affects three large employment-generating sectors: retail, agriculture, and food-and-beverages sectors.

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V. CONCLUSION Using Chenery-Watanabe (1958) framework, combined with the Chanas-Kobylanski (1996) solution method, this paper establishes that, despite a trend of declining contribution to the GDP, financial intermediation sector remains one of the most important sector in the Indonesian economy since 1995. Financial sector is one the foundation in Indonesian economic structure, in terms of inter-industry linkages, by serving a non-negligible amount of input to a large number of sectors. Given the importance of the financial intermediation sector, this paper shows that the financial intermediation sector has mixed record toward small- and medium-scale enterprises. Financial intermediation supports retail trade sector while playing only a negligible role in the cost structure of the agricultural sector. Also, inasmuch as retail trade uses a relatively large share of input from financial intermediation sector, such cost structure implies the high interest rate charged by banks to the sector, which in turn reflects high risk associated with retail trade and SMEs in general. The analysis in paper suggests further studies in how a more focused government efforts to improve efficiency while reducing risk in the SMEs and in the retail trade sector will not only increase SMEs participation in the economy but also improve the economic activities in the agricultural and food-beverages sectors which the combination of these two sectors contribute to around 19 percent of Indonesia’s GDP.

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REFERENCES Beck, Thorsten, Asli Demirguc-Kunt, Luc Laeven, and Ross Levine. (2008). “Finance, Firm Size, and Growth.” Journal of Money, Credit and Banking. 40 (7). Wiley Online Library: 1379–1405. Bernini, Michele. (2011). Financial Development and Industrial Structure in Developing and Emerging Economies: A Microeconomic Analysis. Blundell-Wignall, Adrian, and Paul Atkinson. (2010). Thinking beyond Basel III. OECD Journal: Financial Market Trends 2010 (1). Organisation for Economic Cooperation and Development (OECD): 9–33. Brunnermeier, Markus K. (2008). Deciphering the Liquidity and Credit Crunch 2007-08. National Bureau of Economic Research. Campos, Vicente, Fred Glover, Manuel Laguna, and Rafael Martí. (2001). An Experimental Evaluation of a Scatter Search for the Linear Ordering Problem. Journal of Global Optimization. 21 (4). Springer: 397–414. Campos, Vicente, Manuel Laguna, and Rafael Martí. (1999). Scatter Search for the Linear Ordering Problem. New Ideas in Optimization. McGraw-Hill New York, 331–39. Chanas, Stefan, and Prezemysław Kobylański. (1996). A New Heuristic Algorithm Solving the Linear Ordering Problem. Computational Optimization and Applications 6 (2). Springer: 191–205. Chang, Ting Fa Margherita, Livio C Piccinini, Luca Iseppi, and Maria Antonietta Lepellere. (2013). The Black Box of Economic Interdependence in the Process of Structural Change. EU and EA on the Stage. Ital J Pure Appl Math 31: 285–306. Chenery, Hollis B, and Tsunehiko Watanabe. (1958). International Comparisons of the Structure of Production. Econometrica 26 (4). [Wiley, Econometric Society]: 487–521. doi:10.2307/1907514. Christopoulos, Dimitris K, and Efthymios G Tsionas. (2004). Financial Development and Economic Growth: Evidence from Panel Unit Root and Cointegration Tests. Journal of Development Economics 73 (1). Elsevier: 55–74. Demirgüç-Kunt, Asli, and Enrica Detragiache. (1998). Financial Liberalization and Financial Fragility. Dietzenbacher, Erik, Bart Los, Robert Stehrer, Marcel Timmer, and Gaaitzen De Vries. (2013). The Construction of World Input–output Tables in the WIOD Project. Economic Systems Research 25 (1). Taylor & Francis: 71–98.

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Fernando, Nimal A. (2006). Understanding and Dealing with High Interest Rates on Microcredit. Asian Development Bank 13. Fisman, Raymond, and Inessa Love. (2004). Financial Development and Growth in the Short and Long Run. Garcia, Carlos G, Dionisio Pérez-Brito, Vicente Campos, and Rafael Martí. (2006). Variable Neighborhood Search for the Linear Ordering Problem. Computers & Operations Research 33 (12), 3549–65. doi:http://dx.doi.org/10.1016/j.cor.2005.03.032. Hartmanis, Juris. (1982). Computers and Intractability: A Guide to the Theory of NP-Completeness (Michael R. Garey and David S. Johnson). Siam Review 24 (1). Society for Industrial and Applied Mathematics: 90. Hewings, Geoffrey J D, and Rodney C Jensen. (1987). Regional, Interregional and Multiregional Input-Output Analysis. Handbook of Regional and Urban Economics 1. Elsevier: 295–355. Iyer, Lakshmi, and David Lane. (2013). Indonesia’s OJK: Building Financial Stability. Keuangan, Otoritas Jasa. (2015). Statistik Perbankan Indonesia. Jakarta: Bank Indonesia. King, Robert G, and Ross Levine. (1993). Finance and Growth: Schumpeter Might Be Right. The Quarterly Journal of Economics 108 (3). Oxford University Press: 717–37. Kondo, Yasushi. (2010). A New Method for Triangulation of Input-Output Tables for Comparing Industrial Structures and Investigating Clusters of Industries. Submitted to Economic Systems Research. ———. 2014. Triangulation of Input–Output Tables Based on Mixed Integer Programs for InterTemporal and Inter-Regional Comparison of Production Structures. Journal of Economic Structures 3 (1): 1–19. doi:10.1186/2193-2409-3-2. Laeven, Luc, and Fabian Valencia. (2008). Systemic Banking Crises: A New Database. IMF Working Papers, 1–78. Martí, Rafael, and Gerhard Reinelt. (2011). The Linear Ordering Problem: Exact and Heuristic Methods in Combinatorial Optimization. In, 17–40. Berlin, Heidelberg: Springer Berlin Heidelberg. doi:10.1007/978-3-642-16729-4_2. Martí, Rafael, Gerhard Reinelt, and Abraham Duarte. (2012). A Benchmark Library and a Comparison of Heuristic Methods for the Linear Ordering Problem. Computational Optimization and Applications 51 (3): 1297–1317. doi:10.1007/s10589-010-9384-9.

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Miller, Ronald E, and Peter D Blair. (2009). Input-Output Analysis: Foundations and Extensions. Cambridge University Press. Mourougane, Annabelle. (2012). Promoting SME Development in Indonesia. OECD Publishing. Parikh, Ashok. (1979). Forecasts of Input-Output Matrices Using the R.A.S. Method. The Review of Economics and Statistics 61 (3). The MIT Press: 477–81. doi:10.2307/1926084. Radelet, Steven, and Jeffrey Sachs. (1998). The Onset of the East Asian Financial Crisis. National bureau of economic research. Romer, David. (1996). Advanced Macroeconomics. mcgraw-hill companies. Statistik, B. (2015).

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Tri-Cycles Analysis on Bank Performance: Panel Var Approach

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TRI-CYCLES ANALYSIS ON BANK PERFORMANCE: PANEL VAR APPROACH Denny Irawan and Febrio Kacarib

1

Abstract

The previous financial crisis has revealed the importance of risk in the financial and business cycle within the economy. This paper examines relationship among three cycles in the economy, namely (i) business cycle macro risk, (ii) credit cycle and (iii) risk cycle, and their impacts toward individual bank performance. We examine the responses of individual bank credit cycle and risk cycle toward a shock in business cycle macro risk and its consequence to the bank performance. We use Indonesian data for period of 2005q1 to 2014q4. We use unbalanced panel data of individual banks’ balance sheet with Panel Vector Autoregressive approach based on GMM style estimation by implementing PVAR package developed by Abrigo and Love (2015). The result shows dynamic relationship between business cycle macro risk and financial risk cycles. The study also observes prominent role of risk cycles in driving bank performance. We also show the existence of financial accelerator phenomenon in Indonesian banking system, in which financial cycles precede the business cycle macro risk.

Keywords : Business Cycle Risk, Credit Cycle, Bank Lending, Financial Risk JEL Classification: E320, G210, G310

1 Deni Irwawan ([email protected]) is student at the Graduate School of Economics (PPIE) University of Indonesia and Researcher at the Institute for Economic and Social Research (LPEM) University of Indonesia. Febrio Kacaribu (corresponding author: [email protected]) is lecturer at the Graduate School of Economics (PPIE) University of Indonesia and Head of Research for Macro and Financial Market Studies at the Institute for Economic and Social Research (LPEM) University of Indonesia.

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I. INTRODUCTION The period after the financial crisis is always followed by the introduction of new set of regulations that tighten the activity in the financial sector, particularly banks. In the international sphere this can be seen from the introduction and the implementation period of the Basel Rules by the Basel Committee on Banking Supervision (BCBS), which is a committee of central banks from various countries around the world. Basel I was officially introduced in 1988, triggered by Latin America debt crises in early 1980s (BIS Website, accessed 2017) and US Saving and Loan (S&L) crisis in the late 1980s and early 1990s (FDIC Website, accessed 2017). In the 1997-98 many Asian countries were hit by financial crises. This was followed by the proposal for Basel II. After a long process, it was launched in 2004 and was called the Revised Capital Framework. Likewise, the Basel III, which was introduced in 2010, was a reaction to 2008 financial crisis.

- Early 1980s Latin America Debt Crisis

- Late 1980s US Saving & Loans Crisis

- 1988 Basel I

- 2004 Basel II

- Early 2000s DotCom Crisis

- 1997-1998 Asian Financial Crisis

- 2008 US Subprime Mortgage Crisis

- 2010 Basel III

Source: Collaborated from many sources

Figure 1. Sequence of Financial Crisis and Basel Accord

Each Basel regulation is not introduced to replace the previous one, but rather to revise or to complement with more detailed and tighter regulations on the banking system. Basel I, which is the first attempt to regulate bank’s capital ratio took focus only on the application of minimum ratio of capital to risk weighted assets. This rule is then revised to be more detailed and stringent by Basel II which governs: (i) the application of more extensive minimum capital requirements, (ii) the strict process of monitoring and assessment of capital adequacy, and (iii)

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the implementation of obligations for banks to publish their financial statements to encourage market discipline and disclosure of information. Afterward, Basel III tightened the regulation even more by including: (i) the provision of a layer of additional capital reserves, (ii) the provision of counter-cyclical capital reserves, (iii) a tightening on the limit of leverage ratio, (iv) the application of the Liquidity Coverage Ratio (LCR) and Net Stable Funding Ratio (NSFR) as new indicators of liquidity, and (v) the imposition of a special classification for Systemically Important Financial Institutions (SIFI) as explained by BIS (BIS Website, accessed 2017).

USD TRILLIONS

USD TRILLIONS 35 30

North America Europe

Asia-Pacific Others

25

800 700 600

Foreign Exchange Contracts Equity Linked Contracts Credit Default Swap Contracts

Interest Rate Contracts Commodity Contracts

500 400

15

300

10

200

5

100

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

-

Source: Bank for International Settlements

Figure 2. Derivative Market Transactions across Regions

-

2005 2005 2006 2006 2007 2007 2008 2008 2009 2009 2010 2010 2011 2011 2012 2012 2013 2013 2014 2014 2015 2015 2016

20

I II I II I II I II I II I II I II I II I II I II I II I Source: Bank for International Settlements

Figure 3. Derivative Market Transactions by Type

The trend of tightening financial regulation is generally viewed as necessary to accommodate the rapid development in the financial market. As shown by Figure 3, trend of derivative market transactions grew exponentially since 1990. This growth is not only in terms of value and total transactions, but also the number of the derivative products in the market. The development of necessary regulations is needed to keep up with these developments in the market. The regulations trend which are always coincidence crisis shape regulatory cycle which is in line with business cycle / economy. In general, business cycle is shown by fluctuation of Gross Domestic Product (GDP) of a country from its trend line. However, following a formal model developed by Acharya & Naqvi (2012), this study employs Credit Default Swap (CDS) as proxy of business cycle. Then we prefer to address business cycle as business cycle macro risk, since CDS does not fully represent business cycle. Furthermore, as in BGG (1999), the business cycle is always interconnected with financial cycle, which is usually represented by the credit cycle. This study then tries to relax the financial

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cycle by also examining the risk cycle besides of the credit cycle. The credit cycle is characterized by the flow of bank lending to the economy in order to perform the intermediation function. In line with the credit cycle, the risk cycle is characterized by risk level in each time period of the company both financial institutions and firms in general. In this study the focus is on the bank’ balance-sheet as banking system is accounted for about 78% of asset of financial system in Indonesia (OJK, 2017). The tendency of more tightened revision of regulation might squeeze the ability of financial institutions (banks) to innovate and to conduct risky activity. Although it will result in more resilient banking system, it will also impact bank’s performance, since banks opportunity to make higher return from conducting riskier activity is becoming more limited. Moreover, banks also face direct opportunity costs as a result of tighter regulation. For examples, the implementation of (i) additional layer of reserve requirement; and higher (ii) Loan Loss Provision (LLP), may limit more third-party funds from being disbursed to the market, while it should keep paying the cost of the funds. However, without such policies, the market will be under threat of huge losses in the event of failure (default) of one bank or the entire banking system. So, there is a trade-off between system resiliency and bank performance (profitability) from regulating banking system. As in with business cycle, the credit cycle and the risk cycle also observe fluctuations by time. The relationship between these cycles are interesting and have been becoming focus of regulator, especially whether the regulator should take part to maintain these cycle to prevent excessive lending and risk-taking by the banks. If they should, which cycle they are better to focus on? Credit cycle or risk cycle?

Business Cycle Macro Risk

Bank Performance

Credit Cycle

Risk Cycle

Figure 4. Tri-Cycles and Bank Performance

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Figure 4 describes the main focus of this study in stylized way. Each arrow represent hypothesis to be tested in this study. This study examines the cyclicality relationship between the business cycle toward the credit cycle and the risk cycle. By comparing the relationship of those Tri-Cycles, it can be determined which cycle should receive more attention from the regulators. So that the implementation of the countercyclical regulation could be better targeted. Furthermore, this study examines the relationship of the credit cycle and the risk cycle toward the performance of individual banks. This phase of analysis focus on the impact of the Tri-Cycles to the bank’s performance. Especially, to measure the cost borne by the banks resulted from controlled of credit cycle and risk cycle. The results of this study might provide insight for the regulator to estimate the impact of regulating lending and risk-taking to the performance of individual banks.

II. THEORY Since 1980s, the business cycle literatures has been largely driven by Standard Real Business Cycle (RBC) developed by Kydland and Prescott (1982) which assume no financial frictions in the economy. The theory was revised by Bernanke, Gertler and Gilchrist / BGG (1999) which revealed important role of Financial Accelerator – which is refer to credit market friction and financial cycle – in determining the business cycle dynamics. Then, 2007/8 financial crisis stimulated a new strand of literatures of business cycle which not only accommodate financial cycle, but also risk dynamics or risk cycle. Burns and Mitchell (1946); in Jacobs (1998), one of the first among others, defined business cycle as: “ . . . a type of fluctuations found in the aggregate economic activity of nations that organize their work mainly in business enterprises: a cycle consists of expansions occurring at about the same time in many economic activities, followed by similarly general recessions, contractions, and revivals which merge into the expansion phase of the next cycle . . .” The definition above embodies some notable features of business cycle, which becomes focus of many literature on this field. The first one is expansion, the period of surging business activity and gross domestic product expands until the period reach its peak. This period is also known as an economic recovery. The highest point of a cycle, the peak, is a key period in which the economic bubble get burst and economy the economy turns into contraction period. This period is a phase in which ecoomy as a whole is in decline. The lowest point of this phase, which signals the reversal or revival, is called recession.

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2.1. Development of Business Cycle Theory 2.1.1. Econometric Business Cycle Research (EBCR) Econometric Business Cycle Research (EBCR) is a term referring to strand of literatures combining theoretical and statistical approach in studying business cycle. Term EBCR has been popularized for the first time by Tinbergen (1940). The EBCR approach was developed in some way as a critic toward the then-previous approach that did not combine theoretical framework with data specification (Jacobs, 1998).

Theory

Facts

Model

Data

Economic Model

Refined Data

Combination of Theory and Data

Policy Evaluation

Description

Forecasting

Figure 5. The EBCR Framework

2.1.2. Real Business Cycle (RBC) Theory Standard Real Business Cycle (RBC) model is based on seminal work of Kydland and Prescott (1982). In the model, a competitive market creates resource allocation that maximizes the household utility with limited budget constraint of each resource (Kiyotaki, 2011). On the standard model, the most prominent determinant of the business cycle are shock by the government budget and technological development (Romer, 2012).

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An ongoing debate is still going on about this theory ability to explain the heterogeneity of households and companies in the real world. This assumption is considered too strict. Even so, it encourages the further development/refinement of the RBC theory and not rather neglected its reliability. In general, RBC theory explains how factors of production, namely labor, capital and other factors (such as technology and government budget) affect productivity (output) in the aggregate sense (Kiyotaki (2011) and Romer (2012)). In conditions where there is positive shock to productivity, the marginal product of labor will increase which will lead to a rise in real wage rates and labor supply quantity. The combined effect of rising wages and employment will drive the output to rise. However, because of the increase in productivity is only temporary, then the growth of output in the future will increase by lower pace than the present growth of output, and the growth of income and consumption will not rise as much as the output growth in the present (Kiyotaki, 2011). This condition will then encourage increase in investment and capital stock in the foreseeable future. This process then creates a new expansion phase. It applies in the opposite direction for the contraction phase. One of the most criticized aspect of RBC model is its ignorance of the frictions in financial market. This stylized feature of the model departed from the strong assumption of efficient financial market. The hypothesis, which is very strict, assumes that in time of business fluctuation, every agent in the economy will instantly do recalculation of its economic behavior and decision to adapt with the change. Consequently, there is no such frictions in the financial market. Latter, in the late 1990s, BGG (1999) promoted a model to revised this view in examining business cycle.

2.1.3. Financial Accelerator BGG (1999) are the first to develop a framework which they called as “Financial Accelarator”. In their seminal work, they developed a dynamic general equilibrium model to reveal the frictions in the credit market which play prominent role in determining business fluctuations. The term Financial Accelerator refers to endogenous developments in the credit markets which amplify and propagate shocks to the macroeconomy. They materialized financial frictions in their model in three aspects. First, they internalized money and price stickiness to examine role of the friction in the transmission of monetary policy. Second, they relax the efficient financial market assumption by introducing lags in investment. Third, they relax the assumption of firms homogeneity in order to describe the condition in which every borrower has different access to capital markets. The main contribution of the model is how the financial accelerator give significant influence on business cycle dynamics.

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Departing from the standard RBC model, and influenced by Financial Accelerator model, Kiyotaki (2011) describes the effects of the business cycle toward the credit cycle. In general, relation between the two cycles are influenced by asset quality at every phase of the business cycle. In the period of expansion, there is substantial increase in the value of assets. The increase, including the value of the assets of the firm, make the firm has higher collateral to be utilized to get credit. Moreover, the business boom condition make firm’s balance sheet substantially sound, which is a sign of growth. These conditions make the firm could obtain more credit to finance new investments and expand further. In line with this, the good economic conditions make the firm able to repay their credit and then it has good credit rating. The level of nonperforming loan (NPL) in the banking system is then generally low.

Capital stock, Output

No Friction

Financial Friction

Time Source: Kiyotaki (2011)

Figure 6. Response toward Shock in Asset Quality

The reverse condition happens when the economy is in contraction phase. Impairment of assets (the burst of the boom) and deterioration of economic conditions will generally make the firm experience a decrease in performance and asset values. As a result, loan repayments begin to deteriorate (NPL increase). On the other hand, the banks tend to have lower credit growth. This happens due to (1) deterioration of the financial condition of the firm/debtor; (2) the impairment of the value of assets/collateral; and (3) the bank’s internal condition is getting worse by the rising of NPL rate.

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Expansion Phase Higher collateral value

Increase in asset value

Sound debtor balance sheet

Credit expansion

Contraction Phase Plummeting collateral value

Shrinks in asset value

Deterioration of debtor balance sheet

Credit crash & crunch

Figure 7. Expansion and Contraction Sequence in Business Cycle

2.2. Empirical Studies 2.2.1. Relationship between Cycles Bertay, et.al (2015) in their study analyzed the cyclicality of individual bank lending toward business cycle. They segregated the sample based on the ownership, state-owned banks and private banks. The result shows the state-owned bank proved to be less cyclical than the private banks. The finding applies particularly in countries with higher governance index. In case for developed country, the result even shows counter-cyclical lending behavior by state-owned banks. The study came to the conclusion that state-owned banks can effectively play countercyclical role toward the country business cycle. State-owned banks could play a stabilizing role of the business cycle and financial cycle. However, Bertay, et.al. (2015) also found that loans allocation made by state-owned banks tend to be bad so that from business point of view, the behavior of the state-owned bank is not economically optimal because of its role to support government policy. The study therefore concluded that implementation of micro- and macroprudential banking regulations such as monetary policy and statutory reserves are better tools than altering behavior of the state-owned banks. Empirical model applied in the study are the first-difference GMM of Arellano and Bond (1991) and the system GMM of Blundell and Bond (1998) enhanced by Windmeijer (2005). The study was conducted with a sample of 1,633 banks from 111 countries in the 1999-2010 time period. Ferri, et.al. (2014) conducted an analysis of influence between bank ownership and lending behavior of individual banks and its cyclicality over the business cycle. The sample of the study is banks in Europe in the period 1999-2011. Segregation of the sample conducted between

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profit-oriented bank (conventional bank) and not-for-profit bank (cooperative banks and saving banks). In his study Ferri, et.al. (2014) used first difference GMM by Arellano and Bond (1991). The result showed that there was no significant difference between the two groups of banks based on profit orientation. The main factors that can explain the behavior of bank lending is the monetary policy of the European Central Bank (ECB). Ibrahim (2016) conducted a study to compare the lending cyclicality between conventional banks and Islamic banks in Malaysia. The sample used in the study includes 21 conventional banks and 16 Islamic banks in Malaysia during the period 2001-2013. The data used is unbalanced panel data. The results showed that generally the behavior of bank lending is pro-cyclical to the business cycle. However, by segregating the samples it is observed different cyclicality behavior between conventional banks and Islamic banks. Pro-cyclical behavior only observed at samples of conventional banks. While for Islamic banks, business cycle appears to have no effect on its lending behavior. In fact, the estimation results obtained show a negative value indicating a counter-cyclical lending behavior of Islamic banks. Similar with Bertay, et.al. (2016), the model estimation used in the study is the first-difference GMM and GMM system. As for the case of Indonesia, Pramono, et.al. (2015) in their study examined the influence of Countercyclical Capital Buffer (CCB) policy on the growth of bank-lending in Indonesia. Estimation sample period is 2005Q1 to 2015Q2. Just like Bertay, et.al. (2015) and Ibrahim, et.al. (2016), the study used both the First Difference GMM and System GMM for estimation.

2.2.2. Individual Bank Performance Glen & Mondragon-Velez, (2011) conducted a study of the effects of business cycles on the performance of the credit portfolio of commercial banks in developing countries. The study period is 1996-2008. The results obtained indicate that economic growth is the main determinant of the performance of the loan portfolio. While the interest rate is the second strongest determinant. The estimation results also showed that the relationship between loan loss provision and economic growth is non-linear in conditions where the economy is in a state of stress. Guidara, Lai, Soumare, & Tchana (2013) conducted a study of co-movement between the level of capital buffer and business cycle for the six largest banks in Canada. The results found positive relationship between the two varaibels. The data used are quarterly data for the period of 1982-2010. The study results also showed that the implementation of the Basel regulatory framework does not affect cyclicality behavior of the banking industry in Canada. This suggests that banks in Canada are mostly well-capitalized. Vithessonthi & Tongurai (2016) in his study analyze the effect of business cycles on the development of financial markets and the risk of individual banks. The samples used were 37 bank went public in seven countries in South America. The result shows that the business cycle

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is significantly affecting the banking risks. Besides, the development of financial markets also improve capital ratios and reduce the level of risk exposure of banks, indicating that financial market developments have lowered the banking risk. Psillaki & Mamatzakis (2017) analyzing the impact of financial regulation and structural reforms and their effects on the efficiency of the banking industry. The sample used was a bank from 10 countries in Eastern and Central Europe in the period 2004-2009. Scores of cost efficiency is estimated using stochastic frontier analysis. The results obtained show that structural reforms in the labor market and businesses have a positive impact on the bank’s performance. It was also found that credit regulations raise the cost efficiency of the banks. As well, it appears that banks with stronger capital has a higher cost efficiency. As for Indonesia case, Winata & Viverita (2013) analyzed the effect of the bank’s income structure to market-based performance for listed-banks in Indonesia. The study was conducted using panel data for the period 2004-2012. The result obtained suggest that income diversification does not have a significant effect on the performance of the banks in Indonesia. Meanwhile, other variables such as total assets, asset-to-equity ratio, NPL and ROA have significant influence on the bank’s performance, while the variable cost-to-income and loan growth has no significant effect on the performance of the bank.

Table 1. Empirical Literature Review: Business Cycle and Credit Cycle No

Author

Analysis

Variables

Sample

Result

1

Ibrahim (2016)

• Business cycle • Bank lending • Type of Banks

• GDP • Loan

• Conventional Bank (Malaysia) • Sharia Bank (Malaysia)

• Pro-cyclical • Counter-cyclical

2

Bertay, et. al. (2015)

• Business cycle • Bank lending • Ownership

• GDP • Loan

• 111 countries • State-Owned Bank • Private Bank

• Pro-cyclical • Counter-cyclical

3

Ferri, et.al. (2014)

• Business cycle • Bank lending • Monetary policy

• GDP • Loan • Policy Rate

• Conventional Bank (Europe) • Cooperative Bank (Europe)

• Bank lending significantly correlated with monetary policy

4

Pramono, et. al. (2015)

• Business cycle • Bank lending • Counter-Cyclical Buffer

• • •

• Conventional Bank (Indonesia)

• Pro-cyclical

GDP Loan Counter- Cyclical Buffer

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Table 2. Empirical Literature Review: Business Cycle and Bank Performance No

Author

Analysis

Variables

Sample

Result

• GDP • Loan Loss Provision

• Conventional Bank (Developing Countries)

• Non-linearity

Guidara et. al. (2013) • Business Cycle • Capital Buffer • Basel Framework

• GDP • Capital Buffer • Basel Dummy

• Six largest banks (Canada)

• Pro-cyclical • Basel Framework does not affect cyclicality

3

Vinthessonthi & Tongurai (2016)

• Business Cycle • Financial Development • Bank Risk

• GDP • Risk Exposure • Capital Ratio

• 37 listed banks (South America)

• Bank risk is procyclical

4

Psillaki & Mamatzakis (2017)

• Financial Regulation • Banking industry Efficiency

• • •

5

Winata & Viverita (2013)

• Bank’s income structure • Market based performance

• Income diversification ratio • Stock return

1

Glen & MondragonVelez (2011)

2

• Business cycle • Credit Performance

Credit regulation • 10 countries (Eastern & Central Europe) dummy Cost efficiency Capital ratio • Listed banks (Indonesia)

• •

Regulation increase efficiency Stronger capital raise efficiency

• Income diversification does not affect performance

2.3. Conceptual Framework This conceptual framework refers to model developed by Acharya and Naqvi (2012). The overall economy in this model consists of several sectors, namely, the banking sector, savers, borrowers (both savers and borrowers are referred to as households, for simplicity), and the entrepreneurial sector (corporation).

2.3.1. Bank Lending: Base Case The framework is based on three-date model of a bank, in which at t = 0, the bank receives deposits D from risk-neutral investors (savers of the economy) with reservation utility . Depositors are compensated with rD, the (gross) rate of return on deposits – deposit rate. In t = 1, the bank makes investments in projects (loans) L, while holding a fraction of a deposits as liquid reserves r. In t = 2, the bank-funded projects either success or fail, with the probability of success is given by q. The bank observes q after receiving deposits and sets rL, the (gross) rate of return on loans – lending rate.

Tri-Cycles Analysis on Bank Performance: Panel Var Approach

t=0

-

-

t=1

Bank raises deposits Bank observes success probability θ and seta lending rate rL and borrowing rate rD Investments made and bank sets aside resserves R

-

Bank suffers early withdrawals, xD Bank incurs a penalty cost if xD > R

415

t=2

-

-

Bank projects either succeed with probability θ of fail Payoffs divided among parties

Figure 8. Three-date Model Framework

Bank reserves R are residual after the bank meets the loan demand: (1) The bank could experience withdrawals at , which is represented by random variable given by , where . Thus, the total amount of with drawals at is given by . If , then the bank faces a liquidity shortage, and it incurs penalty, given by , which is proportional to the liquidity shortage, where . The bank owners’ problem is then summarized by: (2) subject to (3) and (4) where

is the expectations operator over the distribution of and profit, , is given by: (5)

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Equation (5) states that the bank chooses deposit and lending rates as well as the level of bank reserves so as to maximize its expected profits, , net of any penalty incurred in case of liquidity shortage and subject to the participation constraint of the depositors given by expression (3) and the budget constraint given by equation (4). The optimal gross lending rate is given by (6)

where deposit rate is given by

is the elasticity of the demand for loans. The optimal gross

(7) And, the optimal level of reserves is given by: (8)

2.3.2. Internal Bank Dynamics and Excessive Lending Acharya and Naqvi (2012) build explicit model to explain the process behind excessive lending phenomenon. The model take focus on how managerial agency problems can have effect on bank lending policies. The bank manager has unobservable effort level, , such that , with assumption that although the loans are affected by effort, they are not fully determined by it.

t=0

t = 0.5

- Principal offers contract - Loan demand L(rL) to manager realized - Manager chooses effort e - Manager makes - Manager receives deposits D investments and sets and observes success aside reserves R probability θ - Manager sets rL and rD

t=1

t=2

- A fraction x of - Bank projects depositors succeed with withdraw early probability θ or fail - Bank incurs a penalty - Payoffs realized and cost if xD > R divided among - Principal decides parties whether or not to conduct audit - Manager is penalized contingent on the audit outcome

Figure 9. Extended Three-date Model Framework

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The manager earns income, , which can be interpreted as bonuses, which increases as the manager sell more loans. But the manager also faces a penalty, , if the principal conducts an audit and it is revealed that the manager had acted over-aggressively to increase loan volume by setting a loan rate lower than the one that maximizes the principal’s expected profits. The managerial penalty is some proportion, , of the penalty cost incurred by the bank due to liquidity shortfalls. However, there is maximum penalty level received by the manager, , so that the managerial penalty is given by , where represents the liquidity shortfall, if any, and . Thus, the net wage earned by the . manager is given by

2.3.3. Bank Liquidity Flush Acharya and Naqvi (2012) assume an economy in which entrepreneurs have access to projects that yield a terminal cash flow if it succeeds and zero otherwise. Macroeconomic risk is given by . The probability of success depends partly on the realization of the state variable, , and partly on the entrepreneurs’ effort decision, e, which identifies whether the entrepreneur is diligent ( ) or shirks ( ) in which case entrepreneurs extract a private benefit . If the entrepreneur is diligent, the probability of success as before is given by , but in the presence of shirking the probability of success is , where . Entrepreneurs promise to pay the risk-neutral investors who invest directly in their projects a face value of . The entrepreneur’s problem as maximizing the expected return is then: (9) Subject to the constraint: (10) and (11) Constraint (10) is the investor’s rationality constraint that says that the expected return to the investor must at least equal the investor’s reservation utility. Meanwhile constraint (11) means the expected entrepreneurial return conditional on the entrepreneur being diligent exceeds his expected return from shirking.

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Then, there is such that, for , the entrepreneur’s offer to the investors are not enough to satisfy the investors’ reservation utility. Intuitively, if macroeconomic risk is sufficiently high, the probability of success is low, and thus, the entrepreneur has little incentive to exert effort and is better off by shirking and consuming his private benefit. However, if the entrepreneurial projects are financed by banks instead of dispersed investors, then such moral hazard can be alleviated via monitoring. Formally, in the presence , the of bank borrowing, entrepreneurs suffer a cost from shirking, say, . As long as entrepreneur has no incentive to shirk. Because investors earn on average from depositing money in the bank, in the presence of entrepreneurial moral hazard, investors are better off by depositing their endowments in banks. However, if , entrepreneurs can attract investors by offering them an expected return slightly above . In summary, if investors observe identically, then all investments are channeled directly into entrepreneurial projects if and into banks if .

2.3.4. Theoretical Framework: Modified This study employs conceptual framework developed by Acharya and Naqvi (2012) with some modification. The main framework implemented in this study is based on equation (5), which is:

First modification, we distinguish the risk in the model, , into two risk. First, macroeconomic risk, , which plays significant role in determining deposit flush received by the banks. Second, individual bank risk, , which is represented as the share of the performing loan compared to total loan made by the bank. By doing so, the conceptual framework in this study becomes as follow: (12)

III. METHODOLOGY 3.1. Characteristic of the Data and Some Related Issues This study employs quarterly unbalanced panel data from balance sheet of all conventional banks in Indonesia in the period 2005q1-2014q4. The main goal of this study is to measure magnitude of dynamic cyclicality between business cycles macro risk, credit cycles and risk cycle toward the performance of individual bank. Therefore, it is necessary for the empirical model to be able describe the dynamic relationship of the Tri-Cycles and its influence on the performance of the bank.

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One of the benefits of the use of panel data is able to provide interpretation that can meet these objectives, in term of variation between individual and over-time (Baltagi, 2005). However, the utilization of unbalanced panel data, although not problematic, requires special attention. First, from the point of view of classical assumption, it makes the OLS estimators remain consistent and unbiased, but the standard error will be biased. Second, ANOVA methods cannot be perfectly applied for unbalanced data thus makes the property of unbiased in the optimal model of standard ANOVA are not met. Third, the asymptotic distribution of critical values of ​​unbalanced panel data becomes unbalanced. Nonetheless, the use of unbalanced panel data remains possible and common statistical software such STATA has automatically accommodated this type of (Cameron & Trivedi, 2009). This study uses STATA 13 MP to perform estimation and data processing. One of the most popular models for cycle analysis is dynamic panel model as used by Ferri, et.al. (2014), Bertay, et.al. (2015), Pramono, et.al. (2015) and Ibrahim (2016) in a study somewhat similar to this study. This model was chosen because of its ability to capture the dynamics between the two variables. So that it is very suitable for use in analyzing the relationship between two variables cycle. The dynamic model in practical is implemented by entering the lag of the dependent variable as one of the independent variables (Baltagi, 2005). Dynamic panel model is characterized by the presence of more than one source of time persistence. Such conditions create dynamic panel model cannot be separated from the autocorrelation lag because of the presence of one of the dependent variables as independent variables. In addition, this model also cannot be separated from the heterogeneity among individuals who became observation. Because of that, dynamic panel model is automatically not met the BLUE (Best Linear Unbiased Estimator) assumptions as owned OLS model standard and standard assumptions of GLS models (Baltagi, 2005). This happens because in dynamic panel model there is correlation between the independent variables with the error term. Because of that, the OLS estimators will be biased and inconsistent. Moreover, the standard GLS estimator cannot be used because there is a correlation between the variables predetermined by the error term. Furthermore, GLS models with instrument variable (IV) also cannot be used because of although it produces a consistent model, it did not provide an efficient parameter. This is due to the model GLS-IV does not use all the conditions of moment conditions. Therefore, the use of dynamic panel model is not necessary to test classic assumptions on the data samples used. Dynamic panel model was essentially developed to be able to accept the conditions in which the classical assumptions are not met. Specifically, dynamic panel model that will be used in this research is Panel Vector Autoregressive (PVAR) developed by Abrigo and Love (2015) based on GMM style estimation from Arellano and Bond (1991). Sprecifically, the GMM model is used in combination with a

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robust estimator of Windmeijer (2005) to avoid problems of overidentification and downward bias (Baltagi, 2005).

3.2. Overview of Panel Vector Autoregressive (PVAR) Model In line with the aim of this study, cyclical relationship between the Tri-Cycles is the object to be observed. Thus, this study needs such method that can observe potential bi-directional relationship between each cycle. Because of that, this study employs Vector Autoregressive (VAR) approach. VAR methodology is categorized as an extension of autoregressive (AR) model in term of its multi-variate characteristic. Furthermore, it also resembles simultaneous-equation modeling (SEM) style estimation (Gujarati and Porter, 2009). The main difference is, each variable in the model is treated as endogenous variable and lag of every variable in the system is considered as independent variable. This approach is then very suitable to fulfill the aim of this study. By placing every variable as dependent variable, it can observe bi-directional relationship of each cycle and also shows how each cycle affects bank performance. In the context of business cycle analysis, the approach might give insight of the dynamics between business cycle and financial cycle, which attracts big concern in the development of the literatures. The other advantage is it has rich of features. VAR estimation has features of Impulse Response Function (IRF), Forecast-Error Variance Decomposition (FEVD), and also GrangerCausality Test. These features make VAR estimation very popular for policy simulation. IRF describe describes response of each variable due to one standard deviation in other variable. Meanwhile FEVD decomposes degree of impact of each variable to the dependent variable. Granger-Causality test is useful in examining the potential bi-directional relationship between variables. However, VAR approach has several disadvantages. First, it can be applied even as an a-theory approach. Researcher does not need basic theory and can simply put any variable into VAR system. This disadvantage then has been avoided in this study since the selection process of the variables in this study is all based on the theoretical framework as explained in Section 3. Second, the coefficient result from VAR estimation cannot be interpreted directly. The result is interpreted only it term of its direction (positive or negative) and significance. Essentially, VAR estimation is developed for time-series process. However, as the availability of cross-individual data is increasing, VAR is getting popular to be implemented in panel estimation. However, standard built-in statistical package is not yet available to estimate Panel VAR. This study then employs PVAR user-based package developed by Abrigo and Love (2015) which uses GMM estimation to estimate Panel VAR.

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Theoretically, Panel VAR have the same structure as time-series VAR model. Each variable is treated as endogenous variable and also interdependent. The main difference is Panel VAR also observe cross-sectional dimension of the data. Panel VAR is also noticeable for several advantages, as revealed by Canova and Cicarelli (2013) that Panel VAR: (i) captures both static and dynamic interdependencies; (ii) in unrestrictive style treats individual variation; (iii) incorporates coefficient variations and the variance of the shocks in term of time series; and (iv) examine cross-sectional dynamic heterogeneities. , while the is vector of variables for unit Think of is stacked version of variable i.e., . Individual is represented by . Meanwhile of represents time. Common time-series VAR empirical form is:

Meanwhile Panel VAR empirical form is then:

where is random disturbances in the form of the unit of observation, and is the lag operator.

vector and

and

depend on

3.3. Empirical Model Recalling equation (13) in Chapter 3, the bank profit is function is as follow: (13) So that the bank profit is function of: (14) Due to data characteristic, rather than using Loan Rate ( ) and Deposit Rate ( ), this study use data of Net Interest Margin, , which is difference of the and . So equation (14) becomes: (15) Based on equation (15), an empirical model might be constructed as follow:

(16)

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where is intercept, and are lag operator.

is parameter. Meanwhile

is the error-term. Both

and

= Bank profit

= Share of performing loans to total loans made by the bank = Macroeconomic risk, Indonesia CDS 1Y – Spread



= Total bank loans



= Total bank deposits, represented by total Third Party Funds



= Net interest margin (NIM)

3.4. List of Variables Table 3 gives complete list of variables employed in the empirical estimation. In total, the data set contain unbalanced panel data of 150 individual conventional bank balance sheet in Indonesia. Focus variables on this study are comprised of four variables. First, the business cycle macro risk, which is represented by CDS spread. In line with theoretical framework on this study which is based on Acharya and Naqvi (2012), this study then uses CDS spread rather than GDP which is very common to represent business cycle. On their paper, Acharya and Naqvi (2012) specifically recommend to use commercial paper spread as the measure of business cycle fluctuations. However, due to limitation and irregularity of commercial paper spread data in Indonesia, this study then employs credit default swap (CDS) spread data, which also represents macroeconomic risk. The second focus variable of this study is bank lending, which represents credit cycle. The data come from individual bank balance sheet. All bank balance sheet data are acquired from website of Bank Indonesia and Otoritas Jasa Keuangan, which are based on monthly and quarterly report of bank balance sheet. Bank lending data employed in this study uses outstanding credit data on the balance sheet. The data is then transformed into natural logarithm and then extracted to its cycle component using Hodrick-Presscott Filter. The third focus variable ), which represents the risk cycle. The data acquired from Net Nonis performing loan ( Performing Loan (NPL) data, specifically = 100 - NPL. The data is then transformed into natural logarithm and the cycle component is extracted. Lastly, the fourth focus variable is bank performance, which is represented by the bank quarter profit. The data is also transformed into natural logarithm and then extracted to its cycle component. Besides of the focus variables, there are also two other variables which are theoretically important, the deposit and net interest margin. The deposit data is sum of the total of third party fund, comprised of giro, savings and time-deposit. The data is transformed into natural log and its cycle component is extracted. Meanwhile the Net Interest Margin (NIM) data is employed as substitute of loan rate and deposit rate. The use of this substitute is due to bank

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do not report their loan rate and deposit rate in their balance sheet, but they report their net interest margin, which is the difference of both. The data is extracted into its cycle component by using HP filter. Table 3. List of Variables Variables

Symbol

Business Cycle Macro Risk Credit Default Swap (CDS) – Spread Credit Cycle Bank Lending Risk Cycle Performing Loan Bank Performance Profit Bank Specific–Theoretically Important Net Interest Margin (NIM) Deposit–Third Party Fund

Unit

Source

Treatment

Percentage

Bloomberg

Million Rp

OJK

Cycle of Natural Log

Percentage

OJK

Cycle

Million Rp

OJK

Cycle of Natural Log

Percentage Million Rp

OJK OJK

Cycle Cycle of Natural Log

Cycle

*OJK stands for Otoritas Jasa Keuangan or Financial Service Authority

IV. RESULT AND ANALYSIS 4.1. Estimation Result The suitability of the theoretical framework and rich feature of VAR estimation method has made it possible to answer all research questions in only one estimation process. VAR estimation is conducted with variables lag of 3 and instrument lag of 1/9. The complete result of tests and estimations are in the Appendix section. Table 4 presented unit-root stationarity test based on Panel ADF-Fisher tests. All variables are stationary at level. Thus, there is no need to conduct cointegration. This condition means VAR model is eligible to be applied to analyze the data. This study applies VAR estimation based on GMM developed by Abrigo and Love. If the variables are found to be non-stationary at level, then VEC Model need to be considered. Table 4. ADF-Fisher Panel Unit-Root Test Variable

ADF-Fisher Prob

Variable

ADF-Fisher Prob

business cycle macro risk

0.000

nim

0.000

credit cycle

0.000

deposit

0.000

risk cycle

0.000

profit

0.000

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With selected lag specification, VAR stability check indicates stability of the model (Figure 10). It can be concluded as all eigenvalue lie inside the unit circle. It means that all eigenvalue has value equal to or less than 1. This stability condition is necessary for VAR estimation otherwise the estimation result become unreliable for analysis and the VAR system need to be recalibrated.

0 -1

-.5

Imaginary

.5

1

Roots of the companion matrix

-1

-.5

0

.5

1

Real

Figure 10. VAR Stabtility Check Result

Results presented on this section are arranged following 8 research questions of this study. Each sub-section (from 5.1.1. to 5.1.8.) address one research question. The results presented are VAR estimation result, Granger Causality wald-exogeneity test result, and Cholesky IRF result. From those result each research question can be comprehensively addressed so that the aim of this study can be fulfilled.

4.1.1. Tri-Cycles Dynamics Estimation result from VAR and Granger Causality test reveal bi-directional cyclicality between business cycle macro risk and credit cycle. VAR estimation result indicate that shock in the credit cycle has lagged impact toward the business cycle macro risk (Table 5). On the other hand, shock in business cycle macro risk also has significant impact toward credit cycle. Granger Causality test results resembles the VAR estimation result. It reveals bi-direction relationship between business cycle macro risk and credit cycle. From the result can be inferred that the credit cycle granger-cause the business cycle macro risk. While on the opposite, the business cycle macro risk granger-cause the credit cycle. Cholesky Impulse Response Function shows that shock in the credit cycle has lagged impact over the business cycle macro risk and the pattern is stable. Meanwhile business cycle macro risk has considerably unstable response toward shock in credit cycle (Figure 11).

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Table 5. VAR Estimation and Granger Causality Result Variable Business Cycle Macro

Granger-Causality

VAR Estimation Result Dependent

Independent

Coefficient

Prob > |z|

Variable

Prob > chi2

7.2628 -6.9603 2.5197

0.002 0.015 0.035

0.000

-0.0063 0.0044 -0.0026

0.000 0.002 0.046

0.000

-0.0054 0.0044

0.665 0.766

0.024

-0.0485

0.004

-0.1132 -0.0261 -0.0030

0.004 0.628 0.953

0.000

Credit Cycle ( )

0.0006 0.0009 0.0018

0.297 0.102 0.002

0.000

Risk Cycle ( )

5.6197 1.5556 1.4232

0.000 0.067 0.047

0.000

Risk (

)

Credit Cycle ( ) Business Cycle Macro Risk (

)

Risk Cycle ) (

Estimation result from VAR and Granger Causality test also reveal bi-directional cyclicality between business cycle and risk cycle. VAR estimation result indicate that shock in the risk cycle has lagged impact toward the business cycle macro risk (Table 5). On the other hand, shock in business cycle macro risk also has significant indirect impact toward risk cycle. Granger Causality test results resembles the VAR estimation result. It reveals bi-directional relationship between business cycle macro risk and risk cycle. From the result can be inferred that the risk cycle granger-cause the business cycle macro risk. While on the opposite, the business cycle macro risk also granger-cause the risk cycle. Based on IRF pattern, risk cycle exhibit unstable response toward shock in business cycle macro risk (Figure 11).

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credit_cycle : buscyc_risk

buscyc_risk : credit_cycle 0 10 -.01 5 -.02

95% CI

-.03 0 impulse : response

IRF

95% CI

0 5

10

0

step

IRF 5

impulse : response

buscyc_risk : risk_cycle

step

10

risk_cycle : buscyc_risk .4

0

95% CI

Cumulative IRF

.2

-.1

0 -.2

95% CI

-.3 0 impulse : response

-.2

IRF

5

10

0

5

step

10

step impulse : response

credit_cycle : risk_cycle

credit_cycle : risk_cycle 15

95% CI

100

IRF

95% CI

Cumulative IRF

10 50 5

0

0 0

impulse : response

5

step

10

0 impulse : response

5

10

step

Figure 11. Tri-Cycles Dynamics

As for credit cycle and risk cycle, VAR estimation result indicate that shock in the credit cycle affect the risk cycle. On the oppposite, shock in risk cycle also has significant impact toward credit cycle (Table 5). The result are also supported by Granger-Causality test, in which

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it reveals two-way relationship between credit cycle and risk cycle. From the result can be inferred that the credit cycle granger-causes the risk cycle. While on the opposite, the risk cycle also granger-causes the credit cycle. The IRF pattern reveals considerably unstable response of risk cycle toward shock in credit cycle. Meanwhile at the opposite, credit cycle has stable and increasing response toward shock in risk cycle.

4.1.2. Tri-Cycles and Bank Performance Estimation result from VAR and Granger Causality test reveal one-way cyclicality between business cycle macro risk and bank performance (profit). VAR estimation result indicate that shock in business cycle macro risk has impact toward bank performance (Table 6). On the other hand, as shown by the estimation result, shock in bank performance does not seem to have significant impact toward business cycle macro risk. Granger Causality test results confirm the VAR estimation result. It reveals one-way relationship between business cycle macro risk and bank performance. Business cycle macro risk granger-cause bank performance. While on

Table 6. Estimation Result: Business Cycle and Bank Performance Variable

Granger-Causality

VAR Estimation Result Prob > |z|

-0.0117 0.0193 -0.0006

0.312 0.077 0.957

0.246

0.1404 0.0275 -0.2195

0.227 0.827 0.014

0.006

Credit Cycle ( )

-0.0003 0.0004 -0.0005

0.473 0.345 0.232

0.530

Bank Performance ) (

-13.8393 29.0063 -13.4835

0.063 0.003 0.010

0.011

Bank Performance ) (

-0.0203 -0.0074 0.0152

0.223 0.666 0.307

0.430

0.0994 -0.0363 0.0493

0.036 0.524 0.284

0.075

Business Cycle Macro Risk ( ) Bank Performance ) (

Risk Cycle ) (

Independent

Prob > chi2

Coefficient

Dependent

Variable

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the opposite, bank performance does not granger-cause business cycle macro risk. Figure 12 presents Cholesky Impulse Response Function of Tri-Cycles and bank performance. Shock in business cycle macro risk has lagged impact over bank performance. The IRF pattern show cyclical behavior of bank performance caused by the shock from business cycle macro risk. Credit cycle has lagged impact toward bank performance as shown by estimation result (Table 6). On the other hand, shock in bank performance does not have impact toward credit cycle. Granger Causality also confirm VAR estimation result. Credit cycle granger-cause bank performance meanwhile on the opposite, bank performance does not granger-cause the credit cycle. The Cholesky IRF pattern result exhibit cyclical pattern of the response of bank performance caused by shock in credit cycle.

profit : buscyc_risk

buscyc_risk : profit .6

95% CI

IRF

95% CI

.05

IRF

.4 .2

0

0 -.2

-.05 0

5

0

10

step

5

impulse : response

impulse : response

credit_cycle : profit

10

step

profit : credit_cycle .002

20

95% CI

IRF

0

0

-20

95% CI 0

impulse : response

5

step

-.002

IRF 10

0

5

step impulse : response

10

Tri-Cycles Analysis on Bank Performance: Panel Var Approach

risk_cycle : profit .2

429

profit : risk_cycle 95% CI

.05

IRF

.1 0 0

-.1

95% CI

-.05 0 impulse : response

5

step

10

0 impulse : response

5

IRF 10

step

Figure 12. Impulse Response Function: Business Cycle Macro Risk and Bank Performance

Shock in the the risk cycle has weak impact toward bank performance (Table 6). On the oppposite, shock in bank performance does not has impact toward the risk cycle. Granger Causality test results resembles the VAR estimation result. It can be inferred that the risk cycle weakly granger-causes bank performance. While on the opposite, bank performance does not granger-causes the risk cycle. The Cholesky IRF of the risk cycle and bank performance reveals unstable response pattern of bank performance toward shock in risk cycle.

4.1.3. Business Cycle Macro Risk Dynamics: Credit Cycle and Risk Cycle Figure 13 present Cholesky IRF of response of the credit cycle and risk cycle due to shock from the business cycle macro risk. The left side is the IRF of the credit cycle. Meanwhile the right side is the IRF of the risk cycle. By comparing the left picture and the right picture, it can be seen different response of the credit cycle and the risk cycle toward shock of business cycle macro risk. Credit cycle reveals stable response pattern toward shock in business cycle macro risk. Meanwhile risk cycle exhibit cyclical response toward shock in business cycle.

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buscyc_risk : credit_cycle

buscyc_risk : risk_cycle

0

0

-.01

-.1

-.02

-.2

-.03

-.3 0

5

10

0

5

10

step 95% CI

impulse : response

IRF

Figure 13. Response of Credit Cycle and Risk Cycle toward Shock in Business Cycle Macro Risk

Comparison of these two IRF responses give comprehensive answer toward the first of two main research questions addressed by this study, expecially to explain cyclical relationship of the Tri-Cycles. By the magnitude of response, the result is clear. Risk cycle tends to be more sensitive toward business cycle macro risk shock rather than credit cycle.

buscyc_risk : credit_cycle

buscyc_risk : risk_cycle

0

0

-.1

-1

-.2

-2

-.3

-3 0

5

10

0

5

10

step impulse : response

95% CI

Cumulative IRF

Figure 14. Cumulative Response of Credit Cycle and Risk Cycle toward Shock in Business Cycle

Meanwhile Figure 13 presents Cumulative Cholesky IRF of the credit cycle and risk cycle in cumulative version. The IRF show negative relationship of shock in business cycle macro risk toward credit cycle. The IRF of risk cycle also show similar result. The overall impact of

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shock business cycle macro risk toward risk cycle is negative. Since business cycle macro risk is represented by Indonesia CDS 1Y spread, increase in the spread means contraction in business cycle macro risk. Negative relationship among business cycle macro risk and credit cycle thus has meaning of pro-cyclicality of lending behavior in Indonesia banking system. As the CDS spread increase, which is sign of increase in macroeconomic risk, means the economy is in bad condition. The condition then result in the slowing down of lending given by banks. Meanwhile for the risk cycle, which is represented by Performing Loan, increase in CDS spread will lower the performing loan level at individual bank. The result is then in line with the logic mentioned in figure Figure 7, where deterioriation of economic condition will further give negative effect toward both bank and firm balance sheet.

4.1.4. Bank Performance Dynamics: Credit Cycle and Risk Cycle Figure 15 presents Cholesky IRF of the response of bank performance caused by shock in credit cycle and risk cycle. The left side is the IRF describing impact in profit due to shock in the credit cycle. Meanwhile the right side is the IRF of shock in the risk cycle toward profit. By comparing the left picture and the right picture, it can be seen common response of bank performance, as represented by profit, caused by shock in the credit cycle and the risk cycle. Both credit cycle and risk cycle cause cyclical response toward bank performance. However, shock from risk cycle shows a bit more unstable response compared to shock from the credit cycle.

credit_cycle : profit

risk_cycle : profit .2

20

.1 0 0

-20

-.1 0

5

10

0

5

10

step impulse : response

95% CI

IRF

Figure 15. Response of Bank Performance toward shocks in Credit Cycle and Risk Cycle

Comparison of these two IRF responses give answer toward the second question of two main research questions addressed by this study, especially to explain loss of profit opportunity borne by banks due to risk control and credit control regulation. By the magnitude of response,

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the result is clear. Bank performane tends to be more sensitive toward credit cycle shock rather than risk cycle. As presented by model of Acharya and Naqvi (2012), bank lending is direct risk-taking action. Bank takes more risk when it decide to give more lending. Meanwhile risk cycle can be interpreted as indirect form of risk-taking as its not only affected by credit, but also other factors. This result strengthens the notion of “high risk – high return” in banking business. So that credit-control and risk-control regulation imposed by regulator might impact the profitability of banks.

credit_cycle : profit

risk_cycle : profit

20

.4

0

.2

-20

0

-.2

-40 0

5

10

0

5

10

step impulse : response

95% CI

Cumulative IRF

Figure 16. Cumulative Response of Bank Performance toward shocks in Credit Cycle and Risk Cycle

Figure 16 presents Cholesky IRF of bank performance as impacted by the credit cycle and risk cycle, in cumulative version. The IRF show negative relationship of shock in credit cycle toward bank performance. This result means that by expanding its lending, the bank takes more risk, which might lower the profitability. While the IRF of bank performance due to shock in risk cycle show positive relationship. Positive shock in risk cycle, which is represented by increase in performing loan level, will have positive impact toward bank profit. This result emphasize the importance of risk management at internal bank level both in the form of prudence credit assessment and supervision. Profit maximization at individual bank level is very sensitive toward dynamics of credit cycle and crisk cycle.

4.2. Discussion 4.2.1. A Tale of Two Cycles: Business Cycle (Macro Risk) and Financial Cycle Seminal work of Bernanke, Gertler and Gilchrist / BGG (1999) has broadened the scope of business cycle literatures by promoting the important role of financial cycle in business cycle analysis. The term “Financial Accelerator” became popular and subject of many researches in

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macroeconomics. Financial accelerator phenomenon addresses the dynamics of financial cycle which propagates to output fluctuation. However, the formal model presented by them is not suitable enough for study at individual bank level. The framework is too macro for such study. This study unexpectedly exhibits the financial accelerator phenomenon in Indonesia banking system in the period of 2005q1 to 2014q4. VAR estimation and Granger-Causality yield result that support the existence of financial accelerator phenomenon. However, this claim only applies in financial market context, because this study employs macroeconomic risk – rather than GDP – as representation of business cycle. VAR estimation result give a sign of significant relationship between credit cycle to business cycle macro risk and between risk cycle to business cycle macro risk. As in BGG (1999), such phenomenon is caused by the existence of credit market frictions. In such ideal world, financial market is perfectly efficient so that every agent in economy can recalculate their decision instantly if any shock happens. Three features of credit market frictions in the BGG model are (i) money and price stickiness; (ii) lags in investment; and (iii) heterogeneity among firms. This study, even though is not specifically intended to examine BGG framework, accommodates those three features in some way. First, money and price stickiness are accounted in the estimation with net interest margin as representative of cost of fund. Second, lags in investment, which is represented by credit cycle, is accounted in the estimation which the result reveales lagged / indirect / non-contemporaneus effect between credit cycle and business cycle macro risk. Third, the heterogeneity of firms is presented by the span of the data which cover the whole conventional bank population in Indonesia. In total, the observation covers up to 119 banks which differs significantly in term of size and market specialization. However, since BGG model is not the fundamental of this study, this study cannot infer any conclusion based on the model. There is a room for future study to examine this phenomenon with BGG model in Indonesia banking industry.

4.2.2. Inside the Financial Cycle: Credit Cycle and Risk Cycle As Acharya and Naqvi (2012) published their work about the “Seeds of Crisis”, scope of the business cycle once again became more comprehensive. Financial cycle, which was solely represented by credit cycle, started to account the importance of risk dynamics. Their model talks about the process of building-up of of a bubble in the economy, which then turns into crisis when it bursts. This study only implements the beginning phase of the model presented by Acharya and Naqvi (2012). This study focuses on the propagation of shock in business cycle macro risk toward balance sheet of individual bank. The whole study is conducted in the business cycle context, in which all variables are extracted to its cycle component.

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Estimation result exhibits pro-cyclicality behavior of the credit cycle toward business cycle macro risk. Deterioration of business condition, represented by increase in macroeconomic risk, will further decrease bank lending. When this context is about to happen, such counter-cyclical policy by the regulator is very important to prevent deeper deterioration of the economy. Meanwhile risk cycle is shown to be unstable along the fluctuation in business cycle macro risk. The relationship is also pro-cyclical. Deterioration of business condition, represented by increase in macroeconomic risk, will further decrease the rate of performing loan. Therefore, such counter-cyclical policy which focuses on risk cycle need to be enhanced to prevent deeper contraction.

4.2.3. Fund Allocation and Performance Risk As the bank get liquidity flush resulted from business cycle swing, it has two option to increase risk; it can either simply lend more money to borrower with similar risk profile or conduct riskshifting activity by giving credit to other borrower with worse risk profile. In other words, the bank can simply switch into riskier assets. Unfortunately the data and capability of the model applied in this study cannot observe this phenomenon closer. The only available channel to explain risk-taking activity in the model is represented by the bank distribute more credit. This feature then make the model assume that risk in the model are solely caused from increasing amount of the credit. Meanwhile the risk profile of the projects to invested by the banks does not vary. This disadvantage gives the room for further improvement of the model. Separating low-risk project and high-risk project will make the model able to explain the risktaking behaviour more resourceful. The dynamics of risk cycle is then no more solely depend on the growth of credit, but also structure of asset of the bank.

4.2.4. Liquidity and Risk-Taking As the framework predict, we get a sign of pro-cyclicality between deposit and credit cycle. In the model, risk-taking activity is proxied by bank-lending. When the bank lend more money, that means the bank take more risk and this is the exact moment of the emergence of the seeds of a crisis. Flush of liquidity into the bank will induce risk-taking behavior, which is represented by the bank channeling more credit into the economy.

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deposit : credit_cycle 1.5

95% CI

Cumulative IRF

1

.5

0 0

5

impulse : response

10

step

Figure 17. IRF of Credit Cycle and Deposit

In the next episode of the model, this risk-appetite will stimulate excessive credit volume channeled into the market, which then result in asset price bubbles. However, that episode is beyond the scope of this study and is subject for further research. From bank’s internal perspective, the model tells that this behavior is supposed to result in punishment if the manager of the bank get “caught” for practicing excessive lending. Another concern of the bank manager behavior in the moment of liquidity flush is that the manager might underprice of risk of projects or a credit. This will result on asset prices bubble in the long run. Further the model also addresses a more detail aspect of excessive lending activity. The model tells that flush of liquidity into the bank will trigger excessive lending through the bank manager will set lower rate of lending. This logic is strongly supported by the estimation result. Net Interest Margin (NIM), which represents rate of lending and deposit react negatively to shock in deposit. This counter-cyclical behavior indicate that banks tend to lower its lending rate, as shown by low NIM. The result also confirm counter-cyclical relationship of credit cycle to shock in NIM. Lower NIM will trigger over-lending and then will result on bigger compensation received by the bank manager.

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deposit : nim 0

95% CI

nim : credit_cycle 0

Cumulative IRF

-10

-.01

-20

-.02

-30

95% CI

-.03 0

5

10

0

step impulse : response

Cumulative IRF 5

10

step impulse : response

Figure 18. NIM, Deposit, and Credit Cycle

At practical level, the story is more interesting. Many banks in Indonesia are outsourcing some of its activity to the third-party service provider. This behavior makes Otoritas Jasa Keuangan (OJK) or Indonesia Financial Service Authority impose a regulation, even though very loose, to remind banks to pay close attention about this outsourcing practice. One of the most popular practice is on marketing division, both at deposit side and credit side. The regulation states that the bank may outsource some only its low-risk activities to the third-party. Some activities on this classification for instance are call center services and telemarketing services, as clearly stated by the regulation (OJK, 2017). This regulation is a clear example that macroprudential regulation has thouched deeply into the bank daily activity. On the one hand, this regulation is a good example of comprehensive microprudential regulation in Indonesia. On the other hand, as much concerned by this study, every regulation has tendency to overstring bank activity, which may impact the bank performance.

4.2.5. Performance Dynamics and Reponse of Banker and Regulator This study further contributes by accommodating bank performance dynamics in the discussion on context of the business cycle dynamics. For the bankers, profit is one of the most important barometer of their success. This notion applies since bank as a financial institution is basically similar with other business entity, in sense that their objective is to maximize profit. The model presented by Acharya and Naqvi (2012) is very suitable not only to discuss policy-making context, but also profit maximization behavior of the bank. The existance of relationship between Tri-Cycles and bank performance in the model make it possible to explain the sensitivity of bank performance along fluctuation of business cycle. Business cycle, through credit cycle and risk cycle, is shown to play important role in determining bank performance (profitability). The result obtained in this study also shows that

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the notion “high risk - high return” applies in banking business. Therefore, regulator need to be aware of the trade-off faced in regulating the banking system. Shock in the business cycle macro risk clearly give more unstable effect toward risk cycle rather than credit cycle. The result also reveals considerably similar cyclical and unstable behavior of bank profit toward shock in credit cycle and risk cycle. For the bank, this result means the bank needs to pay close attention to both credit cycle and risk cycle. Both variable are significant, sensitive and unstable in affecting dynamics of profit. Role of internal audit to ensure compliance of credit process needs to be strengthen. The separation of credit analyst autority and marketing division is one of a good example of internal control in the bank. Meanwhile for regulator, they clearly need to give focus on risk cycle due to its higher sensitivity rather than credit cycle toward shock in business cycle macro risk. The regulation on bank lending might be made a bit more adjustable as long as the bank can maintain its NPL level. Overall, the model presented by Acharya and Naqvi (2012), which is applied in this study, seems to be very resourceful in addressing the dynamics of Tri-Cycles and bank performance. In the context of crisis, the model on its complete setting can be applied to examine the stepby-step of risk built-up and the burst of the bubble in the economy.

V. CONCLUSION 5.1. Tri-Cycles Dynamics and Bank Performance The result of this study, as presented in Section 5, has successfully answered two main topics addressed. First, dynamic cyclical relationship among the Tri-Cycles: (i) business cycle macro risk; (ii) credit cycle; and (iii) risk cycle. Second, dynamic relationship between the Tri-Cycles and bank performance, or exactly the profit.

Stong Weak

Business Cycle Macro Risk

Bank Performance

Credit Cycle

Risk Cycle

Figure 19. Result of the Study: Tri-Cycles and Bank Performance

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Figure 19 wraps up the conclusion of this study. First, business cycle macro risk and credit cycle exhibit two-way strong relationship, in which shock in credit cycle has significant impact toward business cycle macro risk and vice versa. Second, business cycle macro risk and risk cycle shows bi-directional strong relationship. Shock in business cycle macro risk has significant impact toward risk cycle. However, shock in risk cycle only has weak impact to business cycle macro risk. Third, credit cycle and risk cycle both are inter-dependent which means shock in each cycle has significant impact toward its counterpart. Fourth, business cycle macro risk and bank performance indicates one-way relationship. Shock in business cycle macro risk has weak impact to bank performance. Fifth, credit cycle and bank performance has one-way relationship in which credit cycle has significant impact toward bank performance. Sixth, risk cycle and bank performance has one-way relationship in which risk cycle has weak impact toward bank performance. Seventh, when comparing impact of shock of business cycle macro risk toward credit cycle and risk cycle, it can be inferred that risk cycle is more sensitive. The Cholesky IRF reveals response of response of risk cycle toward shock in business cycle macro risk. Last, eighth, bank performance seems to be sensitive toward both shock of risk cycle and credit cycle.

5.2. Notable Contributions This study exhibits the initial finding of the existence of financial accelerator phenomenon in Indonesia. The result show dynamics of financial cycle – in the form of credit cycle and risk cycle - preceded the business cycle macro risk. This study then has contributed to the business cycle literature by revealing this phenomenon especially in emerging country. This finding is very important in order to deeply understand the financial cycle characteristic in Indonesia. Further research need to extend the analysis by examining real output fluctuation, which was not addressed in this study. This study is also one of the first to employ CDS spread as alternative representative of the business cycle fluctuation in Indonesia. Especially since previous studies mostly employed GDP as representative of business cycle fluctuation. In fact, Indonesia did not experience GDP contraction in 2007/8 financial crisis. So GDP can explain almost nothing when examining business cycle fluctuation in the episode of 2007/8 financial crisis in Indonesia. Furthermore, most studies addressed financial cycle issue by only focusing on credit cycle. This study is then one of the first to examine financial cycle in the form of credit cycle and risk cycle at individual bank level in Indonesia. Risk cycle – besides of credit cycle – has attracted many attentions after 2007/8 crisis. Credit cycle can no more solely explain the dynamics of financial cycle and its relationship with business cycle.

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In term of econometrical method, this study attempted to employ a newly developed statistical package – Panel VAR / PVAR – from Abrigo and Love (2015). VAR approach which had long historical implementation in time series context is now possible to be implemented in panel context because of this package.

5.3. Recommendations From the stance of the policy maker, specifically banking regulator, the result obtained in this study reveal the cyclical and unstable response of the financial cycle (credit and risk) due to shock in business cycle macro risk. The result then gives important insight for the regulator in the implementation of counter-cyclical policy to maintain the bank balance sheet stability. For market participants, especially the bankers, this study has revealed unstable response of profit due to the shock in business cycle through the risk and credit channel. This result somewhat strengthens the existence of the notion of “high risk – high return” in the banking business. The bankers then need to give special attention in the internal bank risk cycle, along with the internal bank credit cycle. As modeled by theoretical framework used in this study, the bankers are assumed to get more bonuses by selling more credit. The bankers might need to find the alternative scheme of incentive in lending system. Separation of the marketing department with the credit approval analyst is one of good example. The bankers might also consider the effectiveness of flat incentive system in credit selling. However, this study does not intend to examine these alternatives. Further research is needed to provide comprehensive discussion toward this topic.

5.4. Disadvantages and Suggestion for Further Research From the point of view of econometrical approach, Panel VAR approach employed in this study gives satisfying solution. If it seems possible, further study might explore a more complex statistical setting such as Panel VECM approach to conduct more comprehensive examination. For further research, it might be very fruitful if the design of this research can be uplifted to cover cross-country experience, such as ASEAN countries. By doing so, the study will reveal cross-country variation of the Tri-Cycles dynamics. Such setting is very important regarding the fact that Indonesia as single country did not experience contraction in GDP cycle in financial crisis 2007/8. While other countries in ASEAN such as Singapore and Malaysia did. Finally, recalling the complete formal model presented by Acharya and Naqvi (2012), the design employed in this study has not yet captured the whole feature of the model. Their model essentially was designed to explain the full story of the birth of crisis, which they called as “The Seeds of Crisis”. So, the story presented by this study is only the beginning phase of the complete tale covered by the model. Further research certainly need to address the model in complete setting.

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REFERENCES Abrigo, Michael and Love, Inessa. (2015). Estimation of Panel Vector Autoregression in Stata: a Package of Programs. University of Hawaii Working Paper. Acharya, V., & Naqvi, H. (2012). The Seeds of a Crisis: A Theory of Bank Liquidity and Risk Taking Over the Business Cycle. Journal of Financial Economics, 106, 349-366. Arellano, M., & Bond, S. (1992). Some Tests of Specification for Panel Data: Monte Carlo Evidence and Application to Employment Equations. Review of Economics Studies, 58, 277-297. Baltagi, B. H. (n.d.). Econometric Analysis of Panel Data (Vol. 3). West Sussex, England: John Wiley & Sons Ltd. Bank for International Settlement. (2017, January 3). Basel Committee for Banking Supervision. Retrieved from BIS Website: http://www.bis.org/bcbs/history.htm. Bernanke, S. B., Gertler, M., & Gilchrist, S. (1999). The Financial Accelerator in a Quantitative Business Cycle Framework. In J. B. Taylor, & M. Woodford, Handbook of Macroeconomics (Vol. 1, pp. 1341-1393). Elsevier Science B.V. Bertay, A. C., Demirguc-Kunt, A., & Huizinga, H. (2015). Bank Ownership and Credit Over the Business Cycle: Is Lending by State Banks Less Procyclical? Journal of Banking and Finance, 50, 326-339. Blundell, R., & Bond, S. (1998). Initial Conditions and Moment Restrictions in Dynamic Panel Data Models. Journal of Econometrics, 87, 115-143. Burns, A. F., & Mithcell, W. C. (1946). Measuring Business Cycle. In N. B. (NBER), Studies in Business Cycles. New York. Cameron, A. Colin and Trivedi, Pravin K. (2009). Microeconometrics Using Stata. Texas: StataCorp LP. Canova, Fabio and Ciccarelli, Matteo. (2013). Panel Vector Autoregressive Models: A Survey. European Central Bank Working Paper Series No 1507 / January 2013. Federal Deposit Insurance Company. (2017, January 3). FDIC. Retrieved from FDIC: https:// www.fdic.gov/bank/historical/sandl/ Ferri, G., Kalmi, P., & Kerola, E. (2014). Does Bank Ownership Affect Lending Behavior? Evidence from the Euro Area. Journal of Banking and Finance, 48. Glen, J., & Mondragon-Velez, C. (2011). Business Cycle Effecs on Commercial Bank Loan Portfolio Performance in Developing Economies. Review of Development Finance, 150-165.

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Guidara, A., Lai, V. S., Soumare, I., & Tchana, T. F. (2013). Banks’ Capital Buffer, Risk and Performance in the Canadian Banking System: Impact of Business Cycles an Regulatory Changes. Journal of Banking and Finance, 3373-3387. Gujarati, D. N., & Porter, D. C. (2009). Basic Econometrics (Vol. 5). Boston: McGraw-Hill Irwin. Ibrahim, M. H. (2016). Business Cycle and Bank Lending Procyclicality in a Dual Banking System. Economic Modelling, 55. Jacobs, J. (1998, February). Econometric Business Cycle Research: An Assessment of Method. University of Groningen Working Paper. Kiyotaki, N. (2011, September 1). A Perspective on Modern Bussiness Cycle Theory. FRB Richmond Economic Quarterly, 97(3), pp. 195-208. Kydland, F. E., & Prescott, E. C. (1982). Time to Build and Aggregate Fluctuations. Econometrica, 50, 1345-1370. Minetti, R., & Peng, T. (2013). Lending Constraints, Real Estate Prices and Business Cycle in Emerging Economies. Journal of Economic Dynamics and Control, 37. Otoritas Jasa Keuangan. (2017). Surat Edaran OJK Nomor 11/SEOJK.03/2017 Tentang Prinsip Kehati-hatian bagi Bank Umum yang Melakukan Penyerahan Sebagian Pelaksanaan Pekerjaan kepada Pihak Lain. Pramono, B., Hafidz, J., Adamanti, J., Muhajir, M. H., & Alim, M. S. (2015). Dampak Kebijakan Countercyclical Buffer Terhadap Pertumbuhan Kredit di Indonesia. Working Paper Bank Indonesia. Psillaki, M., & Mamatzakis, E. (2017). What Drives Bank Performance in Transitions Economies? The Impact of Reforms and Regulations. Research in International Business and Finance, 578-594. Romer, D. (2012). Advanced Macroeconomics. New York: McGraw-Hill. Tinbergen, J. (1940, February). Econometric Business Cycle Research. The Review of Economic Studies, 7, 73-90. Vithessonthi, C., & Tongurai, J. (2016). Financial Market Development, Business Cycle, and Bank Risk in South America. Research in International Business and Finance, 472-484. Winata, A., & Viverita. (2013). Analisis Pengaruh Struktur Pendapatan Bank terhadap MarketBased Performance: Studi Empiris Bank yang Terdaftar pada Bursa Efek Indonesia Periode 2004-2012. Working Paper Fakultas Ekonomi dan Bisnis, Universitas Indonesia. Windmeijer, F. (2005). A Finite Sample Correction for the Variance of Linear Efficient Two-Step GMM Estimators. Journal of Econometrics, 126, 25-51.

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What Protect Emerging Markets From Developed Countries Unconventional Monetary Policy Spillover?

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WHAT PROTECT EMERGING MARKETS FROM DEVELOPED COUNTRIES UNCONVENTIONAL MONETARY POLICY SPILLOVER?1 Eko Sumando2

Abstract

This paper investigates the macro-characteristics that reduce the spillover effect of unconventional monetary policy (UMP) from developed countries to the emerging market ones. We use event study method to examine 24 UMP announcements and a panel fixed effects model to examine the characteristics of the emerging markets. The spillover channel considered in this paper is the exchange rate. The results show inconclusiveness of the macroeconomic fundamentals role on emerging markets’ currency resilience. From three main fundamental economic indicators, only inflation was found to significantly and positively contribute to exchange rate depreciation. Deeper financial markets contribute to better resilience. Trade linkages with China provide less vulnerable currency position of the emerging markets while trade linkages with developed countries provide mixed evidence. The macro-prudential policy and the capital flow measures that the emerging markets countries implemented before to the announcements are moderately effective on reducing the spillover effect.

Keywords: unconventional monetary policy, emerging markets, international spillover JEL Classification: E52, E58

1 Author want to thanks Prof. Renée Fry-McKibbin (Crawford School of Public Policy, Australian National University) for her input to this research. 2 Author is a dual-degree post-graduate student in MSc (in Economics), Faculty of Economics and Business, Universitas Gadjah Mada and MIDEC, Crawford School of Public Policy, Australian National University ([email protected]).

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I. INTRODUCTION The global financial crisis in 2008 had a significant impact to the design and implementation of developed countries monetary policy. In response to the crisis, the Federal Reserve Bank (FED), the European Central Bank (ECB), the Bank of England (BOE), and the Bank of Japan (BOJ) adjusted their short term interest rates to zero and applied Quantitative Easing (QE) as an alternative monetary policy strategy (Fawley & Neely 2013). The QE action included large scale purchases of government bonds and private securities, and several lending programmes (Bean 2012). Recently there has been wide interest in the impact of developed countries unconventional monetary policy not only on the country themselves but also on their international spillovers. The International Monetary Fund (IMF) (2013) suggests that developed countries unconventional monetary policy generate greater global financial spillovers when the policy objectives were to restore the financial market stability. Global trade, liquidity and portfolio rebalancing may transmit the impact of unconventional monetary policy measures adopted in the developed countries to other countries (Chen et al., 2012). Despite this new challenge in the monetary system, few studies in literature focus on the effect of developed countries unconventional monetary policy on emerging market. Having a better understanding of the international implications of developed countries unconventional monetary policy is important for emerging market policy makers to cope better with the challenges implied by such policies. Few studies have discussed this issue and the evidence about the effect of developed countries unconventional monetary policy on emerging market is mixed (Chen et al. 2012; Fic 2013; Fratzscher et al. 2013; Mishra et al. 2014; Moore et al. 2013). A question remains whether the emerging market macroeconomic fundamentals, financial system characteristics and macroprudential policy are significant in reducing the spillover impacts of unconventional monetary policy. However, the focus has been on the effects of the FED’s QE. This study contributes to the existing literature in two ways: First, it fills a gap by analyzing all developed countries unconventional monetary policy, including not only the FED, but also the policy from the ECB, the BOE and BOJ between 2008 and 2012. Secondly, this study sheds new understanding on the importance of macroeconomic fundamentals, financial depth, trade linkages and macroprudential policy in reducing the spillover of developed countries unconventional monetary policy, particularly through exchange rate as the spillover channel. This paper examines 24 developed countries unconventional monetary policy announcement events and 15 emerging markets reactions using a two-step method. First, an event study method is used to analyze the significance of the announcement to the exchange rate change with a two-day window event. Then, the panel data fixed effects model is used to reveal the individual characteristics of the countries prior to the event that may enforce the spillover.

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The results show unclear significance of the macroeconomic fundamentals. From three main indicators, only inflation was found to significantly contribute positively to exchange rate depreciation. Deeper financial markets contribute to better country resilience to exchange rate depreciation. Countries with higher stock market capitalization and larger liquid assets (M3) are more stable than countries that are not. Trade linkage with China provides less vulnerable currency position to the emerging markets. On the other hand, trade linkages with developed countries provide mixed evidence. Higher imports from the US and the UK contribute to higher currency volatility. Furthermore, macro-prudential policy and capital flow management measures implemented by the emerging markets prior to the unconventional monetary policy announcement were found to be moderately effective in reducing the country exchange rate depreciation from the spillover. The rest of the paper is organized as follows: Section 2 provides literature review on unconventional monetary policy and its international spillovers, particularly to the emerging market. Section 3 describes the method and the data used in this study. Section 4 presents the estimation result and its analysis, while Section 5 concludes.

II. THEORY 2.1. Unconventional Monetary Policy Generally, central banks implement monetary policy through managing the short term policy rate. However, facing near-zero short term interest rates, developed countries central banks have turned to unconventional monetary policies involving large scale asset purchases. The FED, the BOE, the ECB and the BOJ began to apply QE and asset purchases on a very large scale since 2008 until 2013 (Figure 1). The consequence of these policies was a large increase in central bank balance sheets. Figure 1 shows the evolution of short term interest rates in the US, the UK, the Euro Area and Japan, and the size of individual banks’ balance sheets (normalized to 100 at the beginning of August 2007) which reflects the scale of unconventional monetary policy actions (Fic 2013). The IMF (2013) suggest that this large scale asset purchases contributed to some portfolio rebalancing across the world and generated strong capital flows between developed countries and emerging markets. Furthermore, Fratzscher et al. (2013) argues that when financial markets are thin, capital inflows can cause rapid currency appreciation, which can affect the country’s export sectors and returns from net foreign assets. However, the rapid credit expansion induced by the unconventional monetary policy can encourage exchange rate instability in the emerging markets if the capital inflows are followed by rapid flow reversals.

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7

600

Interest Rates

6

Euro Area

Japan

UK

Central Bank Assets (2007Q3=100)

500

US

5

Euro Area

Japan

UK

US

400

4

300

3

Source: Fawley & Neely (2013)

2013Q1

2012Q3

2012Q1

2011Q3

2011Q1

2010Q3

2010Q1

2009Q3

2009Q1

2008Q3

2007Q3

2013Q1

2012Q3

2012Q1

2011Q3

2011Q1

2010Q3

2010Q1

2009Q3

2009Q1

0

2008Q3

0

2008Q1

100

2007Q3

1

2008Q1

200

2

Source: Fawley & Neely (2013)

Figure 1. Developed Countries Unconventional Monetary Policy

The IMF (2013) noted that at first the QE program was meant to prevent a financial system meltdown and strengthen financial intermediation but later the objective shifted to stimulating the economy. The QE policies are different across central banks and depend on their specific objective and different economy structures of the individual countries. For example, the FED’s and the BOE’s QE program is focused on bond purchases while the BOJ and the ECB focused on direct lending to banks. Fawley and Neely (2013) suggested that the different tools reflected different structures of these developed countries economies, in the US and the UK bond markets play a relatively more important role than banks, while it is the opposite for Europe and Japan.

2.2. The International Spillovers The developed countries unconventional monetary policy measures had a definite impact to the emerging market countries. Emerging markets countries leaders, particularly Brazil, describes the quantitative easing is comparable to a monetary tsunami that can trigger sudden capital reversal away from emerging market countries (Fic 2013; Fratzscher et al. 2013). Some studies provide evidence about the international spillovers of developed countries unconventional monetary policy to the emerging markets. However, the studies available mainly focused on the FED policy spillovers. Chen et al. (2012) studied the cross-border spillovers of developed countries QE to 16 emerging market countries. Using an event study method, they examine the cross-border financial market impact of the FED QE announcements of asset purchase during 2008 to 2012. They find that the FED QE announcement influenced a broad range of emerging market assets prices, raising equity prices, and lowering government and corporate bond yields.

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This evidence supports the view that developed countries QE program influenced international market expectations about the strength of international capital flows to the emerging market. In other words, the QE measures increased the global liquidity through the immediate re-pricing of assets in international financial markets. Furthermore, they used a global vector error-correcting model (VECM) to measure the estimated size of the spillover and found that the size effects differed across regions. In some economies such as Hong Kong and Brazil, the expansionary impact of US quantitative easing was significant and associated with rapid credit growth and strong capital inflow, currency appreciation and inflationary pressures. In addition, Moore et al. (2013) studied the FED large-scale asset purchases (LSAPs) announcement impact on capital flows from the US to 10 emerging market countries from 2008 to 2010. Using panel ordinary least square, they find that a 10-basis-point reduction in long-term U.S. Treasury yields contributes to almost 0.4-percentage-point increase in the foreign ownership share of emerging market debt which in turn estimated will reduce the emerging market government bond yields by almost 1.7 basis points. Their study suggests the significance of the US capital outflows to the emerging markets and its impact on the long-term emerging market government bond yields. Furthermore, the author assessed the robustness of these estimates by employing event study and vector autoregression method and find similar results in aggregates. They also find different marginal effects across emerging market countries but do not explore the country specific characteristics. To complement the evidence about the international spillover of developed countries unconventional monetary policy, Fic (2013) investigates the effect of all unconventional monetary policy implemented four major developed countries central bank on four major developing countries: Brazil, Russia, India and China from 2008 to 2012. This means the analysis not only focused on the FED policy, but also the ECB, the BOE and the BOJ. Using the event study method, she finds that developed countries unconventional monetary policy affected exchange rates, equity prices, and long term yields. The unconventional monetary policy has had a significant spillover through channels such as global trade, global liquidity and global portfolio rebalancing. Her event study analysis shows that the quantitative easing policy contributed to long term yields decreasing by almost 175 basis points in Brazil, and 25 basis points in Russia, India and China. The quantitative easing policy has also contributed to increases in equity prices in the Brazil, China and India. The impact of developed countries unconventional monetary policy on the developing economies may vary. Chen et al. (2013), Fic (2013) and Moore et al. (2013) argues that it depends on the scale of their exposure to the developed countries in terms of trade and financial linkages or other spillover channel, and the type and scale of response of the monetary authorities in that country to capital flows. However, three studies above do not mention the characteristics of macroeconomic fundamentals, financial system and capital flow regulation of each country.

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2.3. The Country Characteristic and the Spillover Fratzscher, et al. (2013) address this emerging markets characteristics issue by analyzing the global impact of the FED unconventional monetary policy from 2007 to 2010 on 65 foreign financial markets, including emerging markets, through net capital inflows and price of bonds and equity as the spillover channel. They found that the FED unconventional monetary policy has influenced the reallocation of portfolio in worldwide financial markets. The first phase of quantitative easing triggered a rapid capital outflow from the emerging markets to the US but the next phase triggered it to the opposite direction. Furthermore, they found no evidence that exchange rate or capital account policies may aided the emerging market countries in protecting themselves from the US monetary policy spillovers. This study illustrates how US unconventional monetary policy has contributed to portfolio reallocation as well as a re-pricing of risk in global financial markets. In addition, Eichengreen and Gupta (2014) use cross sectional regression of emerging market foreign reserves, equity prices, and exchange rates and relate the reaction of these variables to macroeconomic fundamentals and country’s financial markets characteristics. They examine the spillover effect of the FED tapering announcement from April to August 2013 and found that the tapering talk brings immediate sharp negative impact on emerging markets. Furthermore, they show that the impacts vary across countries. Countries with higher financial depth experienced higher exchange rate depreciations. However, countries with better macroeconomics fundamentals characteristics such as lower budget deficit, lower public debt, high level of reserves, or high rate of economic growth did not experienced a significant different impact compared to those who are not. It shows that the size of the country’s financial market is a more important determinant than their macroeconomic fundamentals. This may interpreted as the global investors’ capacity to rebalance their portfolios when the target country has a relatively large and liquid financial market (IMF 2013a). In the opposite of both studies, Mishra, et al. (2014) argue that macroeconomic fundamentals and macro-prudential policy are important in minimizing the spillover effect of US monetary policy. They conducted an event study analysis of the FED QE announcement using government bond yields, stock prices and exchange rates daily data, between January 2013 and January 2014, for 21 emerging markets. The results suggest that markets differentiated across countries during the episodes unconventional monetary policy announcement based on countries characteristics, including macroeconomic fundamentals and economic and financial structures. Countries with deeper financial markets, better macroeconomic fundamentals, and better macro-prudential policy experienced less exchange rate depreciations and less increase in government bond yields. They also found that having strong trade exposure with China can help reduce markets negative reaction.

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The different period of announcements, length of data sets of countries and methodology in each paper may explain the ambiguous findings. The mixed evidence of previous studies also shows two gaps which this research will address. These are the need to cover all developed countries unconventional monetary policy spillover effects on emerging markets, and also to examine whether macro fundamentals, financial structure and macro-prudential policy is significant in minimizing spillover.

III. METHODOLOGY 3.1. Data This study examined 24 unconventional monetary policy announcement events by the FED, the BOE, the ECB and the BOJ as described in Table 8 below. The key spillover channel are daily exchange rates (local currency/US$) from January 2008 to December 2012. Table 1. Developed countries unconventional monetary policy announcements No

Date

Description

1

28 March 2008

ECB announced and expanded long term refinancing operations (LTRO)

2

15 October 2008

ECB announced and expanded long term refinancing operations

3

28 November 2008

FED announced of $100 billion in Government Sponsored Enterprise debt and

4

19 January 2009

BOE announced the purchasing of nearly £50 billion of high quality private sector assets

5

5 March 2009

BOE announced the purchasing of £75 billion in assets

6

18 March 2009

FED announced the purchasing of $300 billion in long term Treasuries and

7

7 May 2009

$500 billion in mortgage backed securities (MBS) purchase.

$750 billion in MBS, and $100 billion in MBS. BOE announced the purchasing of nearly £125 billion in assets; ECB announced the purchasing of €60 billion in Euro-denominated covered bonds; 12 month LTRO announced 8

6 August 2009

BoE announced the purchasing of nearly £175 billion in assets

9

5 November 2009

BoE announced the purchasing of £200 billion in assets

10

1 December 2009

BOJ offered 10 trillion JPY in 3 month loans

11

11 March 2010

FED announced the purchasing of $600 billion in Treasuries

12

17 March 2010

BOJ expanded the size of the fixed rate operations to 20 trillion JPY

13

10 May 2010

ECB conducted interventions in the Euro Area private and public debt securities markets.

14

21 May 2010

BOJ offered 3 trillion JPY in 1-year loans to private institutions

15

30 August 2010

BOJ added 10 trillion JPY in 6 month loans to the fixed rate operations

16

21 September 2011

FED announced the purchasing of $400 billion in Treasuries

17

6 October 2011

BOE announced the purchasing of nearly £275 billion in assets; ECB announced the purchasing of €40 billion in Euro-denominated covered bonds

18

8 December 2011

ECB LTRO expanded, 36 month LTRO announced

19

20 February 2012

FED announced the purchasing of long term securities and sell short term securities; at the pace of about $45 billion per month

9

5 November 2009

BoE announced the purchasing of £200 billion in assets

10

1 December 2009

BOJ offered 10 trillion JPY in 3 month loans

11 450

11 March 2010

FED announced the purchasing of $600 billion in Treasuries

12

17 March 2010

BOJ expanded the size of the fixed rate operations to 20 trillion JPY

13

10 May 2010

ECB conducted interventions in the Euro Area private and public debt securities markets.

14 15

21 May 2010 BOJ offered 3 trillion JPY in 1. 1-year loans to private institutions Table Developed countries unconventional monetary announcements Developed countries unconventional monetary policypolicy announcements (Lanjutan) 30 August 2010 BOJ added 10 trillion JPY in 6 month loans to the fixed rate operations

No 16

Date2011 21 September

FED announced the purchasing of Description $400 billion in Treasuries

17 1

6 2011 28October March 2008

BOE announced and the purchasing of nearly billion operations in assets; (LTRO) ECB expanded long term £275 refinancing

2

15 October 2008

the purchasing of €40 billion in Euro-denominated ECB announced and expanded long term refinancing operations covered bonds

18 3

8 28December November2011 2008

ECB announced LTRO expanded, 36billion monthinLTRO announced FED of $100 Government Sponsored Enterprise debt and

19

20 February 2012

FED announced the purchasing long term securities and sell short term securities; $500 billion in mortgage backed of securities (MBS) purchase.

4

19 January 2009

at theannounced pace of about billion per month £50 billion of high quality private sector assets BOE the $45 purchasing of nearly

20 5

13 September 5 March 2009 2012

FED announced per month BOE announced the the purchasing purchasing of of $40 £75 billion billion of in MBS assets

21 6

12 18 December March 20092012

$45 billion FED announced the purchasing of $300 billionofinlong longterm termTreasuries Treasuriesper andmonth

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without sterilization $750 billion in MBS, and $100 billion in MBS. 22 7

9 7 February May 20092012

£325 billion assets BOE announced the purchasing of nearly £125 in billion in assets; ECB announced

23

5 July 2012

BOEpurchasing announced of £375 billion in assets the ofthe €60purchasing billion in Euro-denominated covered bonds;

24

6 September 2012

ECB announced the potential purchasing of the debt of countries that applied 12 month LTRO announced

8

6 August 2009

to theannounced European Stabilization Mechanism in unlimited amounts BoE the purchasing of nearly £175 billion in assets on the secondary market.

Source: Fawley & Neely2009 (2013) 9 5 November

BoE announced the purchasing of £200 billion in assets

10

1 December 2009

BOJ offered 10 trillion JPY in 3 month loans

11

11 March 2010

FED announced the purchasing of $600 billion in Treasuries

The study used data from 15 emerging markets including Brazil, Colombia, Chile, 12 17 March 2010 BOJ expanded the size of the fixed rate operations to 20 trillion JPY Indonesia, India, Korea, Mexico, Malaysia, Philippines, Peru, Russia, South Africa, Singapore, 13 10 May 2010 ECB conducted interventions in the Euro Area private and public debt securities markets. Turkey and Thailand. The selected country macro-characteristics are decomposed into several 14 21 May 2010 BOJ offered 3 trillion JPY in 1-year loans to private institutions categories: (a) macroeconomics fundamentals: growth rate, inflation, and the ratio of current 15 30 August 2010to GDP; (b)BOJ added 10depth: trillion JPY in 6ratio month of loans to themarket fixed rate operations account balance financial the stock capitalization to GDP, 16 ratio 21 September 2011 purchasing of $400 billion Treasuries the of M2 to GDP andFED theannounced ratio oftheM3 to GDP ratio; (c)intrade linkage with Developed 17 6 October BOE announced purchasing of nearly £275 billion in assets; Countries: the2011 United States, the Unitedthe Kingdom, Europe area and Japan; (d) trade linkage ECB announced the purchasing of €40 billion in Euro-denominated covered bondsby the with China, and (e) macro-prudential policy and or capital flow measures implemented 18 8 December 2011 monetary ECBauthorities. LTRO expanded, 36 month LTRO announced countries fiscal and Table 9 (Appendix) listed the variables description 19 data 20 February announced the purchasing of long before term securities and sell short term securities; and source2012 are. All the FED variables occurred a quarter the event. at the pace of about $45 billion per month

The data for the macro-prudential policy and the capital flow management measure 20 13 September 2012 FED announced the purchasing of $40 billion of MBS per month conducted by emerging market countries during 2008 to 2012 are taken from Zhang and Zoli 21 12 December 2012 FED announced the purchasing of $45 billion of long term Treasuries per month (2014), and the IMF (2012) policy paper about the interaction of monetary and macro-prudential without sterilization policies and the Annual Report on Exchange Arrangements and Exchange Restrictions (IMF 22 9 February 2012 BOE announced the purchasing of £325 billion in assets 2013c). 23

5 July 2012

BOE announced the purchasing of £375 billion in assets

24

6 September 2012

ECB announced the potential purchasing of the debt of countries that applied

3.2. Method Source: Fawley & Neely (2013)

to the European Stabilization Mechanism in unlimited amounts on the secondary market.

Following Mishra et al. (2014) we employ two steps method; first, an event study analysis to examine the significance of each event to the change of the exchange rate in two-day window (a day before and a day after the event). Secondly, panel fixed effect model is used to examine

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the relation of the change in the exchange rate with the countries macroeconomic characteristics a quarter before the event.

Examining the Announcement Event An event study analysis was used to examine the developed countries unconventional monetary policy announcements, because this method is able to capture the impact of an event in a short time window, since the spillover effects of developed countries unconventional monetary policy is expected to rapidly transmit around the date of the event. This method is also used in previous studies (Chen et al. 2012; Fic 2013; Mishra et al. 2014). The “events” or the dates of the FED, the ECB, the BOE and the BOJ announcements and market reactions around these events will be pooled around each year period. These events will be analyzed using model (1). (1) is the exchange rate change (two-day window) of country c at the event i; where is a dummy for the announcement event i. This model will provide a matrix of the change in exchange rate of emerging market countries when an unconventional moneta2ry policy announcement happened. A pooled time-series analysis will be used to measure the significance and the size of the coefficient β. Positive β coefficient indicates exchange rate depreciation while negative β coefficient indicates exchange rate appreciation. In this study, the event that contribute to exchange rate depreciation will be classified as a negative event ( ).

Measuring the Change in the Exchange Rate In order to standardized the change of the exchange rate (local currencies/US$) of emerging markets countries ( ), this study used two measurement of change which are the logarithm change after and before the event described in (i) and the mean deviation of the log change in the year when the event i occurs described in (ii). (i) where is the change in exchange rate of country c, at the announcement event i which come from the log of exchange rate at the day after the event ( ) minus the log of exchange rate at the day before the event ( .) (ii)

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where is the change in exchange rate of country c, at the announcement event i which come from the log of exchange rate of country c, at the time of the event i ( ) minus the mean of log of exchange rate of country c in the year when the event i occurs ( ) divided by the standard deviation of log of exchange rate of country c in the same year ( ).

Significance of Macroeconomic Characteristics Secondly, panel regressions will be estimated by pooling the events across the emerging markets. The panel fixed effects regressions in model (2) will examine the relationship of the exchange rate changes of the negative events with each country characteristics and the event and characteristics interactions. The panel fixed effects model also used in previous studies (Eichengreen & Gupta 2014; Moore et al. 2013) The model specification is as follows: (2) where is a dummy for negative event i (when the country experienced exchange rate depreciation), is the country c macroeconomics and financial system characteristic a quarter before the event, is the country-fixed effect. controls country characteristics that are not varying over time. Country fixed effects also control for other country variables which are not likely to change over the one year period. can be time varying and non-time varying. For regressions where is time-invariant, the variable will be collinear with the country fixed effect and will drop out. is the coefficient of interaction variable that capture the effect of the event and country characteristics at the same time.

IV. RESULT AND ANALYSIS 4.1. The Announcements Effect of Unconventional Monetary Policy An event study method used to examine the immediate markets reactions around the unconventional monetary policy event announcement listed in Table 8. The method employs Equation (1) which relates the two-day logarithm change (i) and the mean deviation to the exchange rate logarithm change (ii) to a constant and a dummy of the events. The regression result in Table 2 shows that emerging markets reacted negatively or experienced exchange rate depreciation in response to announcements (2), (15), and (16) referred to in Table 8. The opposite reaction occurred in the (13) and (17) announcement. Interestingly, the result shows that the FED, the ECB, the BOE and the BOJ have an effect on the emerging market significantly in some announcements.

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The first significant and strongest markets’ reaction is on October 15 2008, when the ECB announced and expanded long term refinancing operations. This event is happen at the same time with the beginning of the GFC episode in 2008. The significant currency depreciation among emerging market on this day is consistent with Fic (2014) and Chen et al. (2012), where they noted that the ECB measures to stabilize the macroeconomic condition in Europe and the GFC contributed significantly to net capital outflows from emerging markets. These findings may indicate that ECB unconventional monetary policy measures may have intensified the magnitude of the capital outflow at the time of crisis. The next significant reaction is on September 21, 2011, when the FED announced the purchasing of US$ 400 billion US Treasury securities. This action by the FED that focused on purchases of US Treasury securities primarily aimed to stimulate the US economy by lowering yields, and pushing up asset prices in riskier market segments (Fratzscher et al. 2013). This action builds pressure to the emerging markets currency. Furthermore, Figure 3 (Appendix) shows that 13 out of 15 countries experienced exchange rate depreciation at this date. Only Turkey and Indonesia do not experience the currency depreciation impact. The last significant depreciation event is on August 30 2010, when the BOJ added 10 trillion JPY in 6 month loans to the fixed rate operations. The BOJ central bank balance sheet policies have a small but significant financial spillover. This policy might have had negative spillover on some trade competitors via the exchange rate channel (IMF 2013b). Beside the depreciation episode, two announcements significantly contribute to exchange rate appreciation. First is when the ECB conducted an intervention in the Euro Area private and public debt securities markets in May 2010. This finding bring into line what the IMF policy paper (IMF 2013a) mention about the importance of the ECB announcements in sending the market stability signal in the Euro area. The announcement of the debt intervention in some core euro area countries and in much of the rest has significantly lowered bond yields in the euro area. This reduction in the yield in the euro area led to a generalized rally in of capital flows into the emerging markets (IMF 2013a).

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Table 2. Significance of the unconventional monetary policy announcements Event 1

2

3

4

5

6

7

8

9

10

11

12

Log Change

Mean Deviation

-0.089

-0.005

(0.290)

(0.029)

2.457***

0.236***

(1.013)

(0.099)

0.535

0.068

(0.436)

(0.045)

-0.318

-0.044

(0.366)

(0.036)

0.262

0.018

(0.340)

(0.029)

-0.181

-0.008

(0.386)

(0.040)

-0.290

-0.039

(0.232)

(0.024)

0.018

0.001

(0.254)

(0.024)

0.159

0.011

(0.286)

(0.031)

-0.290

0.026

(0.268)

(0.027)

-0.062

-0.006

(0.176)

(0.020)

-0.107

-0.009

(0.249)

(0.024)

Event

Log Change

Mean Deviation

13

-0.804**

-0.073**

(0.357)

(0.040)

-1.115

-0.107

(0.811)

(0.084)

0.400**

0.038**

(0.186)

(0.015

0.981**

0.096**

(0.476)

(0.045)

-0.441***

-0.049***

(0.037)

(0.003)

-0.165

-0.022

(0.232)

(0.026)

0.237

0.021

(0.211)

(0.024)

-0.297

-0.031

(0.139)

(0.016)

0.160

0.012

(0.234)

(0.025)

-0.251

-0.023

(0.185)

(0.017)

-0.347

-0.028

(0.326)

(0.031)

0.023

0.004

(0.084)

(0.009)

14

15

16

17

18

19

20

21

22

23

24

***, **, and * denote statistical significance at 1, 5, and 10 percent levels respectively This event study method results used a pool time-series regression to analyze relation in the change in the emerging markets exchange rate and the dummy for the unconventional monetary policy announcements.

The next event is interesting because it involves two central bank policies. In October 2011, the BOE announced the purchase of nearly £275 billion in assets. At the same time, the ECB announced the purchasing of €40 billion in Euro-denominated covered bonds. This policy resulted in significant capital inflows to emerging markets and exchange rate appreciation described in Figure 4. Hosono and Isobe (2014) study confirm that these two policies contribute to the lower bond yield in this two big economies. This may generate capital inflows from developed countries to emerging markets.

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Overall, these findings are consistent with the results from Chen et al. (2012) and Fic (2013) that suggest developed countries unconventional monetary policies contributes to some capital flow episodes between developed countries and the emerging markets.

4.2. Market Reaction and Country Characteristics Macro Fundamentals The results in Table 3 show that the exchange rates for emerging markets are differentiated only on the basis of inflation. Countries with higher inflation have higher depreciation of their exchange rate. This is rather unexpected because unlike the current account balance and economic growth which is a macroeconomic indicator for countries’ trade and output, inflation only indicates price changes. The coefficient is significant and suggests that a country with a 1 per cent higher inflation rate will have higher currency depreciation by 0.11 percentage points compared to other countries. The interaction term shows that inflation will reinforce exchange rate depreciation only if it occurs at the same time with the announcements.

Table 3. Exchange rate log change & macro fundamentals Exchange rate log change Dummy

Growth

1.010***

0.286

1.022***

(0.203)

(0.356)

(0.204)

-0.006 (0.022)

Inflation

0.118*** (0.040)

CA/GDP

0.000 (0.001)

Interaction with Growth

-0.015 (0.045)

Inflation

0.133** (0.059)

CA/GDP

-0.001 (0.002)

Observation

360

360

360

R-squared

0.07

0.12

0.07

Note: ***, **, and * denote statistical significance at 1, 5, and 10 percent levels respectively

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These findings contradict the Mishra et al. (2014) study that suggests the current account and growth rate before the announcements affects emerging market resilience. However, it is consistent with evidence from Chen et al. (2012) that shows that countries with better fundamentals such as a current account surplus does not perform differently to countries that are have a deficit. This may happen due to the different episodes in the unconventional monetary policy announcements in each studies. Mishra et al. (2014) studied 2013-2014 FED announcements, Chen et al. (2012) studied 2008-2012 FED announcements and this paper studied the FED, the BOE, the ECB and the BOJ announcements from 2008 to 2012. In other words, there might be less heterogeneity on the basis of macro-fundamentals around the announcements in this study and Chen et al. (2012) study.

Financial Depth Table 4 shows countries with deeper financial markets are less vulnerable compared to those that are not. The result holds for standard measures of financial depth such as the stock market capitalization to GDP ratio and the M3 to GDP ratio except the M2 to GDP ratio. Table 4. Exchange rate log change & financial depth Exchange rate log change Dummy

1.225***

0.662*

0.703***

(0.373)

(0.390)

(0.269)

Stock Market

-0.019***

Capitalization/GDP

(0.004)

M2/GDP

-0.012 (0.010)

M3/GDP

-0.008* (0.004)

Interaction with Stock Market

0.000

Capitalization/GDP

(0.003)

M2/GDP

0.004 (0.003)

M3/GDP

0.002** (0.0008)

Observation

360

360

339

R-squared

0.27

0.19

0.09

***, **, and * denote statistical significance at 1, 5, and 10 percent levels respectively

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This result indicates countries with more liquid capital market and had large liquid assets have better stance in mitigating the spillover than countries that are do not have large liquid assets. However, the magnitude of the impact is relatively weak. This result is consistent with Mishra et al. (2014) and Bowman et al. (2014) who find that financial depth tends to enhance countries’ resilience to shocks because their deep markets facilitated the fine-tuning needed in capital flows and portfolios rebalancing

Trade Linkage with Developed Countries The effects of developed countries unconventional monetary policy on emerging markets could also transmit directly through the external demand or trade channel. Quantitative easing may increase the demand for emerging markets goods and services through easier trade credit and increase developed countries spending (Chen et al. 2012). However, such impacts depend on the developed countries trade elasticity. Table 5. Exchange rate log change & trade linkage with developed countries Exchange rate log change Dummy

0.735*** (0.232)

ME MUS

0.813*** (0.314) 0.00 0.00

0.825*** (0.310)

0.787*** (0.307)

1.61 (2.17)

MUK

6.71 (8.65)

MJPN

1.18 (2.18)

XE

0.00 (0.00)

XUS

0.00 (0.00)

XUK

0.00 (0.00)

XJPN Interaction with ME MUS

0.00 (0.00) 1.63** (8.17)

MUK

2.23*** (7.66)

MJPN

1.19* (0.64)

XE XUS

0.00 (0.00) 0.00 (0.00)

XUK

0.00 (0.00)

XJPN Observation R-square

360 0.09

360 0.07

360 0.09

Note: ***, **, and * denote statistical significance at 1, 5, and 10 percent levels respectively

360 0.1

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Table 5 describes the impact of trade linkages with developed countries. It shows that countries with higher import from the US and UK experience a higher depreciation of currencies. This result indicates quantitative easing’s ability to initiate carry trades and capital flows into developed countries when it is able to push consumer demand and increase asset prices.

Trade Linkage with China Results in Table 6 show that countries with higher trade exposure to China have a much lower currency depreciation at the announcement event. Exposure to China is measured by the sum of a country’s exports to and imports from China as a ratio of its GDP. This result supports Mishra (2014) that suggest that greater trade linkage to other big economies offers better opportunity for diversifying risks, which helps reduce emerging market countries reactions to the developed countries unconventional monetary policy. Table 6. Exchange rate log change & trade with China Exchange rate log change Dummy

0.863*** (0.204)

Export to China

-1.93 (5.48)

Interaction with Export to China

6.40** (2.72)

Observation

360

R-squared

0.09

Note: ***, **, and * denote statistical significance at 1, 5, and 10 percent levels respectively

The coefficient on the interaction between the negative event dummy and exposure to China is negative and statistically significant. Countries with stronger trade links to China were less hit during the volatility episodes. These results can be interpreted as linkages with China acting as a buffer, whereby investors tend to display more confidence in countries which have greater exposure to China.

Macro Prudential Policy & Capital Flow Measures Table 7 describes 13 countries out of 15 that are recorded to have implemented Macroprudential policy and or capital flow measures (ARERA 2013; Zhang & Zoli 2014; IMF 2012). The macro-prudential policies considered includes loan-to-value policy, debt-to-income policy,

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reserve requirements, limits on assets acquisition and limits on bank lending in foreign exchange. While, the capital flow measures includes limits on borrowing abroad, restrictions on purchase of foreign assets, taxes on capital inflows and minimum stay requirements for new capital inflows. Table 7. Macro-prudential policy and capital flow management measure in emerging markets Policy Measures

2008

2009

Macro-prudential

Indonesia

Indonesia

Policy:

2010

2011

2012

Indonesia,

Indonesia, India, Peru,

Indonesia, Brazil, India,

Peru, Turkey

Turkey, Chile, Mexico,

Peru, Turkey, Chile,

Philippines, Malaysia

Philippines, Mexico, Malaysia, Korea

Capital Flow Measures:

Indonesia

Indonesia

Indonesia,

Indonesia, India, Peru,

Indonesia, Brazil, India,

India, Peru,

Turkey, Chile, Mexico,

Peru, Turkey, Chile,

Turkey

Philippines, Thailand,

Philippines, Mexico,

Malaysia

Malaysia, Korea

Source: Zhang & Zoli (2014), The interaction of monetary and Macroprudential policies (IMF 2012) and Annual Report on Exchange Arrangements and Exchange Restrictions (IMF 2013c).

Table 8 shows that emerging market countries with tighter macro-prudential policies and capital flow measures prior to the event experienced less exchange rate depreciation. The coefficient in the estimation results shows that capital flow measures are far more effective to maintain exchange rate stability. These results support Mishra et al. (2014) and contradict Fratzscher et al. (2013) about the importance of macro-prudential policy and capital control measures to mitigate sudden capital reversals. This may imply that such measures tend to change the composition of investment in emerging countries towards less volatile and risky items. The interaction terms between the event dummy and the policy measures suggesting that the marginal benefits of capital flow management is higher than macro-prudential policy when it is implemented before the event. Overall, the findings suggest that a tighter stance on both macro-prudential policy and capital flow measures in the run-up to the developed countries unconventional monetary policy episodes in 2008-12 helped mitigate the negative market reactions.

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Table 8. Macro-prudential Policy & Capital Flow Measure Exchange rate log change Dummy MPP

1.011**

1.105**

(0.483)

(0.569)

-0.310** (0.001)

CFM

-3.683**

Interaction with MPP

(0.174) -0.660** (0.028)

CFM

-2.371** (0.101)

Observation

312

312

R-squared

0.08

0.12

Note: ***, **, and * denote statistical significance at 1, 5, and 10 percent levels respectively

At the same time, the results may imply that capital flow liberalization carries risks, which are magnified when countries do not have sufficient levels of financial and institutional development. The risks include heightened macroeconomic volatility and vulnerability to crises or external spillover. In the absence of adequate financial regulation and supervision, financial openness can create incentives for financial institutions to take excessive risks, leading to more volatile flows that are prone to sudden reversal (IMF 2013b).

V. CONCLUSION This study examined the impact of unconventional monetary policy measures adopted in developed countries (the US, UK, Euro Area and Japan) on 15 emerging market countries from 2008 to 2012. The method was decomposed into two steps. First, event study techniques were used to study the impact of 24 QE announcements on the exchange rate change of emerging market countries. Then, panel fixed effect model was used to analyze the characteristics of the country that suffer the spillover effect before the announcements. The event study results shows five announcements events that contributes significantly to exchanges rate changes in 15 emerging markets. Three announcements contribute to exchange rate depreciation while two announcements contribute to exchange rate appreciation. The significant exchange rate changes indicate sudden capital flows between developed countries and the emerging market countries. The panel fixed effect regressions reveals the country characteristics prior to the events. The importance of Macro fundamentals is not clear. The interaction term showed only inflation reinforced the spillover effects when it occurs at the same time with the announcements. A deeper financial market enhances country resilience to the exchange rate depreciation.

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This result consistent with other studies and indicate that the more liquid the countries financial market the better the country in adjusting to global monetary change (Eichengreen & Gupta 2014; Mishra et al. 2014). The trade linkage with China is beneficiary for emerging markets, the higher the net export volume to china, the less vulnerable they are to currency depreciation. On the other hand, higher imports from the US and the UK contribute to higher exchange rate depreciation. Macro-prudential policy and capital flow measure contributes significantly to the country resilience to the spillovers. This study is consistent with Mishra et al. (2014) and contradicts Fratzscher et al. (2013) about the importance of macro-prudential policy and capital flow management measures. This paper also highlights the IMF (2013) notes about the efficacy of macro-prudential policy and capital flow management regulation to mitigate global risk on the emerging markets. One policy implication for emerging market country policy makers is the need to employ macro-prudential policy and capital flow management measure to face possible sudden capital reversal when developed countries central banks conduct large scale asset purchases. Furthermore, policymakers in emerging markets need to emphasize to developed countries policymakers the importance of conducting unconventional monetary policy in an orderly manner to avoid turbulence in the global markets (IMF 2013b). One limitation of this study, especially in the event study method, was the investor was assumed to be forward looking and the time-window for this assumption was very short (only two days) suggesting immediate reaction of the investors. Future research could incorporate a longer time window, for example: a week or more to consider a longer time reaction of investors.

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REFERENCES Aizenman, J, Binici, M, & Hutchinson, M . (2014). Transmission of Federal Reserve tapering to emerging financial markets. NBER Working Paper. No. 19980, National Bureau of Economic Research, Cambridge. Bean, C. (2012). Panel remarks: Global Aspects Of Unconventional Monetary Policies, Conference On Quantitative Easing And Other Unconventional Monetary Policies. Bank of England. Viewed on 20 October 2014. Bowdler, C, & Radia, A. (2012). Unconventional Monetary Policy: The Assessment. Oxford Review of Economic Policy. Vol. 28 (4), 603-621. Oxford. Bowman, D, Londono, JM, & Sapriza, H. (2014). US Unconventional Monetary Policy And Transmission To Emerging Market Economies. International Finance Discussion Papers. No. 1109. Board of Governors of the Federal Reserve System. Chen, Q, Filardo, A, He, D, & Zhu, F. (2012). International Spillovers Of Central Bank Balance Sheet Policies. BIS Working Paper. 66. Bank International Settlements. Directorate General for Internal Policies, European Parliament (2012). Unconventional monetary policy measures: a comparison of the ECB, FED and BOE. Directorate General for Internal Policies. European Parliament. Fic, T. (2013). The Spillover Effects Of Unconventional Monetary Policies In Major Developed Countries On Developing Countries. DESA Working Paper. 131. Department of Economic and Social Affairs, United Nations. Fawley, BW & Neely, CJ. (2013). Four Stories of Quantitative Easing. Federal Reserve Bank of St. Louis Review. 95 (51-88). Fratzscher, M, Lo Duca, M, & Straub, R. (2013). On The International Spillovers of US Quantitative Easing. ECB Working Paper. 1557. European Central Bank. Hosono, K and Isobe, S. (2014). The Financial Market Impact of Unconventional Monetary Policies in the U.S., the U.K., the Eurozone, and Japan. PRI Discussion Paper Series. (14A05). Policy Research Institute. MOF. Japan. International Monetary Fund (IMF). (2012). The Interaction of Monetary And Macro-Prudential Policies. IMF Policy Paper. Washington DC. International Monetary Fund. (2013a). Global Impact And Challenges Of Unconventional Monetary Policy. IMF Policy Paper. Washington DC.

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International Monetary Fund. (2013b). Unconventional Monetary Policies-Recent Experience and Prospects. International Monetary Fund. Washington DC. International Monetary Fund. (2013c). The Annual Report on Exchange Arrangements and Exchange Restrictions 2013. International Monetary Fund. Washington DC. Mishra, P, Moriyama, K, N’Diaye, P, and Nguyen, L. (2014). Impact of Fed Tapering Announcements on Emerging Markets. IMF Working Paper. WP/14/109. International Monetary Fund. Moore, J, Nam, S, Suh, M, and Tepper, A. (2013). Estimating The Impacts of US Large Security Asset Purchases on Emerging Market Economies Local Currency Bond Markets. Federal Reserve Bank of New York Staff Reports. No. 595. Taylor, JB. (2013). International Monetary Policy Coordination: Past, Present, and Future. BIS Working Papers. No. 437. Bank for International Settlements. Zhang, L and Zoli, E. (2014). Leaning Against The Wind: Macroprudential Policy in Asia. IMF Working Paper. No. WP/14/109. International Monetary Fund.

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APPENDIX Table 9. Variables description and data source No 1

2

3

Factors Macro Fundamentals

Variables

Description

Data Source

GROWTH

Economic Growth

CEIC

CPI

Inflation

CEIC

CA

Current Account % of GDP

CEIC

CAP

Stock Market Capitalization

CEIC

M2

M2 % of GDP

CEIC/Trading Economics/OECD

M3

M3 % of GDP

CEIC/Trading Economics/OECD

Trade Link with

ME

Import from Europe

CEIC

Developed Country

MUK

Import from England

CEIC

MUS

Import from US

CEIC

MJPN

Import from Japan

CEIC

XE

Export to Europe

CEIC

XUK

Export to England

CEIC

XUS

Export to US

CEIC

XJPN

Export to JPN

CEIC

Financial Depth

4

Trade Link China

XC

Export China

CEIC

5

Macro Prudential Policy

MPP

Macro Prudential Policy

Zhang & Zoli (),

CFM

Capital Flow Measures

Annual Report on Exchange Arrangements and Exchange Restrictions (), The interaction of monetary and Macroprudential policies—IMF ().

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Brazil Colombia India Mexico Peru Russia Singapore Turkey

3/28/08

-7

-6

-7

-7

-4

-3

Brazil Colombia India Mexico Peru Russia Singapore Turkey

11/28/08

3/5/09

-5

Chile Indonesia Korea Malaysia Philippines South Africa Thailand

-6

-5

-4

-2

-1

-3

-2

-1

Chile Indonesia Korea Malaysia Philippines South Africa Thailand

-6

-3

-4

0

-6

-5

-4

2

3

4

5

6

-2

-1

-3

-2

-1

-7

7

0

1

2

3

4

5

6

-7

7

Chile Indonesia Korea Malaysia Philippines South Africa Thailand

-6

-3

-5

-4

Brazil Colombia India Mexico Peru Russia Singapore Turkey

1/19/09

-6

-5

-4

-2

-1

0

1

2

3

4

5

6

7

0

1

2

3

4

5

6

7

Chile Indonesia Korea Malaysia Philippines South Africa Thailand

-3

-2

-1

Brazil Colombia India Mexico Peru Russia Singapore Turkey

3/18/09

0

5/7/09

-7

1

Chile Indonesia Korea Malaysia Philippines South Africa Thailand

Brazil Colombia India Mexico Peru Russia Singapore Turkey

-5

10/15/08

Brazil Colombia India Mexico Peru Russia Singapore Turkey

0

1

2

3

4

5

6

Brazil Colombia India Mexico Peru Russia Singapore Turkey

Chile Indonesia Korea Malaysia Philippines South Africa Thailand

1

4

2

3

5

6

-7

7

8/6/09

7

-7

-6

-5

-4

Brazil Colombia India Mexico Peru Russia Singapore Turkey

-6

-5

-3

-2

-1

Chile Indonesia Korea Malaysia Philippines South Africa Thailand

0

1

2

3

4

5

6

7

0

1

2

3

4

5

6

7

Chile Indonesia Korea Malaysia Philippines South Africa Thailand

-4

-3

-2

-1

*above zero (the mean) shows exchange rate depreciation, while below zero shows exchange rate appreciation.

Figure 2. Mean deviation of emerging market countries log exchange rate (announcement (1) to (8))

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Brazil Colombia India Mexico Peru Russia Singapore Turkey

11/5/09

-7

-6

-5

-4

-3

-2

-1

0

1

2

3

Chile Indonesia Korea Malaysia Philippines South Africa Thailand

4

Brazil Colombia India Mexico Peru Russia Singapore Turkey

3/11/10

-7

-6

-5

-4

-3

-2

-1

0

1

2

3

Brazil Colombia India Mexico Peru Russia Singapore Turkey

5/10/10

-7

-6

-5

-4

Brazil Colombia India Mexico Peru Russia Singapore Turkey

8/30/10

-7

-6

-5

-3

-2

-1

0

1

2

3

5

6

5

6

5

6

-7

7

3/17/10

7

Chile Indonesia Korea Malaysia Philippines South Africa Thailand

4

Chile Indonesia Korea Malaysia Philippines South Africa Thailand

0

1

2

3

4

5

6

7

0

1

2

3

4

5

6

7

0

1

2

3

4

5

6

7

12/1/09

Chile Indonesia Korea Malaysia Philippines South Africa Thailand

4

Brazil Colombia India Mexico Peru Russia Singapore Turkey

-7

5/21/10

7

Chile Indonesia Korea Malaysia Philippines South Africa Thailand

-7

-6

-5

-3

-2

-1

Brazil Colombia India Mexico Peru Russia Singapore Turkey

Chile Indonesia Korea Malaysia Philippines South Africa Thailand

-6

-3

-5

-4

-2

-1

Brazil Colombia India Mexico Peru Russia Singapore Turkey

Chile Indonesia Korea Malaysia Philippines South Africa Thailand

-6

-3

-5

Brazil

9/21/11

-4

-4

-2

-1

Chile

Colombia

Indonesia

India

Korea

Mexico

Malaysia

Peru

Philippines

Russia

South Africa

Singapore

Thailand

Turkey

-4

-3

-2

-1

0

1

2

3

4

5

6

7

-6

-4

-2

0

2

4

6

8

*above zero (the mean) shows exchange rate depreciation, while below zero shows exchange rate appreciation

Figure 3. Mean deviation of emerging market countries log exchange rate (announcement (9) to (16))

10

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10/6/11

-7

-6

-5

-4

Brazil Colombia India Mexico Peru Russia Singapore Turkey

2/9/12

-7

-6

-5

-7

-6

-5

-2

-1

0

Chile Indonesia Korea Malaysia Philippines South Africa Thailand

1

4

3

-4

-3

-2

-1

0

1

2

3

-5

6

-4

-3

-2

-1

-4

-3

-2

-1

-7

7

4

5

6

7

Chile Indonesia Korea Malaysia Philippines South Africa Thailand

Chile Indonesia Korea Malaysia Philippines South Africa Thailand

-6

-3

-5

-4

-2

-1

0

1

2

0

1

2

3

0

1

2

3

4

5

6

7

Chile Indonesia Korea Malaysia Philippines South Africa Thailand

4

5

6

-7

-6

-5

-4

-3

-2

-1

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Figure 4. Mean deviation of emerging market countries log exchange rate (announcement (10) to (24))

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Indonesia’s FDI – Exports – GDP Growth Nexus: Trade or Investment - Driven?

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INDONESIA’S FDI – EXPORTS – GDP GROWTH NEXUS: TRADE OR INVESTMENT - DRIVEN?

Panky Tri Febiyansah1

Abstract

This paper examines the relationship between foreign direct investment inflow, export and economic growth in Indonesia in a dynamic framework. We uses vector error correction model to estimate the causal relationship between FDI, exports and GDP. The findings in Indonesia’s case verify the proposition that FDI plays an important role which, in turn, together with joint FDI-exports can promote economic growth in the immediate short run and improve the competitiveness for Indonesia’s commodity exports. Nonetheless, the absence and existence of economic growth effects to FDI and exports respectively indicate that Indonesia still has several domestic economic constraints. It denotes that Indonesia’s economic structure is still transforming and far from adequate. Indonesia needs bold policies in order to accelerate its economic growth and further integrate trade chains for reducing domestic economic restrictions and improving Indonesia’s degree of competitiveness.

Keywords: Export, Foreign Direct Investment, Growth. JEL Classification: C32, F14, F21, F41

1 Panky Tri Febiyansah, MIDEC is the reseacher in Research Centre for Economics (P2E) – The Indonesian Institute of Sciences (LIPI). Email: ([email protected]).

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I. INTRODUCTION The global economic condition is usually considered to be one of the most important determinants of developing-small open economy (DSOE) countries (Worldbank 2014). This relationship has grown increasingly significant in recent years because world trade and capital movement are more integrated. As a consequence, the world economic activities generate higher world income growth. A recent trend is that there is a huge capital movement inflowing to DSOE countries, especially foreign direct investment (FDI), which can improve economic growth domestically (Gossel & Biekpe 2013). The role of FDI in influencing domestic growth occurs by creating an increase in product competitiveness, technological transfer and facilitating access to world market (Andraz & Rodrigues 2010). However, empirical evidence shows different experiences in different developing countries that have resulted in highlighting different directional relationships between a country’s openness and GDP growth. The variation of a country’s openness to investment and trade has implications for the country’s economic growth. Some findings show that FDI has a significant effect in developing countries. There are two ways in which this occurs. In general cases, the channel of FDI in influencing the economy is by stimulating productivity growth and exports. This is called the FDI-led export growth hypothesis (FLEH). Principally, FDI comes from multinational companies that create better productivity, technological transfer, managerial skill and higher capacity of production in the host country leading to enhancing its capacity to export (Temiz & Gokmen 2011). A clear evidence-based example about the role of FDI is the case of China which was conducted by Gharana and Adhikari (2011). Also, the increasing in capacity of host country can attract FDI due to its growing market size, the improvement in infrastructure and growing human resources which can be called the growth-led FDI hypothesis (GLFH). This hypothesis can be seen in India as the distinct example. There is evidence that exports are a significant determinant of a country’s output growth. Exports will deliver an incentive to producers for supplying foreign demand for goods and services. This incentive leads to wide production capacity, higher endowment utilization and decreased unemployment (Balassa 1978). It is called export-promoted hypothesis. It is based on the principle that by generating a spillover effect can drive economic growth (Shabaz et. al. 2011). Alternatively, economic growth can enhance technological progress, capital capacity and human productivity. It leads to a more attractive endowment factor which, in turn, will generate the ability of firms to gradually increase their production and export their products. However, other studies show that the relationship between exports and growth is bi-directional (Andraz & Rodrigues 2010 and Doyle 2001). These indications imply that either export-led economic growth (ELGH) or economic growth-led exports hypothesis (GLEH) can generate a country’s

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economic performance. How effectively then the above characteristics are interacting in a country is a matter for empirical study and can be examined by testing the FDI-exports-GDP growth nexus in the country. Since the Asian financial crisis (AFC) in 1997/98, Indonesia has been recovering rapidly that can be seen by higher economic growth, export growth and inward FDI growth. This performance was certainly remarkable from 2004 to 2006. Unfortunately, the global financial crisis (GFC) in 2008 affected almost all countries in the world including Indonesia. However, Indonesia’s economic performance was relatively stable in terms of the ability to absorb external shock. Hence, this paper aims to examine the relationship between foreign direct investment inflow, export and economic growth in Indonesia. According to several theoretical and empirical views, obtaining a better insight for the real determinants of economic growth in Indonesia is straightforwardly required by assessing the FDI-export-GDP link as a fundamental basis. Up to this point, examining the FDI-export-GDP nexus has not yet been made in the suitable learning of Indonesia’s experience. Drawing on Toda and Phillips (1993) and Andraz and Rodrigues (2010), this paper uses current advanced modelling for time series framework by using vector error correction model, through the maximum likelihood estimator that comes from Johansen (1988) method, to identify and estimate the above causal relationship between FDI, exports and GDP. Empirical outcomes denote the presence of a long run relationship in the FDI-exportsGDP growth nexus, in turn, there is bi-directional causality in the FDI-exports relation and single-causal direction moving from FDI and GDP to GDP and exports respectively. This paper is arranged as follows. Section 2 presents a brief review of literature. Section 3 discuses analytical framework, and model used. Section 4 describes the set of data that used in the estimation. At last, concluding remarks and policy implications are provided in section 5.

II. THEORY The literature review will provide the applied findings to build a framework. However, there is no previous study which has a similar aim of this paper using Indonesia time series data. In order to solve this issue, empirical results from the experience of other countries will be applied. Moreover, large empirical studies which related to this study have been undertaken, whether the estimation of FDI and exports over economic growth was employed either separately or continuously. To some extent, comparing the findings in terms of direction of causality and magnitude of effect are relatively complicated. The complexity comes from the difference in the stage of economy development, the characteristic of economic building and additional variables included in the FDI-export-GDP model, such as: human capital, exchange rate, imports, legal binding, infrastructure and domestic investment. These imply that the compared procedures will be slightly challenging and the estimated outcome is inevitably different.

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The literature, which talking about the relationship between investment inflow, export and economic growth on this paper’s focus, is extensive. For example, Giles and Williamson (2000a, b), Tarzi (2005), Kondeker and Kalirajan (2010) and Kinda (2010) describe comprehensively the interaction between FDI and economic growth in the host country. On the other hand, Balassa (1978) and Sannassee et al. (2014) work for the underlying study about how the exports and economic activities can be closely related. Another research is also convincing in order to explain the exports-GDP nexus was conducted by Felipe (2003) and provides comprehensive perspective about the importance and constraints of the exports-led growth paradigm in developing Asian countries post Asian financial crisis 1997s. Relating to above reviews, the focus on this paper can only be separately illustrated and cannot emphasise interactively the effects of both FDI and exports. As a result, this study needs to provide several criteria to shape this review of literature in order to assess the impacts of FDI, exports and both FDI and exports on economic growth. First criteria are to provide studies which simply test the influence of FDI on economic growth with no exports. Zhang (2001), Li and Liu (2005) and Oladipo (2013) point out that there was significant linkage between FDI and economic growth in DSOE countries. Chakraborty & Basu (2002) supports GDP-led FDI hypothesis for India, while Nabende et al. (2003) demonstrate that FDI has encouraged economic growth in selected East Asian countries through both direct effect and indirect effect as a result of spillover process. However, Attari et al. (2011) find in Pakistan case that there was no causality between FDI and GDP, although there was the long-run relationship when trade variables were included in the model. Furthermore, Qi (2007) presents diverse results on the relationship between FDI and GDP in 47 countries which suggest that FDI performance was more convincing in developed countries than in developing countries. A second criteria of studies categorises exports as the only determinant of economic growth with no FDI. Parida and Sahoo (2007) found the relationship in long-run equilibrium in four South Asian countries between exports and growth was significantly correlated which supported ELGH. Similarly, Doyle (2001) found that both ELGH and GLEH have been identified in Irish that clearly illustrated a virtuous circle of exports and GDP. Though, Tang and Ravin (2013) also describe the exports-driven growth hypothesis in Cambodia has existed. On the other hand, Gagnon (2008) points out that the long run trend of GLEH in 96 countries was considerable coming from stable terms of trade. Regarding those studies, the dominant effect between ELGH and GLEH is varied, which is similar to Bahmanian and Economidou’s (2009) finding that there is no identical pattern either in the long run relationship or short run effect suggesting that the nexus depends on country-specific attribute. Finally, several studies exhibit evidence on the link between FDI, exports and economic growth. Andraz and Rodrigues (2010) demonstrate that FDI, exports and growth had a long run relationship, while FLGH and GLFH prevailed on short run causal-directional by using vector error correction model and causality test to estimate Portugal data. Making similar finding

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on Thailand time series data, Kohpaiboon (2003) presented FDI as a main driver in boosting economic openness and growth using Engle-Granger causality test. Furthermore, there is a different story from China: Gharana and Adhikari (2011) used augmented vector autoregression (A-VAR) model and found convincing outcomes that FDI and exports drove China’s growth in the post-liberalisation era.

III. METHODOLOGY 3.1. Estimated Model This paper applies a standardized procedure of unit-root test, co-integration test, and causality tests. The aim of the procedure is to present empirical calculation of the relationship between FDI, export and economic growth. Granger proposes the ‘Granger test’ to examine the causality that is suitable tool for the process of integrated series. However, Granger and Newbold (1974) also describe that time series data tend to be random-walks and it can cause miss-specification equations when estimating causality test. Moreover, if the co-integrated data cannot hold, granger test will be inacceptable and the results are ambiguous (Granger, 1988). As a result, Granger (2010) suggesting error correction model can be used to test causality when the data series are not co-integrated. Thus, there is a proper procedure in examining causality that also including stationary and co-integration tests. Unit-root test is procedure to examine stationarity of the time series whether the data have a unit-root or that the series data is a random-walk process (Dickey & Fuller 1979). This test is beneficial to avoid a spurious regression. Stationarity test in this study follows modified test of standard dickey-fuller procedure by Elliott et al. (1996) named dickey fuller with generalized least squares (DF-GLS) which is more powerful and works satisfactorily in a small data series. The DF-GLS test is constructed as below:

(1)

where ∆ = -(L-1) is built by standard lag operator. et is a white-noise signal, while zo characterise the locally de-trended time series. The difference between dickey fuller (ADF) and DF-GLS is to estimate GLS prior to test ADF. Moreover, the null hypothesis is b=0, which shows that the series variable has a unit root, while the estimated unit root is binding with the lag length of model that provided selection information criteria (Ng & Perron 2001). It implies that DF-GLS will provide suitable stationarity test with lag selection.

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After estimating unit-root test, if the two series are Ct ~ I(1) and Dt ~ I(1), degree one integration, and the estimation of Et = Ct – ψ Dt results in Et ~ I(0), the co-integration of the two series exists (Engle & Granger 1987). In this instance, the linear combination of these series can be represented as at least one-way relationship in the long-run equilibrium. In addition, Granger performs the co-integrated I(1) data series in the procedure of vector autoregression (VAR) model. By using n lags, a VAR equation is built as follows: (2) where Zt is a k × 1 vector of variables, u is a k × 1 vector of intercept’s parameters , A1-An are k × k matrices of equation’s parameters, and εt is a k × 1 vector of shocks. εt should have mean around 0, have covariance matrix Σ ;, and be normal upon a time in terms of independent and identically distributed. For the aims of this study by using Zt = [GDPt,FDIt,EXt], VAR model with n lags need to be converted into vector error correction model (VECM). The form of VECM can be stated as below:

(3)

where

(4)

and (5)

Based on the assumption that Zt must be linear combination of variables in I(1), these first-order integrated variables prevail. Afterwards, the rank of matrix π reduces and can be rearranged becoming αβ’. The decomposition of (3) using αβ’ can be presented as:

(6)

Moreover, π is called as identified effect matrix which α and β are the vector of correction coefficients and the cointegrating links respectively.

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According to previous explanation about the importance of cointegrating relationships, this study need to first look for long run relations for generated variables within each model. In order to estimate cointegration relationship on VAR process, Johansen (1988) procedure is satisfied to support VECM by deriving maximum likelihood and generating independent identically distributed Gaussian errors. Note which r representing cointegration rank, satisfying αβ’ needs r greater than zero and less than k as a restriction to avoid non-singular matrix. It implies VECM can be used to distinguish appropriate cointegrating area. In practice, before finding cointegration system, optimal restricted can be beneficial to find optimal lag length which can perform suitable cointegration rank. Technically, restricted r is placed as null hypothesis which is examined by likelihood statistics through trace test (Johansen 1991). Extending equation (6) in terms of employed variables, the form of GDP-FDI-exports is demonstrated as below:

(7)

Where Δ represents first difference process, n is the order of lags and εi,t is a non-serially correlated error periods. Furthermore, the parameters of ϑkj,i show the dynamics in short-run terms. By the assumption, μi,t-1 has to be under stationary level which performs the I(1) lagged significance of the errors from the cointegration equations as follows:

(8)

The determinants of τij are the coefficients of long run equilibrium. In order to confirm causal relationship, this study based on equation (7) needs an additional verification that is performed by both t-test and joint significance test to first order lagged GDPt, FDIt and EXt. It

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infers to Granger test in generating causality. However, since granger causality method is really responsive to the lag creteria, the schwartz information criterion (SIC) is essential to handle in order to determine proper lags on the model.

3.2. Data The set of data consists of quarterly observations of real gross domestic product (GDP), real inflow foreign direct investment (FDI), and real exports (EX) in Indonesia from 2000:01 to 2012:04. This aims to isolate the effect of structural change in the Indonesia economy which was converting from fixed exchange rate to floating exchange rate regime caused by AFC. The data set was obtained from the Indonesia’s central bank (BI) and bureau statistics databases (BPS) and international financial statistics (IFS-IMF). The preference of FDI inflow than FDI outflow comes from the framework of DSOE countries which inward FDI plays a more significant role in the Indonesia economy. Furthermore, FDI inflow in total in time t is first-difference significance as a channel to deliver advanced technical transfer and as an incentive to generate innovation. Specifically, FDI is the flow variable. In order to observe production capacity in the growth process, production function context can be adopted to frame. It implies that FDI should be converted into capital stock to explain the existence of lifetime impacts on Indonesia economy. Coming along with exports, it can measure the expanding level of Indonesia development. Instead stock capital is available on UNCTAD, this study still prefers developing the data by estimating equation (9) due to the absence of quarterly data. Clearly, variable FDI is used to generate capital stock data by following difference equation: (9) Where Kt+1 is the capital stock in next period, Kt is capital stock in current period, δ presents annual depreciation rate, that is constant about 5 per cent, and FDIt+1 is inward FDI in next period. According to the definition, equation (9) requires initial capital stock. Noting 1999:04 as the initial period (K1999:04), this equation can be assumed that K2000:01 = FDI2000:01, which is, FDI is equal to Capital stock in the first period assuming K1999:04 is K in period zero. Since the depreciation is annual, this rate needs to be converted into quarterly series. Figure 1 exposes distinct evidence that GDP, FDI and exports move in upward trends and tend to mutually progress each other. This pattern exhibits convergence development of Indonesia economy along time path. However, the move of FDI and exports is interesting to explore. Indonesia FDI looks like better because, since 2006 quarter 1, the move of FDI have preceded exports trend.

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The cross trend is important to understand that caused by not only significantly increasing FDI but also flatter exports performance in the time periods. It implies Indonesia’s exports does not have steady phase in terms of maintaining export growth.

15.00 14.00 13.00 12.00 11.00 10.00 9.00 8.00

GDP

EXPORTS

FDI

7.00 Q1Q3Q1Q3Q1Q3Q1Q3Q1Q3Q1Q3Q1Q3Q1Q3Q1Q3Q1Q3Q1Q3Q1Q3Q1Q3Q1Q3

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Source: BPS, BI and IMF-IFS (2014)

Figure 1. Gross domestic product (GDP), stock of foreign direct investment (FDI) and exports in Indonesia, 2000:Q1 to 2012:Q4 (natural logarithm)

Figure 2 presents the growth rates of GDP, exports and FDI in year-on-year (YoY) approach to likewise capture seasonality effects in one year. The average quarterly YoY growth of GDP was relatively stable around 5.45 per cent over the periods. In particular considering to the effect of GFC in 2008, the data can be separated into 2000-2007, 2008-2009 and 2010-2013 which the average growth rates were 5.07 per cent, 5.32 per cent and 6.19 per cent, respectively. Meanwhile, the FDI as a stock capital performed relatively impressive prior to GFC’s shock compared to preceding periods. The average growth of FDI was around 46 per cent within 2000-2007, while lower growths were 15 per cent and 14.22 per cent in the periods of 20082009 and 2010-2013. Instead it had decreasing trend of FDI growth, but it was still high and realistic to boost economic growth. Overall, this condition still leads to convincing expansionary stage of Indonesia’s economic performance.

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15.00 GDP

13.00

EXPORTS

FDI

11.00 9.00 7.00 5.00 3.00 1.00 -1.00 -3.00 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Source: BPS, BI and IMF-IFS (2014)

Figure 2. Year-on-year (yoy) growth rates of gross domestic product (GDP), stock of foreign direct investment (FDI) and exports (EX) in Indonesia, 2000:Q1 to 2012:Q4 (per cent)

From the pattern of exports, it had less productive performance in preventing growth phase of Indonesia. This condition may come from the increasing costs per unit worker, world price and demand of exported commodities then primary-based export compositions. One important thing to underline is in developing country, Indonesia’s case, exports performance tends to have no time trend. It is realistic because Indonesia exported goods highly depend on world price and demand which drive exports to move volatile. Placing the time prior to and preceding GFC period, Figure 2 presented exports were 0.73 per cent, -4.55 per cent and 6.64 per cent, respectively. The growth rate about 6.64 per cent was interestingly caused by increasing demand of its exported commodities which is from Asian countries for example China as one of Indonesia’s partner.

IV. RESULTS AND ANALYSIS This study conducts the formal test to confirm the variables are stationary. Table 1 presents estimated result of unit root test. The outcomes propose that all log form of series are stationary on the first difference and have non-stationary condition on level. This indication can also be seen visually on figure 1 which sketched moving-upward trends. These outcomes confirm that GDPt, FDIt and Ext are considered having order one integration I(1) and possibly lead to cointegrating relation.

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Table 1. Unit-root tests Variable

Level

First difference

GDP

-0.776

-3.687**

FDI

-1.089

-4.463*

EX

-1.862

-3.722*

Note: P-value shows the significance level by * = 1% ; ** = 5% ; *** = 10% Source: Stata result

The next step of the procedure is to verify the variables are either cointegrated or not. In order to undertake this step, we follow the Johansen and Julius cointegration assessment (JJcointegration test). However, prior to execute JJ-cointegration test, there is a test which should provide the optimal lag selection from five information criteria, such as: Likelihood Ratio, Final Prediction Error, Akaike Information Criterion, Hannan and Quinn Information Criterion and Schwarz Bayesian Information Criterion. The result of lag length selection is presented on table 2 which proposes VEC model to use lag 4.

Table 2. The selection of lag length criteria Lag

0

LR

1

2

3

4

391.74

34.602

24.873

128.43O

FPE

4.20E-06

3.10E-09

2.30E-09

2.00E-09

2.5E-10O

AIC

-3.87659

-11.0639

-11.3832

-11.5153

-13.6389O

HQIC

-3.83343

-10.8913

-11.0811

-11.0838

-13.0779O

SBIC

-3.76401

-10.6136

-10.5952

-10.3896

-12.1755O

Note: LR, FPE, AIC, HQIC and SBIC stand for like lihood ratio, final prediction error, akaike information, hanan-quinn information and schwartz bayesian information creterion, respectively that are shown by O as lag selected Source: Stata result

To some extent, a distinctive attribute of error correction form in equation (3) contains variables in first-difference and level orders. It results in a deviation of asymptotic distribution of test-statistic to the deterministic elements over cointegrating test. In resolving this constraint, table 3 shows trace statistic which developed by Johansen (1995). Trace statistic generates a joint test to reveal cointegrating rank which showing this model is appropriate for the deterministic elements. The principal use is to look for maximum rank by pointing to trace statistic.

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Table 3. Cointegration test - Johansen approach Maximum rank

Eigenvalue

Trace Statistic

5 percent critical value

0

-

45.8639

29.68

1

0.44718

15.0419**

15.41

2

0.2168

2.335

3.76

3

0.04391

Note: ** indicates 5 per cent of significance level Source: Stata result

The results of cointegration - Johansen test are presented in table 3 which maximum eigenvalue and trace statistic recommends rank 1 by rejecting null hypothesis at 5 per cent of significance level. It means that the model has one cointegrating vector. As a result, cointegration test confirms that there are long run nexus between GDPt, FDIt and Ext inferring the tendency to all of variables to move simultaneously.

0.7 0.6

ect

0.5 0.4 0.3 0.2 0.1 0 -0.1 -0.2 -0.3

Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Source: Stata result

Figure 3. The relationship on cointegration estimation

To some extent, Figure 3 also supports that the model is cointegrated in the long run with respect to GDP at time series. The residual behaviour of cointegrating calculation (ect) is stationary over the time periods that verifies the model is at one cointegrated vector. According to cointegration test, the equation corresponding to the presence of long run relationship can be seen as below: (10)

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Indonesia’s FDI – Exports – GDP Growth Nexus: Trade or Investment - Driven?

Equation 10 exhibits the crowding-in result of FDI and exports over Indonesia’s output in the long run. In particular, since all variables are in logarithm forms, the predicted coefficients of explanatory variables are translated into elasticity’s values. The critical significant level at 1 per cent shows that the raise of 1 per cent in FDI and exports will increase GDP around 0.24 per cent and 0.29 per cent, respectively, within ceteris paribus condition. In other words, this equation reveals that external factors expressed by FDI and exports have created valuable effects on the growth of GDP.

Table 4. Multivariate error-correction model DGDPt = -0.05*

(-0.4

FDIt-1

DFDIt-1

+

-

0.44

EXt-1

-

3.27)

-

(-4.6) - 0.01 DFDI

= 0.04

-

0.04* DFDIt-3

(0.71) (-0.4

FDI

t-1

-

+

(-2.50) 0.44

EX

t-1

-

3.27)

+

0.1

DFDIt-3

+

DEX

= 0.04

+

0.19* DFDIt-2

-

(1.76) (-0.4

FDI

t-1

-

(-1.21) 0.44

EX

t-1

+

-

0.08

3.27)

+

DGDP

+

(0.16)

+

0.23 DFDIt-2

-

(0.99)

0.45* DFDIt-3

-

+

(-2.60)

+

t-1

-

t-2

+

(-0.29)

-

0.23

DGDPt-3

+

0.16*

DEXt-3

(2.38)

0.51 DGDP 0.04

DEXt-3

(0.82) DEXt-2 t-2

-

(-0.81) DEXt-1

0.03* (2.29)

0.004 DGDP 0.01

DGDPt-3

(-16.40) DEXt-2

(0.19)

1.22* DGDP 0.04

0.03*

0.95*

(0.01) DEXt-1

(2.13) DFDIt-1

-

(2.03) t-1

(1.06)

(0.78) + 0.34

0.15

1.03* DGDPt-2 (-17.19)

DEXt-1

(0.55) DFDIt-1

(2.98) t

0.05* (3.56)

(0.78) + 0.3*

-

(-16.46) 0.02 DFDIt-2

(-0.64) t

0.89* DGDPt-1

0.14

DGDPt-3

(-0.23) DEXt-2

-

(-0.27)

0.09

DEXt-3

(-0.64)

Note: ( ) is t-statistics Source: Stata result

The estimated outcomes of the multivariate error-correction model that are presented in table 4. At the same time, table 5 also shows that FDI-export-GDP nexus model has no autocorrelation and the residual is normally distributed. Moreover, figure 4 presents all eigenvalues place on the circle which means the model is not misspecified.

Table 5. the analysis for residual behaviour Diagnostic on Autocorrelation

Diagnostic on Normality

Lagrange-Multiplier (lag-1)

Lagrange-Multiplier (lag-4)

Jarque-Bera

Statistics

14.34

7.47

3.46

P - values

0.11

0.59

0.18

Note: Null hypothesis for Lagrange-Multiplier: no autocorrelation at lag order Null hypothesis for Normality: residual is normally distributed Source: Stata result

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These imply that all fundamental assumptions of the FDI-export-GDP nexus model hold. Consequently, the coefficient of error-correction term around -0.05 on DGDPt denotes the ability of adjustment’s speed is relatively slow in order to drive converging to equilibrium condition at the 1 per cent critical level. This can be illustrated as follows. If a disturbance creates the variation on GDP level during recent period, the deviation will be adjusted by 5 per cent to move back into equilibrium level in the following period. However, the adjustment values of both DFDIt and DEXt are not statistically significant on the critical level and have positive signs. This condition may arise in error-correction modelling because the adjustment multiplier effects are frequently counterbalanced by the dynamics of short-run circumstance.

1

Imaginary

5

0

-5

-1 -1

-5

0 5 1 Real The VECM specification imposes 2 unit moduli Source: Stata result

Figure 4. Stability analysis

Granger (1988) has developed the causality test that expressing the ability of variable’s explanation to another variable. Granger causality estimation exists if, by definition, the two variables are I(1), in turn, either feedback or one-directional relationships must hold in at least integrated order zero. Hence, according to table 5, we can draw the directed trend of Granger causality in both long run and short run and confirm the presence of whether FLGH or/and ELGH as well as GLFH or/and GLEH or both conditions exist in Indonesia.

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Table 6. Multivariate granger causality estimations Dependent Variable

∆ In GDPt-1

∆ In FDIt-1

∆ In EXt-1

t-statistic for ECT t-1

-4.6*

0.780

1.010

p-value

0.000

0.433

0.311

Wald statistic for ∆ In GDPt-1

-

0.476

3.0803**

p-value

-

0.753

0.027

3.0364*

-

3.1945**

p-value

0.029

-

0.023

Wald statistic for ∆ In EXt-1

1.426

2.6644**

-

p-value

0.244

0.047

-

3.0884*

1.474

3.2008*

0.009

0.199

0.007

Wald statistic for ∆ In FDIt-1

Wald statistic for ALL p-value

Note: - The two last rows reports The Wald statistic for The significance of joint estimation - P-value shows the significance level by * = 1%; ** = 5%; *** = 10% Source: Stata result

The empirical results of the Granger causality test and adjustment term are presented in table 6. This table is constructed by the statistically critical level of coefficients which express the dynamics of short-run circumstance in the multivariate error correction model. It can be seen from the table, there is a significant causality from inward FDI to Indonesia’s growth not in reversal way. Instead exports do not cause growth, in turn, FDI and exports jointly influence growth by 1 per cent of significance level. Moreover, the Granger test also shows that growth considerably causes exports. There is likewise another interesting result that FDI has causality on exports. Hence, joint variables between growth and FDI cause exports at 1 per cent critical level. It implies FDI lead directly to both Indonesia’s exports and growth. According to all results provided, several findings can be illustrated. The existence adjustment term in the FDI-export-GDP model leads to the significance of relevant effects in long run coming along with the interactive relationship between FDI and exports to GDP. It is likely to infer in the long run that the FDI-led growth and export-led growth hypothesis in Indonesia exist with the relatively identical effects on GDP. The significant intuition on the relationship of variables is also presented by the short-run investigation. By definition, Indonesia can adjust their FDI and exports performances through some effective policies to drive back output into steady state output level when it is deviated away from equilibrium phase.

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V. CONCLUSION This paper presents an empirical study of the FDI-export-GDP nexus in Indonesia using data from 2000:01 to 2012:04. By operating current advanced modelling for time series framework, this paper investigates the causality relationship among FDI, exports and GDP growth in the short run and long run of time dimension. The findings show that the FDI-exports-GDP growth nexus has a positive long run relationship supported partially from the convincing performances of exports and FDI. In turn, it confirms that the FDI-led growth and export-led growth hypothesis exist in the long run. The findings in Indonesia’s case verify the proposition that FDI plays an important role which, in turn, together with joint FDI-exports can promote economic growth in the immediate short run and improve the competitiveness for Indonesia’s commodity exports. This performance is also strengthened indirectly by the increasing FDI that Granger cause exports in order to boost output growth. This condition may occur due to technology spillover and the increasing production capacity. Nonetheless, the absence and existence of economic growth effects to FDI and exports respectively indicate that Indonesia still has several domestic economic constraints, such as lack of infrastructure improvement, the rigidity in labour market and resources-driven income. It denotes that Indonesia’s economic structure is still transforming. At the same time, current export performance is attracting foreign investors to increase investment in Indonesia. The reason can be seen as the attractiveness of trade relations between Indonesia and other countries, in particular South-East-South East Asian regions, and the presence of abundant factor endowments which creates both lower operational and trade costs. The results reveal important insights of policy implication that Indonesia’s economic policy is still far from adequate. Indonesia needs bold policies in order to accelerate its economic growth and further integrate trade chains. In terms of policies, this study proposes a reform in economic policy which can confirm reducing domestic economic restrictions and improving Indonesia’s degree of competitiveness. These reforms are desirable to ensure the change in Indonesia’s economic structure is on the right path for prosperity.  

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REFERENSI Andraz, JM & Rodrigues, PMM. (2010). What Causes Economic Growth In Portugal: Exports or inward FDI?. Journal of Economic Studies. 37 (3), 267-287. Attari, MIJ; Kamal, Y & Attaria, SN (2011). The Causal Link Between Foreign Direct Investment (FDI) and Economic Growth In Pakistan Economy. The Journal of Commerce. 3 (4), 61-68. Bahmani-Oskooee, M & Economidou, C. (2009). Export led Growth vs. Growth led Exports: LDCs Experience. The Journal of Developing Areas. 42 (2), 179-209. Balassa, B. (1978). Exports and Economic Growth. Journal of Development Economics. 5, 181 -189. Chakraborty, C & Basu, P. (2002). Foreign Direct Investment And Growth in India: a CoIntegration Approach. Applied Economics. 34 (9), 1061-1073. Dickey, DA & Fuller, WA. (1979). Distribution of The Estimators for Autoregressive Time Series With A Unit-Root. Journal of the American Statistical Association. 74 (366), 427-431. Doyle, E. (2001). Export-Output Causality and The Role of Exports in Irish Growth: 1950-1997. International Economic Journal. 15 (3), 31-54. Elliott, G; Rothenberg, TJ and Stock, JH. (1996). Efficient Tests For an Autoregressive Unit Root. Journal of Econometrica. 64 (4), 813-836. Engle, RF & Granger, CWJ. (1987). Co-integration and Error Correction: Representation, Estimation, and Testing. Journal of Econometrica. 55 (2), 251-276. Felipe, J. (2003). Is Export-led Growth Passe? Implications for Developing Asia. ERD Working Paper Series. 48. Asian Development Bank. Gagnon, JE. (2008). Growth-led Exports: Implications for The Cross-country Effects of Shocks to Potential Output. International Finance Discussion Papers. 822. The Federal Reserve. USA. Giles, JA & Williams, CL. (2000a). Export-led Growth: A Survey of The Empirical Literature and Some Non-Causality Results. Journal of International Trade and Economic Development. 9 (3), 261-337. Giles, JA & Williams, CL. (2000b). Export-led Growth: A Survey of The Empirical Literature and Some Non-Causality Results. Journal of International Trade and Economic Development. 9 (4), 445-470. Gharana, KKG & Adhikari, DR. (2011). Econometric Investigation of Relationships among Export, FDI and Growth in China: an application of Todayamamoto-Dolado-Lutkephol Granger Causality Test. Journal of International Business Research. 10 (2), 31-50.

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Gossel, SJ & Biekpe, N. (2013). Economic Growth, Trade and Capital Flows: a Causal Analysis of Post-liberalised South Africa. The Journal of International Trade and Economic Development: An International and Comparative Review, (http://dx.doi.org/10.1080/09638199.2013.78 6118) Granger, CWJ. (2010). Some Thoughts on The Development of Cointegration. Journal of Econometrics. 158, 3-6. Granger, CWJ. (1988). Some Recent Developments in The Concept of Causality. Journal of Econometrics. 39, 199-211. Granger, CWJ & Newbold, P. (1974). Spurious Regressions in Econometrics. Journal of Econometrics. 2, 111-120. Johansen, S. (1991). Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian. Econometrica. 59 (6), 1551-1580. Johansen, S. (1988). Statistical Analysis of Co-integration Vectors. Journal of Economic Dynamics and Control. 12, 231-254. Kinda, T. (2010). Investment Climate and FCI in Developing Countries: Firm-level Evidence. World Development. 38. (4), 498–513. Kohpaiboon, A. (2003). Foreign Trade Regimes and The FDI–Growth Nexus: A Case Study of Thailand. The Journal of Development Studies. 40 (2), 55-69. Kondeker M & Kalirajan, K. (2010). Determinants of Foreign Direct Investment in Low-income and Lower-middle Income Countries: A Comparative Analysis. ASARC Working Paper Series. 13. The Australian National University. Australia South Asia Research Centre, Australia. Li, X & Liu, X. (2005). Foreign Direct Investment and Economic Growth: An Increasingly Endogenous Relationship. World Development. 33 (3), 393-407. Makki, SS & Somwaru. (2004). Impact of Foreign Direct Investment and Trade on Economic Growth: Evidence from Developing Countries. American Journal of Agricultural Economics. 86 (3), 795-801 Nabende, AB; Ford, JL; Santoso, B & Sen, S. (2003). The Interaction between FDI, Output and The Spillover Variables: Co-Integration and VAR Analyses for APEC, 1965-1999. Applied Economics Letters. 10 (3), 165-172. Narayan, PK & Smyth, R. (2004). Temporal Causality and The Dynamics of Exports, Human Capital and Real Income in China. International Journal of Applied Economics. 1 (1), 24-45.

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Ng, S & Perron, P. (2001). Lag Length Selection and The Construction of Unit Root Tests with Good Size and Power. Journal of Econometrica. 69 (6), 1519-1554. Ngoc, PM. (2008). The Roles of Capital and Technological Progress in Vietnam’s Economic Growth. Journal of Economic Studies. 35 (2), 200-219. Oladipo, OS. (2013). Does Foreign Direct Investment Cause Long Run Economic Growth? Evidence from The Latin American and the Caribbean Countries. International Economics and Economic Policy. 10 (4), 569-5

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PETUNJUK PENULISAN 1. Naskah harus merupakan karya asli penulis (perorangan, kelompok atau institusi) yang tidak melanggar hak cipta. Naskah yang dikirimkan, belum pernah diterbitkan dan tidak sedang dikirimkan ke penerbit lain pada waktu yang bersamaan. Hak cipta atas naskah yang diterima, TETAP menjadi hak penulis. 2. Setiap naskah yang disetujui untuk diterbitkan, akan mendapatkan kompensasi finansial sebesar Rp 5.000.000,-. 3. Naskah dapat dikirimkan dalam bentuk softcopy (file). Sangat disarankan untuk mengirimkan softcopy anda ke: [email protected] (Cc. to: [email protected], [email protected])



Jika tidak memungkinkan, file tersebut dapat disimpan dalam disket atau CD dan dikirimkan melalui pos ke alamat redaksi berikut: BULETIN EKONOMI MONETER DAN PERBANKAN Departemen Riset Kebanksentralan, Bank Indonesia Menara Sjafruddin Prawiranegara, Lt. 21, JI. M. H. Thamrin No.2 Jakarta Pusat, INDONESIA Telpon: 62-21-2981-4119, Fax: 62-21-3501912

4. Naskah dibatasi.+ 25 halaman berukuran A4, spasi satu (1), font Times New Roman dengan ukuran font 12. 5. Persamaan matematis dan simbol harap ditulis dengan mempergunakan Microsoft Equation. 6. Setiap naskah harus disertai abstraksi, maksimal satu (1) halaman ukuran A4. Untuk naskah yang ditulis dalam bahasa Indonesia, abstraksi-nya ditulis dalam Bahasa Inggris, dan sebaliknya. 7. Naskah harus disertai dengan kata kunci (Keyword) dan dua digit nomor Klasifikasi Journal of Economic Literature (JEL). Lihat klasifikasi JEL pada, http://www.aeaweb.org/journal/ jel_class_system.html. 8. Naskah ditulis dengan penyusunan BAB secara konsisten sebagai berikut, I. JUDUL BAB I.1. Sub Bab I.1.1. Sub Sub Bab

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9. Rujukan dibuat dalam footnote (catatan kaki) dan bukan endnote. 10. Sistem referensi dibuat mengikuti aturan berikut, a. Publikasi buku: John E. Hanke dan Arthur G. Reitsch, (1940), Business Forecasting, PrenticeHall, New Jersey. b. Artikel dalam jurnal:

Rangazas, Peter. “Schooling and Economic Growth: A King-Rebelo Experiment with Human Capital”, Journal of Monetary Economics, Oktober 2000,46(2), hal. 397-416.

c. Artikel dalam buku yang diedit orang lain: Frankel, Jeffrey A. dan Rose, Andrew K. “Empirical Research on Nominal Exchange Rates”, dalam Gene Grossman dan Kenneth Rogoff, eds., Handbook of International Economics. Amsterdam: North-Holland, 1995, hal. 397-416. d. Kertas kerja (working papers): Kremer, Michael dan Chen, Daniel. “Income Distribution Dynamics with Endogenous Fertility”. National Bureau of Economic Research (Cambridge, MA) Working Paper No.7530, 2000. e. Mimeo dan karya tak dipublikasikan: Knowles, John. “Can Parental Decision Explain U.S. Income Inequality?”, Mimeo, University of Pennsylvania, 1999. f. Artikel dari situs WEB dan bentuk elektronik lainnya: Summers, Robert dan Heston, Alan W. “Penn World Table, Version 5.6” http:// pwtecon.unpenn.edu/, 1997. g. Artikel di koran, majalah dan periodicals sejenis: Begley, Sharon. “Killed by Kindness”, Newsweek, April 12, 1993, hal. 50-56. 11. Naskah harus disertai dengan biodata penulis, lengkap dengan alamat, telepon, rekening Bank dan e-mail yang dapat dihubungi. Disarankan untuk menulis biodata dalam bentuk CV (curriculum vitae) lengkap.