BULLETIN OF MONETARY ECONOMICS AND BANKING

ANALISIS TRIWULANAN: Perkembangan Moneter, Perbankan dan Sistem Pembayaran, Triwulan II - 2007 BULLETIN OF MONETARY ECONOMICS AND BANKING Center for...
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ANALISIS TRIWULANAN: Perkembangan Moneter, Perbankan dan Sistem Pembayaran, Triwulan II - 2007

BULLETIN OF MONETARY ECONOMICS AND BANKING Center for Central Banking Research and Education Bank Indonesia Patron Dewan Gubernur Bank Indonesia Board of 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. Andi M. Alfian Parewangi Dr. M. Edhie Purnawan Dr. Burhanuddin Abdullah Editorial Chairman Dr. Perry Warjiyo Dr. Iskandar Simorangkir Managing Editor Dr. Andi M. Alfian Parewangi Secretariat Rita Krisdiana, Skom., ME Wahyu Yuwana Hidayat, SE., MA Tri Subandoro, SE Aliyah Farwah, SP., MSEI The Bulletin of Monetary Economics and Banking (BEMP) is a quarterly accredited journal published by Center for Central Banking Research and Education,Bank Indonesia. The views expressed in this publication are those of the author(s) and do not necessarily reflect those of Bank Indonesia. We invite academician and practitioners to write on this journal. Please submit your paper and send it via mail: [email protected]. See the writing guidance on the back of this book. This journal is published on; January – April – August – October. The digital version including all back issues are available online;please visit our link:http://www.bi.go.id/web/id/publikasi/ jurnal+Ekonomi/.If you are interested to subscribe for printed version, please contact our distribution department: Statistic Disemination and Management Intern Division - Department of Statistic, Bank Indonesia, Sjafruddin Prawiranegara Building, 2nd Floor – Jl. M.H. Thamrin No. 2 Central Jakarta, Indonesia, phone (021) 2981-6571/2981-5856, fax. (021) 3501912, email: [email protected].

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BULLETIN of monetary Economics and banking

Volume 16, Number 3, January 2014

QUARTERLY ANALYSIS : The Development of Monetary, Banking, and Payment System, Quarter IV - 2013 179 The Quarterly Report Team, Bank Indonesia Interest Rate Risk In Turkish Financial Markets Across Different Time Periods Durmus Özdemir, Harald Schmidbauer 183 The Impact of Capital Reversal and the Threshold of Current Account Deficit on Rupiah M. Noor Nugroho, Ibrahim, Tri Winarno, Meily Ika Permata 205 Fund Management And the Liquidity of The Bank Gantiah Wuryandani, Ramlan Ginting, Dudy Iskandar, Zulkarnain Sitompul

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The Dynamics Of Total Factor Productivity of medium and Large Manufacturing In Indonesia Ndari Surjaningsih, Bayu Panji Permono

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QUARTERLY ANALYSIS The Development of Monetary, Banking, and Payment System, Quarter IV – 2013

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QUARTERLY ANALYSIS The Development of Monetary, Banking, and Payment System, Quarter IV – 2013 The Quarterly Report Team, Bank Indonesia The economic growth of Indonesian economy in quarter IV 2013 is relatively better than Bank Indonesia’s prediction, and is with more balance growth structure. The growth increased from 5.63% (yoy) in quarter III to 5.72% (yoy) in of quarter IV 2013. The growth is supported by the improving real export along with the increase of demand from trading partners. On the other side, domestic demand increase moderately reflected from the slowing down household consumption and investment, particularly non-building investment. Overall, the economic growth of Indonesia reached 5.78% in 2013. The increasing export led to significant decrease of current account deficit and sustained the improving performance of Indonesia Balance of Payment in quarter IV 2013. The current account deficit of quarter IV 2013 decreased significantly to 1.98% of the GDP, which is much lower than the current account deficit in the quarter III 2013 of 3.85%. Two major contributions for export were from export in manufacture along with the increasing demand from the United States and Japan, and export of natural resources regarding the anticipation of export banning for mineral resources and coal (UU Minerba)1. On the other hand, the deficit reduction on current account was also influenced by import reduction due to moderate growth of domestic demand. The improvement of Indonesia’s current account deficit in quarter IV 2013 was also supported by surplus on financial capital transaction, generated from corporate foreign debt, domestic with drawal from savings abroad, and foreign direct investment. Bank Indonesia predicted that the strengthening Indonesia’s current account will continue to 2014, considering the declining current account deficit along with the increasing surplus of capital and financial transaction. On December 2013, Indonesia’s foreign exchange reserve increased to USD 99.4 billion or equivalent to 5.4 months of importand the installment of the government foreign debt, which is above the international adequacy standard of 3 months import. Depreciation pressure on Rupiah was lower inquarter IV 2013. This is influenced by the improving performance of Indonesia’s Balance of Payment inquarter IV 2013 and several policies taken by Bank Indonesia and the government. The Rupiah exchange rate, based on,

1 Act No. 4, 2009 on Mineral and Coal (Undang-undang No. 4 tahun 2009 tentang Pertambangan Mineral dan Batubara).

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depreciated by 4.85% (qtq), much lower than depreciation in quarter III 2013 by 14.29% (qtq). On average, Rupiah depreciate by 8.76%, slightly increased from 8.18% in quarter III 2013. Along with the lower pressure, the volatility of Rupiah’s was also lower. For the whole year, the rate of rupiah tended to depreciate in 2013. On average, Rupiah depreciated by 10.4% from Rp9,358 per USD in 2012, to Rp10,445 per USD in 2013. Nevertheless, the situation was manageable with lower volatility compared to other Asian countries. Responses on policies by Bank Indonesia and coordination with the Government performed positive impact on the reduction of inflation pressure in quarter IV 2013. After the period of high inflation pressure led by food volatility and the increase of fuel price in quarter II and III 2013, CPI inflation in quarter IV 2013 decrease from 4.08% (qtq) in quarter III 2013 to 0.75% (qtq) in the quarter IV 2013; or equivalently from 8.40% to 8.38% (yoy). Major contributions for the lower inflation pressure above were the deflation on some volatile food groups and lower inflation on administered price group following the increase of fuel price subsidy. Deflation on volatile food by 0.58% (qtq) in quarter IV 2013 was supported by the increase of supply following the harvest period on some commodities such as red onion and various chilies. Moreover, the relaxation of import regulation also contributed to the availability of the commodity such as garlic. On the other hand, administered price inflation of 1.40% (qtq) was relatively lower and decreased sharply from 8.94% (qtq) in quarter III 2013. The sharp decrease is in accordance with the less price regulation over strategic goods after the government increases their subsidy on fuel price in quarter II 2013. The only inflation pressure within administered price group in quarter IV 2013, was tariff increase on electric power stage IV in November 2013 along with the increase of the fuel price for household, particularly the liquid natural gas (LPG) related to the adjustment of distribution tariff at the end of the year. Along with the two groups above, the core inflation decreased by 2.59% (qtq) to 1.00% (qtq).This is the subsequent impact of lower depreciation pressure on inflation, the improving expected inflation after the increase of subsidized fuel price, as well as the decrease of international price on non-food commodities, particularly gold. The adjustment of Indonesia’s economy was well managed, sustained by the strong and the stability of financial system. The banking industry resiliency remained solid with tolerable credit risk, good liquidity and market condition, as well as the strong capital resiliency. The growth of banking loan decreased from 21.9% on November 2013 to 21.4% on December 2013 (or equivalent to 17.4% by neutralizing the exchange rate depreciation). This is supported by the slowing domestic demand and an increase of interest rate. Bank Indonesia coordinated with OJK (Financial Service Authority) to direct the loan growth in accordance with moderate growth of domestic demand. Meanwhile, the performance of stock market improved in 2014 as

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indicated by the increase of Stock Price Index (IHSG). In contrast, the performance of government bonds market weakened as indicated on the higher yield on government bond (SBN). Moderate growth of domestic economy in 2013 reduced the payment system transaction. The transaction value of the payment system decreased by Rp1,930 trillion (-5.31%) to Rp34,419.79 trillion, relative to quarter III 2013. The reduction was mainly occurred on Bank Indonesia Real Time Gross Settlement (BI-RTGS) transaction; due to lower transaction for monetary operation. Nevertheless, the transaction volume in quarter IV 2013 increased by 94.7 million transactions or increased 9.36% compared to quarter III 2013. The increasing volume of transaction applied for all types of payment system with the highest increase in Card based Payment Instrument (APMK), particularly ATM card and debit/ATM card, which is commonly used by public during holidays.

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Halaman ini sengaja dikosongkan

Interest Rate Risk In Turkish Financial Markets Across Different Time Periods

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INTEREST RATE RISK IN TURKISH FINANCIAL MARKETS ACROSS DIFFERENT TIME PERIODS

Durmus Özdemir1 Harald Schmidbauer2

Abstract

A measuring the risk associated with interest rates is important since it is beneficial in taking measures before negative effects can take place in an economy. We obtain a risk measure for interest rates by fitting the generalized Pareto distribution (GPD) to positive extreme day-to-day changes of the interest rate, using data from the Istanbul Stock Exchange (ISE) Second Hand Bond Market, namely Government Bond interest rate closing quotations, for the time period 2001 through 2009. Although the use of the GPD in the context of absolute interest rates is well documented in literature, our approach is different insofar and contributes to the literature as changes in interest rates constitute the target of our analysis, reflecting the idea that risk arises from abrupt changes in interest rate rather than in interest rate levels themselves. Our study clearly shows that the GPD, when applied to interest rate changes, provides a good tool for interest rate risk assessment, and permit a period-specific risk evaluation.

Keyword: Interest rate risk; covered interest parity; Turkey; generalized Pareto distribution JEL Classification: G1; C1

1 Corresponding author: Istanbul Bilgi University, Department of Economics, Dolapdere Campus, Kurtulus Deresi Cad., Yahya Köprüsü Sok.No: 1, 34440 Beyoglu, Istanbul, Turkey, Tel.+902123115326, Fax+902122970134, E-mail: [email protected]. 2 Department of Business Administration, Bilgi University, Santral Campus, Eski Silahtaraga Elektrik Santrali, Kazım Karabekir Cad. No: 2/13, 34060 Eyup, Istanbul, Turkey, Tel.+902123117789, E-mail: [email protected].

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I. INTRODUCTION The risk expresses the chance of occurrence of an undesired event or events and nonaccrual of an intended and/or planned expectation. In an economic sense risk is the probability of a monetary loss regarded with a transaction or loss resulting due to decreasing financial returns. Cyclical fluctuations and price changes can increase the risk of occurrence of the undesired situations. Risk is divided into two as systemic and systematic risks. All securities in financial markets are subject to systematic risks, and systematic risks arise for example when fluctuations within political and economic conditions affect the behavior of assets in financial markets. As a result systematic risks are unavoidable in the sense that keeping them under control in a way is impossible. Systemic risks on the other hand are risks related with controllable processes such as intra-firm investment risks or a risk that may be likely to occur due to a decision on a financial issue (Turanlı, Özden and Demirhan (2002)). Interest rate risk should therefore be considered within the context of systematic risks. The fluctuations in interest rates could not totally be controlled but some measures may be taken or certain tools may be applied to cope with interest rate risk. Measuring the interest rate risk is important since it may be beneficial in taking measures before negative effects can take place in an economy (see Woodford (1999)). From the perspective of finance interest rate should be considered not only with economy but with many other factors as well. According to Ang and Bekaert (2002), risk hidden in the behavior of interest rates has a direct effect on the functioning of markets. Duffie and Kan (1996) and Dai and Singleton (2002) show that interest rates not only affect the functioning of markets but also have the power to alter the structure of the markets. There are many other perspectives as well. For example financial income perspective says that the income going to be generated in the future is effected by interest rates because today’s value calculation is made by an assumed interest rate level. If there is an unexpected change in the interest rates there is a risk that the value of income may be lower than expected. From an institutional perspective, changes in interest rates affect a financial institution’s market value (Carneiro and Sherris (2008)). Because the value of a financial institution’s assets and liabilities on the one hand and off-balance-sheet contracts written on interest rates on the other are affected by a change in rates, the present value of future cash flows and in some cases even the cash flows themselves can change. The focal point of the present study is to measure the interest rate risk in the Turkish spot market for government bonds. We will first look at what has happened in the Turkish economy within the period under investigation (2001– 2009). After this we will look at the statistical properties of changes in the daily series of interest rates.

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Our analysis is based on the tail index of the generalized Pareto distribution (GPD), applied to threshold excesses of changes in interest rate series. The tail index characterizes whether the underlying distribution has heavy tails. A similar approach (however, applied to interest rate figures themselves, not to changes of rates) was used by Bali and Neftci (2001) in order to compute a VaR for interest rates in the American market. Meyfredi (2005) has used the estimation of risk measures associated with fat tails for stock market returns in several countries. The behaviour of joint threshold exceedances of returns on international stock indices is investigated by Schmidbauer and Rösch (2004). They show how a bivariate GPD can contribute to financial risk assessment among markets in bull and bear periods. Gencay, Selçuk and Ulugulyağcı (2002) applied this setting to data from Istanbul Stock Exchange and derive a VaR measure meant to be an alert system for the market. Gencay and Selcuk (2001) had already applied a similar methodology for overnight interest rates of Turkish money markets in order to derive a measure querying whether the ex-ante interest overnight levels are indicators of the 2001 crisis or not. Next section outline the literatures study and the underlying theory on interest rate. Section 3 discuss the data and methodology, while section 4 provide result and analysis. Conclusion will be provided on last section and concludes the study.

II. THEORY Many factors may determine the shape of interest rate distribution. From micro level behaviour, macro aggregated, and external factors including negative shock may change the dynamics of interest rate; gradually or drastically. On international perspective, the rule of covered interest provides basic relationship among interest rate, exchange rate, and inflation. Common dynamic of interest rate is well recognized, one of them is that the interest rate volatilities is stochastic. It is also recognized that interest rate tends to cluster, particularly when shifting form low to high volatility; see Andersenand Lund (1997) in Allan Sall Tang Andersen (2011). Borodin and Strokov (2011), investigates the interrelations between the interest rates and international trade within the BRIC countries, and found that countries with lower interest rates experience growth of the share of machinery industry exports rather than agriculture and food products, and, on the contrary. On the other hand, in countries with higher interest rates, the share of agriculture and food exports increases and the share of machinery industry products declines. The investigation has shown that a relative shift in the interest rate can affect the specialization of countries. David Andolfatto (2012) used simple neoclassical model and show that liquidity shock at home and foreign potentially contribute to trade imbalances and push down the interest rate, which is claimed to be inline with Bernanke’s global saving glut hypothesis.

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Turner (2014) noticed many advanced countries including the FED, Bank of Japan and the Bank of England purchase government bonds on a massive scale to lower the long-term interest rate, and to stimulate aggregate demand. It shows low long-term rate has recently become an important intermediate target of central banks in the advanced economies, which affect the short term rate as well. The relationship between the exchange rate, short term rate, and long term interest rate may be illustrated as follows:

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In a more formal formulation, Conway and Orr (2002) construct a global interest rate model (GIRM) based on the concept of efficient debt markets, where by any arbitrage opportunity is exploited by global investors. This model indentify three categories of the determinants of interest rate dynamics; economic fundamentals, short run deviations of bond yields from their long run levels, and global financial integration. Desroches and Francis (2007) identify the behaviour of the world real interest rate which is determined by a number of key variables that change relatively slowly over time. They include the growth of labour force, which affects investment demand, and the age structure of the world economy, which influences savings, and also the level of financial development. Recently, Abbritti, Salvatore, Moreno, and Sola (2013) use FAVAR model to model the global term structure. Using panel of international yield curves, they show that global factors account for more than 80 percent of term premia in advanced economies, while domestic factors are more relevant to explain the short-run dynamics of the rate. Those factors explained above will determine the shape of interest rate, which is appearing to have a fat tail distribtution. Fisher and Tippett (1928) originally introduced the traditional

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method for modeling extreme-value data is based on the extreme-value limiting distributions. Pickands (1975) introduced the Generalized Pareto Distribution (GPD) as a two parameter family of distributions or exceedances over a threshold. Later on there other studies extended the theory such as the article by Hosking and Wallis (1987). Use of GPD in economics is mainly done in last decade. Bali and Neftci (2001), Gencay and Selcuk (2004), Gencay, Selçuk and Ulugulyagcı (2002), Schmidbauer and Rösch (2004) and Meyfredi (2005) are to name a few. Carr and Wu (2007) show that currency options have time-varying skewness. By using model-free estimates of the volatility and skewness priced in interest-rate options, it can be shown that interest rate distributions also show time-varying skewness (see Trolle and Schwartz (2010)). The main purpose of the paper is to provide a consistent framework for modeling the stochastic volatility and skewness. Finally, calibrating the model to time-series of the market data is interesting, as it shows the applicability of the model.

III. METHODOLOGY 3.1. The Generalized Pareto Distribution (GPD) Let (it) designate a daily series of interest rates (t indicates the day), and define the series of daily changes as

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The focal point of this paper is to study the upper threshold exceedance behaviour of the series (rt) on the basis of the generalized Pareto distribution (GPD), which is a model for excesses of a random variable. The rationale behind using the GPD is a limit theorem which states3: Let r1,..., rn be iid random variables, and let R be distributed like ri. Then, for large n and u, there are ξ and σ such that the distribution function of the excess (r – u) conditional on R > u, is approximately given by:

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Here, σ > 0 is a scale parameter; it depends on the threshold and on the probability density function of ri. The shape parameter ξ is called the tail index, since it characterizes the tail of the density function: • The case ξ > 0 corresponds to fat-tailed distributions; in this case, the GPD reduces to the Pareto distribution. • The case ξ = 0 corresponds to thin-tailed distributions; the GPD then reduces to the exponential distribution with mean σ. • The case ξ < 0 corresponds to distributions with no tail (i.e. finite distributions). • When ξ = 1, the GPD becomes a uniform distribution on the interval [0, σ ].

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Figure 1. Fitting the GPD to data

A typical example of fitting the GPD to the upper tail of one of the (r_t) series under consideration is shown in Figure 1. The histogram represents the upper tail of the empirical distribution of daily changes in the series interest rate (456 daily closing quotations of interest rates to maturity government bonds trading at the ISE Bounded Bond Purchasing Market; faiz456) during period 2), where we used the 80% quintile as cutoff point. This quintile was used as cut off point through out our study. The red line is the density of the normal distribution with the same mean and variance as faiz456 in period 2, and the green line is the density of the GPD fitted to the data. It is obvious that the normal distribution overestimates the probability of moderate changes and underestimates the probability of large changes. This makes it inappropriate for risk analysis in our case. Computationally, we use the package “evd” (see Stephenson, 2002) within the statistic software environment R (The R Core Team, 2013) tofit the GPD to data.The estimation method implemented in “evd” is maximum likelihood. Standard errors were cross-checked using bootstrap to ensure the reliability of results.

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3.2. Data We use daily closing quotations of interest rates of 90, 182, 273, 365 and 456 days to maturity government bonds trading at the ISE Bounded Bond Purchasing Market. The periode of analysis covers January 2001 to December 2009. This data is available upon request from ISE. Prior the application of GPD method, we identify the presence of structural break over the observation horizon. The investigation provides us four periods. A plot of the series is shown in Figure 2 for the four periods under investigation. There are no corporate bonds in this market. The Turkish Bond Market is dominated by Treasury Bonds.

IV. RESULT AND ANALYSIS 4.1. Structural Breaks in the Interest Rate Series We shall now approach the question of how to divide the period under consideration into sub-periods by applying a statistical test for structural changes to the time series of daily interest rates. This will provide further arguments for a separate risk analysis in the three subperiods.4 In addition, we will clearly see the limitations of regression models when applied to the interest rate series. The method we use will find breakpoints in a regression relationship, with interest rates as dependent variable and time (i.e. day) as independent variable. This method is based on Bai and Peron (2003); its implementation is described in Zeileis, Kleiber, Kramer and Hornik (2003). Breakpoints are computed with the objective of minimizing the residual sum of squares under the constraint that no segment should be shorter than 15% of total time period considered.

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Figure 2. Breakpoint analysis of faiz0915

4 We analyzed the period January 2001 through August 2008, based on structural breaks. The subsequent period, here called Period 4, was adjoined for economic reasons. 5 Faiz091 is a daily closing quotations of interest rates of 90 days to maturity government bonds trading at the ISE Bounded Bond Purchasing Market.

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(Our time series, beginning with January 2001 and ending in August 2008, are 1930 days long). The number of breakpoints is not predetermined, but results from the procedure. The test for structural changes finds four breakpoints in the series faiz091, which we chose for this purpose to represent interest rate evolution. The results of the breakpoint analysis are displayed in Figure 3. In our subsequent analysis, we shall ignore the first breakpoint and form period 1 with 2003-10-06 as last day. This is justified because of the relative homogeneity of circumstances and events in this period. We are there fore led to a definition of sub-periods and their characterization as shown in Table 1.

4.2. Characteristics of the Sub-Periods First of all, it is justifiable to separate this whole period into only two periods: the period until 2002, and the period from 2003 through 2009. Starting from the beginning of 2001 and ending with the end of 2002 there were three events that mainly shaped this period: the economic crises experienced on 28 February 2001, the September 11, 2001; and the Turkish General Elections in November 2002. The period was comprised of many instabilities in terms of both economy and politics throughout the period (see Insel, 2003). ������� ����������������������������������������������������������������� ������

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Between 2003 and 2008, a growth of 7% growth was seen in the Turkish economy on the average. Per capita GDP had increased by 30%, the domestic currency has revalued 30% as well. On the other hand a 100% set back was seen on trade and balance of payments deficit. Inflation dropped to 12% from 40%, while the interest rate level dropped to a figure of 21% from a rate of 76% at the end of 20016. 6 All quoted figures are taken from: Banking Regulation and Supervision Agency (BDDK) Financial Markets Report, March-June 2006, Number 1-2. Available online at http://www.bddk.org.tr/english/Reports/Financial Markets Report/1971fprMart Hazi­ran2006ingilizce. pdf. Accessed October 2008.

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The period between January 2001 and September 2003 As mentioned above this period was stricken with economic and political insta­bilities. The resolution which authorized the Turkish National Assembly to send troops to Iraq was approved with a 50% majority on 2003-10-06. According to the news expressed the day after this was perceived as a manifestation of “political integrity” by the markets7. It should also be mentioned that the inflation was reported to be the lowest in 30 years in October 20038. Shortly afterwards the Treasury explained a debt structuring in the sense of swapping the short term government bonds with longer maturities. Interest rates had dropped 200 basis points, and the Turkish Govern­ment was subsequently able to borrow for longer term.9

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Figure 3. The faiz series, Perıod 1

The Period between October and May 2006 There were four main events 2003 shaping this period: WTO abolished trade barriers, capital flows rendered more liberalised, growth of developed economies had increased, and inflation in developed countries. It can be said that this period was a period of capital flows between diverse mar­kets. Total volume of capital circulation throughout the world had reached ap­proximately $15 trillion

7 Hurriyet Online: “TezkereGeçti Asker IrakaGidiyor, Kabul 358 Red 183”, date: 2003-10-07. Available online at http://webarsiv. hurriyet.com.tr/2003/10/07/hurriyetim.asp. Accessed October 2008. 8 Hurriyet Online: “Enflasyona Eylül Çelmesi”, date: 2003-10-04. Available online at http://webarsiv.hurriyet.com.tr/2003/10/03/ hurriyetim.asp. Accessed October 2008. 9 Hurriyet Online: “Para KuruluToplandı”, date: 2003-10-15. Available online at http://webarsiv.hurriyet.com.tr/2003/10/15/hurriyetim. asp. Accessed October 2008.

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Figure 4. The faiz series, Period 2

according to the IMF Economic Outlook10. Developing countries in this sense were also among the beneficiaries. An amount of $2 trillion out of $15 trillion had flown to them, Turkey’s share be­ing $90 by foreign investment, according to the Turkish Central Bank Inflation Report11.

The Period between 2006-06-02 and 2008-08-29 There were four main events that shaped the period12: inflation fear of developed countries, the increase in interest rates, the sub-prime crises through the end of the year 2007, and the banking crises throughout the world.

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Figure 5. The faiz series, Period 3 and 4

10 International Monetary Fund (IMF) World Economic Outlook, October 2006, pp. 1–6. Avail­able online at http://www.imf.org/ external/pubind.htm.Accessed October 2008. 11 Turkish Central Bank, Inflation Report 2006-IV, pp. 41–46. Available online at http://www.tcmb.gov.tr/.Accessed October 2008. 12 International Monetary Fund (IMF) World Economic Outlook, October 2008: Financial Stress, Downturns and Recoveries, pp. 1–46. Available online at http://www.imf.org/external/pubs/ft/weo/2008/02/pdf/text.pdf. Accessed October 2008.

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4.3. Statistical Properties of Daily Interest Rate Changes Tables 4 and 5 in the Appendix give an analysis of the distributional properties of the percent point changes in the five series for the four periods in terms of mean, variance and standard deviation, skewness, kurtosis, minimum, median, and maximum. Our goal in the present paper is an evaluation of the interest rate risk. Therefore, the two most important items in the list are the variance and the kurtosis. There are obvious differences between the periods: The range of daily changes is widest for period 1; the variance and the kurtosis are largest for period 1. The behaviour of the five series within the periods gives insight into the characteristics of the different maturities, but also reveals further differences between the periods. In particular, some of the characteristics resulting from Tables 4 and 5 are: • The arithmetic mean of the daily changes in the faiz series increases from faiz091 through faiz456 most pronouncedly in period 1. An explanation may be that period 1 was regarded as risky by many investors in the sense that the Turkish financial market’s risk premium is still high. As a conse­quence, investors demanded high long-maturity interest rates as a compen­ sation for risks in future periods. • The variance increases from faiz091 through faiz456 through out all periods, in other words: The interest rate risk increases with maturity. • The tail behaviour of the distributions, as expressed in the kurtosis, is more complex. The kurtosis becomes larger as maturity increases only in pe­riod 1. This points again to an elevated risk for higher maturities in period 1. The results of Tables 4 and 5 point to a high risk in period 1, lower (and similar) risks in periods 2 and 3, and somewhat higher risk (albeit reduced “surprise potential”, indicated by the lower kurtosis) in period 4. The kurto­sis generally points to heavy tails in all periods across all series, with a few exceptions. The more complex kurtosis structure justifies using the GPD as a means to study the tail behaviour of the interest rate change distributions. • The ratio between minimum and maximum percentage point change is in­creasing with maturity during periods 1, 2, 3, with a reduced rate during period 4. This is also clearly visible in the boxplots in Figure 4. • The days when minima and maxima occurred is always the same or very close in period 1.

4.4. GPD-Based Interest Rate Risk Measurement The estimation results are reported in Table 2. In our context of risk measurement, the estimated tail index ξˆ is more important than σˆ. As stated above, a positive tail index indicates that the distribution of interest rate changes has a heavy upper tail (see Table 2).

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• Estimates of the GPD parameters ξ and σ, together with their standard errors, based on daily interest rate changes (computed as rt = ln (it - it-1))) are above their empirical 80% quantile (that is, based on threshold exceedances of the 80% quantile) for each period, • 95% and 99% quantiles of the interest rate changes (the columns designated as q95 and q99, respectively), • The corresponding quantiles are obtained by adding a GPD-based quantile to the empirical 80% quantile (which served as the threshold). The relatively close agreement between the latter pairs throughout the periods we considered and across the interest rate maturities can be seen as a confirmation of the model accuracy. A comparison of the four periods with respect to the tail properties of the interest rate change distribution leads to the following remarks: • Period 1 has exceptionally high values of ξ for each interest rate series con­sidered: All five tail indices are significantly positive (which indicates heavy tails, here: an elevated risk that tomorrow’s interest rate is much higher than today’s) at the 5% level of significance. • There is little difference between Periods 2 and 3, as far as the tail index is concerned. None of the interest rate change distributions is heavy-tailed, with the exception of faiz456. This points to an elevated overnight increase in interest rate only for long-term bonds. • The exceptional status of faiz456 has disappeared in Period 4. • The normal distribution is not appropriate to measure the risk associated with interest rates in Turkey. The GPD, derived as an explicit model for distribution tails, fits very well and provides a close fit between the theoretical VaRs and empirical quantiles.

4.5. Discussion In this section we will try to discuss the economic implications of our study to explain how our study fits into economic arguments. We have examined the interest rate risk in the Turkish economy. Statistical modelling is the key to the development of such risk scores. There are many economic reasons to have dif­ferent levels of risks in interest rates. Statistical risk scores can make a useful contribution to economic decision making under uncertainty. A general assump­tion of a decision or action depends on the occurrence of some adverse event, and that we have data which indicate how the likelihood of this event depends on values of observable risk factors.

Interest Rate Risk In Turkish Financial Markets Across Different Time Periods

Period 1

Period 2

Period 3

Period 4

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Figure 6. Boxplots of Interest Rate Changes, Four Periods

A risk indicator can be provided by the ex­amination of extreme values of interest rates in the framework of extreme value theory. Extreme value theory is a powerful framework to study the tail behaviour of a distribution; hence it can be a good indicator for risk.

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For this study we use the generalized Pareto distribution (GPD) to asses the interest rate risk for the pe­riod starting in 2001 till the end of 2009. Estimating GPDs to the data resulted in a good fit between the model and our data for all periods and maturities. It turned out

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that the tail indices, indicating the weight of the upper tail of distribu­tions of daily interest rate changes, became smaller and smaller, indicating that tails became thinner from period to period (except for the maturities second pe­riod faiz456 and faiz456, faiz356 on period3), thus reducing interest rate risk. As summarized in Figure 3, the interest rates have three significant structural brakes in this period. Due to justifiable economic reasons, the entire period is divided into four sub-cycles. These cycles are important because, for example, during re­cession consumers are likely to cut back on luxury items, and thus firms in the consumer durable goods sector should see their credit risk increased. Moreover, there is considerable evidence that macroeconomic conditions impact the emer­gence of risk. For example, during economic crises reduced (or even negative) growth will slow down the adjustment speed of capital. Economic agents borrow less. Thus GDP growth is positively associated with the likelihood of debt issue (see Hackbarth, Miao and Morellec (2006)). Keeping this argument in mind, in our sample periods, the highest risk is in the first period with a highest value of ξ. As far as high risk is concerned, the first period is followed by the third period, but the only risky periods of borrowings are the longer maturity borrow­ings, i.e. a year or more maturity borrowings. The second period, starting with 2003-07-10 and ending in 2006-06-01, has zero ξ values except for the longest maturity (faiz456). A general economic rule applying here is that the longer is the maturity, the more risk will emerge. Period 4 shows zero ξ values for all ma­turity levels, which indicates that this period is associated with a lowest interest rate risk period. This result appeared to be contradictory at the beginning because this period is the first period of the impact of the global financial crisis on the Turkish economy. An obvious question is, why does an economic crisis affect the risk associated with interest rates in Turkey? An interpretation of this result can be found in the macroeconomic implications of an economic crisis. During global crises, price levels and the level of interest rates will generally decrease. Prices decrease because of lowering demand for goods, and the rates of interest will also decrease for a similar reason. Compared to the earlier periods, in the fourth period, the sensitivity of interest rates with respect to investment decreases, implying a reduced demand for loans. Figure 5 depicts the weekly observations of the total consumer loans and the claims under legal proceedings between 2004­-06-25 and 2010-01-01. It is very clear that the consumer borrowing demand is slowing down for the latest period, and there is a dramatic increase in the claims under legal proceedings. Hence the decrease in the risk of the rates of interest for this period is smaller than expected. These interpretations are statistically confirmed by the results of Table 3: Statistical properties of interest rate changes for the four periods indicate that the results are significant. Higher kurtosis means that a larger share of the variance is due to infrequent extreme deviation, as opposed to frequent modestly sized deviations.

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Moreover, our results are also in line with the covered interest parity link. Table 3 depicts the joint threshold exceedances for our four sub-periods in the Turkish economy. We have used the dollar TL exchange rate as a proxy measure of the Turkish exchange rate. The joint behaviour of changes in Turkish interest rates and the USD/TL is in line with our approach to interest rate risk assessment to investigate the occurrence of joint daily threshold exceedances. For each period, we define indicator variables as follows: a USD-return exceedance happened on day t, { 10 IfOtherwise, 1 If an interest rate change exceedance happened on day t, ={ 0 Otherwise,

Xt = Yt

Here, we speak of a USD-return exceedance if the change in price of a USD in TL was larger than its 90% quantile or lower than its 10% quantile, where the quantile is periodspecific. Likewise, an interest rate change exceedance is said to happen if the change in interest rate is larger than its 90% quantile or lower than its 10% quantile, where quantiles are again period-specific. Contingency tables for X and Y, together with their odds ratios, are shown in Table 3. An odds ratio larger than 1 indicates a positive association of X and Y, that is, the main diagonal of the contingency table has higher frequencies than expected under the hypothesis that X and Y are independent. Table 3 reveals that, as expected, a positive association was found for all periods, the odds ratio indicating the strongest link for Period 1. Furthermore, a significantly positive association (at a significance level of 5%) was only found for periods 1 and 3. Any interest rate exceedance

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will also entail a change in interest rates. This link is similar for all four periods. Thus we observe a covered interest parity condition in Turkish financial markets.

V. CONCLUSION Measuring the interest rate risk is important for the emerging markets as well as the globalised financial system. A risk hidden in the behavior of interest rates has not only directly effect the functioning of markets but also have the power to alter the structure of the markets. It is obvious that the normal distribution overestimates the probability of moderate changes and underestimates the probability of large changes. This makes it inappropriate for risk analysis in our case. The use of the GPD in the context of absolute interest rates is well documented in literature, our approach is different insofar as changes in interest rates constitute the target of our analysis, reflecting the idea that risk arises from abrupt changes in interest rate rather than in interest rate levels themselves. Our study clearly shows that the GPD, when applied to interest rate changes, provides a good tool for interest rate risk assessment, and permit a period-specific risk evaluation.

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REFERENCES

Andolfatto, David. 2012. Liquidity Shocks, Real Interset Rates, and Global Imbalances, Federal Reserve Bank of St. Louis, Review, May/June 2012, 94 (3), pp. 187-95. Ang, A., and Bekaert, G. 2002. International asset allocation with regime shifts. The Review of Financial Studies 15, 1137–1187. Bai, J., and Peron, P. 2003. Computation and analysis of multiple structural change models. Journal of Applied Econometrics 18, 1–22. Bali, T.G., and Neftçi, S.N. 2001. Estimating the term structure of interest rate volatility in extreme values. Journal of Fixed Income, March 2001. Available at SSRN: http://ssrn.com/ abstract=262418. Brigitte Desroches and Michael Francis. 2007. Global Savings, Investment, and World Real Interest Rates, Bank of Canada Review, winter, 2006-2007. Carneiro, L.A. F., and Sherris, M. 2008. Corporate interest rate risk manage­ment with derivatives in Australia: Empirical results. Revista Contabilidade & Financas 19, 86–107. Coles, S. 2001.An Introduction to Statistical Modeling of Extreme Values. Springer, Berlin. Dai, Q., and Singleton, K.J, 2002. Specification analysis of affine term structure models. Journal of Finance LV, 1943-1978. Duffie, D., and Kan R, 1996. Yield factor model of interest rates. Mathematical Finance 6, 379-406. Fisher, R.A. and Tippett, L.H.C. 1928. Limiting forms of the frequency distribution of the largest and smallest member of a sample,Proc. Camb. Phil. Soc.24, 80-190. Gençay, R., and Selçuk, F. 2006. Overnight borrowing, interest rates and ex­treme value theory. European Economic Review 50, 547–563. Gençay, R., Selçuk, F., and Ulugülyağcı, A. 2002. High volatility, thick tails and extreme value theory in value at risk estimation, Insurance: Mathematics and Economics 33, 337–356 Gençay, R., and Selçuk F. 2004. Extreme value theory and value at risk: Relative performance in emerging markets. Journal of Forecasting 20, 287–303.

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Hackbarth, D., and Miao, J., and Morellec, E. 2006. Capital structure, credit risk, and macroeconomic conditions. Journal of Financial Economics 82, 519– 550. Hosking, J. R. M., and Wallis, J. R. (1987), “Parameter and Quantile Estimation for

the GeneralizedPareto Distribution,” Technometrics, 29, 339-349.

Insel, A. 2003. The AKP and normalising democracy in Turkey. The South At­lantic Quarterly 102, 293–308. Konstantin Borodin and Anton Strokov. 2011. Central bank Interest Rate and International Trade in BRIC Countries: Agriculture vs. Machinery Industry?, conference paper on “Will the „BRICs Decade continue?–Prospects for Trade and Growth”, Halle (Saale), Germany. Meyfredi, J. 2005.Is There a Gain to Explicitly Modelling Extremes? Working Paper, Edhec Risk and Asset Management Research Centre Publications, Nice. Mirko Abbritti, Salvatore Dell’Erba, Antonio Moreno, andSergio Sola. 2013. Global Factors in the Term Structure of Interest Rates, International Monetary Fund, working paper, WP/13/223, November. Paul Conway and Adrian Orr. 2002. The GIRM: A Global InterestRate Model , occasional paper, Westpac Institutional Bank. Philip Turner. 2014. The global long-term Interest Rate, Financial Risks and Policy Choices in EMEs, BIS Working Papers, No. 441 Pickands, J. (1975), “Statistical Inference Using Extreme Order Statistics,” The Annals of Statistics, 3,119-131. R Core Team 2013R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/. Schmidbauer, H., and Rösch, A. 2004. Joint threshold exceedances of stock index returns in bull and bear periods. Central European Journal of Operations Research 12, 197–209. Stephenson, A.G. 2002. evd: Extreme Value Distributions. R News 2, 31-32. Turanlı, M., andOzden, H.U., and Demirhan, D. 2002.SeçimTartışmalarının Hisse Senedi Piyasalarına Etkisi, Istanbul Ticaret Universitesi Dergisi 2. Woodford, M. 1999.Optimal Monetary Policy Inertia. NBER Working Papers 7261, Cambridge, Massachusetts. Zeileis, A., and Kleiber, C., and Kramer W., and Hornik, K. 2003. Testing and dating of structural changes in practice. Computational Statistics and Data Analysis 44, 109–123.

The Impact of Capital Reversal and the Threshold of Current Account Deficit on Rupiah

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The Impact of Capital Reversal and the Threshold of Current Account Deficit on Rupiah

M. Noor Nugroho Ibrahim Tri Winarno Meily Ika Permata1

Abstract

This paper studies the effects of foreign capital flows toward the exchange rate of rupiah both in total and across types of capital investment. This paper also analyzes the thresholds of current account deficit which significantly affect the rate of Rupiah. The estimation shows the capital outflow affect the rate of Rupiah to depreciate and is larger than the appreciation pressure of capital inflow (except when invested in Certificate of Bank Indonesia, SBI). Furthermore, the rate of Rupiah is more sensitive on government bond (SUN) than stock or SBI. The yield of this government bond largely affects the probability of the capital reversal. Related to the current account, the estimation shows that after exceeds the threshold of USD980 million monthly deficit orabout 2% of GDP, the exchange rate will depreciateby 12.7% (m-o-m) with the lag effect of 4 months.

Keywords: Capital flows, exchange rate, current account deficit, threshold. JEL Classification: F31, F32

1 Authors are researcher on Economic Research Bureau, Directorate of Economy Research and Monetary Policy, Bank Indonesia. The views on this paper is solely of the auhtors and do not necessarily represent the views of Bank Indonesia. Corresponding author: [email protected].

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I. INTRODUCTION The activity of external sectors in the economy is generally reflected on the balance of payment dynamics, which later will affect the exchange rate of Rupiah. The main component of the balance of payment is the current account (CA) and capital account (Capital and Financial Account) and the change of foreign exchange reserve. When current account declines, it commonly related to the reduction of competitiveness and export activity, or the increase of import due to the strong domestic demands. After that, they then observe the other factors such as the economic development of the trading partner, the import content in production structure and in export products, etc. On the other hand, the decline of capital account is sometimes linked to the falling of foreign capital’s value (sudden stop). In many countries, the dynamics of the foreign capital flow is often associated with the flow of investment portfolio considering the fact that this capital flow is more volatile than the other capital flows – direct investment and foreign debt. Unlike current account of which activity is more determined by fundamental and domestic factors, the dynamics of capital account depends more on investor’s appetite (investor’s portfolio). Furthermore, the dynamics of portfolio investment can directly affect the exchange rate2, therefore potentially cause instability in exchange rate and in domestic stock market. In its development, the studies which investigate the external balance are getting to be more intensive following the Balance of Payment Crisis (BOP crisis), such as the crisis in Mexico (1992), Asia (1997/8) and Russia (1998). The sudden stop and capital reversal phenomenon which are linked to the deficit of CA (CA reversal) also occur in many countries. This phenomenon is usually marked with the movement of the capital outflow in a gigantic amount, while the capital inflow is limited. The imbalance of the capital outflow and inflow in one economy directly influences their exchange rate. When the capital outflow is a lot bigger than the capital inflow, the demands for foreign exchange will increase drastically and the exchange rate will depreciate. On the next stage, the sudden stop can influence the current transaction activities. Edwards (2004) observed 157 countries during the period of 1970-2001 and found 5.1% of sudden stop events and 11.8% of CA reversals. Furthermore, he found from the total amount of 2.228 observations, 46.1% experienced both sudden stop and capital account reversal; showing that these two phenomena are related. The sudden stop phenomenon which defined as the falling of the net inward flow of foreign capital in a gigantic amount occurred in Indonesia in after the 1997/98 crisis. The large of capital outflow increase the demand for foreign exchange much larger than the supply, which put large pressure for Rupiah to depreciate. In some periods of sudden stop, Bank Indonesia implemented a mix of policies which included foreign exchange intervention in considerable amount, reflected on the decline of the foreign exchange reserve. 2 The influence of CA activity on exchange rate is indirect. This is because the foreign exchange resulting from export activity will not directly enter the forex market. On the other hand, the needs of foreign exchange for import is not taken directly from foreign exchange market as well. Instead of buying forex right at import transaction, the importer can buy the forex periodically when the exchange rate is conducive for them.

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After the crises 2008, the global capital flew to the developing countries with high growth, supported by their strong domestic demand. However, the economic recovery process of developed countries was full of uncertainties and made the finance market condition to be susceptible to various negative sentiments. The finance market will fluctuate whenever the global risk indicators are getting worse. For recipient developing countries, the worsening global risk will make their foreign capital flow to be more volatile. The larger and the more volatile foreign capital flow will cause instability in foreign exchange and financial asset prices. Indonesia is one of few developing countries survived from the 2008 crisis and recorded positive economic growth after the crisis. The foreign capital flow to Indonesia increased significantly. The capital inflow reached the highest number of more than USD26.6 million in 2010 (USD13.2 million of it was portfolio investment). The capital inflow pushes the appreciation of Rupiah for 12.5% to the average level of Rp9,080 per US dollar. However, the government bond price (Surat Utang Negara, SUN) increased as its yield declined from 10.07% in the end 2009 to the lowest level of about 7.01 in October 2010 and closed at the level of 7.83 in December 2010 (for 10 years SUN). The stock price index also increased from the level of 2,534 at the end of 2009 to the level of 3,703 by the end of 2010. Indonesia in general enjoyed a net inflow condition. However, in several point in 2010 Indonesia also experienced capital reversal, for example in May where the net outflow was significant and reached the number of USD4.8 billion, and USD865 million in November. The sudden reversal commonly occurs when there is negative sentiment in the developed countries who are still trying to get out of the crisis. The sudden reversal in Indonesia occur also on the second half of the 2011with the net outflows of USD11.9 billion, and by mid of 2012 with USD2.9 billion. Those events significantly affected the rate of Rupiah. On the other hand, slow recovery of US and EU from the crisis and the falling of Japan economy also influenced the decline of export activities in Indonesia. On the contrary, the strong domestic demand still pushed the import to grow, resulting current account to decline and even became deficit since quarter four 2011. Higher current account deficit affected the exchange rate significantly, and when current account deficit is relatively low it does not affect the exchange rate significantly. Since the capital flow and current account activity can influence the movement of exchange rate, it is important to do a more comprehensive research on it. Moreover, the movement of the exchange rate will in turn affect the inflation which is the main task of Bank Indonesia. On the other hand foreign capital flow may affect the financial management and the financial system stability. This paper measure the influence of foreign capital flow on the exchange rate, both in total and across investment types; SUN, SPN, SBI and stock. Furthermore, this paper also measure the influence of current account balance on the exchange rate and also test whether there is a current account balance threshold which significantly affecting the exchange rate of Rupiah.

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The next session of this paper outline the underlying theory and some previous studies. Section three discusses the data, the model and the method. Section four explain descriptive analysis, estimation result, and discussion, while section five outline the conclusion and close the presentation of this paper.

II. THEORY The standard theory of open economy commonly refers to Mundell-Flemming model which combines the internal and the external balance. Internal balance is the equilibrium between IS and LM curve, where IS is the expenditure curve (Y=C+I+G+NX) and LM is the money rill curve (M/P). The external balance is shown by the balance of payment curve where the net position of the current account and the capital account in total is zero. The internal equilibrium is the combination of the real sector and the financial sector. IS curve or the product market equilibrium is the accumulation of consumption, investment, and real interest rates deduced by inflation expectation equation. On the other hand, investment is the function of the income and the nominal interest rate. The net export is the function of the exchange rate, domestic income, and trading partner income. The financial (factor) market equilibrium is the function of income and real interest rate. Consumption function is c = c[Y - T,r - E (p)]; investment function: I = I[i - E (p), Y-1]; net export function: NX = NX (e,Y,Y*); and money market that represent the financial market is M/P = f (Y,r); whereas the government expenditure is an autonomous factor (G=G).

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Figure 1. Internal and External Equilibrium

The internal and external equilibrium is shown by point B on Figure 1 above which is the intersection of IS curve, LM and BOP = 0 curve. The central bank policy to add the active circulation will shift LM to LM’. Moreover, due to the capital inflow phenomenon, the capital

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inflow to developing countries causes domestic currency demand to increase also. The result is in point A, where the interest rate is lower and the acquired output is bigger. Some policies which can be done are when the inflow is decentralised with the accumulation of reserve, it causes point A to move back to point B. This will give implication to the longer capital inflow period due to the higher interest rate. The second policy which is possible to be done is when the central bank allows the entrance of capital and does not do sterilization so there is an appresiation on exchange rate. This will cause the decline of competitiveness and export. It will then shift IS from IS to IS’. Other policy which can be done is to do sterilization by limiting the capital inflow. This causes the domestic efficiency to decline, so domestic investment requires bigger cost (higher exchange rate) compared to the foreign one which offers lower cost. In relation with external equilibrium, the exchange rate in floating exchange rate regime is determined by the interaction between the supply and demand as the standard theory of supply and demand. Demand is the amount of goods or service which can be bought by the consumers in different price levels, where the higher the price, the fewer the amount of goods or service the consumers want to buy. On the contrary, the supply is the amount of goods or service which can be sold by the producers at various price levels. The spread between demand and the amount of goods or service offered by the sellers is proportional with the price: the higher the price, the more amounts of products offered by the sellers of the intended goods. The equilibrium between the supply and the demand where the transaction take place is what we call as a market. The market is in the equilibrium condition when the interaction between the supply and demand exists, and produces one equilibrium price on certain supply and demand quantities. The foreign exchange market is another type of market where the traded commodity is foreign exchange such as US dollar, Euro, or Japan Yen. The interaction between the foreign exchange supply and demand which determine the foreign exchange rate is illustrated below.

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Figure 2. Supply-Demand Foreign Exchange

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The negative correlation between the demand for US dollars with its price in Rupiah (IDR/$) is represented by ‘Demand for USD’ curve. The higher the exchange rate (the increase of US dollar value), the lower the demand for US dollar will be. On the other hand, the correlation between the exchange rate and US dollar supply is reflected in positive slope line. Figure 5 shows the equilibrium of USD at the rate of Rp9,000/USD. Due to certain effect – for example when export increases, the supply of US dollar will shift to the right and create new equilibrium at E2 (Rp8,500/USD). In this case Rupiah appreciate or strengthen relative to USD. Since the monetary crisis in the 1990s, economists have focused their attention on the global capital flow behavior. Some researches show that in globalization era, sudden capital reversal or sudden stop gives negative impacts on domestic economy. According to some economists (Dornbusch, Goldfajn and Valdes, 1995), sudden capital reversal also affects the Current Account (CA) reversal and expensive economic adjustment. Most of economic crises occurred in emerging market nowadays are marked by the sudden capital reversal (Calvo, 1998) which is then followed by the significant decline of output. Edwards (2004) did a research using the panel data to answer the phenomenon on the impact of CA reversal toward the economic activity. They concluded that CA reversal will likely take place if the CA deficit reaches 4% of GDP. The CA reversal is also influenced by other factors such as foreign debt, domestic credit growth, and volatility of capital flow. Sula (2008) used probit method and panel data to investigate the main factor causing the sudden capital reversal. It indicated that the main cause of sudden capital reversal is the high capital inflows in the previous period (around one to three years), and the composition of capital inflows which is dominated by non FDI. Kaminsky, Lizondo, and Reinhart (1998) built and estimated empirical model on currency crisis by observing some variables with certain threshold and one of the variables are sudden capital outflows. They found the level of exchange reserve of less than three months import equivalent and a more than 5% GDP current account deficit will trigger the currency crisis. Meanwhile, Frankel and Rose (1996) developed a method to provide early signal for exchange rate crisis occurrence by using probit model for developing countries. They found that sudden stop capital inflows and foreign debt composition significantly lead to currency crisis. Sachs, Tornell and Valasco (1996) used panel data of 1995 to analyze the occurrence of currency crisis which is known as ‘tequila effect’ after the Mexico crisis. The found the significant effect of the vulnerable banking system, the overvalued exchange rate, and the falling of national foreign exchange reserve. Another research using independent variable with threshold was carried out by Kan and Senhaji (2001). They used panel data and found supporting inflation threshold for the economic growth was 1-3 percent for developed countries and 11-12 percent for developing ones.

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Related to trade balance, Stahn (2006) carried out research on German exports and imports both with European Union and outside European Union (particularly the United States) using error correction model (ECM). He used REER and total sales deflator as price competitiveness, and regional export as a proxy for the demand for German export. The result confirmed the significance of price and demand on affecting the Germany’s export. Chinn and Prasad (2003) analyzed determinant of current account in developed and developing countries during the period of 1971-1995. Particularly for developing countries, Chin and Prasad found that the surplus on government budget, the net foreign assets position, and the trade volatility are closely related to current account balance. Calderon et.al (2000) investigated current account deficit in 44 developing countries during 1966 to 1995. The study found three conclusions; first, the higher the economic growth of a developing country, the more likely that the occurrence of CA deficit to be higher; second, the higher the economic growth of a developed country the more likely that the CA deficit occurrence is lower; third, the higher the interest rate will result on the lower CA deficit happens; and fourth, the appreciation of the real exchange rate increases the CA deficit, and (v) the international interest rate which is slowly decreasing will lead to the rise of CA deficit. On Indonesian case, Sahminan et.al (2009) carried out research on the determinant of Indonesian current account as well as its dynamics using annual data from 1993 until 2008. The consumption rate, the real exchange rate and investments are the factors which explain the CA fluctuation in Indonesia. Moreover, by implementing inter-temporal approach, the research also found that the CA fluctuation in Indonesia is optimum. This paper attempts to contribute to the scientific literature which relates to the impacts of sudden capital reversal phenomenon and CA on the exchange rate of Rupiahin the period subsequent to the global financial crisis in 2008. The next session outline the data we use and empirical model we estimate.

III. METHODOLOGY 3.1. Empirical Model By the law of demand, an increase (decrease) of the commodity price will decrease (increase) the demand for the commodity and on the other hand increase (decrease) the supply of the commodity. The foreign exchange market also follows this law. The increase of US dollar’s price toward Rupiah (depreciation of Rupiah) will reduce the demand of US dollar, and on the other hand will elevate the supply; vice versa. The interaction of both can be formalized on the following reduced form equations:

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Foreign exchange supply:

ܳ௧ௌ ൌ  ܽ଴ ൅  ܽଵ ܵ௧ ൅  ܽଶ ܺ௜௧ ൅  ‫ݑ‬௧

(1)

Foreign exchange demand:

ܳ௧஽ ൌ  ܾ଴ െ  ܾଵ ܵ௧ ൅  ܾଶ ܺ௜௧ ൅  ‫ݒ‬௧

(2)

D where Qst and Q t are the foreign exchange supply and demand; St is the exchange rate (standard quotation of the domestic currency per US dollar); and Xit is other explanatory variables. The market equilibrium exist when Qst = QDt at equilibrium price St. However, in forex market bank determines the buying and selling exchange rate therefore supply and demand of foreign exchange does not always in equilibrium (Qst = QDt). Those excess demand or supply will be absorbed by the bank. Since the bank tends to be risk averse and is limited on its net open position,then the excess within the market is minimized or limited. To suppress the excess, the bank can change the selling/buying exchange rate offered to their customers. In this condition, the market condition for the foreign exchange faced by the bank can be represented by the following reduced from equations [(1)-(2)]:

ܳ௧ௌ െ ܳ௧஽ ൌ ܽ଴ െ ܾ଴ ൅ ሺܽଵ ൅ ܾଵ ሻܵ௧ ൅ ሺܽଶ െ ܾଶ ሻܺ௜ ൅ ‫ݑ‬௧ െ ‫ݒ‬௧

(3)

By isolating St we can get: ሺ௕ ି௔బ ሻ భ ା௕భ ሻ

ܵ௧ ൌ ሺ௔బ

െ ሺ௔



భ ା௕భ ሻ

ሺܳ௧ௌ െ ܳ௧஽ ሻ ൅ ቀ

ሺ௔మ ି௕మ ሻ ቁ ܺ௜ ሺ௔భ ା௕భ ሻ

൅ ሺ‫ݒ‬௧ െ ‫ݑ‬௧ ሻ

(4)

and after simplifying the coefficients we have:

ܵ௧ ൌ ܿ଴ െ ܿଵ ሺܳ௧ௌ െ ܳ௧஽ ሻ ൅ ܿଶ ܺ௜௧ ൅ ݁௧

(5)

The foreign exchange net supply (Qst - QDt) – in an open economy – might come from the international trade transactions (export-import) and the capital flows among the countries. All of those transactions are recorded on Current Account(CA) and Capital and Financial Account (FA) in the balance of payment.

ܵ௧ ൌ ܿ଴ െ ܿଵ ‫ܣܥ‬௧ െ ܿଶ ‫ܣܨ‬௧ ൅ ܿଷ ܺ௜௧ ൅ ݁௧

(6)

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This paper will focus on the study of the impact of capital reversal to the exchange rate of Rupiah. This is the reason we use equation 6 – and FA is defined as the short term capital flow (hot money)3. With a few modifications, we can highlight some important parts below: • The asymmetrical influence of capital inflows and capital outflows: FA is separated into outflows and inflows;

ܵ௧ ൌ ܿ଴ െ ܿଵ ‫ܰܫ̴ܣܨ‬௧ െ ܿଶ ‫̴ܷܱܶܣܨ‬௧ ൅ ܿଷ ܺ௜௧ ൅ ݁௧

(6A)

• Reversal influence based on the foreign divestment in SUN: FA is separated into foreign investment/divestment in SUN and other capital flows;

ܵ௧ ൌ  ܿ଴ െ ܿଵ ܷܵܰ௧ െ ܿଶ ሺ‫ܣܨ‬௧ െ ܷܵܰ௧ ሻ ൅ ܿଷ ܺ௜௧ ൅ ݁௧

(6B)

• Reversal influence based on the foreign divestment on the stock: FA will be divided into foreign investment/divestment on the stock and other capital flows;

ܵ௧ ൌ  ܿ଴ െ ܿଵ ܵ‫݇ܿ݋ݐ‬௧ െ ܿଶ ሺ‫ܣܨ‬௧ െ ܵ‫݇ܿ݋ݐ‬௧ ሻ ൅  ܿଷ ܺ௜௧ ൅ ݁௧

(6C)

• Reversal influence based on the foreign divestment on the stock: FA will be divided into foreign investment/divestment on SBI and other capital flows:

ܵ௧ ൌ  ܿ଴ െ ܿଵ ܵ‫ܫܤ‬௧ െ ܿଶ ሺ‫ܣܨ‬௧ െ ܵ‫ܫܤ‬௧ ሻ ൅  ܿସ ܺ௜௧ ൅ ݁௧

(6D)

• The reversal influence originated from foreign divestment on stock: FA will be divided into foreign investment and divestment on SPN and other capital flows;

ܵ௧ ൌ  ܿ଴ െ ܿଵ ܵܲܰ௧ െ ܿଶ ሺ‫ܣܨ‬௧ െ ܵܲܰ௧ ሻ ൅  ܿସ ܺ௜௧ ൅ ݁௧

(6E)

On the model specified above, the foreign capital flows variables invested in SUN, stock, SBU and SPN will be estimated in three alternatives; first, the net flows or net (inflows minus outflows); second, in the form of gross inflows and outflows (divided into 2 variables); and three,in the form of gross outflows only. This method allows us to see specific influence of each investment type/outlets on capital reversal occurrence. Instead of observing the influence of sudden capital reversal towards the exchange rate, equation 6 also observe the influence of CA activity towards exchange rate, particularly on the efforts to determine the deficit of CA threshold which significantly trigger the occurrence of exchange rate depreciation.

3 The short term capital flow includes foreign investment on SUN, stock, Certificate of Bank Indonesia or SBI, and also some portion of foreign debt (assumed to be 50%).

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To complete this research, we will estimate the probabilities of the sudden stop occurrence which is influenced by the push (foreign) and pull factors (domestic).

ܴ݁‫݈ܽݏݎ݁ݒ‬௧ ൌ ߔሾܿ଴ ൅ ܿଵ ‫ܣܫܬܦ‬௧ ൅ ܿଶ ‫ܩܵܪܫ‬௧ ൅ ܿଷ ܷܵܰ௬௜௘௟ௗ ௧ ൅ ܿସ ܸ‫ܺܫ‬௧ ൅ ݁௧

(7)

Where Φ a is the normal cumulative distribution function; DJIA is the stock price index in the US; IHSG is the composite stock price index in Indonesia; SUNyield is the return of investments in SUN; and VIX is the volatility index which reflects the risk factor. DJIA and VIX represent the push factors, whereas IHSG and SUNyield represent the pull factors.

3.2. Estimation Technique This paper estimate the threshold of current account (CA) when it starts significantly affects the rate of Rupiah against USD. To find the threshold, we use non-linear model approach threshold autoregressive (TAR). TAR is a regime-switching model which enables a variable to behave differently. Generally, the amount of threshold is often unknown and must be estimated al together with the other parameters. TAR model also accommodates the possibility that the length of the adjustment process for the regime to change after certain period, d. The value of d is usually recognized as delay parameter. Enders (2004)4 and Chan (1993) give the guidance to gain super consistent threshold value. Some of required conditions are: 1. Threshold should be placed within the range of observation period. 2. Estimate TAR model with different threshold levels and store the sum of squared residuals (SSR), then choose the smallest SSR. Another criteria to choose the best model is using smallest Akaike Information Criterion (AIC) and Schwarz Bayesian Criteria (SBC). We apply the following threshold autoregressive (TAR) model: ௞



ο݁௧ ൌ ൭ߙ଴ ൅ ෍ ߙଵ௜ ‫ܣܥܮ‬௧ି௜ ൱ ሺͳ െ ‫ܫ‬௧ିௗ ሻ ൅ ൭ߚ଴ ൅ ෍ ߚଵ௜ ‫ܣܥܮ‬௧ି௜ ൱ ሺ‫ܫ‬௧ିௗ ሻ ௜ୀ଴

௜ୀ଴ ௟





൅ ෍ ߛ௜ ο‫ܦܴܫ‬௧ି௜ ൅ ෍ ߜ௜ οܴ‫ܭܵܫ‬௧ି௜ ൅ ෍ ߠ௜ ο݁௧ି௜ ൅ ‫ݑ‬௧ ௜ୀ଴

௜ୀ଴

4 Walter Enders. 2004. Applied Econometric Time Series. Wiley.

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which represent: ௞







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൭ߙ଴ ൅ ෍ ߙଵ௜ ‫ܣܥܮ‬௧ି௜ ൅ ෍ ߛ௜ ο‫ܦܴܫ‬௧ି௜ ൅ ෍ ߜ௜ οܴ‫ܭܵܫ‬௧ି௜ ൅ ෍ ߠ௜ ο݁௧ି௜ ൅ ‫ݑ‬௧ ൱ ݆݅݇ܽ‫ܫ‬௧ିௗ ൏ ߛ

ο݁௧ ൌ

൭ߚ଴ ൅ ෍ ߚଵ௜ ‫ܣܥܮ‬௧ି௜ ൅ ෍ ߛ௜ ο‫ܦܴܫ‬௧ି௜ ൅ ෍ ߜ௜ οܴ‫ܭܵܫ‬௧ି௜ ൅ ෍ ߠ௜ ο݁௧ି௜ ൅ ‫ݑ‬௧ ൱ ݆݅݇ܽ‫ܫ‬௧ିௗ ൒ ߛ

The equation above is the application of TAR model on distributed lag model. On the above model, the change of exchange rate (depreciation/appreciation) is function of current account level (CA), change of interest rate differential, the global risk movement, and the adaptive expectation on exchange rate. The time lag from the independent variable is represented by i, where as the time lag from threshold variable is represented by d. Variable I is the dummy variable, where It-d = 0 when CA level is smaller than the threshold γ, and It-d=1 when CA level is equal or larger than the threshold γ. We use monthly data from January 2008 until June 2012. The data includes the nominal exchange rate in log change (De); current account level (LCA); interest rate differential change (DIRD); and the global risk change (DRISK).

IV. RESULT AND ANALYSIS 4.1. Descriptive Analysis The Dynamics of Balance of Payment The Balance of Payment in Indonesia fluctuates dynamically. Prior to the 1998 crisis, its current account was more often deficit and after the crisis it recorded surplus (Figure 3). The dynamic represent the economic structure of this country. Any changes either structural in nature or temporary will influence CA. Structural change usually evolves gradually in a relatively long period and mostly relates to the development of the domestic economy. On the other hand,the domestic changes which are temporary in nature usually affect the balance of payment quickly in shorter period. As small developing and open economy, for Indonesia, all changes in international markets (commodity price and world trade volume) can give significant and immediate impact on its domestic economy, including on its current account. Those external changes will be transmitted via trading network.

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Figure 3. The Development of Transaction (% GDP)

Figure 4. The Development of the Main Indicator BOP

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After 2000 or post the crisis, the management of macro policy was better. One of the most fundamental changes was the floating exchange rate regime which can detect the signal much faster when there is external side imbalance. During this regime however, the external indicator were also suffers from several shocks as reflected on balance of payment (BOP). On domestic side, one source of economic instability is the change in national political system. Indonesian economy contains substantial politic uncertainties since the political reformation in 1998. The crisis of national leadership which is also characterized by the changing of president through the Special Session worries the foreign investors. The Standard and Poor (S&P) rating reduced the investment rating in Indonesia in May 2001 and in November 2001. The international geopolitics particularly the event of September 11, 2001 affected the domestic economy and negative reaction for foreign investors within this country. The strings of political and economic instability affected the economic recovery process until 2002. The period where the external equilibrium in Indonesia run relatively normal without significant political fluctuation and disturbances is the period after 2002. Statistic and BOP data also showed improvement relative to before 2000. Consequently, the external equilibrium fluctuation is mainly originated from external factor, hence relatively easy to compare. In general there are three episodes of huge pressure on Indonesia balance of payment; they are in the year of 2005, 2008, and in 2012. We recognized the fluctuation in 2005 as mini crisis, where Rupiah depreciate drastically to the level of Rp11,000 per USD. This is equivalent to a depreciation of 8,5% annually, which is higher than in 2008. The main trigger of the fluctuation in 2005 was the increase of the world commodity price, especially crude oil. The Fed’s responded by increasing his rate by 200bps and made the foreign investors to relocate their investment portfolio to United States. With the increase of the world crude oil price, the domestic fuel price also increased by more than 100% and triggered sharp rise of inflation.

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In 2008, the source of fluctuation also came from external factor, particularly the raise of commodity price and the subprime mortgage crisis in United States. In the last quarter of 2008, the Lehman Brothers collapse added the strings of crises in global financial market. The massive foreign capital outflow put a high pressure on exchange rate and depreciate Rupiah to Rp12,000 per USD. The latest episode of external shock for Indonesian balance of payment was in 2012, and was mostly caused by the crisis in Europe, and the decline of China and India’s economy. The decrease of non-oil and natural gas commodities and the current rise of the world crude oil price also played a role in lowering Indonesian trade as evident in first and second quarter of 2012. These three episodes of high external pressure on Indonesia balance of payment above are well described on Figure 4. The accumulation of net capital and financial transactions also decline along with the depreciation of Rupiah.

The Characteristics of Short Term Capital Flow in Indonesia The component of balance of payment can be divided into two big components; the current account balance (CA), and the capital and financial balance (KA). In general, the components of CA contributing surplus are trade balance and transfer, while service and income contribute deficit (see Figure 5). The roles of those four components are varied depending on the nominal value. Since 2000-2011, the trade balance contributed most by 49%, while transfer transaction only contributed 6%. Income and services contributed 25% and 19% consecutively. On service trade balance, Indonesia as most of developing countries has weak competitiveness within the service products. This is the reason for their services transaction to

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Figure 5. The Development of CA Balance Component

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Figure 6. The Development of Transaction Portfolio and PMA

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be deficit. The use of transportation and insurance services which is closely related to import activities causes the decline of CA which is relatively faster when there is a leap of import. On the other hand, income transaction is related to the profit of foreign company on their investment; either in portfolio and direct investment (PMA). The raise of foreign interest to invest in 2010 and 2011 was one of the reason for the increase of deficit income. On capital and financial balance (KA), the foreign capital flow is relatively limited and fluctuate. The accumulation of the capital flow was relatively stable from 2000 until 2008 (Figure 4). The significant rise occurred after 2009 along with the domestic economy recovery (see Figure 6). However, the role of the portfolio capital is still dominant. Since the instrument for foreign exchange transaction is still limited, and on the other hand the domestic foreign exchange market is shallow, then the short term negative sentiment can easily trigger the foreign capital flow, and then alter the Rupiah rate. The global economic crisis in 2008 due to subprime mortgage problems in the US leaded foreign capital flow to the emerging countries. The crisis which then spread to the European Union and the world forced the developed countries to apply a quantitative easing and expansionary fiscal policy to save their economy. The abundant global liquidity flew to the emerging countries. However, due to the elevation of the global economic recovery uncertainties, this capital flow was very volatile.

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Figure 7. CA and FA Balance

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Figure 8. Portfolio Investment

As we have witnessed, the foreign capital flow to Indonesia also tended to increase continuously, but most of them are short term capital in the form of portfolio investment. After the 2008 crisis, the capital flow raced along to Indonesia was recorded to amount of USD49.7 billion during third quarter 2009 until 2011 quarter three. USD29 billion or 58 percent of this inflow was portfolio investment (PI). The incoming flow of this capital pushed up the Rupiah

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financial asset price as reflected in the increase of capital stock index (Indeks Harga Saham Gabungan, IHSG) and the decline yield of government bond (Surat Utang Negara, SUN). Among several studies on the foreign capital flow to Indonesia after the 2008 global crisis, Agung et.al (2011) confirmed the rise of foreign capital flow to Indonesia which is dominated by the short term fund (PI). The implication is that the foreign capital flow in Indonesia tends to be more volatile which is reflected by the rise of coefficient of variation in portfolio investment (PI). Similar pattern was found during the global crisis of 2008. This research comprehensively reveals the foreign capital flow to Indonesia both in the form of FDI, portfolio (FPI), as well as Other Investment (foreign debt). Portfolio investment flows directly affect the demand and supply of foreign exchange; hence the Rupiah rate. Agung et.al (2011) further analyzed it through the investment outlet (SUN, stock and SBI). The investment on SBI was the most volatile investment (temporary in nature or not persistent) and became more volatile subsequent to the 2008 crisis. Bank Indonesia responded by implementing minimum holding period on SBI for six months. The foreign capital flow in stock market was also volatile. The volatility on this type of investment tended to increase after the 2008 crisis. Capital flow invested on foreign bond (SUN) is different. Unlike SBI and stock, the foreign investments on SUN in fact became more persistent. It was parallel with the economic activity in Indonesia which still runs well – including the stable fiscal deficit. Furthermore, Agung et.al (2011) also showed that the foreign capital to Indonesia was relatively short investment (1-2 months). Nugroho (2010) also shown that the foreign capital duration invested in Indonesia is relatively short (one month). This is in accordance with the fact that most of the foreign agent transacting in foreign exchange market are traders, and not truly investor. They do the foreign exchange buying and selling in the short term period to gain profit. Another significant characteristic from the foreign capital flow is its asymmetrical impacts on the exchange rate. Agung et.al (2011) proves that the capital outflow has bigger influence on the exchange rate of Rupiah and has longer duration compared to the impacts of capital inflow. Sugeng et.al (2009) also proves that the capital outflows influence (the rise of foreign exchange demand or the decline of foreign exchange supply from the foreign agent) has bigger impacts on the depreciation of Rupiah compared to the capital inflows impact on Rupiah appreciation. Sugeng (2009) also proves that the impacts of foreign exchange transaction which is carried out by the foreign agent has more significant impacts on the exchange rate of Rupiah than the impacts of foreign exchange transaction by domestic agents. This section has briefly described the actual condition of the market, the agent behavior and the external condition of Indonesian economy. The next section will confront this reality with the result of the estimated model.

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4.2. Estimation Result The empirical test result (Table 1) shows that the capital flow– both the inflows and the outflows- significantly affects the rate of Rupiah. However, the impact is asymmetrical where the capital outflows give bigger impacts than the capital inflows. The estimation shows that whenever the capital flow out – especially capital reversal – in about USD100 million, the Rupiah will depreciate by Rp3,2 on the same day. The Rupiah depreciation due to this capital outflow will continue to the next day, though with smaller depreciation; see Figure 9 below. The accumulative impact of USD100 million capital outflow will depreciate Rupiah by Rp16 in about 15 days. On the other hand, the impact of same amount of capital inflow will appreciate Rupiah by Rp3, with a three days lag.

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Figure 9. Impulse Response of Exchange Rate towards Shock Capital Reversal (USD100 million)

The effect of monetary operation by central bank using government bond (SUN) is in accordance with the theory. A liquidity expansion when Bank Indonesia increases their buy on SUN will depreciate Rupiah rate. However, this impact is not statistically significant. On the other hand, the exchange rate level in the previous period largely determines the current Rupiah rate. The capital inflow in Indonesia in the form of portfolio investment is generally on SUN (the long term government bond, up to 30 years), the SPN (short term government Treasury bill), SBI and stock. A more intensive research shows that the effect of capital inflow differs across those types of portfolio investment. Generally, the foreign capital flow – either in the form of gross inflow, gross outflow, or net flows – invested in all investment types above (except stock), significantly affect the rate of Rupiah. Capital inflows lead the Rupiahto appreciate, while capital outflow lead Rupiahto depreciate, whereas net inflows lead Rupiah appreciation.

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Based on our estimation (see Table 2), the foreign capital flow in SPN gives the largest impacts on Rupiah’s rate. The impact of foreign capital flow on SUN is the second largest, followed by stock, and finally SBI. Interestingly, the estimation result indicates the asymmetrical influence between the inflow and the capital outflow. The estimated coefficient for the capital outflow is larger than the inflow one, except for foreign investment in SBI. The next section will describe the effect of capital flow on Rupiah rate for each types of investment. ��������������������������������������������������������������������������������� ���������������������������������� ��������

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1. The Impact of Investment Flows on SUN toward Exchange Rate Foreign capital flow placed in SUN significantly affect the movement of Rupiah rate, either we measure it as capital inflow, outflow, or net capital flow. Its affect is proven to be asymmetrical. A USD100 capital outflow will depreciate Rupiah by Rp13 based on the estimation using the equation 2. A slightly different of Rp14 is estimated using equation 3. On the other hand, the same amount of capital inflow will appreciate Rupiah rate by Rp7. ������� ��������������������������������������������������������������������������������������� ��������������

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When we use the net capital flow, we found smaller coefficient. An USD100 million net capital inflow placed on SUN will appreciate Rupiah by Rp1,8 – Rp2,9. Accordingly, the same amount of net capital outflow will lead Rupiah to depreciate by the same figures. 2. The Impact of Investment Flows on the Stock towards Exchange Rate For stock investment, not all capital flow affects the Rupiah’s rate. The net capital flow and capital outflow affected the exchange rate; however, the capital inflow does not. The outflow of capital from the stock investment to the amount of USD100 million will depreciate Rupiah by Rp11 – Rp12. Whereas the net capital inflow of the same amount will appreciate Rupiah by Rp4.8. When we use the by net capital flow (inflow minus outflow), the estimated coefficient showed an USD100 million of net capital inflow will appreciate Rupiah by Rp3 – Rp5.3; and vice versa, same amount of net capital outflow will depreciate Rupiah also by Rp3 – Rp5.3. ������� �������������������������������������������������������������������������������������������� ��������������

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3. The Impact of Investment Flows on SBI toward Exchange Rate The foreign capital invested on SBI behaves slightly different with the other types of capital flow investment. The capital inflow invested on SBI affect the exchange rate much stronger than the capital outflow from SBI. A USD100 million capital inflow to SBI will appreciate Rupiah by Rp3.6, while the net foreign investment inflows on SBI by the same amount will encourage the appreciation of Rupiah by Rp2.9. For this type of investment (SBI), we estimate three model variants. The result show the net flow of Other Investment (OI) also affected the exchange rate. The net capital inflow (out)

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to the amount of USD100 million will appreciate (depreciate) Rupiah by Rp3. The estimation result above also shows that the buying/selling operation of SUN by BI significantly affects the rate of Rupiah. If Bank Indonesia run monetary expansion by buying long term government bond (SUN) to the amount of Rp1,000 billion, then Rupiah will depreciate by Rp18.

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4. The Impact of Investment Flows on SPN toward Exchange Rate Similar to foreign capital invested on long term government bond (SUN), the foreign capital investment on Treasury Bill (SPN) also significantly affected the exchange rate of Rupiah. This is evident either when we evaluate the capital flow with inflow, outflow, or with net flow. An USD100 million capital outflow will depreciate Rupiah by Rp24 – Rp25. Contrary to this, the capital inflow with the same amount will only appreciate Rupiah by Rp21, showing an asymmetric effect. When we use the net value, the result show an USD100 million net capital inflow (out) on SPN will push the Rupiah to appreciate (depreciate) by Rp15. The result of the estimation shows the impact of monetary expansion when Bank Indonesia buy SUN significantly affect the rate of Rupiah. The buying of SUN of Rp1,000 billion will push down Rupiah to depreciate by Rp19; vice versa.

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5. The Impact of Current Account Activity on Exchange Rate We estimate several variants threshold estimation using TAR model, from which we choose the best threshold values on three models: AR-4, AR-5 and AR-6. The three models show the existence of CA deficit threshold, and if the CA deficit level exceed the threshold, then the effect on exchange rate becomes significant. On the contrary, when the level of CA deficit is under the threshold, then its impact will likely be tolerable for the market without causing any exchange rate fluctuation. On model AR-4, the threshold of current account deficit is USD980 million, while in AR-5 model, the threshold is USD810 million. On the last model, AR-6, the current account deficit threshold is USD730 million. The result of the estimation in January 2008 until June 2012 period shows the occurrence of CA threshold level to the amount of deficit of USD980 million (monthly) with 4 months lag. The impulse response figure shows that whenever the shock level of CA exceeds the threshold ( > USD 980 million), the impact transmitted to the exchange rate with 4 months lag is 12.7% (m.o.m). The cumulative effect within12 month year is the depreciation of Rupiah by 13% (figure 10 and 11). On the other hand, if the CA deficit does not exceed the threshold, the effect to the change of exchange rate is relatively small. The impulse response show the impact of USD500 million CA deficit (monthly), and the cumulative effect within 1 year is only 0,4%.

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Figure 10. Impulse Response on the Decline of CA toward Exchange Rate (Deficit USD980 million)

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Figure 11. Impulse Response Cumulative on the Decline of CA toward Exchange Rate (Deficit USD980 million)

The second variant of the model AR-5 also cover the same period of January 2008 until June 2012. The impulse response shows that when deficit of CA exceed the threshold of USD810 million (monthly), the impact will be transmitted to the exchange rate with 4 months lag is depreciation of Rupiah by 32% (m.o.m). The accumulative impact after 1 year is the depreciation of Rupiah by 29% (graph 12 and 13). If the CA deficit level does not exceed the threshold, the effect of the change on exchange rate is also small. Cumulatively, the impulse response shows the shock of USD500 million CA deficit will depreciate Rupiah by 0.3% within one year.

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Figure 12. Impulse Response on the Decline of CA toward Exchange Rate (Deficit USD810 million)

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Figure 13. Impulse Response Cumulative on the CA Decline toward Exchange Rate (Deficit USD810 million)

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On the last model, AR-6, the threshold of current account deficit is USD370 million. The impulse response shows that a shock of USD370 million CA shock deficit will depreciate Rupiah by 4.4% (m.o.m) with 4 months lag, and after one year, the cumulative effect is the depreciation of Rupiah by 3.3% (figure 14 and 15). When the shock is less than the threshold-say USD500 million, the cumulative impact is only 0.2% after one year.

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Figure 14. Impulse Response to the Decline of CA toward Exchange Rate (Deficit USD730 million)

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Figure 15. Impulse Response Cumulative on the Decline of CA toward Exchange Rate (Deficit USD730 million)

6. The Determinant and Probabilities of the Sudden Stop Occurrence To capture the capital reversal behavior, we apply probit model and this section outline its estimation result. Probit model capture the probability on capital reversal occurrence by observing the changes on some determinant factors such as the global stock price index (using Dow Jones Industrial Average index or DJIA), the global risk factor (VIX index), the stock price index in domestic market (IHSG), and the yield of the long term government bond (SUN). We estimate three variants of econometrical model to explain the behavior of capital reversal. The first equation uses DJIA and yield SUN as explanatory variables. The estimation result shows the global stock price index negatively affect the probability of capital reversal occurrence. This means a global stock price index decrease will increase the probability of capital reversal.When the global stock price index decrease, investor would expect the domestic stock price will be affected hence they will withdraw their investment from Indonesia. The yield of government bond (SUN) also negatively related to the probability of capital reversal; when the SUN yield decrease, the probability of capital reversal will increase. The yield is negatively related to the bond price. The decline of bond yield is associated with the price increase and leads the investor to gain profit by releasing their holding on SUN, particularly when the bond price is already over value. This is in line with the recognized behavior of foreign

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investors who attract more on short term profit. They buy the asset when its price is low and sell it right away when its price increases. The government bond (SUN) by foreign investors is identical with capital reversal. On the second empirical model, we specify the yield of government bond (SUN) and the risk index (VIX) as explanatory variables. The influence of yield SUN toward capital reversal is relatively similar with the first model. On the other hand, the risk index also significantly affects toward the tendency of capital reversal occurrence. A worsening risk condition reflected by the increase of VIX index will raise the probability of capital reversal. ������������������������������������������������������������������� �������������������������������������������� ��������������

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The third model use the risk index (VIX) and domestic stock price index (Indeks Harga Saham Gabungan, IHSG) as explanatory. Both VIX index and IHSG index significantly affect the probability of capital reversal. An increase of risk index or stock price tends to increase the probability of capital reversal.

V. CONCLUSION The foreign capital flow particularly the short term significantly affected the rate of Rupiah. The capital inflow will encourage the appreciation of Rupiah against USD and vice versa; however the marginal effect is asymmetric. In general the capital outflow depreciate Rupiah’s rate more than when same amount of capital inflow appreciate Rupiah. An exception is for capital flow invested in Certificate of Bank Indonesia (SBI). This paper analyze the behavior of capital flow across investment types, including long term government bond (SUN), Treasury Bill (SPN), stock, SBI, and Other Investment. We found that the exchange rate of Rupiah is relatively more sensitive toward foreign capital flow changes

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invested on SUN than stock or SBI. This is quite reasonable since the investors in the stock market can switch across stocks where the price movement provides them larger profit. Thus, the stock release by the foreign investors does not always imply capital reversal. A different case is for SUN, which price movement of each series tends to be linear, hence leaving the investors with only 2 options either invest in SUN or reverse their capital out (capital reversal). On the other hand, the investment on SBI is relatively more limited and is related more to liquidity management. Moreover, SBI has a short span of maturity date (less than 1 year) so the investors automatically have to release SBI when they reach the maturity date, and any attempts to get it back in the primary and secondary market is constrained with limited supply. The exchange rate is also sensitive toward foreign capital flow on SPN. However, considering its limited volume, then its influence on the rate of Rupiah is also limited. The foreign investors decision to invest in Indonesia is influenced by the level of investment return and risk. This paper found that the probability of capital reversal is significantly affected by global monetary assets (DJIA) and global risk factor (VIX), as well as domestic investment return (IHSG and SUN yield). The SUN yield significantly affects the probability of capital reversal, more than other explanatory variable, as far as the global risk is in good condition. The fund flow originated from current account (CA) transaction is also significantly influence the rate of Rupiah. Moreover, the impact of worsening CA activity on Rupiah’s rate will be larger when it exceeds certain threshold level. The estimation result shows that whenever the CA deficit exceed the deficit threshold of USD980 million (about 2% of GDP), the exchange rate will depreciate by 12.7% (m-o-m) with a 4 months lag. Following the above findings, we derive some recommendations to keep the stability of Rupiah’s rate; first, improving the monitoring on foreign investments especially on SUN and stocks. Second, increasing the monitoring of stock price index (IHSG), the SUN yield, and the global risk indicator, and use them to predict the capital reversal. Third, increase the performance of current account by any means to avoid its deficit exceeds the threshold. Fourth, to maintain the stability of Rupiah’s rate when potential of capital reversal is detected, policy maker need to maintain sufficient foreign reserve to enable them intervene on forex market.

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REFERENCES

Agung, Juda, M. Noor Nugroho and Yanfitri (2011), “Arus Modal Jangka Pendek di Indonesia Pasca Krisis Global: Karakteristik, Prospek dan Respon Kebijakan”, Bank Indonesia Working Paper, Juni 2011. Berg, Andrew, and Pattillo Catherine (1999)” Are Currency Crisiss Predictable? A Test” IMF Economic Review, Jun 1999; 46,2. Calderon, Cesar, Alberto Chong and Norman Loayza, 2000.” Determinant of Current Account Deficit in Developing Countries,” The World Bank Policy Research Working Paper, No. 2398. Chinn, Menzie D., and Eswar S. Prasad. 2003.” Medium-Term Determinants of Current Account in Industrial and Developing Countries: An Empirical Exploration” Journal of International Economics, Vol. 59, pp 47-76. Calvo G (1998)” Capital Flows And Capital – Market Crises: The Simple Economics Of Sudden Stops” Journal of Applied Economics 1:35-54. Edwards, Sebastian (2004),” Financial Opennes, Sudden Stops and Current Account Reversals” National Bureau of Economic Research, Working Paper 10277, January 2004. Edwards, Sebastian (2005),” Capital Controls, Sudden Stops and Current Account Reversals” National Bureau of Economic Research, Working Paper 11170, 2005. Frankel, Jeffrey A., and Andrew K. Rose, (1996)” Currency Crashes In Emerging Markets: An Empirical Treatment” Journal Of International Economics, Vol. 41 (nov), pp. 351-66. Hansen, Bruce (1999), “Threshold Effects in Non-Dynamic Panels: Estimation, Testing, and Inference,” Journal of Econometrics, Vol. 93, No. 2. pp. 345-68. Hutchinson M, Noy I (2006),” Sudden Stops And The Mexican Wave: Currency Crisis, Capital Flows Reversal And Output Loss In Emerging Markets” Journal Of Development Economics 79(1): 225-249, 2006. Kaminsky, Graciela, Saul Lizondo, and Carmen Reinhart, 1998” Leading Indicators Of Currency Crisis” Staff Papers, IMF, Vol. 45 (March) pp.1-48. Khan, Mohsin S., and Senhaji Abdelhak S.2001” Threshold Effects In The Relationship Between Inflation And Growth”, IMS Staff Papers, Vol. 48 No. 1.

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Nugroho, M. Noor (2010), “Perilaku Aliran Dana Jangka Pendek Asing dan Pengaruhnya pada Nilai Tukar Rupiah”, Bank Indonesia Working Paper, Juni 2010. Radelet S, Sachs J (1998) The East Asian financial crisis: diagnosis, remedies, prospects” Brooking Paper, economic Article 1:147-212. Rodrik D, Velasco A (1999)” Short Term Capital Flows” National Bureau of Economic Research, Working Paper 7364, 1999. Rudi Dornbusch, Ilan Goldfajn, dan Rodrigo Valdes. 1995”Currency Crisis And Collaps”, Brookings Papers on Economic Activity, pp. 219-70. Sachs, Jeffrey, Aaron Tornell, and Andres Velasco, 1996” Financial Crisis In Emerging Markets: The Lessons From 1995,” Brooking Papers On Economic Activity:1, Brooking Institution, pp. 147-215. Sahminan, Ibrahim, Yanfitri. 2009.“ Determinants and Sustainabality Indonesia’s Current Account Balance”. Working Paper No. WP/09/2009. Bank Indonesia. Stahn Kerstin, 2006” Has The Impact Of Key Determinants Of German Exports Changed? Results From Estimations Of Germany’s Intra Euro-Area And Extra Euro-Area Exports” Deutsche Bundesbank, Discussion Paper Series 1: Economic Studies No. 07/2006. Sugeng, M. Noor Nugroho, Ibrahim and Yanfitri (2009), “Dampak Dinamika Penawaran dan Permintaan Valuta Asing Terhadap Nilai Tukar Rupiah dan Perekonomian”, Bank Indonesia Working Paper, Juni 2009. Sula, Ozan (2010)” Surges and Sudden Stops of Capital Flows to Emerging Markets”, Open Economic Rev (2010) 21:589-605.

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Fund Management And the Liquidity of The Bank1 Gantiah Wuryandani Ramlan Ginting Dudy Iskandar Zulkarnain Sitompul 2

Abstract

This paper analyzes the liquidity of the banks, both precautionary and involuntary liquidity. We apply dynamic panel estimation on individual bank data covering the period of January 2002 to November 2011. The result shows precautionary liquidity is more determined by the operation of the bank. On the other hand, the involuntary liquidity is more affected by the financial system condition. Controlling the size of the bank, the effect of the financial system condition and the macro economy is larger for the small banks. Moreover, the monetary policy in the form minimum reserve requirement affects the precautionary liquidity of the small banks; while the central bank rate is less influential to the bank liquidity.

Keywords: Banking, Liquidity, General Method of Moment JEL classification: G21, G11, C33

1 Authors thank to VimalaDewi, AnggayastiHayu, Indri Tryana, and RizkaRosdiana for their great help on data and estimation. We also thank to Dr. Telisa Aulia for their great discusiion. 2 Authors are researche on Center for Central Bank Education – Bank Indonesia; [email protected], [email protected], diskandar@ bi.go.id, [email protected].

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I. INTRODUCTION Bank is an intermediary institution that accepts from and channels funds to the society. The performance individual banks and the banking system in aggregate will highly depend on how the banks manage their assets and their liability.The banks manage their assets and their liability to gain profit and to raise the company value within some rules. The rules include sufficient liquidity, low risk and sufficient capitals. Therefore, the management of assets and liability are highly related to the bank liquidity. According to Keynes (1936), the three motivations in holding cash or liquidity are for transaction, precaution, and speculation. Furthermore, Edgeworth (1888) with the square root of law of precautionary reserves principle, the liquidity reserve of the bank will increase equivalently to the root of the number of transactions. Liquidity is vulnerable and can be suddenly drained from a bank. The liquidity problems at one bank can spread to other banks that will eventually pose a systemic risk. A shock can create liquidity spiral that leads to the loss of liquidity and may form financial crisis. Learning from the history, the banking crises lately was mainly due to the liquidity crisis that caused the banks fail to pay their obligations. Within the framework of the financial system safety net (FSSN), as also proposed by Bagehot (1873), the central bank as the lender of last resort (LLR) provides temporary liquidity loans with specific requirements to maintain the stability of banking system. The liquidity assistance is granted especially when the failure of bank may cause contagion effect and lead a systemic risk. Goodhart (1987) states that there is no clear distinction between the condition of illiquid and insolvent, but banks that need liquidity through LLR are suspect to be in the process of becoming bankrupt. In general, the liquidity reserve of a bank is a guarantee or a precaution over a possible financial penalty due to the increasing withdrawals or an increase in the minimum reserve requirement. Some banks choose a strategy to have excess liquidity to give a signal of strong liquidity to the market. However, excess liquidity may also be interpreted that the banks have a bad liquidity management and is sub optimal on managing their assets portfolio and liabilities. The excess liquidity can also be the result of poor infrastructure in the payment system and inter bankmoney market. Di Giorgio (1999) argues that the level of financial system development can be reflected by the participation cost within the financial system. In developed countries, the cost to process information, project evaluation and monitoring of borrowers is relatively low. This allows the banks to manage their liquidity with a relatively low liquidity reserves. On the contrary, the country with poor payment systems and with limited infrastructure on interbank money market tends to complicate the banks in managing their liquidity. This leads them to hold higher liquidity reserve. In micro-banks, the asset and liability management of banks concerns the aspects of liquidity risk, market risk, trading risk, capital and fund raising, profit target and growth plans. Generally the banks face three types of risk; credit risk (transaction, counterparty, concentration,

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and settlement), market risk (interest rate, exchange rate, liquidity), and operational risks (processes, systems infrastructure, human resources). The main focuses in asset and liability management by the banks are liquidity risk, exchange rate, and interest rates. In this case, the optimal liquidity is the liquidity that is able to create optimal revenue and prevent the occurrence of liquidity risk. At macro level, from the central bank perspective, an optimal asset and liability management by the banks is the one that create liquidity in accordance with the target of monetary policy. Currently, the management of assets and liabilities by the banks in Indonesia indicates a fair amount of surplus liquidity3. This excess is absorbed by the central bank through monetary policy with market or non-market approach. The market operations involve monetary transactions between the central bank and the banks in order to lower or to increase the liquidity in the market. This includes the selling or buying the government securities or Certificate of Bank Indonesia. In Indonesia, monetary policy tends to be contractionary to absorb the excess liquidity in banking. This condition arises as the consequence of the bail out policy in financial crisis of 1998 when dealing with the bank run within banking system. The non-market approach to the monetary policy is conducted through minimum reserve requirement which dictate the banks to place minimum amount of their funds at central bank. Ganley (2004) stated that the surplus of liquidity can cause problems for central banks on transmitting his monetary policy. It can also create difficulties for the central bank to intervene the currency market. Moreover, excess liquidity may disturb the balance sheet and the income statement of the central bank. The distortion on monetary policy effectiveness will likely lead to a problem on the financial sustainability of the central bank, particularly when the main instruments for monetary policy is the central bank securities. The main source of bank liquidity is capital inflows such as long-term foreign investment, short-term portfolio investment, and the fiscal deficit financing. With free foreign exchange regimes, the capital inflow to Indonesia either long-term or speculative, is a significant determinant for national liquidity. Speculative capital inflow is more distortive for the financial system and monetary stability. On the other hand, a long term capital inflow will support the domestic economic growth and job opportunities. This paper will analyze the impact of banks behavior in Indonesia in accepting deposit and channeling funds on their liquidity. Furthermore, this paper will also identify the determinants of banking system liquidity, and the role of monetary policy on the liquidity management of the bank.

3 Surplus of liquidity occurs when the cash flow on the market exceeds the needs for reserves, (Ganley, 2004). Within market equilibrium, this is an ex ante disequilibrium and tends to be persistent due to autonomous increase of liquidity on central bank.

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The next section of this paper provides theoretical background and existing literatures on liquidity. Section three present the data and the research methodology, while section four discussed result and analysis. Section five present the conclusion and close this paper.

II. THEORY 2.1. Determinant of Bank Liquidity In terms of micro-enterprises, the Bank for International Settlements (2008) defines liquidity as the ability of the bank to finance the asset increase and meet liabilities without incurring losses. Valla, Escorbiac and Tiesset (2006) and Vodova (2011) define liquidity as the ability to meet cash liabilities, and can be distinguished into funding liquidity and market liquidity4. Borio (1997, 2001) argues that it is necessary to distinguish between ex ante liquidity balance before the central bank intervention and ex post after central bank intervention. Edlin and Jaffee (2009) stated that high liquidity is due to the ‘credit crunch’ or the reluctance of banks to channel the credit. The development and liquidity conditions are not only affected by the bank’s business activities but are also influenced by the money market. With a well functioning money market, the banks can better manage their liquidity and avoid deficiency or excess liquidity. The money market in a country is determined by the market structure, the available instrument, development, regulation, and the market liquidity. The liquidity condition of the financial system will determine the monetary policy taken by the central bank in order to achieve the target of inflation and to maintain the sustainable growth momentum. The interbank money market (PUAB) or also called interbank call money market is a place for the banks to lendor to borrow funds to keep their liquidity. The transaction is short-term and is used to deal with daily liquidity gap. The PUAB is executed over the counter (OTC) with direct communication among banks through Reuter Dealing Monitoring System (RDMS). The structure of interbank money market in Indonesia tends to be oligopoly and segmented, and is very shallow. This condition makes the banks less flexible in obtaining and allocating optimal liquidity. Most major banks tend to hold excess liquidity and more often serve as a lender. The segmentation in the interbank market creates sub system of money market within them. In this case, the same lender banks and same borrower banks will transact within their group continuously. These conditions led to different levels of counterparty risk and variation across segments including price disparity. Under conditions of tight liquidity, the segmentation of this interbank market tends to be stronger with increasing counterparty risk. This condition tends to encourage interbank rate to increase and the price disparity to widen. In this case, the motivation of banks to not release liquidity grows higher in order to maintain adequate liquidity. 4 A funding liquidity (Valla, Escorbiac, and Tiesset, 2006) is the asset that is ready to be converted into cash to meet liabilities or for operational activity, while market liquidity is defined as the activities of banks in trading assets shown by the ability of banks to sell non-liquid assets.

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The structure of the Indonesian financial markets has a very limited instrument of which there is a short-term securities instrument, which is no more than one year, such as commercial paper, certificates of Bank Indonesia, repurchase agreements, banker’s acceptances, and certificates in the interbank money market instruments deposit. The ever shallow instrument in PUAB encourages banks to manage short term liquidity by holding onto a limited variation of the instrument. In general, banks tend to have instruments that are highly liquid with low risk like government securities (sovereign), the central bank securities, and other short-term securities. Besides market conditions, various regulations related to risk management and liquidity urges banks to behave in certain way in managing both liquidity and asset and liability portfolios. The money market is an outlet or the foremost means of most major banks in managing liquidity. The liquidity condition of the bank will be directly reflected in money market both in the volume of transactions and the dynamic of interest rate. The tight liquidity in the banking is marked by the rising interbank rates and the widening spreads between the purchase and the selling price. The tight level of bank liquidity is reflected in the loan rate (borrowing) and financing rate (lending). The indicative rate for interbank market is reflected in JIBOR (Jakarta Interbank Offered Rate), which is the average price of the quotations from the contributor banks. These JIBOR were published through Reuters and Bloomberg and also reported by the bank on daily basis on their daily reports (Laporan Harian Bank Umum, (LHBU). In addition to JIBOR, the average price of all banks can be monitored via LHBU on the central bank website. In Indonesia commercial banks participating in interbank money market possess wide gap in assets and capital. By the end of 2011, the capital of the bank ranged from 0.15 - 54 trillion rupiahs, while their asset ranged from 0.17 trillion to 465 trillion Rupiahs. There are only 7 (seven) banks with asset above 100 trillion rupiah, while banks with assets below 1 trillion rupiahs were 20 banks out of a total of 122 banks. The results of Vodova’s research (2011) indicates that the liquidity of the banks in Czech, measured by several indicators, were positively determined by capital adequacy ratio (CAR), interest rate loans, the nonperforming loans (NPL), and inter-bank rate. On the other hand, the financial crisis, inflation, and economic growth had a negative impact on liquidity. The unemployment, interest rate margins, profitability, and the monetary interest rate do not significantly affect the liquidity of the banks. Vodova measure the liquidity in his study with the ratio of liquid assets (cash, demand deposits at the central bank) to total asset, bank liabilities, and credit line with the other bank’s counterparties. Shen, Chen, Kao, and Yeh (2009) conducted a study to determine the factors of liquidity risk by using panel data from 12 countries. The result of the study indicates that liquidity risk is affected by illiquid assets, external financing, supervision, regulation, and macroeconomics. Liquidity risk is negatively correlated with the performance of the banks in countries with a market-based financial system. On the other hand, in countries with bank-based financial system, the liquidity risk is not associated with the bank’s performance.

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Saxegaard (2006) states that banks will hold excess liquidity more than precautionary reserve when the economy is in liquidity trap condition. Under these conditions the yield is too low compared to credit intermediation cost, of which the returns from the funds placed at the central bank is better than channeling credit. The result of a study by Aizenman Agenor, and Hoffmaister (2000) indicates that the contraction in credit financing in Thailand after crisis, caused by the phenomenon of supply resulting in involuntary excess liquidity. Research by Bathaluddin et al (2012) stated that the tendency for the banks in Indonesia to store excess liquidity is influenced by the need for currency fluctuations, economic growth, cost of funds, and significant liquidity lag. The precautionary liquidity in the study was defined as the ratio of the bank funds placed in central bank securities (excess liquidity) to the third-party funds. On the other hand, involuntary liquidity is the residual of the estimated precautionary liquidity. Pontes and Sol Murta (2012) found that excess liquidity occurs due to the weak development of the financial sector where the interbank market is less efficient, a low diversification of financial instruments, and weak credit intermediation due to the expensive costs. The total liquidity of the banks in agregate will not change despite the change in the liquidity ratio at bank level. However, these changes affect the composition of liquidity in the presence of excess liquidity. Based on the research conducted by Keister and McAndrew (2009), the amount of liquidity available in the bank is determined by central banks policy and does not reflect their financing behavior. On the other hand, Ganley (2004) argues that some factors that determine the liquidity of banks are beyond the control of the central banks. These include the flow of their reserve to and from central bank,and the amount of money hold by the public. Aspachs, Nier, and Tiesset (2005) conducted a study on bank liquidity in the UK using quarterly data of individual banks from 1985 to 2003. The results showed that the greater the support of central bank liquidity in the times of crisis, the lower the liquidity reserves held by the bank. Most banks in the UK also tend to do a counter-cyclical liquidity strategy with low liquidity back up when the economy grows. The liquid assets in this study consist of cash, reverse repo, and commercial paper. The dependent variable is the liquidity ratio, which is measured by the ratio of liquid assets to total assets, or the ratio of liquid assets to total deposits. The explanatory variables consisted of Net Interest Margin (NIM), the profit, the credit growth, bank size, the growth of Gross Domestic Product (GDP), and the short-term interest rates. Interest rates and GDP have a strong influence on liquidity, as well as the opportunity for future financing. On the other hand, Acharya and Merrouche (2010) analyzed the bank demand for liquidity and settlement in UK as well as its effect on the interbank market before and after the sub-prime crisis. The result showed that the bank in UK hold liquidity 30 percent higher after the interbank suspension in August 9, 2007, showing precautionary action. This creates a tight liquidity conditions and created crisis. The increased demand for liquidity by the banks has raised the interest rate and potentially created systemic risk.

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Some actions required to reduce the stress and the volatility of interest rate in interbank money market are monitoring, early stress test, recapitalization for the troubled banks, and increase the liquidity above the emergency liquidity reserves. The results of the study by Berger and Bouwman (2009) shows that monetary policy does not have a significant effect on the liquidity for large and medium-sized banks with 90 percent possession of the market liquidity, or above. The monetary policy is instead effectively affecting the liquidity of small banks. In addition, there is no significant difference between the effects of monetary policy during the financial crisis and in normal times. Henry, Birchwood, and Primus (2010) conducted a study to estimate the demand for precautionary reserves and dynamic impact of involuntary reserve on monetary policy in Trinidad and Tobago, by using the GMM (generalized method moments) and VAR (Vector Autoregression) method. Their results showed that the bank holded excess reserve as precautionary action against liquidity shortages. The spread between lending rates and policy rate negatively affects precautionary reserves of the bank. In addition, the dynamics of involuntary reserves are influenced mainly by fiscal operations. Similarly, a decreasing allocated credit during slow economic growth tended to increase the liquidity of the bank. Another study conducted by Pontes and Sol Murta (2010) using TSLS (two stage least squares) show that the credit growth, the government securities and the crisis influence the bank liquidity. High lending rate will disturb the bank intermediation, thus causing liquidity accumulation.

2.2. The Role of Monetary Policy The central bank’s monetary policy is implemented to maintain monetary stability in order to control the national liquidity. By implementing liquidity management we expect to attain sustainable economic development. In Indonesia, the central bank sets policy rate (BI rate) as a reference for the market participants. The instruments of monetary policy currently consist of Certificate of Bank Indonesia (SBI), standing facility of Bank Indonesia, and the minimum reserve requirement (GiroWajib Minimum, GWM). Bank Indonesia use SBI and term deposit for open market operation. The maturity for Certificate of Bank Indonesia (SBI) was initially one month then in 2011 Bank Indonesia extended the holding period to six months. The maturity for term deposits is longer up to nine months. On the other hand, the standing facility of Bank Indonesia set since June 2008 is to fine tune the market operation particularly to control the overnight interest rate (ON) within the interbank money market. This is helpful to keep the ON rate to move around the BI rate (interest rate corridor), hence will ensure the effectiveness of monetary policy transmission through the interest rate channel.

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The standing facility consists of deposit and lending facilities with interest rates based on BI rate plus or minus certain spread. When the bank holds excess liquidity, they can deposit their excessive funds in Bank Indonesia using deposit facility; otherwise use lending facility when they face a liquidity shortage. The interest rate corridor originally used symmetrical spread to the BI rate movement. At the end of 2012 the spread between deposit facility and BI rate was -175 basis points, while the spread between lending facilities amounted to +100 basis points from the BI rate. The non-market monetary policy instruments includes minimum reserve requirement (GWM) rule the banks to place their funds in Bank Indonesia by certain percent of the funds the collect from third parties. Nowdays, the reserve requirement policy is associated with the Loan-to-Deposit Ratio (LDR). The aim is to optimize the use of liquidity by increasing the banking intermediation to support the economic growth. In this case the banks are required to meet the LDR of 78-100 percent, otherwise the central bank will impose additional minimum reserve requirement. The central bank provides time deposit for the bank, which is limited up to 3 percent of their third-party funding. The incentive to deposit their fund is the BI rate minus 2.5 %, therefore will not burden the banks due to their loss of time value of money. The management of assets and liabilities is very essential to keep the operation of the bank. The liquidity conditions may affect the systemic risk and monetary policy transmission. The uncertain withdrawal by depositors encourages the banks to choose best strategy on asset and liability management to ensure they meet their laibilities. In addition, changes and volatility in interest rates, and the exchange rate will determine the compliance with the conditions capable of fund withdrawal liability, either suddenly or massively simultaneous. Freixas, Martin and Skeie (2009) conducted a study of the efficiency in the interbank money market fund allocation and the optimal policy of the central bank in the presence of liquidity shock. The results of the research showed that distributional liquidity shock crisis will increase the market segmentation across bank (disparity), and the central bank should lower the interbank rate. The failure to lower interest rates in the times of crisis will worsen the financial stability with the increasing probability of a bank run (simultaneous withdrawals by depositors). On macro interest perspective, Saxegaard (2006) mentioned the need to distinguish between precautionary excess liquidity with involuntary excess liquidity (excess liquidity that exceeds the precationary). He found that in Sub-Saharan Africa countries (SSA), banks tend to have excess liquidity that is involuntary due to the underdeveloped financial markets, the lack of credit allocation, and the increasing government deposits at the bank. If a bank has excess liquidity to meet the needs of anticipation (precautionary), the central bank does not need to sterilize the economy since it potentiallytrigger inflation. The behavior of the banks in SSA above may indicate a structural problem that causes the inefficient allocation of funds. The involuntary liquidity in general serves as a secondary reserve and is intended to address the possibility of liquidity gap in bank operation or the

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likelihood of liquidity shocks. By holding the involuntary liquidity it naturally means the banks pay opportunity cost to obtain income. In this case, Sacerdoti (2005) argues that SSA countries need development of debtor information, accounting and auditing standards, and also legal and regulatory framework. O’Connell (2005) also argues that involuntary excess liquidity will disturb the mechanism of monetary policy transmission. Thus, an understanding of the sources of excess liquidity becomes important to choose optimal monetary policy. On his study about the pattern of excess liquidity banks in SSA countries above, Saxegaard (2006) used SVAR (Structural Vector Autoregression). He also found the excess liquidity of the banks weaken the mechanism of monetary policy transmission, and leave the monetary authority unable to control the demand within the economy. Slightly in line with Saxegaard, Ganley (2004) also stated that the liquidity absorption operation by the authority tended to use weak monetary instruments such as central banks securities with high interest rates, making it less effective in transmitting their monetary policy. In the long term, this raises important implications for the central bank to finance the rising costs of monetary operations. This condition can seriously affect the income of the central bank and its independency from the government. A continuous loss of the central bank will require government recapitalization. Meltzer (2009) and Feldstein (2009) argue that the increased liquidity can lead to an inflationary pressure with rapid money creation through credit, and the central banks should absorb this excess liquidity. However, Keister and McAndrews (2009) stated that the above phenomena may occurs only when the central bank use a traditional monetary operation framework. Currently, the Federal Reserve gives interest reward on bank liquidity deposited at the central bank, thereby increasing market interest rates and restrain credit growth rate without changing the amount of liquidity. By providing interest for reserves at the central bank, the central bank may control the short-term interest rate which is independent from the level of liquidity; therefore will not create inflationary pressures. In other words, the excess liquidity in the bank does not always lead to inflationary pressures. A study by Bathaluddin, Adhi, and Revelation (2012) using TVAR (threshold vector autoregression) indicates that there has been a regime switching from the low liquidity to the high liquidity in Indonesia in 2005. Additionally, excess liquidity caused ineffective monetary policy on controlling inflation. Berger and Bouwman (2009) in his research show that monetary policy creates significant effects of liquidity only to small banks. However, there is no significant difference of the impact of monetary policy on liquidity creation during normal condition or crisis.

III. METHODOLOGY The framework of this research is shown in the diagram below. The business activities of the banks in collecting and allocating fund will affect their liquidity. On the other hand, the liquidity conditions of the bank will affect the economic activity reflected on Gross Domestic Product (GDP), hence the rate of inflation. The actual inflation and inflation expectations will

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determine the monetary policy reaction of the central bank on controlling liquidity in order to achieve his inflation target. The policy will influence macroeconomic conditions such as interest rate movements, the exchange rates, and the economic growth. The changes of these macroeconomic measures will be anticipated by the individual banks to choose strategy on collecting and allocating fund. The sources of fund to the banks are demand deposits, savings deposits, time deposits, loans, and capital. On the other hand, the allocation of fund may take the form of cash, demand deposits at the central bank, demand deposits in other banks, securities, loans, and other placements. All the process is inter-related and form continuous cycle.

Macroeconomy Nilai tukar Suku bunga Krisis Penempatan dana Kas Giro dibank sentral Giro dibank lain Surat berharga Kredit Penempatan lainnya

Penghimpunan dana Giro Tabungan Deposito Pinjaman Modal

Transmisi Moneter

Reaksi Kebijakan Moneter

Likuiditas Precautionary Involuntary

PDB

Inflation

Diagram 1. Research Framework

We use monthly data of individual banks from January 2002 to November 2011 published on financial report of banks. The samples include 110 banks from the total population of 122 banks. In this study, the sampling covers only conventional banks, considering that Islamic banks have a different operational activities and different money market. We classify the banks based on asset;large banks with asset above 50 trillion rupiahs, medium banks with asset of 10 to 50 trillion rupiahs, and small banks with asset lower than 10 trillion rupiahs. Within our sample, the smallest bank had assets above 100 billion rupiahs, following the provision of capital limit of minimum 100 billion rupiahs since 2010. The selection of number

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of banks and period of observation meet the requirement that the cross section dimension is larger than the period (N > T), which is useful on controlling individual heterogeneity when there are unobservable behavior. We specify a dynamic panel model and make use the estimation technique of generalized method of moments (GMM). The selection of the best model is based on a panel data modeling framework as shown in the diagram below.

Panel Data Model Static Panel Data HAUSMAN TEST Fixed effects Jika error berkorelasi dengan Xit (regressors),

Random effects Jika error tidak berkorelasi dengan Xit (regressors),

Dynamic Panel Data Jika lag dependent variable digunakan: heterogeneity

Generalized Method of Moments (GMM) First difference GMM

System GMM

Diagram 2. Panel Data Model

The specification of empirical model for dynamic panel data model is below

Yit  Yi ,t 1   X it   it

(1)

Where Yit is the dependent variable (endogenous), Xit are explanatory variables (exogenous), and mit are the residual. The instrument variable (IV) used is a certain lag of the endogenous and exogenous of variables that are not correlated with the shock at time t. In general, symbols and descriptions are used as variables in Table 1 appendix A. Our endogenous variable is liquidity and we distinguish between precautionary and involuntary as defined in Saxegaard (2006), Valla et al (2006) and Aspachs et al (2005). Yitis a precautionary liquidity and Y2t is involuntary liquidity. The precautionary liquidity (Yit), is the ratio of the cash plus minimum reserve requirements (RR), plus the placement on Bank Indonesia and other banks (ODD), relative to total assets (TA). We specify the precautionary liquidity to be more determined by the needs of the banks for operational activities. Each bank needs different liquidity depending on their operation, market segments, and tolerable risks. Based on focus group discussions, some banks consider the minimum

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precautionary liquidity to be approximately 2-10 percent. The threshold for liquid to total assets ratio for each bank depends on business operations, historical liquidity needs and risk appetite. The involuntary liquidity Y2t is the ratio between central bank securities (CBSEC) plus government securities (GSEC) and other securities (OSEC), relative to total asset (TA). The deposit of the bank on central bank may take the form of Bank Indonesia certificates, term deposits and bank Indonesia standing facilities. In general, the banks consider the threshold of involuntary liquid asset ratios to be 15-18%.

Y1t 

Y2t 

C  RR  ODD TA

(2)

i, t

CBSEC  GSEC  OSEC TA

i, t

(3)

To explain the determinant of precautionary and involuntary liquidity, we use the balance sheet component, representing the source and the allocation of the fund, monetary policy, financial system and the macroeconomic condition. We focus more on variables that are considered to affect the precautionary liquidity directly including their business activities. On the other hand, the involuntary liquidity variable is driven more by market conditions and financial systems, as well as the macro economy. We use the empirical specification below: Y1i ,t  Y1i ,t 1  1RRRATE t   2 DTi ,t   3CREDITi ,t   4 FSI t   5ON t   i ,t

DTi ,t 

DD  S  TD i, t i, t i, t TA i, t

CREDITi ,t 

CRED TA

i, t

(4)

(5)

(6)

i, t

wherei = 1,...., N is the observations and t = 1.... T is the monthly time dimension from January 2002 to November 2011. Y1i,t-1 is the lag of the endogenous variable, RRRATE.t is the level of reserve requirement, DTi,t in equation (5) is the ratio of deposits (including demand deposits (DDi, t), savings (Si, t), and deposits (TDi, t) to total assets (TAi,t); CREDITi,t in equation (6) is the ratio of credit (CREDi, t) to total assets (TAi, t). FSIt is financial stability index which represent the pressure on financial system stability both in banking systems and capital markets; the ONt is the interest rate for overnight interbank money market, and εi,t is error term.

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The equation for involuntary liquidity (Y2t) is below. Y2 i ,t   Y2 i ,t 1   1 BIRATE t   2 ER t   3 CREDIT i ,t   4 FSI t   5 CAR i ,t   6 GDPt   i ,t

DIT i ,t   4 FSI t   5 CAR i ,t   6 GDPt   i ,t

(7)

wherei = 1,...., N is the observations and t = 1.... T is the monthly period of January 2002 to November 2011. Y2i,t-1 is the lag of involuntary liquidity; BIRATEt is the interest policy rate; ERt is the nominal exchange rate, CREDITi,t is the ratio of loans to total assets, FSIt is financial stability index, CARit is the capital adequacy ratio or the percentage of minimum bank capital requirement, GDPt is gross domestic product, and εi,t is the error term.

IV. RESULT AND ANALYSIS 4.1. Descriptive Statistics We outline the statistics of variables used in this study in the Table 2. The high standard deviation of involuntary liquidity shows that liquidity volatility involuntary (Y2t) tends to be dynamic throughout the observation period range, as depicted on Figure 1. On the contrary, the precautionary liquidity tends to have a relatively stable volatility compared to the involuntary liquidity, as indicated with its low standard deviation. The movement range of precautionary liquidity is relatively lower about 500 basis points. This indicates that involuntary liquidity movements tend to follow the dynamic of the economic situation which move from 1300 basis points (13 percent). At the time of 2005 mini-crisis and the international financial crisis of 2008, the financial stability index (FSI) was above the threshold 2, and involuntary liquidity decreased quite significantly. This indicates that with a liquidity problem in the market, the involuntary liquidity will be used as primary buffer. Banks tend to hold high reserve when the liquidiy of the financial system is relatively high, and then use it when the financial system downturn and become more volatile. These behavior are countercyclical to the market liquidity condition. On the other hand, the volatility of the exchange rate (ERt) is relatively stable. A significant surge was in late 2008 until mid of 2009 when the global financial crisis occurred due to the subprime mortgage crisis in United States as illustrated in chart 3. The relatively stable exchange rate movement is influenced also by the policy intervention by the Central Bank. Maintaining the stability of the exchange rate is one of central bank target as mandated under the Central Bank Act, in addition to the target on inflation. Based on Figure 4, the policy rate does not affect the decision of the bank to allocate their fund in central bank. When BI rate decline, the bank keep increasing their placement of funds on central bank.

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4.2. Precautionary Liquidity The estimation result of precautionary liquidity using all banks is good and does not biased upwards or downward (see Table 3 in Appendix D). The Sargan test statistics indicate that the instrument variables (IV) used isvalid. The precautionary liquidity of all banks are significantly influenced by previous precautionary liquidity, the minimum reserve requirement (RRRATEt), thirdparty funds (DTi,t), credit (CREDITi,t), financial stability index (FSIt), and interbank rates (ONt). Across bank classification, our estimation indicates that the largest determinant of precautionary liquidity are the level of previous precautionary liquidity, the third-party funds (except for the medium-sized banks), the credit and the overnight rate of interbank market. Banks still consider their historical liquidity conditions in determining the current one, and this is in line with Bathaluddin et al (2012). The positive impact of third-party funds on precautionary liquidity shows that when the third party fund increase, the bank face wider opportunities to allocate funds including to expand their business. The level of allocated credit either in large banks, medium, or small banks, negatively affect the level of precautionary liquidity; which is consistent with Henry et al (2010) and Pontes and Sol Murta (2010). Money market conditions reflected in overnight interbank rates (PUAB ON) negatively influence the precautionary liquidity. The tight liquidity in the money market, as indicated by the rising interbank rates will reduce the precautionary liquidity of the bank to overcome the difficulty on obtaining liquidity. This is consistent with the results of Vodova (2011) and Acharya and Merrouche (2010). On the other hand, the minimum reserve requirement (GWM) and the financial stability index (FSI) do not significantly affect the precautionary liquidity, except on small banks. This indicates that the non-market monetary policy using minimum reserve requirement only affects the small banks, and is consistent with Berger and Bouwman (2009). Small banks have limited activities hence will hold limited amount of liquidity reserve. This is the reason why reserve requirement policy significantly affects their liquidity. On the other hand, large and medium-sized banks hold higher liquidity. The amount is large enough to be buffer for their operational acitivites; therefore a change in GWM will not affect their liquidity. Furthermore, unlike the small banks, the large and medium-sized banks can easiliy fulfill their liquidity needs from the interbank money market. In addition, when the banks deposit their funds in central bank (maximum 3 percent of total third party funds), the central bank compensate with 2 percent interest rate, and this is higher than the real cost. Small bank liquidity is also affected by the stability of financial market. Estimation result shows increasing FSI, which indicatesa lower stability of financial markets and usually followed by a tight liquidity in the money market, will reduce the precautionary liquidity of the small banks. These conditions show that the resilience of small bank liquidity is strongly depends on the financial system condition, including the capital market.

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4.3. Involuntary liquidity The involuntary liquidity equation in Table 4 Appendix D shows that the involuntary liquidity at all banks is determined by its own lag of involuntary liquidity (Y2i,t), the monetary policy rate or BI rate (BIRATEt), the interbank rates (ONt), the exchange rate (ERt), credit (CREDITi,t), the capital adequacy ratio (CARi.t), the financial stability index (FSIt), and the gross domestic product (GDPt). The increase in interbank rates, exchange rates, and the FSI will reduce the involuntary liquidity of the bank. The estimation on all sample showed that the increase in policy rate will increase the involuntary liquidity of the banks. Generally the banks tend to use their involuntary liquidity as a buffer to maintain liquidity in the event of financial market and financial system shock. It is important to note that the estimation using medium-sized banks does not produce a good equation despite not biased upward or downward. This has been indicated by the Sargan test that the instrument variables used were not valid. In general, involuntary liquidity largely depends on the previous involuntary liquidity. On large bank sample, the previous liquidity is the only significant explanatory variable. The strong influence of historical involuntary liquidity is in line with Bathaluddin et al (2012). The interbank rates (PUAB) ON significantly affect the involuntary liquidity, except for the large banks. On the other hand, the effect of capital adequacy ratio (CAR) is significant for small bank sample and overall sample. The significance of capital requirement (CAR) on involuntary liquidity is also in line with the study of Vodova (2011). Using all samples, we find the policy rate has small effect on the bank liquidity. However, this is not the case when we estimate the sample across their size (large, medium, and small banks). The weak impact of the policy rate (BI rate) on involuntary liquidity hold by the bank is in line with Vodova (2012). Policy rate is not the reason for the banks to deposit their fund on central bank. The volume of the placement of the funds in the central bank securities continues to increase, despite of the decrease of the policy rate. In addition, the flexibility of central bank security as liquidity instrument is lower if the maturity of security is longer. The banks may face high liquidity because of the speed of channeling credit is lower then the speed of third party fund increase. On the other hand, the placement of funds in other financial product is still limited due to several limitations to transact in foreign exchange and stock market, as well as under-developed money market instruments. The exchange rate only affects the liquidity of medium-sized banks, while the interbank rates only affect the liquidity of the medium and small banks. The involuntary liquidity on small banks affected by the lag itself, interbank rates, CAR, FSI, and it shows that the liquidity GDP. The condition of involuntary small banks is also determined by the macroeconomic conditions and macroeconomic finance. The condition systems such as FSI and GDP are only affecting small bank liquidity, in line with the results of the study Aspachs et al (2005).

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For the large bank sample, the previous involuntary liquidity significantly affects the current involuntary liquidity. Other variables play minor impact on involuntary liquidity dynamics, which indicates that major banks hold very high liquidity. Good liquidity resiliency of large banks showed their better conditions on liquidity relative to smaller ones, and they tend to be lenders in interbank money market. It is also supported by the fact that major banks are easier in raising funds with lower cost than smaller banks. Furthermore, these large banks are equipped with good infrastructure, networking, and more complete products, as well as better credibility. Within this condition, smaller banks may necessary merger to better maintain their liquidity.

V. CONCLUSIONS On this paper we devide the liquidity of banks into precautionary and involuntary liquidity. Precautionary liquidity is the ratio between cash plus deposits on central banks and other commercial banks, towards total assets; while involuntary liquidity is the ratio of tradable securities (central bank securities, government securities, or other) towards total assets. This paper showed that the accumulation and the management of the fund affect the bank liquidity. Across the size of the bank, monetary policy and financial market condition (minimum reserve requirement policy, interbank money market rate, and financial stability index) affect more the precautionary liquidity of small banks. Furthermore, precautionary liquidity generally depends on the operations of the bank, except for the small-sized banks. The banks tend to use their involuntary liquidity as a buffer for their operational liquidity; this is reflected by the dominant effect of financial market condition on the bank’s involuntary liquidity. The monetary policy rate (BI rate) only affects the involuntary liquidty of the mediumsized banks, and not for the large and small ones. Furthermore, the macroeconomic conditions such as financial stability index and gross domestic product, is also only affect the involuntary liquidity of small banks. Our conclusion above imply the central bank does not need to implement strict liquidity absorption using policy interest rate, since the bank’s liquidity depend more on the operating conditions, the capital, the financial system, and the macroeconomic condition. Our result implicitely show that the banks tend to hold high liquidity involuntarily because of the financial markets in Indonesia is shallow, and because of high uncertainty to get liquidity from the market. Therefore, it is necessary to increase the depth of financial markets to expand liquidity instrument. This will serve as good buffer for the banks and provide them flexibility on managing their liquidity. The banks need to reduce their dependency on central banks, and one way to do this is by reviewing the central bank standing facilities.

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On the other hand, small banks have less immunity against the macroeconomic and financial market condition. This implies a necessity for the small bank to merge and to combine their asset, which will help them better manage their liquidity and increase their credibility. From global perspective, this will also help Indonesian banking system to compete in international markets such as forthcoming ASEAN Economic Community, 2015.

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REFERENCES

Acharya, V.V, and Merrouche, O. 2010. Precautionary Hoarding of Liquidity and Inter-bank Markets: Evidence from the Sub-prime Crisis. NBER working paper No.16395. Agenor, P.R., Aizenman, J., and Hoffmaister, A. 2000. The credit crunch in east asia: What can bank excess liquidity tells us? National Bureau of Economic Research, Working Paper No.7951. Alper, K., Hulagu, T., and Keles, G. 2012.An empirical study on liquidity and bank lending. Central Bank of Turkey. Aspachs,O., Nier, E., Tiesset, M. 2005. Liquidity, Banking Regulation and the Macroeconomy. Evidence on bank liquidity holdings from a panel of UK-resident banks. Bank of England Working Paper. Bagehot, W.1873. Lombard Street, a Description of the Money Market. Henry S. King, Bank for International Settlement. 2008. Principles for sound liquidity risk management and supervision. Bank for International Settlements. 2009. International framework for liquidity risk measurement, standards and monitoring. Bathaluddin, M.B, Adhi, N.M, dan Wahyu, A.W. 2012 Dampak Persistensi Ekses Likuiditas terhadap Kebijakan Moneter. Buletin Ekonomi Moneter dan Perbankan Vol 14., No. 3, Bank Indonesia. Berger, A.N., dan Bouwman, C.H.2009. Bank liquidity creation, monetary policy, and financial crises.University of South Carolina. Borio, C. 1997. The implementation of monetary policy in industrial countries: A survey. Bank for International Settlements, Economic Papers No.47. Borio, C. 2001. A hundred ways to skin a cat: Comparing monetary policy operating procedures in the United States, Japan, and Euro area. Bank for International Settlements, Monetary and Economic Department Papers No.9. Brunnermeier, M. K. 2009. Deciphering the Liquidity and Credit Crunch 2007-2008.Journal of Economic Perspectives, Vol. 23, No. 1, pp. 77-100. Di Giorgio, G.1999. Financial development and reserve requirements. Journal of Banking and Finance.

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Drehmann, M. and Nikolau, K. 2009. Funding Liquidity Risk. Definition and Measurement. ECB Working Paper, No. 1024. Edgeworth, F., 1888. The mathematical theory of banking, Journal of the Royal Statistical Society, 51, 113-127. Edlin, A.S. and Jaffee,D. 2009. Show Me the Money. University of California, Berkeley. Freixas, X. and Rochet, J.C. 1998. Microeconomics of Banking. Massachusetts Institute of Technology. Freixas, X., Martin, A., dan Skeie, D. 2009. Bank Liquidity, Interbank Markets, and Monetary Policy. Federal Reserve Bank of New York Staff Reports, no. 371. Ganley, J. 2004. Surplus Liqudity: Implications for Central Banks. Lecture Series No. 3, Centre for Central Banking Studies, Bank of England. Goodhart, C. 1987. Why Do Banks Need a CB?. Oxford Economic Papers, p 75-89. Henry, L., Birchwood, A., and Primus, K. 2010. The dynamics of involuntary commercial banks’ reserves in Trinidad and Tobago.XLII Annual Monetary Studies Conference. Keister, T. and Mc Andrews, J. 2009. Why are banks holding so many excess reserves? Federal Reserve Bank of New York, Staff Reports. Keynes, J.M., 1936. The General Theory of Employment, Interest and Money.McMillan, London. O’Connell, S. 2005. Note on Excess Liquidity. Working paper, International Monetary Fund. Pontes, G. and Sol Murta, F. 2012. The determinants of the bank’s excess liquidity and the credit crisis: the case of Cape Verde. Faculdade de Economia da Universidade de Coimbra. Sacerdoti, E. 2005. Access to Bank Credit in Sub-Saharan Africa: Key Issues and Reform Strategies. Working paper, International Monetary Fund. Saxegaard, M. 2006. Excess Liquidity and Effectiveness of Monetary Policy: Evidence from SubSaharan Africa. Working paper, International Monetary Fund. Shen, C.H., Chen, Y.K., Kao,L.F., Yeh, C.Y. 2009. Bank Liquidity Risk and Performance. Vodova, P. 2011. Liquidity of Czech Commercial Banks and Its Determinants.International Journals of Mathematical Models and Methods in Applied Sciences, issues 6, volume 5.

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Precautionary (Y1i,t) liquidity is the ratio between the summation of cash, account in BI and account in other banks and total assets as described in equation (2). Involuntary (Y2i,t) liquidity is the ratio between securities composite that is ready sold consisting of fund placement in Central Bank in the shapes of securities, government securities and other securities and total assets as described in equation (3). In this graph precautionary and involuntary liquidity are liquidity aggregate.

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Figure 1. Precautionary (Y1i,t) and Involuntary (Y2i,t) Liquidity

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Appendix C FSI (financial stability index) is composite index in money market consisting of banking and capital market for measuring the durability of financial system. Involuntary (Y2i,t) liquidity is the ratio between securities composite that is ready sold consisting of fund placement in Central Bank in the shapes of securities, government securities and other securities and total assets as described in equation (3).



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Figure 2. FSI and Involuntary Liquidity

BIRATEt is interest rate of monetary policy determined by Central Bank in order to an open market operation. ERt is the nominal of exchange value.

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CBSECi,t is bank fund placement in Bank Central such as in the shapes of term deposit, securities of Bank Indonesia, and facilities of Bank Indonesia. BIRATEt is the interest rate of monetary policy determined by central bank for the operation of open market.

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Appendix D This table shows the result of GMM (generalized method moment) on precautionary liquidity (see table 1. for symbol and variable description). The model of precautionary liquidity follows the equation (4). Instrument of variable (IV) which is used is dependent and independent lag, in which the length of every IV of every model is different to get a significant result. The number of samples for all banks are 110, big bank 17, medium bank 28, and small bank 65, based on the amount of asset. Sampling for the period January 2002 until November 2011 uses the monthly data longitudinal panel.

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Fund Management and The Liquidity of The Bank

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This table shows the result of GMM (generalized method moment) to the determinant (see table 1. For symbol and variable description) involuntary liquidity on equation (7). The Definition of involuntary liquidity is in equation (3). Instrument of variable (IV) what is used is dependent and independent lag, in which the length of IV every model is different to get a significant result. Sampling for all banks are 110, big banks are 17, medium banks are 28, and small banks are 65, based on the amount of assets. Sampling is for period from January 2002 to November 2011 by using the monthly data of longitudinal panel.

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Halaman ini sengaja dikosongkan

The Dinamics of Total Factor Productivity Ofmedium and Large Manufacturing In Indonesia

259

THE DYNAMICS OF TOTAL FACTOR PRODUCTIVITY OFMEDIUM AND LARGE MANUFACTURING IN INDONESIA

Ndari Surjaningsih Bayu Panji Permono1

Abstract

This paper calculates and decomposes the Total Factor Productivity (TFP) for large and medium scale industry in Indonesia covering the period of 2000-2009. By using Data Envelopment Analysis (DEA) method, the result shows there is a shift of the supporting factors on the growth of TFP on manufacturing sector within the 2 (two) sample period. In the period of 2000-2004, efficiency change becomes the main contributor on the growth of TFP. Whereas in the period of 2005-2009, technical change becomes the main supporting factor of TFP,however it goes along with the growth of negative efficiency change or the decline of the company’s catching-up effect ability to adapt with the more advance technology. The grouping of the sample across subsectors, technical change and also efficiency change shows the declining amount of manufacture industry with superior productivity. Furthermore, the number of low and weakening catching-up industry is increasing.

Keywords: Indonesian manufacturing, total factor productivity, technical change, efficiency change, economic scale change, Data Envelopment Analysis JEL Classification: L6, M11

1 Authors are researcher on Economic Research Group – DKM Bank Indonesia. The views on this paper are solely of the authors and does not necessarily represent the views of Bank Indonesia; corresponding author: email here please.

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I. INTRODUCTION Sector of manufacturing in Indonesia is a strategic at least because of four reasons. First, this sector is the largest contributor in Indonesian Gross Domestic Product (GDP). The segment of this sector in GDP 2011 contributed 24.3 percent. Second, this sector absorbs high employment, after the farming and the trading, hotel and restaurant, as well as service sectors. Third, this sector is the main contributor in the total non-oil and gas export. About 38 percent of the total export value or about 46 percent from the non-oil and gas total export in 2011 arise from manufacturing sector. Fourth, manufacturing sector has high backward lingkage and forward linkage to other sectors. The linkage of this sector to other sectors, both the backward and the forward linkage are above the average within all sectors.

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Figure 1. Manufacturing Sector Segment in GDP

Figure 2. Non-Oil and Gas Export Structure

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The growth of manufacturing sector prior economic crisis of 1998 is relatively high 9.2% (yoy) within the period of 1991-1998. However, the average growth declines after the 1998 crisis, which only reached the amount of 4.6% (yoy) in 2001-2011. Moreover, since 2004 the

The Dinamics of Total Factor Productivity Ofmedium and Large Manufacturing In Indonesia

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growth slowed down but started to increase in 2010 and 2011. In general, the contribution of manufacturing sector to economic growth declined in 2004-2009.

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Figure 3. Manufacturing Sector’s Growth

Considering the importance of manufacturing sector above, it is important to further analysis the productivity of this sector, particularly the viability of its output. The use of Total Factor Productivity (TFP) term on this paper includes the productivity of all production factors; hence we do not analyze the productivity of individual factors as commonly found in many literatures. The first aims of this research is to calculate the Total Factor Productivity (TFP) of the large and medium scale manufacturing companies in Indonesia; second, this paper will identify the determinant of manufacturing productivity; and third, to analyze the technical change and the efficiency change at subsector level. With these aims, we expect to be able to identify the potential and the risk of manufacturing sector performance, as well as the policy required to support it. The second section of this paper will discuss on the theory, the third section will discuss on the data and methodology, whereas part four will discuss on the result and analysis. The conclusion will be provided in the fifth section and will close the paper presentation.

II. THEORY 2.1. The Concept of Productivity and Efficiency The economic performance of a company can be reflected from the level of its efficiency and productivity, that is the ratio of output towards input. The larger the output to input ratio, the higher the performance of the firm will be. If the production process involves more than

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one input, we need to aggregate the input with certain method and make index in order to calculate the productivity ratio. The same case is required when the company produce multiple outputs. This performance measure is a relative indicator across period or across competitors. We need to clarify some terms related to productivity and efficiency; and the first one is productivity. Productivity means the ratio of produced output towards the used input. This productivity is reflected in the slope in a certain production point (case of one output y; and one input x). As shown in figure 5. Company B has higher productivity than Company A.



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Figure 4. Illustration of Productivity

The second term is production frontier. The curve of production OF’ in Figure 5 shows the amount of maximum output which can be produced in each input level. In other word the production curve reflects the level of the use of technology by the company. The third term is efficiency. This is a comparison of the output of certain company towards the maximum output produced by other companies using same set of input. The company is considered to be efficient when it operates exactly on the production line (frontier), which



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Figure 5. The Illustration of Efficiency

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is at the point of B and C. On the contrary, it is considered to be inefficient if the company operates under its frontier, which is at the point of A. At point A, the company still can improve its efficiency to point B without additional input. In Figure 5. we can measure the A efficiency of Company A as AA’/BA’. We can use Figure 6 to distinguish efficiency and productivity. As explained above, the productivity level is depicted by the slope of the straight line from point O. According to Figure 6, both Company A and B have equal productivity, however, the efficiency Company A is lower than Company B. On the other hand, Company B and C have the same efficiency; however, the productivity of Company C is larger than Company B. Thus, companies who have the same productivity do not necessarily have the same efficiency, and the companies which equal efficiency do not always have equal productivity.

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Figure 6. The illustration of Efficiency and Productivity Comparison

Allocative efficiency is another term. This is a combination of input composition which produces output with minimum cost or yield maximum income. We can measure the allocative efficiency only if we know the value of those inputs cost. We also know the term of technical

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Figure 7. Illustration of Technical Change

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change, which can be estimated by how much the production frontier shift say from one period to another. Figure 7 shows the technical change shifts from F0’ to F1’ Another important term is economies of scale. We can calculate this only if we release the assumption of Constant Return to Scale; hence Variable Return to Scale. The value of economies of scale is the distance between CRSand VRS. Figure 8 illustrates the economies of scale (grey area) which is located between OB curve (Production Frontier under CRS) and OF’ (Production Frontier under VRS). The optimal economic scale is the point where the company operates in VRS frontier (OF’) with the highest productivity, compared to other companies within the same OF’ curve. On this figure, the Company C is in its optimal scale.

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Figure 8. Illustration of The Economical Scale

The last term to clarify is Total Factor Productivity (TFP). This is a productivity which includes all factors of production2 and can be decomposed into efficiency, technical change, and economic scale. Thus, the concept of TFP used on this paper is different from common method that measure TFP from residual (technology) in production function with capital and labor as primary inputs.

2.2. Productivity and Efficiency Measurement We have three options to measure efficiency; input oriented, output oriented, and distance function. Using input oriented measure, we target certain output then minimize the use of input. Within this method, the most essential variable to observe is input. On the other hand, the output oriented method target certain level of input and then maximize the output.

2 Including calculating all of the output in the case of multiple-output production.

The Dinamics of Total Factor Productivity Ofmedium and Large Manufacturing In Indonesia



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Figure 9. Input Oriented

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Figure 10. Output Oriented

The third method, distance function, is commonly used on academic literature. Before we explain this method in details, we firstly outline the production technology, which explain the multiple-output production technology. The representation of technology set may refer to Coelli (2005) following Fare and Primont (1995), Let S be the technology set, while x and q represent Nx1 input vector and Mx1 output vector. The vector value is non-negative real numbers in nature. Technology set below consists of both input and output vectors (x,q) in which x produces q. S = {(x,q): x can generateq} Production technology can be represented with output and input set as follows.: a) Output Sets, P(x), is an array of output vector, q, which can be produced using input vector, x. Output set will be our base to construct production possibility curve (PPF) with two output. P(x) = {q: x can generate q} = {q : (x,q) ∈ S} b) Input Sets, L(q), is an array of input vector, x, which generate certain output vector, q. L(q) = {x : x can generate q} = {x : (x,q) ∈ S} Without losing generality, we can explain multi-output technology with one input (x1) and two outputs (q1 and q2). The input is function of these two outputs: x1 = g(q1, q2) The combination of two outputs produced by using certain level of input is our production possibility curve (PPC). When the PPC curve is tangential to isorevenue curve, we have the output combination which maximizes the revenue. The optimum point which produce maximum

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revenue is point A, where the slope of isorevenue line (-p1/p2) is equal to the slope of PPC curve.

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Figure 11. Production Possibility Curve and Maximum Income

In the case of multiple outputs, technical change may alter the production of certain output relative to other output in two ways. From graphic below, we distinguish between neutral dan non-neutral technical changes.

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Figure 12. Technical Change Bias and Production Possibility Curve

We can apply the distance function both on output and input. The distance function for a company operating in point A is the ratio of OA/OB (see Figure 13 and 14). Distance function equal 1 (one) means the company already operates in PPC which correspond to certain isoquant, L(q).

The Dinamics of Total Factor Productivity Ofmedium and Large Manufacturing In Indonesia

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Figure 14. Input Distance Function

2.3. Malmquist Productivity Index The term of productivity in this paper refers to the Total Factor Productivity (TFP) of multifactors and not the partial factor productivity, such as labor productivity or capital productivity. Partial measurement can be misleading when we measure the performance of a company. When a company produces multiple outputs and multiple inputs, we can use the profitability indicator, which is the ratio of total income against total cost from input. For two companies, the TFP is measured by comparing the profit of the two companies. After using the price of output and input, we can derive a simplified productivity measure as equation below. Here we compare the productivity of the two companies using the real output and the real input.

ߨ͸‫ݍ כ‬͸ Τ‫ݔ‬͸ ൌ  ߨͷ‫ݍ כ‬ͷ Τ‫ݔ‬ͷ We can analyze the dynamics of productivity across period. Two periods comparison will involve 2 (two) production technology sets, Ss and St; both for period s and t. Each technology set relates to the vector of output qs and qt, as well as the vector of input xs and xt. Common approach to do this is Malmquist Productivity Index (MPI), and we will apply this method on this paper. Malmquist Productivity Indexwas firstly introduced by Caves, Christensen and Diewert (1982); a distance function method for representing technology in order to define families of input, output and productivity indexes. For the output produced in period sand period t, there is a technology that can produce maximum output using xs and xtinput. For example, if company produce 80% of its maximum capacity using the input vector xs, andin period t he can produce

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output 30% above maximum capacity using input vector xt, then the change of productivity from period s to t is 1.30/0.80 = 1.625. The calculation of MPI with using the technology reference in period s is:

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If the company technically efficient (efficient) in both periods, then dos(qs, xs)= 1, thus:

݉଴ ௦ ሺ‫ݍ‬௦ ǡ ‫ݍ‬௧ ǡ ‫ݔ‬௦ ǡ ‫ݔ‬௧ ሻ ൌ ݀‫ ݋ݏ‬ሺ‫ ݐݍ‬ǡ ‫ ݐݔ‬ሻ If we calculate MPI using the technology reference in period t, we use the following formula:

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With both periods MPI in hand, we can calculate the Malmquist TFP Index (MTFPI) as geometrical average from both indexes as follows:

݉଴ ሺ‫ݍ‬௦ ǡ ‫ݍ‬௧ ǡ ‫ݔ‬௦ ǡ ‫ݔ‬௧ ሻ ൌ ሾ݉଴௦ ሺ‫ݍ‬௦ ǡ ‫ݍ‬௧ ǡ ‫ݔ‬௦ ǡ ‫ݔ‬௧ ሻ ൈ ݉଴௧ ሺ‫ݍ‬௦ ǡ ‫ݍ‬௧ ǡ ‫ݔ‬௦ ǡ ‫ݔ‬௧ ሻሿ଴Ǥହ  MTFPI can be decomposed to 2 (two) components; efficiency change and technical change. By using output orientated measure, the decomposition of MTFPI is:

݀௢௦ ሺ‫ݔ‬௧ ǡ ‫ݍ‬௧ ሻ ݀௢௧ ሺ‫ݔ‬௧ ǡ ‫ݍ‬௧ ሻ ቉ ‫ ܫܲܨܶܯ‬ൌ  ቈ ௧ ൈ ݀௢ ሺ‫ݔ‬௦ ǡ ‫ݍ‬௦ ሻ ݀௢௧ ሺ‫ݔ‬௦ ǡ ‫ݍ‬௦ ሻ

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In reality, a company often operates inefficiently, therefore dos(qs, xs) ≤ 1 and dot(qt, xt)≤ 1. When the company is inefficient, the MTFPI can be calculated as:

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݀௢௧ ሺ‫ݔ‬௧ ǡ ‫ݍ‬௧ ሻ  ݀௢௦ ሺ‫ݔ‬௦ ǡ ‫ݍ‬௦ ሻ ଴Ǥହ

݀௢௦ ሺ‫ݔ‬௧ ǡ ‫ݍ‬௧ ሻ ݀௢௦ ሺ‫ݔ‬௦ ǡ ‫ݍ‬௦ ሻ ቉ ݄݈ܶ݁ܿ݊݅ܿܽ‫ ݄݁݃݊ܽܥ‬ൌ ቈ ௧ ൈ ݀௢ ሺ‫ݔ‬௧ ǡ ‫ݍ‬௧ ሻ ݀௢௧ ሺ‫ݔ‬௦ ǡ ‫ݍ‬௦ ሻ

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The last MTFPI equation above can be decomposed into 2 components. The first component evaluates the efficiency changes between period s and t, while the second component in brackets estimates the changes of technology between the 2 periods. Figure below illustrate productivity changes measures. It is assumed that a company has a constant return to scale production characteristic with one input and one output. In period s, the company operates in point D and move to E point in period t; both points are inefficient. Efficiency and technical change correspondingly are:

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Figure 15. Malmquist Productivity Index

Considering that the calculation of MTFPI is based on CRS assumption, then there are only two sources of productivity growth; efficiency change and technical change. However, under variable returns to scale assumption, along with these two productivity sources, there are also operating scale and efficiency scale. The weakness of MTFPI was covered later by Grifell-Tatje and Lovell (1999) with generalised Malmquist Productivity Index by internalizing the efficiency scale.

2.4. Data Envelopment Analysis Data Envelopment Analysis (DEA) isa data oriented approach, and is used to evaluates the performance of a set of entity called as DMU (Decision Making Units) which convert multiple inputs to multiple outputs. The production frontier estimation with some numbers of homogenous DMU uses non-parametric mathematical programming approach.

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The first frontier estimation with piecewise-linear convex hull approach is pioneered by Farrell (1957). Further development was carried out by Boles (1966) and Afriat (1972) using mathematical programming method on frontier estimation. However, the DEA term become popular after the work of Charnes, Cooper, and Rhodes (1978), who used an input oriented model under Constant Return to Scale (CRS) assumption. After that, Banker, Charnes, and Cooper (1984) modify this model using Variable Return to Scale (VRS) assumption. Let there are N input and M output for each I companies. Each company is represented by the vector of column xi and qi. The NxI input matrix X, and MxI output matrix Q, represents the data for all of the companies. DEA model uses ratio; for each company, we need to calculate the ratio of aggregate output toward aggregate input. In its aggregation, we use weight where the optimum weight will be determined using mathematical programming. DEA model in the form of Fractional Program (FP) is specified below:

݉ܽ‫ݔ‬௨ǡ௩ ሺ‫ݑ‬Ԣ‫ݍ‬௜ Ȁ‫ݒ‬Ԣ‫ݔ‬௜ ሻǡ ‫ݑݐݏ‬Ԣ‫ݍ‬௜ Ȁ‫ݒ‬Ԣ‫ݔ‬௝ ൑ ͳ ‫ݑ‬ǡ ‫ ݒ‬൒ Ͳǡ ݆ ൌ ͳǡʹǡ ǥ ǡ ‫ܫ‬ Optimum weight u and v in the FP above is obtained by maximizing efficiency subject to the condition that efficiency value is less than or equal one. The problem arise from the FP above is infinite solutions. Thus, the model in the form of FP above is converted into Linear Programming (LP) as follows:

݉ܽ‫ݔ‬ఓǡ௩ ሺߤԢ‫ݍ‬௜ ሻǡ ‫ݒݐݏ‬Ԣ‫ݔ‬௜ ൌ Ͳǡ ߤԢ‫ݍ‬௝ െ ‫ݒ‬Ԣ‫ݔ‬௝ ൑ Ͳǡ ߤǡ ‫ ݒ‬൒ Ͳǡ ݆ ൌ ͳǡʹǡ ǥ ǡ ‫ܫ‬ The weight notation for FP and LP is distinguished to differentiate its mathematical programming form. We can solve the formulation of DEA model in LP above, however, the constraint will increase as the number of company is,and thuswe need to specify the LP form above into Dual Programming (DP). In dual programming, the number of constraints will not increase following the number of companies but rather only adding the variables to solve.

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݉݅݊ఏǡఒ ߠǡ ‫ ݐݏ‬െ ‫ݍ‬௜ ൅ ܳߣ ൒ Ͳǡ ߠ‫ݔ‬௜ െ ܺߣ ൒ Ͳǡ ߣ ൒ Ͳǡ We can specify the dual-programming using output orientated approach below (remaining of this paper will use this):

݉ܽ‫ݔ‬థǡఒ ߶ǡ ‫ ݐݏ‬െ ߶‫ݍ‬௜ ൅ ܳߣ ൒ Ͳǡ ‫ݔ‬௜ െ ܺߣ ൒ Ͳǡ ߣ ൒ Ͳǡ On the DEA Constant Return to Scale (CRS), it assumes that all of the DMU operates on the optimum economic scale. However, the existence of imperfect competition, financial limitation, etc., make the DMU cannot operate on optimum economic scale. To deal with this we can use DEA model under assumption of Variable Return to Scale (VRS). CRS model is not starkly different with the VRS model except for the addition of convexity constraint (I1’λ = 1). Below is the DEA model under VRS assumption:

݉ܽ‫ݔ‬థǡఒ ߶ǡ ‫ ݐݏ‬െ ߶‫ݍ‬௜ ൅ ܳߣ ൒ Ͳǡ ‫ݔ‬௜ െ ܺߣ ൒ Ͳǡ ‫ͳܫ‬ᇱ ߣ ൌ ͳ ߣ ൒ Ͳǡ The economic scale estimated from the above model does not indicate whether the company is increasing or decreasing returns to scale. For this reason we impose a non-increasing return to scale (NIRS) restriction to DEA model. If the technical efficiency in NIRS model is different from technical efficiency in VRS model (TE NIRS ≠ TE VRS), then we may conclude it as Increasing Return to Scale (IRS). On the other hand, if the TE NIRS isequal with TE VRS then the case is Decreasing Return to Scale (DRS).

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݉ܽ‫ݔ‬థǡఒ ߶ǡ ‫ ݐݏ‬െ ߶‫ݍ‬௜ ൅ ܳߣ ൒ Ͳǡ ‫ݔ‬௜ െ ܺߣ ൒ Ͳǡ ‫ͳܫ‬ᇱ ߣ ൑ ͳ ߣ ൒ Ͳǡ

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Figure 16. Ilustration on Non-Increasing Returns to Scale

Estimation of dual programming model does not always provide the optimum efficiency point. To ensure the solution of the model provide us optimum efficiency, we can use the following model with slack variable:

݉ܽ‫ݔ‬ఒǡைௌǡூௌ െ ሺ‫ͳܯ‬ᇱ ܱܵ ൅ ܰͳᇱ ‫ܵܫ‬ሻǡ ‫ ݐݏ‬െ ߶‫ݍ‬௜ ൅ ܳߣ െ ܱܵ ൌ Ͳǡ ‫ݔ‬௜ െ ܺߣ െ ‫ ܵܫ‬ൌ Ͳǡ ߣ ൒ Ͳǡ ܱܵ ൒ Ͳǡ ‫ ܵܫ‬൒ Ͳǡ

φ is parameter estimated from step one; OS is Mx1vector of output slacks; IS is Nx1vector of input slacks; while M1 and N1 are column vector of ones with dimension of Mx1 and Nx1consecutively.

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Figure 17. Ilustration of Slack (input oriented)

In DEA, the estimation of Total Factor Productivity use index. Simple illustration, if a certain company can produce the same output in period t and t+1, but in period t+1 only use 75% of the input period t, then the TFP index will increase by 1/0.75=1.3. Similarly, if the company uses the same input in both periods, but produces a 30% higher output at period t+1 compared to period t, then the TFP index will be 1.3. Beside the MTFPI explained in depth above, there are two other TFP index; Hicks-Moorsteen TFP (HM TFP) Index, and TFP Index based on the Profitability Ratio. The earlier illustration use HM TFP index with the following formula:

HMTFP Index =

Growth in Output Growth in Intput

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Output Quantity Index Input Quantity Index

However, HM TFP index cannot explain the sources of the productivity growth (technical change, efficiency change), and does not account for the price effect. On the other hand, the second approach (Profitability Ratio) estimates the TFP index using revenues and costs (price adjusted between period s and t). Similar with HM TFP index, Profitability Ratio approach also neglect the price effects. For these reason, we will use Malmquist TFP Index (MTFPI).

2.5. Research on the TFP of Manufacturing sector in Indonesia There are some numbers of studies on productivity and efficiency in Indonesia. Generally we can classify those researches based on their approach; Data Envelopment Analysis (DEA) and Stochastic Frontier Approach (SFA). The first approach is non-parametric, while the latter is parametric.

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In the SFA group, Ikhsan (2007) examined the TFP growth and changes in technical efficiency in Indonesian manufacturing industry during the period of 1988-2000. By using Medium and Large Industry Statistic (SIBS), the study concluded that the average TFP growth was 1.55 percent. The contributors of the TFP growth mainly came from technical progress of about 1.89 percent, while the contribution of economic scale and technical efficiency are-0.13% and -0.21% respectively. On technical efficiency changes, Ikhsan found a visible learning by doing process in technology adoption because the company is not operating at its maximum production capacity. The National Development Planning Agency or Bappenas (2010) apply Ikhsan method using 2000-2007 data also from SIBS. They found average productivity growth was approximately 0.22 percent. This productivity growth is lower relative to the productivity growth before the crisis 19983. After slowed down during 2000-2004 probably due to post-crisis consolidation, the growth of industrial productivity started to increase 2004-2007. Bappenas found the major contributor of the productivity increase was technical efficiency growth. On the other hand, the growth of technology and economies of scale contributed negatively to the TFP, respectively -0.17% and -0.45%. In 2-digit level disaggregation in ISIC, the Chemical sector recorded the highest TFP growth by averagely 0.21% per year, followed by Non-Metallic Mineral sector (0.14%) and Food and Beverage sectors (0.09%). The lowest productivity growth was in Wood Industry (-1.18%), Other manufacturing (-0.31%), and Textiles sector (-0.08%). Prabowo and Cabanda (2011) examined the productivity of Indonesian manufacturing companies listed in Indonesia Stock Exchange 2000-2005 period. Using stochastic frontier approach (SFA), Prabowo and Cabanda found technical inefficiency in the those companies. The average technical eficiency was 0.7149, showing the company operated below its frontier. Meanwhile, Saputra (2011) and Halim (2010) examined the productivity of the industrial sector using DEA method. Saputra examine the level of technical efficiency of industrial companies in Indonesia. By using the data in the UNIDO 3-digit ISIC level, they concluded that there are 5 sub-sectors with highest efficiency, Tobacco; Iron and Steel; Transportation Equipment; Non-Ferrous Metal; and Chemistry. In general, the efficiency of basic industry was higher than the traditional industry in the category of low and high-tech industry. Nevertheless, the latter industry showed higher efficiency in the last 2 years of their observation. Halim (2010) specifically examined the marketing productivity and the profitability of the company in Indonesia. The inputs used in this study were limited to equity and marketing expenses. DEA method was applied to the five categories of industrial manufacturing company listed on the Stock Exchange during 2001 to 2007; Food and Beverage; Clothing and Textile Product; Plastics and Articles of glassware; Automotive Products; and Pharmaceuticals. The 3 Period1988-1992 and 1993-1996.

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main conclusion of this study is the highest productivity of marketing was in 2005-2006, and the major contributor was technological efficiency. A total of 44 companies were identified operating at their efficient level. Based on their categories, Automotive had the highest productivity and technical efficiency. The TFP for efficient company was positively related to Return on Assets (ROA), reflecting the higher efficiency of marketing productivity, the higher financial performance will be.

III. METHODOLOGY 3.1. Methodology On this paper, the estimation of TFP growth and its components is based on the Malmquist Indexand the application of DEA-Dual Programming method. The components of Total Factor Productivity include technological changes, change of efficiency and change of economic of scale, across companies, sub-sector, and across year. Output Oriented Malmquist DEA model can be defined as follows:

݉ܽ‫ݔ‬థǡఒ ߶ǡ ‫ ݐݏ‬െ ߶‫ݍ‬௜ ൅ ܳߣ ൒ Ͳǡ ‫ݔ‬௜ െ ܺߣ ൒ Ͳǡ ߣ ൒ Ͳǡ Where ϕ is a proportional increase in output produced by company i, given constant input; λ is the weight for each input or output; q is the output of company i; and Q is the output of remaining companies. On the other hand, x is the input company i, while X is the input for remaining companies. The Malmquist TFP index is defined as follows:

݉଴ ሺ‫ݍ‬௦ ǡ ‫ݍ‬௧ ǡ ‫ݔ‬௦ ǡ ‫ݔ‬௧ ሻ ൌ ሾ݉଴௦ ሺ‫ݍ‬௦ ǡ ‫ݍ‬௧ ǡ ‫ݔ‬௦ ǡ ‫ݔ‬௧ ሻ ൈ ݉଴௧ ሺ‫ݍ‬௦ ǡ ‫ݍ‬௧ ǡ ‫ݔ‬௦ ǡ ‫ݔ‬௧ ሻሿ଴Ǥହ Furthermore, the components of Total Factor Productivity Malmquist are derived from the breakdown of Malmquist index, as follows:

ܶ‫ ݄݁݃݊ܽܥݕ݂݂ܿ݊݁݅ܿ݅ܧ݈ܽݐ݋‬ൌ 

݀௢௧ ሺ‫ݔ‬௧ ǡ ‫ݍ‬௧ ሻ  ݀௢௦ ሺ‫ݔ‬௦ ǡ ‫ݍ‬௦ ሻ ଴Ǥହ

݀௢௦ ሺ‫ݔ‬௧ ǡ ‫ݍ‬௧ ሻ ݀௢௦ ሺ‫ݔ‬௦ ǡ ‫ݍ‬௦ ሻ ቉ ݄݈ܶ݁ܿ݊݅ܿܽ‫ ݄݁݃݊ܽܥ‬ൌ ቈ ௧ ൈ ݀௢ ሺ‫ݔ‬௧ ǡ ‫ݍ‬௧ ሻ ݀௢௧ ሺ‫ݔ‬௦ ǡ ‫ݍ‬௦ ሻ

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‫ ݄݁݃݊ܽܥݕ݂݂ܿ݊݁݅ܿ݅ܧ‬ൌ 

௧ ݀௢௩ ሺ‫ݍ‬௧ ǡ ‫ݔ‬௧ ሻ  ௦ ݀௢௩ ሺ‫ݍ‬௦ ǡ ‫ݔ‬௦ ሻ ଴Ǥହ

௧ ௧ ௦ ௦ ሺ‫ݍ‬௧ ǡ ‫ݔ‬௧ ሻȀ݀௢௖ ሺ‫ݍ‬௧ ǡ ‫ݔ‬௧ ሻ ݀௢௩ ሺ‫ݍ‬௧ ǡ ‫ݔ‬௧ ሻȀ݀௢௖ ሺ‫ݍ‬௧ ǡ ‫ݔ‬௧ ሻ ݀௢௩ ൈ ቉ ‫ ݄݁݃݊ܽܥ݈݁ܽܿܵܿ݅݉݋݊݋ܿܧ‬ൌ ቈ ௧ ௦ ௦ ௧ ݀௢௩ ሺ‫ݍ‬௦ ǡ ‫ݔ‬௦ ሻȀ݀௢௖ ሺ‫ݍ‬௦ ǡ ‫ݔ‬௦ ሻ ݀௢௩ ሺ‫ݍ‬௦ ǡ ‫ݔ‬௦ ሻȀ݀௢௖ ሺ‫ݍ‬௦ ǡ ‫ݔ‬௦ ሻ



3.2. Data, Variable, and Proxy The main data used in this study is the Survey of Large and Medium (Survei Industri Besar dan Sedang, SIBS) published by the Central Bureau of Statistics (BPS). The period covers 20002009. Each company (KIPN) is grouped based on 3-digit ISIC code. For each company, we use the following set of variables: output, capital, labor, raw materials, and energy. We can calculate the output of the firm based on their production or sales. This study use the first proxy, since the firm use all resources (capital, labor, raw materials and energy) to produce a number of outputs despite of being sold or stored as inventory. The production value will be deflated using Indonesian wholesale price index correspondingly for each sub sector. The proxy for capital is estimated fixed or durable asset including land, buildings, machinery, vehicles, and other durable asset. Some missing capital data during the survey is estimated. We use capital at year t to estimate the capital for other years, using the firm investment (purchase or maintenance), the value of sales, and depreciation (assuming equal to 14%) during those missing period. To get real capital data, we use Gross Domestic Fixed Capital Formation (GFCF) deflator to deflate the nominal capital. For employment data, we consider the use of working hours to be appropriate, since same amount of labor in a firm may generate different output when their working hours change (due to overtime or temporary production stops). However, due to a lack of data, this study used the number of workers as proxy for employment. The intermediate input included raw materials and supporting materials originated both from domestic and import. This intermediate input is deflated using wholesale price index, which is assumed to be equal across companies. For energy data, we use of fuel and lubricants as well as electricity. Both of these energy were deflated before aggregating them to one energy composite. We deflate fuel and lubricants using their corresponding wholesale price index (premium, kerosene, diesel fuel, diesel oil, fuel oil, and lubricants). For electricity we use sectoral GDP for electricity as deflator. Our data covers 49 subsectors with the total of 3,295 firms. The summary of variable and the distribution of sample is provided on Table 2 and Table 3.

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TFP growth, then its decomposition. ������first part of this chapter analyzes the � ��� The �� aggregate ��� ����� ������������������ � ��� ������������������������� In��� addition to analyzing the entire period ��� (2000-2009), we analyze tow period, 2000-2004 � ��� ����������������������� � ��� ��������������������������������������������� and 2005-2009. Analysis on aggregate industry will proceed to a more detail across subsector � ��� ����������� �� ��� ����������������������� of��� the ���������������������� industry. The analysis on technical change and efficiency change in sub sector level is �� ��� ��� ���������������������������������������� carried out by plotting them into four quadrants. �� ��� ��������������� ��� ��� ����������������������������������������������

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Averagely the TFP grew by 7.44% per annum during 2000-2009. The major sources of TFP growth was dominated by the growth of technical change, and then followed by economic scale change, and finally the efficiency change. This result strongly indicate that during the period of 2000-2009, the companies relied more on the use of new technologies and moved toward optimal scale. ������� ��������������������������������������������������� �������

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The Dinamics of Total Factor Productivity Ofmedium and Large Manufacturing In Indonesia

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The strong efficiency change in 2000-2004 was associated with ongoing consolidation after the financial crisis 1998. This included the improvement of investment climate to raise the investor’s confidence. As the domestic demand and investment activity was weak, the companies improved their productivity by increasing the production efficiency. The company improved the use of intermediate input, improving production layout to shorten the switching between work stations, aligning the workflow across workplaces (the concept of pull systems) to reduce the accumulation of half finished product between work stations, and the application of Lean Manufacturing conceptsto reduce the idle time between work stations. During 20002004, the slowed down technical change means the decline of production frontier, due to the declining production capability of the machines. One possible reason is disturbance on machine replacement as indicated by the low growth of investment (GFCF/PMTB) and the low realization of investment (both FDI and domestic). In contrast, during 2005-2009 period, technical change play greater role on TFP growth. The average growth rate of investment and the realization of domestic and foreign investment were higher compared to the previous period (Figure 18 and 19). The increase of aggregate investment and the realization of domestic and foreign investment generally bring new technologies. During this period, though the technical change increased, the efficiency change or catching up effects decreased. Similar studies on productivity in other countries explain the reason for the decline of catching up effect when technical change increaseis the lack of human resource capacity on adapting the new technologies. The inability to catch-up indicates a low labor competency, either because of the low education and low skill. This weakness may affect the ability of manufacturing sector to operate optimally. In the long run, this may cause the foreign investment in Indonesia is limited to the low technology one.

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Bulletin of Monetary, Economics and Banking, January 2014

280

Unlike Indonesia, the increase of technical change in Malaysia, was not followed by a decrease in efficiency change (Figure 20). The rank of Global Competitiveness Index during 2012-2013 for Indonesia was apparently far behind Malaysia, especially for the fourth pillar (basic health and education) and fifth pillar (secondary education and training). This may explain the difference of the TFP dynamics over the two economies (Table 5).

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The Dinamics of Total Factor Productivity Ofmedium and Large Manufacturing In Indonesia

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4.2. TFP and Its Component Across Manufacturing Subsector The model calculation show the total factor productivity of all subsectors grew during 2000-2009, except for Lamp industry (Table 6). Technical change became the major source for the growth of TFP in most subsectors (about 75 percent of all 49 subsectors we observed). On the other hand, efficiency change play greater role on the TFP growth in the following subsectors: Spinning; Other foods, Leather goods, Footwear; Glass; Clay product; Building and Construction; Equipment and Components of four or more wheel vehicles; and Other Manufacturing. Five subsectors with the highest TFP average growth during 2000-2009 are generally classified in high-tech industry. They are Medical Equipment; Other Electrical Equipment; Electric Motors and Engines; Heavy equipment;and Electricity and Telephone Cables. The source of productivity growth for these subsectors is technical change. ������� ������������������������������������������������������������������������������� �������������������

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We map subsector of industry into four quadrants based on their level of technical ���� ���� ����� ����� efficiency change (positive or negative). ���� ���� Quadrant ���� I, includes ���� subsectors with high technical change and positive efficiency change. This quadrant be ��������������������������� ����� ����� ����� should ����� ���������������������� ���� ����� for high productivity sub sectors, and are considered as supreme sub���� sector. ����� ������������������ change (high and low4), and their ����������������������������������

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The Dinamics of Total Factor Productivity Ofmedium and Large Manufacturing In Indonesia

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Our calculation shows that for the period of 2005-2009 (Table 8), the number of subsectors in first quadrant decline relative to previous period, 2000-2004 (Table 7). In contrast, the number of sub-sectors in quadrant II increased across the two corresponding periods. The decreasing number of industry sub-sectors in quadrant I and the growing number of subsectors in quadrant II indicates a lack of development and innovation on managerial (working procedures) along the production process. This affects the firm in two aspects, first, the ability of manufacturing sector to operate at its potential level; and second, the ability of the labor to adapt increasing technology. The above condition is unfortunate considering the high technical change potentially offer higher productivity. One way to overcome this problem is by developing the skill of the workers to cope with higher technology.

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The mapping of subsector onto the four quadrants may depend on the characteristic of the firm within the subsector industry. Some variables gathered from the Survey of Medium and Large Industry (SIBS) include: the intensity of research and development activities (R&D); innovation; sales orientation; location of the company; the use of foreign investment facility; type of ownership; and years of schooling.These variables are useful to explain our mapping; though it requires formal modeling and statistical testing, which is beyond the scope of this paper. Table 9 below summarizes the characteristic of the firm within quadrant. We recall that in terms of technical change and efficiency change, quadrant I is better than quadrant II and III, and quadrant II or III is better than quadrant IV. However, apple to apple comparison between quadrant II and III is not valid. We see some interesting evident from the survey; first, the use of foreign investment facility is associated with higher technical change and also higher efficiency change, which lead them to quadrant I. Second, similar pattern apply when the company is partially or fully owned by foreign. Third, if the company is located in industrial area, the likelihood to have better infrastructure support increase. On the other hand the motivation for companies to learn from each other is greater. These will help them increase their productivity; hence put them more likely in quadrant I, II, then quadrant III.

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V. CONCLUSION This paper provides several interesting result. First the TFP of medium and large scale companies in Indonesia grew 7.44% on average during the year of 2000-2009. The main source of the TFP growth is technical change, followed by economic scale change, and finally efficiency change. During the period of 2000-2004, the source of TFP growth was efficiency change, while for the period of 2005-2009, the source of TFP growth shifted into technical change, along with the increasing investment activity.This is the second conclusion of this paper. Third, even though the technical change increased, the catching-up capability (efficiency) decreased in 2005-2009, showing the inability of the company to adapt the more advance technology.

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REFERENCES Boediono, DR., “Seri Sinopsis Pengantar Ilmu Ekonomi No. 1 Ekonomi Mikro”, BPFE, edisi 2, 1999. Coelli, Timothy J., et al. (2005), “An Introduction To Efficiency And Productivity Analysis”, Springer Science & Business Media, Inc, edisi kedua. Coelli T.J. (1996), “A Guide to DEAP Version 2.1: A Data Envelopment Analysis (Computer) Program”, Centre for Efficiency and Productivity Analysis (CEPA), Department of Econometrics University of New England, Australia. Cooper, William W.; Seiford, Lawrence M. and Tone, Kaoru, (2007). “Data Envelopment Analysis”, 2nd Edition. Springer. Direktorat Evaluasi Kinerja Pembangunan Sektoral, (2010), “Perubahan Produktivitas Industri Manufaktur Indonesia dan Faktor-faktor yang Mempengaruhinya: Analisis Panel Data 20002007”, Kementerian PPN/Bappenas. Halim, Rizal Edy, (2010), “Marketing productivity and profitability of Indonesian public listed manufacturing firms: An application of data envelopment analysis (DEA)”, Benchmarking: An International Journal, Vol. 17 Iss: 6 pp. 842-857. Kumbhakar, Subal C. and Lovell, C.A.Knox, (2004). “Stochastic Frontier Analysis”. Cambridge University Press. Ikhsan, Mohamad (2007), “Total Factor Productivity in Indonesian Manufacturing: A Stochastic Frontier Approach”, Global Economic Review Vol. 36, No. 4, pp. 321-342. Mahadevan, Renuka (2002), “A DEA Approach to Understanding the Productivity Growth of Malaysia’s Manufacturing Industries”, Asia Pacific Journal of Management, 19, 587-600. Pindyck, Robert S., dan Daniel L. Rubinfeld, “Microeconomics”, edisi ke-4, Prentice-Hall, 1998. Prabowo, Handono E.T., dan Cabanda, Emilyn (2011), “Stochastic Frontier Analysis of Indonesian Firm Efficiency: A Note”, International Journal of Banking and Finance, Vol. 8; Iss. 2, Article 5. Saputra, Putu Mahardika Adi, (2011), “Analysis of Technical Efficiency of Indonesian Manufacturing Industries: An Application of DEA”, International Research Journal of Finance and Economics, Issue 66.

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a. Book:

Hanke John E. and Arthur G. Reitsch, (1940), Business Forecasting, Prentice-Hall, New Jersey.

b. Article in journal:

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

c. Article in book edited by other people:

Frankel, Jeffrey A. and Andrew K., Rose. (1995) “Empirical Research on Nominal Exchange Rates”, in Gene Grossman and Kenneth Rogoff, eds.,”Handbook of International Economics. Amsterdam: North-Holland, page. 397-416.

d. Working papers:

Kremer, Michael and Daniel, Chen. (2000) “Income Distribution Dynamics with Endogenous Fertility”. National Bureau of Economic Research (Cambridge, MA) Working Paper No.7530.

e. Mimeo or unpublished work:

Knowles, John. “Can Parental Decision Explain U.S. Income Inequality?”, Mimeo, University of Pennsylvania, 1999.

f. Article from web or other electronic form:

Summers, Robert and Alan W., Heston. (1997) “Penn World Table, Version 5.6” http://pwt.econ.unpenn.edu/

g. Article in newspaper, magazine or equal periodicals:

Begley, Sharon. (1993) “Killed by Kindness”, Newsweek, April 12, page. 50-56.

9. The paper should be submitted along with curriculum vitae complete with mail address and phone number.

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