Indonesia: Selected Issues

Abstract

Indonesia: Selected Issues

Analysis of Macro-Financial Linkages in Indonesia1

Macro-financial linkages in Indonesia are analyzed using two complementary approaches: a sector-level balance sheet analysis and a panel vector autoregression approach. These analyses confirmed the importance of external funding in Indonesia, particularly through nonfinancial corporations (NFC). In this connection, negative external shocks could propagate through NFCs to the domestic banking system, which replaces some of the reduction in NFC’s foreign financing. In addition, we empirically identify linkages among global risk sentiment, economic activity, bank credit and deposits, and the exchange rate that warrant close monitoring.

A. Introduction

1. This paper presents evidence about macro-financial linkages in Indonesia using two complementary approaches. The first approach is the Balance Sheet Analysis (BSA) which extracts information from annual data on sector-level balance sheets. Within this approach, we demonstrate four different ways of exploiting the same set of data. The second approach is a panel vector autoregression approach which relies on a combination of macroeconomic data and bank-level balance sheet data.

B. Balance Sheet Analysis

2. Sectoral balance sheet data are used to construct a balance sheet matrix that supports a range of different balance sheet analyses (BSA). A BSA matrix provides a snapshot of outstanding gross and net balance sheet positions (stocks) of each sector in the economy vis-à-vis other resident sectors.2 As such, it can be used to study the evolution of exposures and vulnerabilities in individual sectors, as well as cross-sectoral linkages. A matrix can be constructed from monetary and financial statistics (MFS, drawn from the IMF’s standardized report forms), international investment position (IIP), and government finance statistics (GFS). To analyze the Indonesian economy, we use data covering the period 2001–14 for seven sectors:3 (i) Government; (ii) Central bank; (iii) Banks; (iv) Nonbanking financial institutions; (v) Nonfinancial corporations (NFCs); (vi) Households (HHs);4 and (vii) Non-resident (or rest of the world, ROW). We use the BSA to support four types of analysis: matrix, network, sensitivity, and a vector-autoregression (VAR).

3. Analysis of NFC exposures to ROW calculated from the IIP warrants some caveats.

From the point of view of NFCs external assets, the residency concept used in the construction of the BSA may lead to underestimating the actual funds available to NFCs, as NFCs operating in the country sometimes hold their funds abroad (e.g. Singapore) through an affiliate or subsidiary company. Further, external liabilities of NFCs include indistinguishably both equity and debt funding, without a currency breakdown (they are assumed to be all denominated in foreign currency).

Matrix Analysis

4. Table 1 (first panel) shows the matrix in the fourth quarter of 2014. The net values in columns represent net assets and those in rows represent net liabilities. For example, the HH sector was a net creditor to the banking sector (Rp 683 trillion) while the NFC sector was a net debtor to both ROW (Rp 4,933 trillion) and banks (Rp 698 trillion). Reflecting its nature as an open economy, the overall external funding represented the largest share of total inter sectoral net credit–about 33 percent of total allocated liabilities, equivalent to about 60 percent of GDP (Table 1, second panel).

Table 1.

Indonesia: BSA Matrix—Intersectoral Net Positions, 2014:Q4

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Source: IMF; and IMF staff estimates.

5. The matrix results suggest two areas of vulnerability for Indonesia. First, NFCs’ large reliance on cross border funding potentially exposes them to risks from both currency mismatches and sudden withdrawal of funding. Second, the banking sector is mostly exposed to NFCs and thus vulnerable to a shock to NFCs balance sheets (e.g. higher nonperforming loans (NPLs)).5 The net exposure of banks to the nonfinancial sector (NFCs and HHs combined) is slightly negative, as banks are net borrowers from HHs. Indeed, loan to deposit ratio in Indonesia is relatively low as banks fund their assets relying mainly on customer deposits.

Network Analysis

6. Network maps provide a graphical presentation of the BSA matrix. They can be used to visualize the evolution of financial exposures among sectors over time. Figure 1 shows gross cross-sectoral exposures along different dimensions in 2007 and 2014.6 The thickness of the arrow indicates the size of gross exposure, while the color of the nodes distinguishes net creditors (green) from net debtors (red).

Figure 1.
Figure 1.

BSA Matrix in Network Map Form

(Gross exposures)

Citation: IMF Staff Country Reports 2016, 082; 10.5089/9781513584409.002.A003

Sources:IMF; and IMF staff estimates.

7. Three key messages emerge from the analysis of the network maps. In particular, net creditors in 2007 remained so in 2014; the size of both gross exposures (thickness of the arrows) and net exposures (size of the nodes) has generally become larger over the period; and the NFC sector’s borrowing from ROW represented the largest exposure in both 2007 and 2014.

Sensitivity Analysis

8. The BSA matrix is used to assess the sensitivity of the NFC sector to exogenous shocks. Importantly, the analysis allows propagation of a shock from other sectors to NFC. The analysis is conducted using two scenarios: Scenario 1—an exchange rate depreciation of 25 percent, and Scenario 2—an exchange rate depreciation of 25 percent and a capital flow reversal, in which NFCs are forced to replace 10 percent of their foreign funding with domestic funding either by drawing from their funds in banks, or by obtaining new credit from banks. Either assumption has the same implication in the BSA, specifically an increase of net assets (or exposure) of banks with respect to NFCs. The sensitivity analysis, however, does not capture second-round effect. For instance, it does not show if the deterioration of the NFC balance sheet has implications on bank lending or NPLs.

9. There are several key takeaways from the results summarized in Table 2:

  • In Scenario 1 (Table 2, first panel), external indebtedness of NFCs increases by about 14 percent of GDP. Following exchange rate depreciation, all assets and liabilities denominated in foreign currency increase in value proportionally. Therefore, sectors that are net-borrowers in foreign currency become further indebted, particularly the government and NFC sectors, due to their reliance on borrowing from nonresidents.

  • In Scenario 2 (Table 2, second panel), external indebtedness of NFCs increases by about eight percent of GDP, and the exposure of banks to NFCs increases by about seven percent of GDP. NFCs indebtedness initially increases by the same 14 percent due to the exchange rate depreciation shock. However, due the additional capital flow reversal shock, the external borrowing is now partially replaced with borrowing with the banking sector.

Table 2.

Indonesia: Sensitivity Analysis Using the BSA—Difference of Intersectoral Net Positions

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Source: IMF; and IMF staff estimates.

Vector Autoregression (VAR) Analysis

10. The BSA matrix is complemented by macroeconomic variables to implement a VAR analysis to identify exposures of the NFC sector. In particular, a VAR model and its impulse-response functions are estimated to a one standard deviation negative shock to capital inflows.

11. The definition of the VAR is the following:

yt=B0+B1(L)yt+ut,(1)

where y is the vector containing the BSA and macroeconomic variables, B0 is the vector of constants, B1 is the vector of coefficients, L is the lag operator (we use a single lag) and u is the vector of residuals.

12. The model includes two macroeconomic and two BSA variables, respectively. The BSA variables are the growth rate of NFC net positions (i) with banks (G_ODCNFC) and (ii) with ROW (G_NFCIIP). The macroeconomic variables are (i) the first differences of VIX (DVIX) (used as a proxy for capital flows – a higher VIX is associated to lower capital inflows) and (ii) exchange rate depreciation against the U.S. dollar (G_XRATE). Due to first differencing, data are available for periods 2002–14.

13. Impulse-response functions are calculated based on Choleski decomposition. The four variables are stacked to reflect the assumed sequence of propagation of the initial shock: the VIX is at the top of the matrix, followed by the BSA variables, and the exchange rate. Table 3 shows the VAR estimates, which in particular highlights the significance of the coefficients of VIX in the equations for the BSA variables.

Table 3.

Indonesia: BSA VAR Analysis

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Source: IMF staff estimates.

14. The result confirms that NFCs could be a source of vulnerability, transmitting external shocks to the domestic economy. A one standard deviation increase in VIX, representing a negative shock to capital inflows, leads to exchange rate depreciation (Figure 2, left panel), a decrease of foreign funding for NFCs (Figure 2, middle panel) and an increase in the exposure of the domestic banking sector to NFCs (Figure 2, right panel). This supports the assumption in our sensitivity analysis conducted earlier that NFCs may replace some of their foreign funding with domestic bank lending, creating a channel for transmitting balance sheet vulnerabilities. Nonetheless, the relatively few number of observations and the somewhat restrictive assumptions in constructing the BSA matrix call for further robustness analysis as new information becomes available.

Figure 2.
Figure 2.

Indonesia: Response to Negative Shock to Capital Inflows

Citation: IMF Staff Country Reports 2016, 082; 10.5089/9781513584409.002.A003

Source: IMF staff estimates.

C. Panel Vector Autoregression Analysis7

15. Weaker macroeconomic conditions, including growth slowdown and rupiah depreciation can negatively affect bank balance sheets. Vulnerabilities from rising corporate foreign exchange leverage are rising.8 Evidence suggests that weaker real GDP growth and higher rates of rupiah depreciation tend to increase bank NPLs. If there are spillbacks to the macroeconomy, a vicious feedback loop can develop. This note attempts to identify existence of such macro-financial linkages, exploiting information on bank-by-bank heterogeneity.

16. The paper estimates a panel vector autoregression (VAR) model that accounts for bank-level heterogeneity, to identify a positive feedback loop between the macroeconomic and bank-level balance sheet variables:

yi,t=B0+B1(L)yi,t+ui,t(2)

where yi,t is a vector of macroeconomic and bank-level variables, B0 is the deterministic component, (L) is a lag operator and ui,t is the residual. The model was estimated using a panel VAR routine pvar developed by Love and Zicchino (2006), which exploits a System-General Method of Moments (GMM) estimator as in Arellano and Bover (1995).9

17. Five macroeconomic and bank level variables were included. Among macroeconomic variables, the VIX index captures global risk sentiment, commonly found in the literature to be a key determinant of cross-border capital flows. Domestic economic activity is captured by real GDP growth. Real rupiah depreciation against the US dollar affects profits and balance sheet conditions of domestic agents, such as corporates, impacting broader economic activity. Real growth rates of credit and deposits for the individual banks represent the channel through which shocks propagates back to the real economy.

18. The identification of shocks is based on Choleski decomposition, where the variables are stacked to explore how macroeconomic shocks affect bank-level variables first, and how the latter affect the former in the second round. In particular, two macroeconomic variables {VIX, real GDP growth} are stacked at the top. The bank-level variables {real deposit growth, real credit growth} are stacked below the macro-level variables. Real rupiah depreciation against the U.S. dollar is stacked at the bottom as commonly done in the literature. The model is estimated with one lag in view of the short time series dimension (2000–14).

19. The estimated results are summarized as follows. Table 4 presents the estimated coefficients from the system GMM approach in the panel VAR model. The estimated coefficients are mostly statistically significant, except for those of the deposit growth equation.10 Figure 3 visually summarizes the directions and magnitude of responses, which are broadly consistent with findings in the literature.11

Figure 3.
Figure 3.

Indonesia: Macro-Financial Linkages 1/

Citation: IMF Staff Country Reports 2016, 082; 10.5089/9781513584409.002.A003

Source: Bankscope; Haver Analytics; and IMF staff estimates.1/ Panel VAR with one lags Annual data 2000-14. Bank level data for real credit and deposit growth. Numbers represent a percent response to a 1 percent adverse shock. VIX is used in percentage point difference.
Table 4.

Indonesia: Estimated Panel VAR Coefficients and t-Statistics 1/

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Source: Bankscope; Haver Analytics: and IMF staff estimates.

Estimated using a panel VAR routine with one lag. Annual data spanning 2000-14. Bank level data for real credit and deposit growth for [45] banks. “L.” is a lag operator indicating the first lag. vix is the VIX index (in first difference], ryg is real GDP growth, rdpg is real deposit growth, rcrg is real credit growth, rdpr is the rate of rupiah depreciation against the US dollar in real terms.

20. The results illustrate the macro-financial linkages in Indonesia. Starting from a shock to the VIX index, a rise in this variable, which represents lower risk appetite for emerging market assets, leads to weaker GDP growth, slower credit growth, and a greater rate of rupiah depreciation. When the VIX rises by ten percentage points (equal to one standard deviation), real GDP growth declines by 0.3 percentage point, real credit growth declines by four percentage points, and the rate of rupiah depreciation rises by nine percentage points.

21. Weaker economic activity leads to lower real credit growth and greater rupiah depreciation. When real GDP growth rises by one percentage point (slightly above one standard deviation), the rate of credit growth declines by about seven percentage points and that of rupiah depreciation rises by 11 percentage points, both in real terms.

22. Bank balance sheet variables create feedback effects within the balance sheets and spillback to a broader real economy. First, lower deposit growth dampens GDP and credit growth, but leads to rupiah appreciation after rupiah liquidity in the banking system declines. A ten percentage point decline in real deposit growth (about ½ of one standard deviation) leads to a five percentage point reduction in real credit growth as funding conditions tighten. It also leads to a 0.1 percent decline in real GDP growth. The rate of rupiah depreciation declines (i.e., less depreciation or greater appreciation) by one percentage point. Second, credit growth moderation does not systematically affect deposit growth but weakens economic growth and accelerates rupiah depreciation. A ten percentage point decline in real credit growth (about ½ of one standard deviation) leads to a 0.1 percentage point reduction in real GDP growth and a one percentage point increase in the rate of rupiah depreciation.

23. The finding that rupiah depreciation leads to lower economic growth overall warrants further analysis. Taking at face value, a ten percentage point increase in the rate of rupiah depreciation in real terms leads to a 0.2 percentage point reduction in real GDP growth and a four percentage point decline in real credit growth. Rupiah depreciation does not systematically affect deposit growth. One interpretation is that rupiah depreciation captures a negative terms of trade shock, which leads to weaker economic activity. Another interpretation is that the model captures correlation between lower economic growth and the resultant capital outflows and rupiah depreciation. The macroeconomic effects of exchange rate deprecation warrants further research.12

D. Concluding Remarks

24. The results of the BSA analysis highlight NFC’s large reliance on foreign funding and, as a result, a potential source of vulnerability for the Indonesian economy. A depreciation shock or a negative shock to global risk sentiment may affect NFC’s foreign funding. In the case of a negative shock to capital inflows, the shock may propagate the vulnerability to the domestic banking sector, for instance if NFCs are to replace a part of foreign funding with domestic credit.

25. The results from the panel VAR approach using a combination of macroeconomic and bank level data point to the macro-financial linkages in Indonesia. In particular, worsening global risk sentiment, which tends to lower appetite for emerging market assets, leads to moderation in GDP growth, credit growth and a greater rate of rupiah depreciation. Weaker economic activity leads to lower real credit growth and greater rupiah depreciation. Bank balance sheet variables create feedback effects within the balance sheets and spillback to a broader real economy. Finally, rupiah depreciation puts pressure on profits and currency mismatches on balance sheets, leading to lower economic and credit growth.

26. Looking ahead, the authorities should continue to monitor macro-financial linkages and maintain strong macroeconomic fundamentals. In particular, the resilience of the NFC sector is of primary importance, as their funding structure could transmit external shocks to the domestic economy partly through the banking system. This is particularly the case if global and domestic conditions remained unfavorable for a protracted period (e.g. global risk sentiment remained weak, economic growth stuck at low gear, and rupiah deprecation continued). The first line of defense for Indonesia against adverse shocks is to keep its house in order. In other words, the country should maintain strong and credible monetary and fiscal policy and sustain the resilience of the domestic financial and corporate sector partly by continue upgrading the financial stability safety net.

Appendix 1. Indonesia—Macro-Financial Impulse Responses

Figure 1.1.
Figure 1.1.

Indonesia: Response to Macro and Bank Level Variables 1/

Citation: IMF Staff Country Reports 2016, 082; 10.5089/9781513584409.002.A003

Sources: Bankscope; Haver Analytics; and IMF staff estimates.1/vix is the VIX index (in first difference), ryg is real GDP growth, rdpg, is real deposit growth, rcrg is real credit growth, rdpr is the rate of rupiah depreciation against the U.S. dollar in real terms. Relying on a pvar routine with one lag and using annual data spanning 2000-14. The area around the solid line represents the 95 percent confidence interval.
Table 1.1.

Indonesia: Impulse-Response

article image
Source: IMF staff estimates.

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1

Prepared by Elena Loukoianova (APD), Ken Miyajima (MCM), and Giovanni Ugazio (STA).

2

For more details on the recent work on BSA see Caprio (2011), and IMF (2014, 2015).

3

See IMF (2016) for a discussion on how to construct the BSA matrix. GFS data are available for selected recent periods only (2009 until 2013).

4

Data for NFCs and HHs are generally less comprehensive than those for the other sectors.

5

Higher NPLs would be reflected in the BSA matrix when a write-off reduces the stock of loans.

6

Missing links in the 2007 map reflect the data gaps discussed above, which however do not materially constrain the BSA.

8

See accompanied SIP on corporate vulnerabilities for more details.

9

As the fixed effects are correlated with the regressors due to lags of the dependent variables, the mean-differencing procedure commonly used to eliminate fixed effects would create biased coefficients. The orthogonality between transformed variables and lagged regressors is preserved by forward mean-differencing (the Helmert procedure in Arellano and Bover, 1995), which removes the mean of the future observations. Then, lagged regressors are used as instruments to estimate the coefficients by system GMM.

10

The counterintuitive response of real deposit growth to several variables may be due to lack of statistical significance of the estimated coefficients in the deposit growth equation. In particular, real deposit growth rises due to a rise in the VIX index or a decline in real GDP growth.

11

Figure 1.1 in Appendix 1 presents time series plots of the values presented in Table 1.1 (after normalizing by the size each variable’s one standard deviation shock)

12

Another interpretation is that the “risk taking” channel has a stronger impact than the net export channel on economic growth. The growing literature on the risk taking channel in emerging economies finds that currency depreciation weakens bank lending and asset price performance domestically, and creates another round of currency depreciation (Borio and Zhu, 2008; Adrian and Shin, 2009; Chung, Lee, Loukoianova, Park and Shin, 2014; and Hofmann, Shim and Shin, 2016). The mechanism creates a positive feedback loop, which slows domestic economic activity. The effect of the risk taking channel would be stronger in EMs with larger reliance on portfolio capital inflows. Meanwhile, the effect of currency depreciation through the net export channel on economic growth may be less pronounced in economies exporting mainly commodities priced internationally in US dollars because local currency depreciation would not improve competitiveness much, particularly when import content of the trade balance is large. Therefore, greater rupiah depreciation could lead to lower economic growth in Indonesia.

Indonesia: Selected Issues
Author: International Monetary Fund. Asia and Pacific Dept