Abstract
Selected Issues
Impact of Covid-19 on Nonfinancial Corporate Vulnerabilties in Indonesia 1
Nonfinancial corporate (NFC) firms in Indonesia entered the COVID-19 pandemic with a relatively weak debt service capacity and modest liquidity buffers compared with peers in the region. For a sample of 459 Indonesian NFC firms at end-2019, about half of the firms used more than half of their operating income to cover their interest payments and held cash holdings covering less than 20 percent of their outstanding current liabilities. Without adequate policy support, about half of these firms might have become unable to cover their interest payments and may have faced cash shortages by end-2020. Their liabilities are expected to account for about one-third of total outstanding NFC debt, more than double the end-2019 share, which could put significant strains on the banking system.
A. Introduction
1. As elsewhere around the world, nonfinancial firms in Indonesia are coping with unprecedented challenges from the COVID-19 crisis. The unexpected collapse in sales due to stringent containment measures put the survival of many firms at risk, even for otherwise viable firms before the pandemic. Extraordinary emergency support measures, including a large-scale loan restructuring program, have assisted them sustain the impact so far. As the pandemic goes on, however, policymakers will likely face an increasingly difficult tradeoff between preserving policy space for the future and continuing with the support to save more firms and jobs now. An understanding of the scale and nature of the NFC problems, especially relative to other major economies in the region, could help inform this important policy decision.
Retail Sales Volume Growth
(In percent, year-on-year)
Citation: IMF Staff Country Reports 2021, 047; 10.5089/9781513570860.002.A004
Sources: Indonesian authorities; and IM F staff estimates.Loan Restructuring Scheme: Value of Restructured Loans
Citation: IMF Staff Country Reports 2021, 047; 10.5089/9781513570860.002.A004
Source: Otoritas Jasa Keuangan.2. The challenge for Indonesian firms has been compounded by their financial vulnerabilities pre-dating the pandemic. A sustained decline in commodity prices since the Global Financial Crisis (GFC) in 2008, together with the heightened global trade tensions over recent years, weighed down on Indonesian firms’ corporate performance. On the financing side, the reliance on external debt had increased in recent years, which contributed to the persistently high currency risk premium and potentially left Indonesian firms’ balance sheets more susceptible to sudden large fluctuations in the exchange rate. Some of the associated risks were nonetheless mitigated by a set of prudential requirements introduced in 2014 on hedging, 2 liquidity, and minimum credit rating.
External Trade Conditions
(Index, Dec. 2010=100)
Citation: IMF Staff Country Reports 2021, 047; 10.5089/9781513570860.002.A004
Sources: Indonesian authorities; Haver Analytics; and IMF International Finance Statistics.Corporate External Debt
(In billions of U.S. dollar and in percent of GDP)
Citation: IMF Staff Country Reports 2021, 047; 10.5089/9781513570860.002.A004
Sources: CEIC Data Co. Ltd.; and IMF staff estimates.3. This chapter assesses the impact of COVID-19 using a novel firm-level dataset. In the current environment, assessments through the usual banking system soundness indicators could miss an accurate picture of the fast-evolving financial health of NFC firms. The dataset used in this analysis is constructed from S&P’s Capital IQ database and comprises 459 nonfinancial firms in Indonesia as of end-2019. The sample accounts for about 62 percent of total NFC debt in the economy and 22 percent of GDP in terms of revenue. Both publicly listed (439) and non-listed private firms (20) are included in the dataset, and 23 of the 459 sample firms are state-owned enterprises (SOEs). The dataset contains information on the currency composition of individual firms’ outstanding debt, a major advantage over other commercial firm-level databases considering Indonesian firms’ reliance on FX debt. Appendix I provides further details on the firm-level dataset.
B. Financial Health Before COVID-19 Pandemic
4. We first evaluate the solvency of Indonesian NFCs using the interest coverage ratio (ICR) as the organizing framework. The ICR, defined as the earnings before tax and interest expenses (EBIT) to interest payment (INTP) ratio, measures a firm’s capacity to service its debt payments out of its EBIT. To understand the underlying drivers of the ICR dynamics before the pandemic, we decompose the ICR in year t as follows:
ICRt = EBITt/INTPt = ROAt/((INTPt/DEBTt-1) × (DEBTt-1/ASSETt-1)) = ROAt/(EIRt × LEVt-1),
where ROA, EIR, and LEV denote the return on assets, effective interest rate, and leverage, respectively. We analyze the evolution of the ICR by examining each of these components in turn.
5. Indonesian firms’ debt service capacity had been broadly stable in the run up to the pandemic, although at low levels. Since 2015, the median ICR of sample Indonesian firms had remained relatively stable within a narrow range of 2 and 2.5, which stands in contrast with their peers in the ASEAN region that experienced significant declines in the ICR. The median ICR of 2 as of end-2019 was nevertheless among the lowest in the region. Furthermore, the stable ICR ratio over 2015–2019 masks a noticeable fall in the ICR for sample SOEs (“IDN-SOE”), a potential concern from a systemic perspective, given their relatively large firm size.
Interest Coverage Ratio
(Ratio, EBIT/interest payment)
Citation: IMF Staff Country Reports 2021, 047; 10.5089/9781513570860.002.A004
Sources: S&P Global Market Intelligence; and IMF staff estimates.Interest Coverage Ratio
(Ratio, operating income/interest payment, median)
Citation: IMF Staff Country Reports 2021, 047; 10.5089/9781513570860.002.A004
Sources: S&P Global Market Intelligence; and IMF staff estimates.6. Profitability was maintained at adequate levels, despite some deterioration during 2011–2015 and 2018–2019, closely following the trend in the terms of trade. The median ROA of sample Indonesian firms stood at about 5.1 percent as of end-2019, comfortably higher than 3.4 percent for other firms in ASEAN. Notwithstanding some slight decline over 2018–2019, the median profitability of private sector Indonesian firms (“IDN-Private”) in 2019 was comparable to the level in 2015, which contrasts with other sample firms in the region that generally saw their profits decline over the same period. On the other hand, the profitability of Indonesian SOEs dropped sharply, although from a relatively high level in 2015, partly contributing to the deterioration in their debt service capacity.
Profitability: Return On Assets
(in percent, EBIT/assets)
Citation: IMF Staff Country Reports 2021, 047; 10.5089/9781513570860.002.A004
Sources: S&P Global Market Intelligence; and IMF staff estimates.Profitability
(In percent, EBIT/assets, median)
Citation: IMF Staff Country Reports 2021, 047; 10.5089/9781513570860.002.A004
Sources: S&P Global Market Intelligence; and IMF staff estimates.7. Corporate leverage was relatively high in 2019, despite some deleveraging in recent years. As of end-2019, the median debt-to-assets ratio of sample Indonesian firms stood at 27 percent, compared with 22 percent for other NFCs in the region, partly explaining Indonesian firms’ relatively low ICR despite the relatively strong profitability. The surge in the leverage of Indonesian SOEs from about 20 percent in 2015 to 36 percent in 2019 is especially remarkable considering the large decline in their profitability over this period. 3 Meanwhile, private sector Indonesian firms had reduced their leverage over the same period, likely reflecting higher financing costs (discussed below).
Leverage
(In percent, debt/assets)
Citation: IMF Staff Country Reports 2021, 047; 10.5089/9781513570860.002.A004
Sources: S&P Global Market Intelligence; and IMF staff estimates.Leverage
(In percent, debt/assets, median)
Citation: IMF Staff Country Reports 2021, 047; 10.5089/9781513570860.002.A004
Sources: S&P Global Market Intelligence; and IMF staff estimates.8. The cost of financing, proxied by the effective interest rate, had increased since 2015, in line with the trend in ASEAN countries. This increase reflected the rise in the policy rates across the region in the recent years, which led to higher short-term market interest rates. In the case of private sector Indonesian firms, however, the effective interest rates had been markedly higher than the rates elsewhere, which was another factor explaining their relatively low ICRs in addition to high corporate leverage. Meanwhile, the effective interest rate of Indonesian SOEs had actually declined over 2015–2019, indicating a significant degree of subsidization in their financing.
Effective Interest Rate
(In percent, Interest payment/total debt)
Citation: IMF Staff Country Reports 2021, 047; 10.5089/9781513570860.002.A004
Sources: S&P Global Market Intelligence; and IMF staff estimates.Effective Interest Rate
(In percent, interest payment/debt, median)
Citation: IMF Staff Country Reports 2021, 047; 10.5089/9781513570860.002.A004
Sources: S&P Global Market Intelligence; and IMF staff estimates.9. The share of outstanding NFC debt held by low-ICR firms remained comparable to historical levels in 2019, although higher than in other ASEAN economies. As of end-2019, the debt-at-risk share of Indonesia firms with ICR below 2 stood at about 56 percent, significantly higher than even in Singapore (35 percent) where the median ICR was lower in 2019. This difference indicates that the firms with high debt service burden were relatively larger in size in Indonesia than in Singapore. The data also show that, while the debt-at-risk share was not at an alarming level in light of Indonesia’s own history, the risks posed by the NFC sector to the broader financial system was nonetheless the greatest among major ASEAN economies.
Debt-at-Risk (ICR)
(In percent of total debt)
Citation: IMF Staff Country Reports 2021, 047; 10.5089/9781513570860.002.A004
Sources: S&P Global Market Intelligence; and IMF staff estimates.Debt-at-Risk (ICR<2)
(In percent of total debt in each economy, as of end-2019)
Citation: IMF Staff Country Reports 2021, 047; 10.5089/9781513570860.002.A004
Sources: S&P Global Market Intelligence; and IMF staff estimates.10. Across industries, firms in the utilities, materials, energy, and consumer staples and discretionary sectors had the weakest debt service capacity. In 2019, the debt of low-ICR firms in utilities accounted for over 20 percent of total outstanding NFC debt in Indonesia, followed by firms in materials (about 10 percent), together consisting of more than half of the NFC debt-at-risk in Indonesia. While these industries warrant attention from a financial stability perspective, the high share of risky firms in consumer staples and discretionary industries raise concerns from an employment-at-risk perspective, given their relatively high labor intensity. 4
Debt-at-Risk By Industry (ICR) 1/
(In percent of total debt, as of end-2019)
Citation: IMF Staff Country Reports 2021, 047; 10.5089/9781513570860.002.A004
Sources: S&P Global Market Intelligence; and IMF staff estimates.1/ ICR<2Firm-at-Risk By Industry
(In percent of sample firms in each industry, as of end-2019)
Citation: IMF Staff Country Reports 2021, 047; 10.5089/9781513570860.002.A004
Sources: S&P Global Market Intelligence; and IMF staff estimates.11. The reliance on FX debt is another importance source of NFC vulnerability in Indonesia. Specifically, the firm-level data reveal the following regarding Indonesian firms’ use of FX debt prior to the pandemic:
As of end-2019, sample Indonesian firms held about 39 percent of their outstanding debt in foreign currencies, more than double the average share for other sample firms in major ASEAN economies (about 16 percent).
The share of FX debt had increased over 2016–2019, especially among small-sized firms, as indicated by the relatively larger increase in the median FX debt share compared with the asset-weighted share. Furthermore, the FX debt share had increased relatively more for the 75th percentile firms, although still comparable to the post-GFC levels.
Private sector firms and those in utilities had relatively higher FX debt shares in 2016, and their shares increased relatively more in 2016–2019―these were also the firm groups that had relatively weak debt service capacity, as shown above.
While the firm-level data used in this study do not provide information on financial hedging, 5 we attempt to gauge the net FX exposure at the industry level by comparing the average share of foreign sales (as a proxy for natural hedges) in each industry with the average FX debt share in the same industry. The analysis shows that Indonesian firms, compared with their peers in ASEAN, generally had much higher shares of FX debt in their total debt compared to the share of foreign sales in their total sales. Notably, the average high FX debt share in utilities appears to warrant caution, given the increase in recent years and the lack of natural hedges in the form of foreign sales.
12. Indonesian firms encountered the pandemic with relatively modest cash buffers. As of end-2019, the cash ratio (defined as the ratio of cash and cash equivalents relative to total current liabilities) of Indonesian firms stood at about 20 percent, which was relatively low in the region. 6 This situation contrasts with other economies in ASEAN, in which firms either held a combination of low cash buffers with high ICRs (e.g., Vietnam) or low ICRs with large cash buffers (e.g., Singapore). Amongst sample Indonesian firms, the median cash ratio was significantly lower for private sector firms (19 percent) than SOEs (34 percent), and for firms in materials (13 percent) and consumer staples (12 percent).
ASEAN-6: Cash Ratio
(In percent, interquartile range, as of 2019)
Citation: IMF Staff Country Reports 2021, 047; 10.5089/9781513570860.002.A004
Sources: Capital IQ, S&P Global Market Intelligence; and IMF staff estimates.ASEAN-6: ICR and Cash Ratio
(In percent, median, as of end-2019)
Citation: IMF Staff Country Reports 2021, 047; 10.5089/9781513570860.002.A004
Sources: S&P Global Market Intelligence; and IMF staff estimates.C. Expected Impact of COVID-19 on Indonesian Firms’ Financial Health
13. Next, we evaluate the potential impact of COVID-19 by estimating ICRs at end-2020 using two complementary approaches (see Appendix II for more details). In one approach, we directly apply relevant shocks to each firm’s operating income and interest payments at end-2019 to derive the expected ICR value at end-2020. The operating income shock is set based on the information from Consensus earnings forecasts of market analysts, whereas the shocks applied to interest payments—namely, the exchange rate and the interest payment shock—are set in line with the October 2020 IMF WEO forecasts. In an alternative approach, we use a regression-based approach to predict the ICRs at end-2020, where the explanatory variables consist of a set of macroeconomic and global variables.
14. The cash position at end-2020 is estimated by adding the expected net cash flow during 2020 to the cash balance at end-2019. Specifically, the cash flow from operations is assumed to decline in line with the operating income shock assumed for the ICR analysis. Capital expenditure and debt refinancing are set at levels broadly consistent with the magnitude of the macroeconomic shocks in past crisis episodes.
15. Without ample policy support measures, the fallout from the COVID-19 crisis on the NFC sector could be substantial. The analysis shows that, under the October 2020 IMF WEO, the share of sample firms with ICRs below one could reach between 47–50 percent by end-2020. Liquidity pressures would also intensify and become broad-based, potentially leaving about 45 percent of sample firms with cash shortages at end-2020 without liquidity support measures. The actual data from 2020:Q2 for a subset of sample firms 7 are broadly in line with these estimates, with the share of firms at risk (i.e., ICRs below one) at about 49 percent and a median ICR of 0.8. The decline in the ICR primarily reflected lower corporate sales and profitability, while the policy rate reduction by Bank Indonesia provided some relief on interest payments. Meanwhile, the cash ratio has also fallen to 19 percent from 20 percent at end-2019, suggesting significant underlying liquidity pressures.
ASEAN-6: Firm-at-Risk by Country
(Percent share of firms in each economy with interest coverage ratio less than one)
Citation: IMF Staff Country Reports 2021, 047; 10.5089/9781513570860.002.A004
Sources: S&P Global Market Intelligence;and IMF staff estimatesASEAN-6: Cash Ratio by Country
(In percent, cash and cash equivalents to total current liabilities)
Citation: IMF Staff Country Reports 2021, 047; 10.5089/9781513570860.002.A004
Sources: S&P Global Market Intelligence and IMF staff estimates16. The results also show large variations in the COVID-19 impact across industries, partly reflecting their pre-pandemic conditions. Consumer discretionary, which comprises sub-industries such as retailing, consumer durables and apparel, and consumer services, is expected to be hit the hardest in terms of the share of firms facing interest payment difficulties and cash shortages, partly reflecting their weak initial position before the pandemic. This is in contrast with the energy industry, for example, in which a relatively small share of firms is expected to be cash-strapped due to the strong cash position in 2019 (median cash ratio of 43 percent). Other vulnerable industries include materials and industrials, 8 which are the industries that account for a significant share of Indonesia NFC debt-at-risk in 2019 (28 percent).
Indonesia: Firm-at-Risk (ICR<1)
(In percent of sample firms in each industry)
Citation: IMF Staff Country Reports 2021, 047; 10.5089/9781513570860.002.A004
Sources: S&P Global Market Intelligence; and IMF staff estimates.Indonesia: Firms with Expected Cash Shortages 1/
(Percent share of sample firms in each industry, end-2020)
Citation: IMF Staff Country Reports 2021, 047; 10.5089/9781513570860.002.A004
Sources: S&P Global Market Intelligence; and IMF staff estimates.1/ Cash balance expected at or below 0 in 2020.17. The debt-at-risk share at end-2020 is expected to rise markedly from its level at end- 2019, which could put substantial strains on the health of the banking system. The analysis shows that the share of NFC debt held by Indonesian firms with ICR below one and two could rise to 32 percent and 88 percent by end-2020, respectively, which would be 2½ and 1½ times higher than the levels in 2019. Data from 2020:Q2 for a subset of sample firms with available accounting information confirm that these estimates are broadly consistent with the performance up to Q2 in major ASEAN economies (except the Philippines). While some further increase is expected during the remainder of 2020 in the debt-at-risk share based on the ICR threshold of two, the increment would be relatively small due to the overall improvement in mobility and economic activity, which is expected to have led to some recovery in corporate performance.
Debt at Risk (ICR<1)
(In percent of total sample debt in each economy)
Citation: IMF Staff Country Reports 2021, 047; 10.5089/9781513570860.002.A004
Sources: S&P Capital IQ; and IMF staff estimates1/ Projection based on October 2020 IMF World Economic Outlook.Debt at Risk (ICR<2)
(In percent of total sample debt in each economy)
Citation: IMF Staff Country Reports 2021, 047; 10.5089/9781513570860.002.A004
Sources: S&P Capital IQ; and IMF staff estimates.1/ Projection based on October 2020 IMF World Economic Outlook.D. Policy Implications
18. The findings highlight the need to prepare for possible impending NFC problems in Indonesia. Authorities have appropriately responded to the pandemic with bold emergency support measures, including interest rate subsidies, bank loan moratoria for MSMEs, credit guarantees, corporate income tax reduction, and liquidity support and regulatory easing aimed to facilitate loan restructuring by banks. Considering the magnitude and persistence of the COVID-19 crisis, some of these targeted assistance measures should be sustained or even scaled up as needed until recovery firmly takes hold (see Staff Report for more specific recommendations). In this regard, the authorities’ plan to prioritize support high value-added sectors in 2021, including through an ambitious vaccination program, would help accelerate the recovery in corporate performance. At the same time, strengthening the current insolvency framework and social safety nets, together with implementation of active labor market policies aimed at increasing employment opportunities would help minimize the long-term scars on the economy.
Foreign Currency Nonfinancial Corporate Debt in Indonesia
Citation: IMF Staff Country Reports 2021, 047; 10.5089/9781513570860.002.A004
Appendix I. Data Source
1. This study uses a firm-level dataset constructed from the corporate balance sheet database provided by Capital IQ, S&P Global Market Intelligence.
2. One advantage of the Capital IQ database over other commercial databases such as Worldscope and Orbis, is the availability of detailed information on firms’ outstanding debt held in their balance sheets. Its Debt Capital Structure database, in particular, provides information on the individual debt instruments held by each firm at a point in time, including the principal amount due, the currency of denomination, and the type of instrument (for example, whether bank loans or bonds). Information on debt instruments is collected from company financial reports filed to national regulatory agencies, typically available in the supplementary note accompanying the main financial statements.
3. The sample consists of a total of 459 NFC firms for Indonesia and 2,594 firms for ASEAN-6 economies (see text table) as of end-2019. The sample is nationally representative in terms of the key variables of interest, such as NFC debt and GDP. In the case of Indonesia, the debt held by sample firms and their total revenue in 2019 account for about 62 percent of total outstanding NFC debt in the economy and 22 percent of GDP, respectively.
4. The industry classification in this study follows that of Capital IQ’s proprietary system. In terms of the firm distribution, industrials have the highest concentration of sample Indonesian firms (102) and utilities has the smallest (7). The industry distribution for the broader ASEAN-6 economies is also similar, thus providing some assurance that the results for Indonesia in this study are not primarily driven by the economy’s sample industry composition (text chart).
Sample Representativeness 1/
Based on data as of end-2019.
Sample Representativeness 1/
| NFC Debt Share | Revenue/ GDP | Number of Sample Firms | |
|---|---|---|---|
| IDN | 62.0 | 21.9 | 459 |
| MYS | 62.3 | 66.0 | 766 |
| PHL | 44.4 | 42.1 | 153 |
| SGP | 40.7 | 60.1 | 448 |
| THA | 83.5 | 73.9 | 569 |
| VNM | N.A. | 22.0 | 199 |
Based on data as of end-2019.
Sample Representativeness 1/
| NFC Debt Share | Revenue/ GDP | Number of Sample Firms | |
|---|---|---|---|
| IDN | 62.0 | 21.9 | 459 |
| MYS | 62.3 | 66.0 | 766 |
| PHL | 44.4 | 42.1 | 153 |
| SGP | 40.7 | 60.1 | 448 |
| THA | 83.5 | 73.9 | 569 |
| VNM | N.A. | 22.0 | 199 |
Based on data as of end-2019.
Indonesia: Number of Sample Firms By Industry
Citation: IMF Staff Country Reports 2021, 047; 10.5089/9781513570860.002.A004
Sources: S&P Global Market Intelligence;and IMF staff estimatesASEAN-6: Number of Firms by Industry
Citation: IMF Staff Country Reports 2021, 047; 10.5089/9781513570860.002.A004
Sources: SSiP Global Market Intelligence; and IMF staff estimatesFinally, the sample includes 23 SOEs, mostly concentrated in industrials (12) and materials (5) industries (text table). Although small in numbers, these SOEs represent the largest firms in the sample, with a median asset size of US$3,287 million, compared with US$135 million for private sector firms.
Distribution of State-Owned Enterprises
(Number of firms, as of end-2019)
Citation: IMF Staff Country Reports 2021, 047; 10.5089/9781513570860.002.A004
Sources: S&P Global Market Intelligence; and IMF staff estimates.Asset Size By Firm Type
(In percent, interquartile range, as of 2019)
Citation: IMF Staff Country Reports 2021, 047; 10.5089/9781513570860.002.A004
Sources: Capital IQ, S&P Global Market Intelligence; and IMF staff estimates.Indonesia: List of Sample State-Owned Enterprises
(As of end-2019)
Indonesia: List of Sample State-Owned Enterprises
(As of end-2019)
| Company Name | Industry | Total Assets (millions, USD) |
|---|---|---|
| Perusahaan Perseroan (Persero) PT Perusahaan Listrik Negara | Utilities | 114,544 |
| PT Pertamina (Persero) | Energy | 67,086 |
| PTTelekomunikasi Indonesia (Persero) Tbk | Communication Services | 15,986 |
| PT Indonesia Asahan Aluminium (Persero) | Materials | 11,912 |
| PT Pupuk Indonesia (Persero) | Materials | 9,796 |
| PT Waskita Karya (Persero) Tbk | Industrials | 8,859 |
| PT Hutama Karya (Persero) | Industrials | 6,623 |
| PT Semen Indonesia (Persero) Tbk | Materials | 5,767 |
| PT Wijaya Karya (Persero) Tbk. | Industrials | 4,488 |
| PT Garuda Indonesia (Persero) Tbk | Industrials | 4,456 |
| PT Pembangunan Perumahan (Persero) Tbk | Industrials | 4,276 |
| PT Krakatau Steel (Persero) Tbk | Materials | 3,287 |
| PT Kereta Api Indonesia (Persero) | Industrials | 3,245 |
| PT Angkasa Pura II (Persero) | Industrials | 3,180 |
| PT Angkasa Pura I (Persero) | Industrials | 3,092 |
| PT Adhi Karya (Persero) Tbk | Industrials | 2,639 |
| PT Pelabuhan Indonesia III (Persero) | Industrials | 2,223 |
| PT Pembangunan Perumahan Properti Tbk | Real Estate | 1,415 |
| PT Kimia Farma (Persero) Tbk | Health Care | 1,326 |
| PT Pelabuhan Indonesia I (Persero) | Industrials | 1,194 |
| PT Pelabuhan Indonesia IV (Persero) | Industrials | 760 |
| PT Semen Baturaja (Persero) Tbk | Materials | 403 |
| PT Indofarma (Persero) Tbk | Health Care | 100 |
Indonesia: List of Sample State-Owned Enterprises
(As of end-2019)
| Company Name | Industry | Total Assets (millions, USD) |
|---|---|---|
| Perusahaan Perseroan (Persero) PT Perusahaan Listrik Negara | Utilities | 114,544 |
| PT Pertamina (Persero) | Energy | 67,086 |
| PTTelekomunikasi Indonesia (Persero) Tbk | Communication Services | 15,986 |
| PT Indonesia Asahan Aluminium (Persero) | Materials | 11,912 |
| PT Pupuk Indonesia (Persero) | Materials | 9,796 |
| PT Waskita Karya (Persero) Tbk | Industrials | 8,859 |
| PT Hutama Karya (Persero) | Industrials | 6,623 |
| PT Semen Indonesia (Persero) Tbk | Materials | 5,767 |
| PT Wijaya Karya (Persero) Tbk. | Industrials | 4,488 |
| PT Garuda Indonesia (Persero) Tbk | Industrials | 4,456 |
| PT Pembangunan Perumahan (Persero) Tbk | Industrials | 4,276 |
| PT Krakatau Steel (Persero) Tbk | Materials | 3,287 |
| PT Kereta Api Indonesia (Persero) | Industrials | 3,245 |
| PT Angkasa Pura II (Persero) | Industrials | 3,180 |
| PT Angkasa Pura I (Persero) | Industrials | 3,092 |
| PT Adhi Karya (Persero) Tbk | Industrials | 2,639 |
| PT Pelabuhan Indonesia III (Persero) | Industrials | 2,223 |
| PT Pembangunan Perumahan Properti Tbk | Real Estate | 1,415 |
| PT Kimia Farma (Persero) Tbk | Health Care | 1,326 |
| PT Pelabuhan Indonesia I (Persero) | Industrials | 1,194 |
| PT Pelabuhan Indonesia IV (Persero) | Industrials | 760 |
| PT Semen Baturaja (Persero) Tbk | Materials | 403 |
| PT Indofarma (Persero) Tbk | Health Care | 100 |
Appendix II. Methodology
This appendix provides additional details on the analytical approaches used to obtain the results in the chapter. The goal is to estimate firms’ debt service capacity, proxied by the ICR, and their cash positions at end-2020 under the October 2020 IMF World Economic Outlook.
1. Unlike other crisis episodes in the past, the COVID-19 crisis is characterized by large supply disruptions caused by lockdowns aimed at containing the virus transmission. As a result, the cross-industry impact on nonfinancial corporates is expected to differ markedly from the patterns observed in other crisis episodes in which demand contraction was the major driver. For example, the impacts on industries such as transportation, tourism, and other labor-intensive industries, are expected to be disproportionately larger in the COVID-19 crisis. Typical regression-based stress testing approach would not adequately capture this unique aspect of the COVID-19 crisis, however, as they entirely rely the historical data.
2. In light of the large uncertainty surrounding the near-term economic prospects and the impact of COVID-19 shocks, we take the following two complementary approaches to obtain a range of ICR estimates at end-2020:
Approach I. Regression-Based Method
3. In this approach, we use a regression-based approach to obtain alternative forecasts of ICRs for end-2020 (ICR22020, j). The sample used for the regression approach consists of 29,161 firm-year observations over the period of 2002–2019 from the sample ASEAN-6 economies.
4. This approach involves running separate regressions with each firm’s return on assets (ROA), effective interest rate (EIR), and leverage (LEV, defined as the debt-to-assets ratio) as the dependent variable and then constructing firm-specific projections for the ICR using the relationship below:
ICR = ROA/(EIR X LEV)
5. For each subcomponent, we estimate the empirical relationship between their behavior and macroeconomic variables based on the regression equation below:
where yi,j,k,t denotes a subcomponent of ICR (ROA, EIR, LEV) for firm i, industry), and country k, in year t. It considers different types of macroeconomic variables: country-level domestic variables Xk,t including real GDP growth as a proxy for aggregate demand and lending rates; external sector variables Ek,t including the trade partners’ GDP growth weighted by the country k’s exports and the exchange rates (bilateral exchange rate to USD); global variables Wt including commodity prices and LIBOR. It includes the lagged dependent variable, and a dummy variable indicating the year 2010 onward to improve the model fit. We also control for industry and country fixed effects δj and μk. We use the IMF WEO (including the global assumptions) for most macroeconomic data for the period of 2002 to 2019, and the IMF’s International Financial Statistics for the lending rates. Based on the estimated relationship, we forecast the firm-level financial indicators for 2020–21, using the IMF staff projections for the macroeconomic variables as available in the WEO from the October vintage. 1
6. The model predictions are robust to different regression specifications and provide good fits for the ICR (text charts). Several different specifications were considered, including different measures for each variable, as well as running similar regressions at the country- and industry-level separately instead of using the full sample. Given that the main objective is to make out-of-sample forecasts, we prioritize a specification that provides the best fit to the actual data until 2019.
Profitability: Return on Assets
(In percent, EBIT/assets, median)
Citation: IMF Staff Country Reports 2021, 047; 10.5089/9781513570860.002.A004
Sources: S&P Global Market Intelligence; and IMF staff estimates.Interest Coverage Ratio
(In percent, EBIT/interest payment, median)
Citation: IMF Staff Country Reports 2021, 047; 10.5089/9781513570860.002.A004
Sources: S&P Global Market Intelligence; and IMF staff estimates.Approach II. Direct Method Using Consensus Forecast Earnings
7. In this approach, we directly apply relevant shocks to the subcomponents of the ICR ratio— namely, the operating income (numerator) and the interest payment (denominator).
8. To set the shock to the operating income, we first take the consensus earnings forecasts of individual firms for FY 2020 from both the January 2 vintage and the June 30 vintage. 2 Next, for each firm, we calculate the percentage change of the earnings forecasts between these two vintages. Finally, as these forecasts are only available for a relatively small subset of sample firms, we apply the industry-median earning shock to individual firms’ earnings in 2019 (i.e., common operating income shock for firms; in the same industry i) to obtain the estimated earnings for 2020:
EBIT2020,j = (1+ θ x EBIT shocki/100) x EBIT2019,j
where θ is a scaling factor whose value is set such that the median ICR value for the ASEAN-6 sample in 2020 from Approach I and the value from Approach II are equal to each other (at about 1.2). 3
Expected Corporate Earning Shock by Industry 1/
(In percent, interquartile range)
Citation: IMF Staff Country Reports 2021, 047; 10.5089/9781513570860.002.A004
Sources: 5&P Capital IQ; and IMF staff estimates.1/ Interquartile range of changes in analysts’ 12-month-ahead company earning forecasts for each ind ustry between January 2 and June 30.9. Meanwhile, we apply two separate shocks to the interest payment—the interest payment shock and the exchange rate shock. The exchange rate shock is set as the percent change of the bilateral exchange rate vis-à-vis the U.S. dollar between the end-2019 level and the projected end-2020 level in the October 2020 IMF WEO. The exchange rate shock is applied to the actual―not imputed―foreign currency-denominated portion of each firm’s outstanding debt as of end-2019. The interest payment shock is set at 0, which is somewhat more conservative than the macroeconomic scenarios considered where interest rates are projected to decline slightly, and hence the only effective shock to the interest payment is the exchange rate shock (e.g., currency depreciation leading to higher FX debt interest payment in local currency terms). Specifically, the expected interest payment for firm j in 2020 would be given as follows:
INTP2020,j = [FX debt share2019,j × (1+FX shock2020/100) + local currency debt share2019,j] × (1+INTP shock2020/100) × INTP2020,j
Cash Flow Analysis
10. To estimate a firm’s cash position at end-2020, we take the end-2019 stock of cash and cash equivalents and adjust for the expected changes in the cash flow in 2020. Specifically, we assume the end-2020 cash position of a firm to be determined as follows:
cash2020 = cash2019 + EBIT shock × (cash flow from operations2019) – capital expenditure2020 – debt amortization2020 + net interest payment2019 – dividend payment2020,
where
Capital expenditure2020 = 0.75*capital expenditure2019
Debt amortization2020 = 10 percent of maturing debt (i.e. debt rollover ratio = 90 percent)
Dividend payment2020 = 0
11. The operating income shock comes from the consensus earnings forecasts in Approach II. While the shock parameter values are somewhat arbitrary, they are set at plausible levels based on the sample data. In the case of capital expenditure (CAPEX), for example, the sample median value during the GFC in 2009 was about 80 percent of the level in 2008. In the case of the debt rollover ratio, we set it at 90 percent of the maturing debt, which is slightly lower than the median level observed in 2009 (about 100 percent) in the ASEAN-6 sample. 4
Prepared by Minsuk Kim (APD), based on a forthcoming IMF working paper, “Impact of COVID-19 on Financial Health of Nonfinancial Firms in ASEAN,” co authored with Jiae Yoo and Xin Li (all APD).
According to the regulation (No. 16/21/PBI/2014), non-bank corporations with external debt are required to meet a minimum hedging ratio of 25 percent of the negative difference between maturing foreign currency assets and foreign currency liabilities in the next three months and in the next three to six months.
The increase in leverage partly reflects SOEs’ active involvement in government priority projects in infrastructure and energy.
It should also be noted that the majority of informal sector firms―although not included in the sample―likely belong to these industries, which comprise wholesale and retail trade, and accommodation and food services.
According to Bank Indonesia’s March 2020 Financial Stability Review, about 93 percent of 3,807 nonbank corporations with external debt met the regulatory minimum hedging ratio requirements at end-2019:Q4.
The assessment still holds with the asset-weighted average cash ratio, which was about 47 percent for Indonesian firms and only higher than Vietnamese firms (about 20 percent) among ASEAN-6.
The Q2 estimates are based on a sub-sample of 397 NFC firms, compared with the full sample of 459 firms at end-2019.
Industrials comprises transportation, construction, and heavy machinery and equipment.
For the country-specific lending rates we refer to the data available in the IMF’s International Financial Statistics. For projection for 2020, we use the average of the available months (up to September) as a proxy; for projection for 2021, we use the latest available (September 2020) as a proxy assuming that the current low interest rates persist in 2021.
The June 30 vintage is used as the reference vintage as it provides the earnings forecast that is the closest to the magnitude of economic downturn implied by the October 2020 IMF WEO projection. The quantitative results, however, are robust to using alternative reference vintages.
Without this scaling factor, the ASEAN-6 median ICR value from Approach II would be much higher than the median ICR obtained from Approach I.
In normal times, the sample median rollover ratio is estimated at about 110 percent, implying a nominal debt growth of about 10 percent.
