Buoyant or sinking? Tax revenue performance and prospects in developing Asia

Hill, Samuel; Jinjarak, Yothin; Park, Donghyun Working Paper Buoyant or sinking? Tax revenue performance and prospects in developing Asia ADB Economics Working Paper Series, No. 656 Provided in Cooperation with: Asian Development Bank (ADB), Manila Suggested Citation: Hill, Samuel; Jinjarak, Yothin; Park, Donghyun (2022) : Buoyant or sinking? Tax revenue performance and prospects in developing Asia, ADB Economics Working Paper Series, No. 656, Asian Development Bank (ADB), Manila, https://doi.org/10.22617/WPS220182-2 This Version is available at: https://hdl.handle.net/10419/264372 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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TAX REVENUE PERFORMANCE AND PROSPECTS IN DEVELOPING ASIA Samuel Hill, Yothin Jinjarak, and Donghyun Park ASIAN DEVELOPMENT BANK ADB Economics Working Paper Series Buoyant or Sinking? Tax Revenue Performance and Prospects in Developing Asia Samuel Hill, Yothin Jinjarak, and Donghyun Park No. 656 | May 2022 Samuel Hill ([email protected]om.au) is a senior economist at the World Bank. Yothin Jinjarak ([email protected]) is a senior economist at the Macroeconomics Research Division and Donghyun Park ([email protected]) is an economic advisor at the Office of the Chief Economist and Director General, Economic Research and Regional Cooperation Department, Asian Development Bank. Eugenia Co Go and Anton Miguel Ragos provided excellent assistance with tax data. We are grateful for comments from participants at the Asian Development Outlook 2022 ThemeChapter workshop in November 2021 and the Asian Development Bank Economists’ Forum in January 2022. The ADB Economics Working Paper Series presents research in progress to elicit comments and encourage debate on development issues in Asia and the Pacific. The views expressed are those of the authors and do not necessarily reflect the views and policies of ADB or its Board of Governors or the governments they represent. Creative Commons Attribution 3.0 IGO license (CC BY 3.0 IGO) © 2022 Asian Development Bank 6 ADB Avenue, Mandaluyong City, 1550 Metro Manila, Philippines Tel +63 2 8632 4444; Fax +63 2 8636 2444 www.adb.org Some rights reserved. Published in 2022. ISSN 2313-6537 (print), 2313-6545 (electronic) Publication Stock No. WPS220182-2 DOI: http://dx.doi.org/10.22617/WPS220182-2 The views expressed in this publication are those of the authors and do not necessarily reflect the views and policies ofthe Asian Development Bank (ADB) or its Board of Governors or the governments they represent. 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Since papers in this series are intended for quick and easy dissemination, the content may or may not be fully edited and may later be modified for final publication. CONTENTS TABLES AND FIGURES iv ABSTRACT v I. INTRODUCTION 1 II. TESTING HYPOTHESES 2 A. Interpretation of Coefficients of Short-Run and Long-Run Tax Buoyancies 2 B. COVID-19 Crisis and Tax Revenue 3 C. Large-Scale Tax Relief and Tax Buoyancy 3 III. EMPIRICAL STRATEGY AND FINDINGS 3 A. Data and Descriptive Statistics 4 B. Estimation 4 IV. IMPACT OF COVID-19 ON TAX REVENUES 8 A. Estimated Excess Tax Revenue Loss from COVID-19 8 B. COVID-19 Fiscal Measures and Tax Buoyancy 8 V. PROJECTION OF SHARE OF TAX TO GROSS DOMESTIC PRODUCT IN 2030 10 VI. CONCLUSIONS 12 APPENDIX 15 REFERENCES 17 TABLES AND FIGURES TABLES 1 Pooled Mean-Group Estimator 5 2 Projection of the Share of Tax to Gross Domestic Product in 2030 and Changes from 2019 11 FIGURES 1 Long-Run Tax Buoyancy Coefficients 7 2 Excess (Actual Values Minuses Model Estimates) Tax Losses in 2020 9 3 COVID-19 Fiscal Measures and Tax Buoyancy 10 A1 Distribution of Share of Tax to Gross Domestic Product 15 A2 Correlations of Tax Growth, GDP Growth, Inflation, and Share of Debt to GDP 16 ABSTRACT How did developing Asian economies perform with respect to tax revenue mobilization before and during the coronavirus disease (COVID-19) pandemic? An analysis of data from developing Asia suggests that both short-run and long-run tax buoyancies, a measure of how tax revenue responds to gross domestic product (GDP), were close to one before COVID-19, which is indicative of fiscal sustainability. COVID-19 had a negative impact on the region’s GDP and thus its tax base, and spurred significant fiscal stimulus including tax measures. At a regional level, the pandemic subtracted a tenth of a percentage point from tax revenue growth after controlling for changes in GDP. Using estimated economy-level tax buoyancy coefficients, a counterfactual analysis is undertaken to estimate excess tax revenue losses in 2020 because of COVID-19. The average GDP-weighted excess tax revenue loss is about half a percentage point of pre-pandemic GDP. Keywords: tax collection, business cycles, pandemic crisis JEL codes: E32, H12, H20, H71 I. INTRODUCTION In many developing economies, additional tax revenue is needed to meet growing demands for public goods and services, and support development goals. For example, Gaspar et al. (2019) estimate that achieving the Sustainable Development Goals (SDGs) in key areas requires additional spending by 2030 of US$0.5 trillion for low-income developing economies and US$2.1 trillion for emerging market economies. The coronavirus disease (COVID-19) pandemic has battered government finances, adding to the challenge (Benedek et al. 2021). Much of this additional spending will need to be financed by tax revenue. To shed light on how well governments are positioned to meet this challenge, it is important to understand the responsiveness and efficiency of tax collection in developing economies, informed by empirically assessing the links between the tax base and tax revenues. In particular, tax buoyancy measures the response of tax revenues to gross domestic product (GDP), and is therefore a key metric for understanding tax system performance and the outlook for revenues. A buoyancy coefficient greater than one implies that tax revenues grow faster than GDP, and less than one the opposite. In this study, we focus on developing economies in Asia, where tax revenue is comparatively low, and estimate the short-run and long-run association between tax revenue and output with panel and time-series analyses. Our research questions, testing hypotheses, and their relevance are: (i) How buoyant was tax revenue before the COVID-19 crisis? Were tax buoyancy coefficients greater than one indicative of fiscal sustainability, strongly rising revenues, and effective tax collection? (ii) What was the impact of the COVID-19 pandemic on tax buoyancy? How large are the actual tax revenue losses in 2020 compared to the model estimates? (iii) What can we infer about the recovery in revenues after the crisis? What are the implications of an economy’s COVID-19 fiscal measures on its tax buoyancy? Our estimation of tax buoyancy uses both time-series and panel data. Using error-correction models (ECMs), we take the natural logarithm of tax revenues and GDP, test the hypothesis that there exists a cointegrating relationship, and allow for the short-run and long-run tax buoyancies coefficients to differ. Our data covers 24 developing economies in Asia and the Pacific from 1998 to 2020, sourced from the Organisation for Economic Co-operation and Development (OECD), the International Monetary Fund (IMF), and the Asian Development Bank (ADB), and carefully cross-validated. Overall, the sample covers 23 years and 24 economies, giving us a total of 552 observations. The panel results suggest that both short-run and long-run tax buoyancies coefficients are above one. We then apply the regression coefficients to obtain estimates of revenue loss because of COVID-19. Specifically, we first estimate a time-series model with 1998–2019 data. We then compare model predictions of revenues from 2015 to 2020 with actual data to assess the impact of COVID-19 over and above what would normally be expected, given the GDP downturn. We find that tax revenue fell more than the model’s predictions in many economies, while in a few economies predicted actual revenues are very close. Averaged and GDP weighted across 24 economies, the estimated excess revenue loss of developing Asia amounted to –0.5% of 2019 GDP in 2020. The final part of our analysis projects the tax revenue-to-GDP ratio in the 24 economies to 2030, the target year for the SDGs. We find that the model estimates are insignificant for some 2 ADB Economics Working Paper Series No. 656 economies. In addition, the projection is sensitive to growth forecasts and model specification. Nevertheless, the different model specifications are largely in agreement on the 2030 projection. We find that tax-to-GDP ratios are projected to improve toward 2030 in a majority of developing Asian economies, other things held constant. Section II presents the testing hypotheses. We then describe data and estimate tax buoyancy coefficients in Section III, while Section IV concludes with key takeaways and policy implications. II. TESTING HYPOTHESES This section describes the hypotheses motivating our analysis of tax buoyancy in developing Asia. Broadly defined as how tax revenues (either in aggregate and/or by individual types of taxes) vary with changes in nominal GDP, tax buoyancy estimates inform fiscal sustainability and the extent to which taxes are an effective “automatic stabilizer,” and provide a formal metric of structural changes in tax revenues. The testing hypothesis is informed by, and builds on, previous tax buoyancy studies. In a sample of 30 economies in Asia and the Pacific spanning 1980–2017, Jalles (2021) assess tax buoyancy of total tax revenue, personal income taxes, corporate income taxes, general sales taxes, and trade taxes. They control for inflation and tax rates, and draw on data from the World Bank’s World Development Indicators, and the IMF’s World Economic Outlook (WEO) and Tax Policy Division databases. Applying an ECM specification with panel data and using the pooled mean group estimator, they estimate short-run tax buoyancy of one and long-run buoyancy greater than one. For advanced economies, Lagravinese, Liberati, and Sacchi (2020) examine 35 OECD countries for the period 1995–2016, assessing the buoyancy of total revenue (excluding social security contributions), total taxes, personal income taxes, corporate income taxes, and general sales taxes. They control for unemployment, inflation, various policy variables, using data from OECD revenue statistics, and OECD national accounts. They too apply an ECM specification with panel data, and use a Dynamic Common Correlated Effects estimator, and instrumental variables. They report estimates of short-run and long-run tax buoyancies generally less than one. As tax buoyancy estimates have multiple uses and can inform a variety of policies, our testing hypotheses focus on assessing the overall progress with tax revenue mobilization, the impact of the COVID-19 pandemic, and the longer-term implications implied by projections. A. Interpretation of Coefficients of Short-Run and Long-Run Tax Buoyancies Our estimation tests the response of tax revenues to GDP. Where tax buoyancy is estimated to be greater than one, tax revenues are rising more than proportionately to an increase in GDP. In this scenario, tax revenues are structurally increasing and sufficiently buoyant to support fiscal sustainability, even allowing for some increases in the spending share of GDP. However, this is not a stable long-run equilibrium, as taxes cannot continue to grow faster than GDP—the tax base— indefinitely. The tax system is also playing an automatic stabilizer role, providing a countercyclical fiscal impulse. Buoyant or Sinking? Tax Revenue Performance and Prospects in Developing Asia 9 Figure 2: Excess (Actual Values Minuses Model Estimates) Tax Losses in 2020 (% GDP in 2019) BAN = Bangladesh, CAM = Cambodia, GEO = Georgia, GDP = gross domestic product, INO = Indonesia, KGZ = Kyrgyz Republic, KIR = Kiribati, LAO = Lao People's Democratic Republic, MAL = Malaysia, MLD = Maldives, NEP = Nepal, PAK = Pakistan, PNG = Papua New Guinea, PRC = People's Republic of China, RMI = Marshall Islands, SAM = Samoa, SIN = Singapore, SRI = Sri Lanka, THA = Thailand, VAN = Vanuatu, VIE = Viet Nam. Note: Negative values are tax loss beyond what would normally be expected in the GDP downturn. Excludes Federated States of Micronesia whose revenue loss is estimated at 19.8% of GDP. Source: Authors' calculations. As shown in Figure 3, COVID-19 fiscal policy responses appear to be associated with lower tax buoyancy in developing Asian economies. Specifically, we observe a negative correlation (–0.15) between the size of the fiscal policy response, expressed as a share of GDP, and the size of the estimated tax buoyancy coefficients. While the negative association is suggestive, more data postpandemic is required to support the test. 10 ADB Economics Working Paper Series No. 656 Figure 3: COVID-19 Fiscal Measures and Tax Buoyancy ARDL = autoregressive distributed lag, BAN = Bangladesh, CAM = Cambodia, COVID-19 = coronavirus disease, FIJ = Fiji, FSM = Federated States of Micronesia, GDP = gross domestic product, GEO = Georgia, INO = Indonesia, KGZ = Kyrgyz Republic, KIR = Kiribati, LAO = Lao People’s Democratic Republic, MAL = Malaysia, MLD = Maldives, NEP = Nepal, PAK = Pakistan, PHI = Philippines, PNG = Papua New Guinea, PRC = People’s Republic of China, RMI = Marshall Islands, SAM = Samoa, SIN = Singapore, SRI = Sri Lanka, THA = Thailand, TON = Tonga, VAN = Vanuatu, VIE = Viet Nam. Note: Above-the-line refers to measures directly affecting revenue and expenditure; for example, deferral of taxes and cash transfers. Source: Authors’ calculations of tax buoyancy coefficients; International Monetary Fund. Fiscal Monitor Database of Country Fiscal Measures in Response to the COVID-19 Pandemic. https://www.imf.org/en/Topics/imf-and-covid19/Fiscal-PoliciesDatabase-in-Response-to-COVID-19, measures since January 2020 and covers measures for implementation in 2020, 2021, and beyond. V. PROJECTION OF SHARE OF TAX TO GROSS DOMESTIC PRODUCT IN 2030 As a by-product of tax buoyancy analysis, we can analyze whether an economy is on the path of increasing tax revenue as a share of GDP over the long run. By estimating tax buoyancy with the data up to and including 2019, we could answer the question: Were economies making progress in mobilizing revenues prior to the pandemic? If the answer is yes, then one might infer that, postpandemic, when economies recover, progress with revenue mobilization would continue. Having derived estimates of tax buoyancy, a simple forecasting exercise is undertaken to estimate tax revenue in the long-run, specifically in 2030, the target date for achievement of the SDGs. Buoyant or Sinking? Tax Revenue Performance and Prospects in Developing Asia 11 Given   of 2019, growth forecasts, and the estimated long-term tax buoyancy coefficients, we calculate tax revenue from 2019 and 2030. Following Gupta, Jalles, and Liu (2021): 𝑡𝑎𝑥 𝐺𝐷𝑃, =𝑡𝑎𝑥 𝐺𝐷𝑃,×𝛱 󰇧(1+𝐿𝑅𝑡𝑎𝑥𝐵𝑢𝑜𝑦𝑎𝑛𝑐𝑦×𝑔𝑑𝑝𝐺𝑟𝑜𝑤𝑡ℎ, 1+𝑔𝑑𝑝𝐺𝑟𝑜𝑤𝑡ℎ, 󰇨;𝜏=𝑡+1,...,𝑇 In our calculation, 𝑡=2019;𝑇=2030. We use GDP forecasts for 2020–2026 and a 10-year moving average (2017–2026) as the growth projection after 2026. Both historical and forecasted values of GDP are drawn from the 2019 IMF WEO database. In the scenario analysis of Section IV, we use the 2019 forecast of 2020–2026 GDP to assess the pandemic effect on tax buoyancy on GDP. Table 2 reports projected tax-to-GDP ratios in 2030. We note that the model estimates are not significant for some economies (t-values) and that the projection is sensitive to growth forecasts Table 2: Projection of the Share of Tax to Gross Domestic Product in 2030 and Changes from 2019 Country GDP_Growth Tax_Buoyancy taxGDP2030 Change t_stat Bangladesh 10.2 1.1 9.7 0.8 34.36 Bhutan 9.1 1.3 20.3 4.1 13.90 Cambodia 6.8 1.7 34.3 12.4 7.87 Sri Lanka 6.8 1.0 11.3 –0.2 19.70 Indonesia 6.1 1.1 11.7 0.6 41.71 Lao People’s Democratic Republic 7.2 1.2 12.8 1.9 0.64 Malaysia 4.9 0.8 10.8 –1.3 5.11 Maldives 6.1 1.4 24.4 5.4 9.18 Nepal 6.7 1.4 24.9 5.1 28.81 Pakistan 8.5 1.1 13.0 1.2 59.53 Philippines 6.1 1.2 16.4 1.9 9.62 Singapore 2.1 1.1 13.5 0.2 6.87 Thailand 3.5 1.1 16.7 0.6 15.97 Viet Nam 4.1 1.0 14.6 –0.1 28.18 Fiji 4.8 1.3 26.5 3.1 44.69 Kiribati 2.4 1.0 17.4 –0.0 2.28 Vanuatu 4.0 1.0 17.8 0.1 16.32 Papua New Guinea 3.6 0.8 12.0 –1.0 3.54 Samoa 4.5 1.2 27.8 2.2 4.26 Tonga 3.0 1.1 21.6 0.7 8.65 Marshall Islands 1.3 0.9 17.3 –0.1 3.05 Micronesia, Federated States of 1.1 1.8 26.7 1.9 4.49 Georgia 4.7 1.2 26.2 2.2 3.39 Kyrgyz Republic 5.9 0.8 18.6 –1.9 2.29 China, People’s Republic of 5.2 0.5 12.5 –3.5 0.29 GDP = gross domestic product. Note: Long-run estimates of the mean-group estimator (MG); those of the autoregressive distributed lags model are available upon request. Tax buoyancy coefficients from the estimation on 1998–2019 data. Source: Authors’ calculations using data from Organisation for Economic Co-operation and Development. Global Revenue Statistics Database. https://www.oecd.org/tax/tax-policy/global-revenue-statistics-database.htm (accessed 15 September 2021); International Monetary Fund. Government Finance Statistics https://data.imf.org/?sk=a0867067-d23c-4ebc-ad23-d3b015045405 (accessed 22 October 2021). 12 ADB Economics Working Paper Series No. 656 and the model specified. Nevertheless, different model specification is largely in agreement on the projection. Tax-to-GDP ratios are projected to increase in most economies in our sample, other things held constant. However, in some cases the projected increase is modest, leaving economies with still low tax revenues. This underscores the importance of bolstering efforts to mobilize taxes to support development in Asia (ADB 2022). VI. CONCLUSIONS Tax buoyancy, which captures how tax revenues vary in response to changes in GDP, is crucial to understanding tax revenue performance and fiscal sustainability. Tax buoyancy provides estimates of the extent to which tax revenues rise and fall during cyclical upturns and downturns, shedding light on the stabilizing role of taxes over the business cycle. Tax buoyancy also helps assess how tax revenue evolves over the long run. In light of the pronounced economic impact of COVID-19, now is an especially opportune time to visit this issue. During a major downturn like the COVID-19 pandemic, tax buoyancy may be affected because of policy changes or greater tax evasion. Therefore, we are interested in both the impact of the pandemic on revenues and the prospects for the recovery of revenues as the pandemic recedes. Tax buoyancy greater than one implies that tax revenues are rising more than proportionately to an increase in GDP. Therefore, tax revenues are structurally increasing and sufficiently buoyant to support fiscal sustainability, even with some increases in public spending. The tax system is also helping to stabilize short-run output. During upturns, tax revenues increase disproportionately, providing a countercyclical impulse which dampens demand and prevents overheating. During downturns, tax revenues decrease disproportionately, which is analytically equivalent to a tax cut which boosts economic activity. In contrast, if tax buoyancy is less than one, tax revenues are structurally decreasing, and insufficiently buoyant tax revenues pose a risk to fiscal sustainability in the absence of spending cuts (Creedy and Gemmel 2008; Dudine and Jalles 2018; Gupta, Jalles, and Liu forthcoming; and Lagravinese, Liberati, and Sacchi 2020). To estimate tax buoyancy in developing Asia, an ECM of total tax revenue and nominal GDP is estimated for a sample of 24 developing Asian economies for the period 1998–2020, using both timeseries and panel data approaches. The estimation yields two sets of coefficients: the instantaneous impact of changes in GDP on tax revenues or short-run tax buoyancy; and the long-run relationship between GDP and taxes or long-run tax buoyancy. To investigate the impact of COVID-19, the analysis includes a dummy variable, which takes the value of 1 for 2020 and zero otherwise. Regression results from our panel data analysis show that both short-run and long-run tax buoyancies in developing Asia as a whole is very close to one and statistically significant. The results also indicate that the pandemic subtracted a tenth from tax revenue growth after controlling for changes in GDP. To explore tax buoyancy at the economy level, the same model is estimated for individual economies. Consistent with our regional level analysis, we find that long-run tax buoyancy coefficients are close to one in most economies. Using coefficients from economy-level equations, a simple counterfactual analysis is performed to estimate excess tax revenue lost in 2020 because of the pandemic, reflecting the decline in revenue over and above what would normally be expected given the GDP downturn. We first Buoyant or Sinking? Tax Revenue Performance and Prospects in Developing Asia 13 estimate predicted revenues for 2020 and then deduct this from actual revenues. Based on GDP-weighted figures, it is estimated that, on average, developing Asian economies endured excess tax revenues losses equal to half a percentage point of 2019 GDP because of COVID-19. This is consistent with an observed negative association between the size of COVID-19 fiscal stimulus measures and our estimates of tax buoyancy. APPENDIX Figure A1: Distribution of Share of Tax to Gross Domestic Product BAN = Bangladesh, BHU = Bhutan, CAM = Cambodia, FIJ = Fiji, FSM = Federated States of Micronesia, GDP = gross domestic product, GEO = Georgia, INO = Indonesia, KGZ = Kyrgyz Republic, KIR = Kiribati, LAO = Lao People’s Democratic Republic, MAL = Malaysia, MLD = Maldives, NEP = Nepal, PAK = Pakistan, PHI = Philippines, PNG = Papua New Guinea, PRC = People’s Republic of China, RMI = Marshall Islands, SAM = Samoa, SIN = Singapore, SRI = Sri Lanka, THA = Thailand, TON = Tonga, VAN = Vanuatu, VIE = Viet Nam. Note: The sample covers 25 economies from 1998 to 2020. Source: Authors’ calculations using data from Organisation for Economic Co-operation and Development. Global Revenue Statistics Database. https://www.oecd.org/tax/tax-policy/global-revenue-statistics-database.htm (accessed 15 September 2021); International Monetary Fund. Government Finance Statistics https://data.imf.org/?sk=a0867067-d23c-4ebc-ad23d3b015045405 (accessed 22 October 2021). 16 Appendix Figure A2: Correlations of Tax Growth, GDP Growth, Inflation, and Share of Debt to GDP BAN = Bangladesh, BHU = Bhutan, CAM = Cambodia, CPI = consumer price index, FIJ = Fiji, FSM = Federated States of Micronesia, GDP = gross domestic product, GDP = gross domestic product, GEO = Georgia, INO = Indonesia, KGZ = Kyrgyz Republic, KIR = Kiribati, LAO = Lao People’s Democratic Republic, MAL = Malaysia, MLD = Maldives, NEP = Nepal, PAK = Pakistan, PHI = Philippines, PNG = Papua New Guinea, PRC = People’s Republic of China, RMI = Marshall Islands, SAM = Samoa, SIN = Singapore, SRI = Sri Lanka, THA = Thailand, TON = Tonga, VAN = Vanuatu, VIE = Viet Nam. Note: The sample covers 25 economies from 1998 to 2020. Source: Authors’ calculations using data from Organisation for Economic Co-operation and Development. 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Sancak, Cemile, Jing Xing, and Ricardo Velloso, 2010. “Tax Revenue Response to the Business Cycle.” IMF Working Paper WP/10/71. International Monetary Fund, Washington, DC. ASIAN DEVELOPMENT BANK 6 ADB Avenue, Mandaluyong City 1550 Metro Manila, Philippines www.adb.org Buoyant or Sinking? Tax Revenue Performance and Prospects in Developing Asia The response of tax revenue to gross domestic product (GDP) is indicative of fiscal sustainability in developingAsia. A counterfactual analysis shows that excess tax revenue losses in 2020 due to coronavirus disease (COVID-19) averaged about half a percentage point of pre-pandemic GDP for the region. To restore fiscal sustainability after the pandemic crisis, developing economies in Asia urgently need to mobilize their domestic fiscalresources. About the Asian Development Bank ADB is committed to achieving a prosperous, inclusive, resilient, and sustainable Asia and the Pacific, while sustaining its efforts to eradicate extreme poverty. Established in 1966, it is owned by 68 members —49 from the region. Its main instruments for helping its developing member countries are policy dialogue, loans, equity investments, guarantees, grants, and technical assistance.