Does conditionality in IMF-supported programs promote revenue reform?

Abstract

This paper studies whether revenue conditionality in Fund programs had any impact on the revenue performance of 126 low- and middle-income countries during 1993–2013. The results indicate that such conditionality had a positive impact on tax revenue, with strongest improvement felt on taxes on goods and services, including the VAT. Revenue conditionality matters more for low-income countries, particularly those where revenue ratios are below the group average. Moreover, revenue conditionality appears to be more effective when targeted to a specific tax. These results hold after controlling for potential endogeneity, sample selection bias, and when revenues are adjusted for economic cycle.

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Notes

  1. 1.

    The average targeted fiscal adjustment in 133 IMF programs was 1.7 % of GDP during the period 1993–2001 (IEO 2003).

  2. 2.

    An example of structural tax revenue reforms with a positive revenue impact is the move to replace harmful trade taxes with broad-based consumption taxes (Baunsgaard and Keen 2010).

  3. 3.

    Arezki et al. (2012) found that IMF technical assistance and training support structural reforms in the context of IMF programs.

  4. 4.

    Most of the literature has focused instead on the effects of IMF programs on the balance of payments (Reichmann and Stillson 1978; Bird 1996); on inflation (Edwards and Santaella 1993; Killick 1995); on public spending (Conway 1994); social spending (Clements et al. 2013); on economic growth (Dreher 2006a; see also Przeworsky and Vreeland (2000) for a review of the earlier literature); on sovereign risk (Jorra 2012); and on the effect of IMF conditionality on trade openness (Wei and Zhang 2010). See also Dreher (2009) for a review of conditionality in IMF programs and a discussion on its effectiveness.

  5. 5.

    Includes VAT, excise taxes, and other consumption-related taxes.

  6. 6.

    Specific conditionality can also target revenue administration (such as, create a large VAT taxpayers unit).

  7. 7.

    If the revenue conditionality was not met, the dummy variable takes the value zero.

  8. 8.

    While unmet conditionality is not likely to be equivalent to no conditionality in terms of its effects on tax reform, Appendix B (available online only) formally considers a revenue conditionality variable that equals one for all revenue conditionality regardless of compliance record and another variable measuring compliance with revenue conditionality. The results indicate that only met revenue conditionality has an impact on revenue collection.

  9. 9.

    Gujarati and Porter (2009) suggest that the log transformation may be of advantage since it may reduce the incidence of heteroskedasticity and skewness of the data. Auriol and Warlters (2005) suggest that the log transformation may help ensure that out-of-sample fitted values of the tax-to-GDP ratio lie in the 0–100 % range. The results are, however, qualitatively identical when using the ratio in levels.

  10. 10.

    The (Blundell and Bond 1998) system-GMM estimator is used instead of Arellano and Bond (1991) difference-GMM estimator since the first one has much better finite sample properties in terms of bias and root-mean-squared error than the later; the results are not qualitatively different.

  11. 11.

    Moser and Sturm (2011) provide a very detailed survey of the literature on the determinants of IMF programs, including a description of the main economic and political variables. For further recent reviews, see for instance (Steinwand and Stone 2008; Bird 2007; Conway 2006).

  12. 12.

    Alternatively, other economic variables were considered as possible instruments without significant differences in the results, such as the level of external debt-to-GDP, inflation, the change in real GDP per capita, the change in the bilateral exchange rate to the US dollar and the overall fiscal balance.

  13. 13.

    The Hansen statistic’s p value should be high enough to reject correlation between the instruments and the errors but not too high because it weakens confidence in the test.

  14. 14.

    Using, alternatively, augmented Dickey–Fuller or Phillips–Perron unit root tests.

  15. 15.

    Further disaggregation for taxes on corporate profits (CIT) and on personal income (PIT) was performed with no qualitatively difference compared to total taxes on income.

  16. 16.

    Appendix B (online only) presents the results after also controlling for the level of corruption (omitted here because it reduces considerably the number of observations), which are qualitatively identical to that in Table 2.

  17. 17.

    Alternatively, the impact of revenue conditionality on structural revenue performance can be analyzed by using a dummy on revenue conditionality that equals one during and after each IMF program. The results of this are qualitatively similar to those presented in the text and the coefficients imply revenue gains very close to those computed by the third year after the program started.

  18. 18.

    Except perhaps for the tax on corporate profits in low-income countries whose share in total revenue can still be significant (International Monetary Fund 2013).

  19. 19.

    Alternatively, for further robustness, selection bias has been addressed using a fixed-effects model including Heckman’s (1976, 1979) proposed two-stage estimation procedure. As a second alternative to GMM and fixed-effects estimators, we tried the inverse probability weight regression-adjustment method (Hirano et al. 2003). Results from these alternative models are not qualitatively different from those using system-GMM and have been drop here to preserve space but are available from the authors.

  20. 20.

    As in Sect. 3, the diagnostics here are satisfactory, with a tolerable value for the Hansen, Kleibergen–Paap, and Cragg–Donald tests, and with the Arellano and Bond (1991) test for first- and second-order serial correlation (M1 and M2) suggesting the former is present but the latter is not, which is consistent with the underlying assumptions.

  21. 21.

    In addition, we have also included a dummy variable for oil exporter countries to capture potential negative influence of natural-resource revenues on domestic tax effort (Benedek et al. 2014). Alternatively, we have also used non-resource tax revenue only as in Crivelli and Gupta 2014. The results being qualitatively identical to those in Table 2 are omitted to preserve space.

  22. 22.

    Middle-income countries are classified according to the World Bank criterion. Seventy-two low-income countries are now eligible for concessional lending, which the IMF provides via the Poverty Reduction and Growth Trust (PRGT). It currently carries a zero interest rate on its loans. Eligibility for PRGT lending is based on a member country’s annual per capita income and ability to access international financial markets on a sustainable basis. Concessional support credit lines under the PRGT include the Extended Credit Facility (ECF) and the Standby Credit Facility (SCF). Middle-income countries have been supported mainly under Standby Arrangements (SBA), but also under the Extended Fund Facility (EFF), the Flexible Credit Line (FCL), and the Precautionary and Liquidity Line (PLL). Prior to 2001, low-income countries received support under Extended Structural Adjustment (ESAF) facility and Poverty Reduction and Growth Facility (PRGF).

  23. 23.

    This grouping is almost equivalent to considering the 50th percentile of the distribution with less and more corrupt countries, respectively, also on the basis of the ICRG ranking of corruption.

  24. 24.

    Control variables include the current inflation rate to ensure that the results are not driven by high inflation episodes and a linear time trend.

  25. 25.

    Overidentifying restriction tests (notably Wooldridge’s 1995 score test) do not reject the validity of the selected instruments.

  26. 26.

    Alternatively, qualitatively identical results were obtained when using the total number of revenue conditions.

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Acknowledgments

We would like to thank the Editor Ron Davies and two anonymous referees for excellent suggestions. We are grateful to Santiago Acosta-Ormaechea, Celine Allard, Katherine Baer, Sabina Bhatia, Martin Cerisola, Karla Chaman, Francesco Columba, Ruud De Mooij, Jonathan Dunn, Nisreen Farhan, Katherine Ferry, Geoff Gottlieb, Michael Keen, Christina Kolerus, Svitlana Maslova, Masahiro Nozaki, Iva Petrova, Marcos Poplawski-Ribeiro, Saad Quayyum, and Philippe Wingender for many helpful suggestions on an earlier draft of the paper, and to Haoyu Wang for outstanding assistance with consolidating the data. The views expressed herein are those of the authors and should not be attributed to the IMF, its executive board, or its management.

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Appendix: Data

Appendix: Data

The countries in the sample are the following:

Low-income countries Bangladesh, Benin, Burkina Faso, Burundi, Cambodia, Central African Rep., Chad, Comoros, Congo, Dem. Rep. of, Eritrea, Ethiopia, The Gambia, Ghana, Guinea, Guinea-Bissau, Haiti, Kenya, Kyrgyz Republic, Lao People’s Democratic Republic, Liberia, Madagascar, Malawi, Mali, Mauritania, Mozambique, Myanmar, Nepal, Niger, Rwanda, Sierra Leone, Solomon Islands, Tajikistan, Tanzania, Togo, Uganda, Zambia, Zimbabwe

Middle-income countries Albania, Algeria, Angola, Antigua and Barbuda, Argentina, Armenia, Azerbaijan, Belarus, Belize, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Bulgaria, Cameroon, Cape Verde, Chile, China, P.R.: Mainland, Colombia, Republic of Congo, Costa Rica, Côte d’Ivoire, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Fiji, Gabon, Georgia, Grenada, Guatemala, Guyana, Honduras, India, Indonesia, I.R. of Iran, Jamaica, Jordan, Kazakhstan, Kiribati, Lebanon, Lesotho, Libya, Lithuania, Macedonia FYR, Malaysia, Maldives, Mauritius, Mexico, Moldova, Mongolia, Morocco, Namibia, Nicaragua, Nigeria, Pakistan, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Russian Federation, Samoa, Senegal, Seychelles, South Africa, Sri Lanka, St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines, Sudan, Suriname, Swaziland, Syrian Arab Republic, São Tomé and Príncipe, Thailand, Tonga, Tunisia, Turkey, Ukraine, Uruguay, Uzbekistan, Vanuatu, Rep. Bol. Venezuela, Vietnam, Republic of Yemen.

Data on total tax revenue, taxes on goods and services, VAT, income tax revenue, and trade tax revenue are taken from three different sources: the IMF’s Government Financial Statistics (GFS) database, the IMF’s World Economic Outlook (WEO) database, and the Organization for Economic Co-operation and Development (OECD) Revenue Statistics in Latin America database, relative to GDP. Data for the construction of the dummy variables on IMF program with and without conditionality are taken from the IMF’s Monitoring of Fund Arrangements (MONA) Database. Among the economic descriptors for conditionality in the MONA database, considered in this paper are those related to revenue conditionality, which are: revenue measures and revenue administration. Total revenue conditionality and only met revenue conditionality were considered separately. For IMF program without conditionality, the dummy takes the value 1 if the country has a program with no revenue conditionality in the year t and zero otherwise. The starting year of a program is defined as the year in which it was approved. The end year is the year in which the program expired. For IMF program with revenue conditionality, the dummy takes the value 1 if the country has a program that contains revenue conditionality for a given tax in year t and zero otherwise, as discussed in Sect. 2. In cases in which revenue conditionality cannot be identified with a specific tax in year t (general conditionality), it is assumed that the revenue conditionality applies for each and all of the taxes in that country.

Share of agriculture in aggregate value added, taken from the World Bank’s World Development Indicators (WDI) database. Trade openness is calculated as imports plus exports in percent of GDP, taken from the IMF’s International Financial Statistics (IFS) database. Per capita GDP is calculated in constant (2000) US dollars, taken from the WDI database, expressed in logs. Inflation is the annual change in the CPI, taken from the IFS database. International reserves, nominal foreign exchange rate to the US dollar is taken from the IMF’s IFS database. The overall fiscal balance, in percent of GDP, is taken from the WDI database. Foreign debt, relative to GDP, is taken from the WDI database. The ICRG corruption scores, produced by Political Risk Services Group, are assessments by staff and relate to actual and potential corruption in the following forms: excessive patronage, nepotism, job reservations, ‘favor-for-favors,’ secret party funding and suspiciously close ties between politics and business. The scores range from 0 to 6, where 0 indicates the highest potential risk of corruption and 6 indicates the lowest potential risk for any country.

Other political economy variables include: past IMF program, measured as the lag of a 5-year-moving average of the IMF program dummy, taken from the MONA database; the KOF index of political globalization as in Dreher (2006b), measured by the number of embassies and high commissions in a country, the number of international organizations of which the country is a member, the number of UN peace missions the country has participated in, and the number of international treaties that the country has signed since 1945. Two indicators from the World Bank’s Database of Political Institutions as in Beck et al. (2001): the index of political plurality, in which legislators are elected using a winner-take-all/first past the post-rule, taking the value 1 if this system is used or 0 otherwise; and an indicator for chief executive years in office, measure in number of years. Two indicators from Freedom House: one on political rights as a measure of free participation in the political process, including the right to vote freely for distinct alternatives in legitimate elections, compete for public office, join political parties and organizations, and elect representatives who have a decisive impact on public policies and are accountable to the electorate, taking the value 1 (most free) to 7 (least free); and the imputed polity index, which in addition to political rights, also measures civil liberties, allowing for the freedom of expression and belief, associational and organizational rights, rule of law, and personal autonomy without interference from the state. The imputed policy index takes the value 0 for least democratic and 10 for most democratic countries. In addition, Kuncic (2014) indicator on legal institutional quality is considered, taking the value 1 (high) to 0 (low). Finally, Transparency International’s Corruption Perception index has been considered, which measures the level of corruption in 152 countries, transformed to take the value 0 (high corruption) to 100 (low corruption). All these indicators are available online at www.qog.pol.gu.se, from Dahlberg et al. (2015). Appendix Table 10 summarizes the data.

Table 10 Descriptive statistics

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Crivelli, E., Gupta, S. Does conditionality in IMF-supported programs promote revenue reform?. Int Tax Public Finance 23, 550–579 (2016). https://doi.org/10.1007/s10797-015-9379-7

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Keywords

  • Tax revenue reform
  • Structural conditionality
  • IMF

JEL Classification

  • C33
  • E62
  • F33
  • H2