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Distributional Effects of Structural Reforms in Developing Countries: Evidence from Financial Liberalization

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Abstract

This paper examines the redistributive effects of financial liberalization, including domestic and external finance reforms, implemented in 64 emerging and low-income countries over the past four decades. To identify these effects, we employ a “doubly robust” estimation approach and generate impulse responses using the local projections method. Our findings reveal that financial reforms significantly reduce income inequality. These results are robust and hold across various specifications and alternative methods. We find that reducing income inequalities through financial reforms depends on several factors, including improved access to financial services, level of public expenditures, and institutional quality. Furthermore, we demonstrate that governments adopting a reform approach that considers sequencing and potential complementarity of measures can significantly reduce income inequality. Taking the business cycle into account, we observe that implementing financial reforms during periods of relatively slower economic growth would be more beneficial for developing countries. Financial reforms have an impact on reducing income inequality by increasing the income of individuals located at the bottom of the distribution while decreasing the income of individuals located at the top of the distribution.

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Data Availability

The data used in this study, with the exception of the data on structural reforms, are sourced from public sources and are available from the corresponding author upon request.

Notes

  1. The concept of inequality, as defined by KNBS and SID (2013), pertains to the degree of variance between the distribution of economic well-being produced within an economy and the hypothetical equitable allocation among its populace. Recognizing the multidimensional nature of inequality, which encompasses disparities in access to fundamental services, opportunities, income, education, etc., this investigation specifically addresses the domain of income inequality.

  2. Ostry et al. (2009) analyzes the impact of a sequencing strategy or complementarity of structural reforms on economic growth.

  3. Furthermore, we conduct a thorough analysis to test the reliability of our findings by employing an alternative approach as defined by Alesina et al. (2020) to identify episodes of reform. This approach involves identifying episodes as those in which the absolute annual alterations in the reform indexes surpass the \(75^th\) percentile across the sample.

  4. Refer to Table A1 in the Online Appendix for specific information categorized by time periods and income levels. Additionally, Fig. A1 in the Online Appendix illustrates the years in which each country underwent reform episodes. Online Appendix Table 10 presents the list of countries.

  5. This observation aligns with the depiction of reform episodes using definitions from Alesina et al. (2020), wherein an episode involves a financial reform index exceeding the average annual change over all observations by two standard deviations or using the Kaopen indicator.

  6. Refer to Fig. A2 in the Appendix Online for a visual representation of the diminishing relationship between financial reforms (domestic and external finance) and income inequality (Gini index).

  7. However, it’s worth noting that local projection methods do have certain drawbacks in terms of efficiency.

  8. Notably, the number of lag terms is determined based on the economic literature regarding the use of local projection methods, with a maximum autocorrelation lag set at \(h+1\).

  9. An alternative choice is the enhanced inverse probability weighted (AIPW) estimator. This estimator includes an adjustment term that corrects for bias in the IPW estimator. Consequently, if the treatment model is accurately defined, the bias correction term becomes zero, rendering the estimator identical to the IPW estimator. In instances where the treatment model is improperly specified but the outcome model is correctly specified, the bias correction term fine-tunes the estimator. Notably, the inclusion of the bias correction term confers upon the AIPW estimator the same dual robustness attribute as the IPWRA estimator.

  10. This approach may not be the most robust identification strategy, but it can serve as a starting point for developing one.

  11. Qualitatively, in terms of the intensity of the estimated effects, we observe that the size of the estimated effects for domestic financial reforms, when used in continuous form, is relatively larger compared to those of external capital account liberalization reforms. However, quantitatively, Alesina et al. (2020) argue that it is not possible to compare reform indices across different sectors.

  12. It’s important to note that achieving perfect alignment of covariates through conventional models like probit and logit is exceedingly rare.

  13. These reform variables are incorporated as continuous variables rather than dummy variables. Nevertheless, when introduced as dummy variables according to the definition of reform episodes, the outcomes remain consistent with the baseline findings.

  14. Similar to the Gini index, a lower Theil index signifies lower income inequality.

  15. The calculation of the output gap relies on the approach outlined by Hamilton (2018), which employs the logarithm of GDP per capita. While conventional empirical practices often use the Hodrick-Prescott filter (Hodrick and Prescott 1997), this entails selecting a parameter value, a choice that warrants discussion. In contrast, we adopt the alternative method put forth by Hamilton (2018), which obviates the need for parameter selection and avoids the results’ sensitivity to the chosen parameter value.

  16. The data is obtained from https://data.imf.org/?sk=f8032e80-b36c-43b1-ac26-493c5b1cd33b

  17. If this volatility results in an inefficient allocation of resources, it is preferable to reform the domestic financial sector prior to opening the external capital account, rather than following a reverse sequencing strategy. By adopting this approach, higher economic growth can be expected (Ostry et al. 2009).

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Acknowledgements

I would like to thank Chris Papageorgiou for kindly sharing the database on structural reforms. I would like to express my gratitude to the editor, the associate editor, and the two anonymous reviewers for providing highly valuable feedback on previous versions of my paper. Their input has played a crucial role in shaping the final version. I would also like to thank Estel Ablam Apeti for engaging discussions and valuable comments. Additionally, I am thankful to UNU-WIDER for providing me with the opportunity to conduct this study. Special thanks are extended to Jesse Lastunen, Michael Danquah, Abrams Tagem, Nyemwererai Matshaka, and Rodrigo Oliveira for their insightful comments that significantly enriched this paper. Any remaining errors or omissions are my sole responsibility.

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  1. Equipe d’Economie - Le Havre Normandie (EDEHN), EA 7263, Normandie Université, 25 Rue Philippe Lebon, BP 1123, 76063, Le Havre Cedex, France

    Kwamivi Mawuli Gomado

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Gomado, K.M. Distributional Effects of Structural Reforms in Developing Countries: Evidence from Financial Liberalization. Open Econ Rev (2023). https://doi.org/10.1007/s11079-023-09740-7

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