during the Millennium Era
Ⅲ. Methodology and Data
1. Model Specification
To test how foreign aid affects fiscal variables in aid recipient countries, the following equation considers a fiscal variable, , as a function of ODA, allowing for disaggregation between Grants and Loans. A large set of control variables are included in :
. (1) where represents a vector of selected fiscal variables including public investment, government consumption, tax revenue, and domestic borrowing, all measured as a ratio to GDP. Since all dependent variables take the natural log value in actual estimation, the coefficients of Grants and Loans relative to GDP, as well as all other independent variables included in measure the semi-elasticity of the fiscal variable in response to a one-percentage point change in the Grants and Loans variables, respectively. Country and time indicators are referred to =1,
…, and =1, …, , respectively. Countries included in the actual estimation are listed in Appendix 1. In addition, the one-year lagged dependent variables are included as an independent variable, in all equations, since the current levels of fiscal variables may be significantly affected by the past levels. A full set of year dummies is included in all regressions to account for the time effects.
For the other major independent variables, as in the previous fiscal response studies, it is assumed that the level of public investment, government consumption, tax revenue and borrowing depends on the lagged dependent variables, the country’s level of economic development, which is represented by GDP per capita and external debt burden of the country, which is measured by debt servicing to GDP. The level of external indebtedness, which reflects the need to generate
revenue to service debt, may have positive correlation with tax revenue (Gupta et al. [2004]), while it may crowd out other government spending for debt interest payments. Export and import are also included as a ratio to GDP based on the fact that many developing countries obtain revenues from exports as well as imports, which in turn affect the level of other fiscal variables.
For a robustness of the model, a set of other relevant variables is drawn from previous fiscal response literature and are included in the model. Firstly, the share of agriculture in total value-added is expected to be negatively associated with tax revenue and positively associated with public investment and government consumption. This is due to the fact that agriculture is harder to tax, and rural areas are relatively in more need of infrastructure, leading to more demand on public investment and consumption. The share of industry in total value-added is expected to be positively associated with tax revenue but negatively associated with public investment and government consumption for the opposite reason. For the same reason, Urbanization is included and measured by the proportion of urban residents in total population. Secondly, inflation may have revenue effects through both unindexed tax systems and the generation of seigniorage (Gupta et al. [2004]).
Inclusion of inflation can also take cyclical factors into account.9) Thirdly, foreign direct investment (FDI) as a ratio to GDP is included, as a government could increase public capital spending and tax revenue to attract FDI. Lastly, in order to control the impact of the quality of institutions, political stability and corruption indicators from the Worldwide Governance Indicators (WGI) are included as proxies in the model. The remaining symbols in Equation (1) represent error terms related to countries, times, and country-time effects.
2. Methodology
For estimation, this study uses fixed effects model,10) which has been favored by the Hausman test for all equations and can control systematic tendency of individual-specific effects and time varying effects.11) Heteroskedasticity of error terms is detected through Breusch-Pagan Test and corrected with robust standard errors in all equations. In addition, the possibility of serial correlation (AR(1)), which is raised due to the persistence of fiscal variable data over time, is confirmed by Wooldridge test for serial correlation and is corrected.
9) In the case of counter-cyclical policy,one may expect public spending to be restrained when inflation accelerates and to be increased with rising unemployment levels (Aubin et al. [1988]).
10) The fixed-effects model controls for all time-invariant differences between the individuals, so the estimated coefficients of the fixed-effects models cannot be biased because of omitted time-invariant characteristics such as culture, religion, gender, or race - Ulrich Kohler, KreuterFrauke, Data Analysis Using Stata, Stata press, (2008), 245.
11) In order to find out whether potential endogeneity of the foreign aid variable exists, the author carried out Durbin-Wu-Hausman (DWH) Test. The result shows no existence of endogeneous variables in the model.
3. Data
The data used in this paper comes from three main sources and covers the period of 1990-2011, which is relevant to the Millennium Development Goals (1990-2015).
Data on foreign aid (net official development assistance: ODA) and its disaggregated data (grants and loans), relative to GDP, are from the OECD Credit Reporting System Database. Data on government consumption, tax revenue, debt service, exports, imports, GDP per capita, agriculture, industry, urbanization, population, FDI, and inflation are obtained from the World Bank World Development Indicator 2012 and supplemented with NYU Development Database. Data on corruption and political stability are taken from the Worldwide Governance Indicators (WGI) and used as proxies for institutional quality. Data on public investment is calculated by the gap between gross fixed capital formation (private sector) and the gross fixed capital formation12) (total) from IMF World Economic Outlook Database 2013 since official public investment data published by World Bank Global Development Network Growth Database is available up to only 1998 and not suitable for the time length of the analysis of this paper. Data on borrowing is the public sector borrowing requirement, obtained as residual from the budget constraint equation applied to the decision makers in aid recipient governments, as below:
, (2)
. (3) where for public investment; for government consumption; for tax revenue; for foreign aid disbursements; and for domestic borrowing. However, if we use as a dependent variable in the estimation of Equation (1), the coefficient of GRANTS and LOANS can be biased since GRANTS and LOANS are parts of the dependent variable , and are also used as independent variables in the same equation at the same time. To avoid this problem, is transformed into
′ , which is used as a dependent variable in the estimation of Equation (1). The estimated coefficients of GRANTS and LOANS were transformed by subtracting 1 from them, and the results are reported as the coefficients of GRANTS and LOANS of Equation (1) with dependent variable (BORROWING).
For estimation purposes, all variables except GDP per capita, urbanization, population, political stability, and corruption indicators are expressed as a percentage of GDP. Also, all dependent variables are transformed into natural log.
Full details of the dataset and summary statistics are provided in Appendix 2.
12) Formerly gross domestic fixed investment.