properly control for this potential endogeneity issue, a dynamic panel GMM estimation technique is implemented (Arellano and Bond, 1991; Arellano and Bover, 1995). The system GMM technique controls for endogeneity by using lags of the endogenous variables as instruments in the econometric equation. It also controls for eventual autocorrelation issues that could appear after introducing lags of the dependent variable as regressors. ESpendit−1 , Fisconit−1 and GDPgwhit are treated as endogenous variables in the system GMM regression.<sup>3</sup>

# **3. Data**

Table 1-Panel A shows the data used to estimate equation 1, and Panel B describes the data used to estimate equation 4. Data are collected from the World Economic Outlook and the World Development Indicators databases. The data cover the period 1990-2019. To address the data availability issues, all variables are averaged by a three-year interval.

Panel A data show that about 9.5% of the observations experienced a relative decline of total government expenditure over the last decades and the average debt service-to-reserve ratio varies between 0.5% and 397%. The average debt-to-GDP ratio is over the IMF's recommended benchmark of 40% in about 60% of the countries, showing that several countries may be affected by debt sustainability issues. The data are available for a total of 50 low- and middle-income countries from 1991 to 2019 to estimate equation 1.

Education spending per school-age individual (in primary, secondary, and tertiary) is the dependent variable in equation 4 (Table 1-Panel B). Because countries included in the analysis have different demographic structures, the size of the school-age population may vary across countries. Government spending on education is usually designed to cover the school-age population, and education expenditure by school-age child appears to accurately capture government's effort toward education financing.

Panel B shows that the average spending per school-age child is about $300. The spending per child hugely varies across income groups, from $39.5 in low-income countries to $636.7 in upper-middle income countries. This illustrates the large cross-country inequality in the volume of resources available to finance education. A total of 96 countries has available data to estimate equation 4. Almost half of these countries are lower-middle income countries.

**Table 1:** Descriptive statistics

||Obs|Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|---|
|**Panel A: Probit model (equation 1)**||||||
|Change in government expenditure compared to revenue (_C_)|577|0.095|0.294|0|1|
|External debt to reserve ratio (_DebtRes_)|577|6.095|18.612|0.492|397.016|

3 It is worth mentioning that debt service obligations exist because of countries’ borrowing activities in the past. Past education spending financed by debt could play a role in the relationship between the current debt service and education spending. Although equation 4 is dynamic and takes into account education expenditure at time t-1, it may not fully capture the long-term relationship between past education spending on the one hand and the current debt service and education spending on the other hand.

5