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Does oil rents dependency reduce the quality of education?

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Abstract

The resource curse hypothesis suggests that resource-rich countries (especially oil-dependent economies) show lower economic growth rate as compared to resource-poor countries. We contribute to this literature by providing empirical evidence on a new transmission channel of the resource curse, namely the negative long-run effect of oil rents on the quality of education. Our empirical analysis for more than 70 countries from the period of 1995–2015 shows a significantly positive effect of oil rents on the quantity of education measured by government spending on primary and secondary education. However, we find a robust and negative long-run effect of oil rents dependency on the objective and subjective indicators of quality of education. Further, panel regressions with country and year fixed effects support our cross-country findings on the negative effect of oil rents dependency on the quality of education.

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Notes

  1. Resource rents refer to difference between international market price and average unit production costs multiplied by production volume of specific kind of natural resource. Our main analysis is focusing on point resources, e.g., crude oil.

  2. http://www.worldbank.org/en/news/feature/2016/03/10/education-quality-measuring-learning-outcomes-in-francophone-africas-primary-schools.

  3. We see the same impressive funding of education, when we consider its share of government total spending (16.3% in the MENA vs. 13.9% world average). In addition, educational spending per student at the secondary level (in relation to GDP per capita) is higher in the resource-rich MENA region (19.5%) than in East Asia (14.7%) or Latin America (13.9%).

  4. The literature shows that the curse is in point resources such as oil and gas, which offer significant economic rents (Ross 2012). In this paper, we focus on oil rents dependency.

  5. Davis (1995) sees positive effects of resource rents on schooling, as literacy rates and enrollment are higher in resource-rich countries than in other (developing) countries.

  6. We have also investigated the effect of education spending and the quality of education on the long-run economic growth rates in our sample controlling for other possible drivers of growth. The results are shown in Appendix B Tables 14 and 15. Our cross-country results support the argument of scholars who are questioning the effectiveness of education spending per se on long-term growth. However, we find that there is a robust positive effect of education quality on long-term growth, which is also in line with the findings of Hanushek and Woessmann (2013).

  7. We also re-examine the resource curse hypothesis by regressing the degree of oil dependency in 1990 on the GDP per capita growth rates from 1990 to 2015, controlling for a couple of other growth drivers and regional dummies. The initial dependency on oil rents has a negative and significant effect on the long-term growth rates in our sample, which is in line with resource curse hypothesis (see Table 13 in Appendix B). There is also a line of literature, which argues for positive long-run growth effect of natural resource wealth (see Alexeev and Conrad 2009). Alexeev and Conrad (2011) also find little evidence for the resource curse in a sample of transition countries.

  8. We are not considering the tertiary level of education in our analysis because our main focus is only on the quality of the educational system, which is measured by the secondary level of schooling.

  9. As the World Bank (2017) data on education expenditures is expressed in relation to GDP per capita of a country, we first multiply the government expenditure per student (percentage of GDP per capita) by GDP per capita in the local currency of each country. The outcome is then divided by the PPP conversion factor.

  10. We also control for different indicators of trade and investment openness (trade and net inflow of FDI as a share of GDP and KOF economic globalization index) in both quantity and quality of education regressions. Due to their mostly insignificant effects, we have dropped them from the analysis. Our main results on the education–oil nexus are not sensible to inclusion or exclusion of the trade and FDI indicators. (Results are available upon request.)

  11. Kaarsen (2014) uses data from various TIMSS rounds from 1995, 1999, 2003, 2007, and 2011. Some countries such as the USA have participated in all rounds and some others only in few of them. The downside of using these data is that we cannot conduct a panel data analysis, as the Kaarsen index is available only as a cross section.

  12. http://reports.weforum.org/global-competitiveness-report-2015-2016/.

  13. The report is available at: http://unesdoc.unesco.org/images/0017/001776/177683e.pdf.

  14. The mean VIF for Model 3.7 is under the critical level of 10 (9.31). However, the VIF for the logarithm of government spending—per student at the secondary and primary levels—and the logarithm of GDP per capita show VIF of above 10.

  15. The role of some dimensions of governance such as corruption and rent seeking for the effectiveness of education spending is examined by Rogers (2008).

  16. We have also used the average test score in math and science (primary through the end of secondary school, all years, 1964–2003), which is used in Hanushek and Woessmann (2013), as our index of education quality. The negative effect of oil rents remains robust in most specifications.

  17. For more details on robust regressions, see http://stats.idre.ucla.edu/stata/dae/robust-regression/.

  18. The result is obtained by first multiplying the absolute value of the coefficient 0.010 associated with the individual effect of oil rents dependency by the within-country standard deviation of oil rents 2.7 and then by dividing the product by the within-country standard deviation of WEF quality of education 0.26 (− 0.01 * 2.7/0.26 = −0.10).

  19. World Values Survey (Wave 6 2010–2014) Official aggregate v.20150418. World Values Survey Association (www.worldvaluessurvey.org).

  20. Because of this effect, the literature suggests that higher resource rents can reduce the income gap between the rich and the poor in the short term (Goderis and Malone 2011).

  21. The direct relevance of institutions for the allocation of talent is examined empirically by Natkhov and Polishchuk (2012).

  22. Farzanegan (2014b) also shows that rent dependency leads to a low prevalence of entrepreneurship.

  23. Other regional dummies turn out to be insignificant when included.

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Acknowledgements

We thank the anonymous referees, participants and discussants at the 23rd International Congress of the German Middle East Studies Association (2016, Tübingen); Annual Meeting of the European Public Choice Society (2016, Freiburg); International Iran Forum (2017, Marburg); 11th Workshop on Political Economy (2017, Dresden); and 3rd Turkish-German Frontiers of Social Science Symposium (2017, Berlin) for their helpful comments on earlier versions of this study.

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Authors and Affiliations

  1. Economics of the Middle East Research Group, Center for Near and Middle Eastern Studies (CNMS), School of Business and Economics, Philipps-Universität Marburg, 35032, Marburg, Germany

    Mohammad Reza Farzanegan

  2. CESifo, Munich, Germany

    Mohammad Reza Farzanegan

  3. Faculty of Business and Economics, Technische Universität Dresden, Dresden, Germany

    Marcel Thum

  4. ifo Dresden, Dresden, Germany

    Marcel Thum

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  1. Mohammad Reza Farzanegan
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Correspondence to Mohammad Reza Farzanegan.

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Appendices

Appendix A

See Tables 9, 10, 11, and 12.

Table 9 List of 94 countries included in Table 1 (Model 1.3) example of education spending for secondary school
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Table 10 List of 72 countries included in Table 3 (Model 3.8)—quality of education (Kaarsen index)
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Table 11 List of 123 countries included in Table 4 (Model 4.8)—quality of education (WEF index)
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Table 12 Data description and sources
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Appendix B

2.1 Resource curse in our sample of analysis

The resource curse hypothesis states that countries that are more dependent on resource rents (in most cases, this is oil) experience lower economic growth rates in the long term compared to countries with lower initial dependency on rents. To (re-)test this hypothesis, we consider the effects of oil rents dependency in 1990 on GDP per capita growth rates from 1990 to 2015, controlling for other possible drivers of growth in 1990. These control variables are trade openness ([import + exports]/GDP); foreign aid (percentage of GNI); domestic capital formation (percentage of GDP) as a proxy for the domestic investment rate; foreign direct investment (FDI, percentage of GDP); the fertility rate and a dummy for the East Asian and Pacific region.Footnote 23 All variables are from the World Bank (2017).

We follow a specific to general approach in our specification, starting in Model 1 with oil rents only. There is a negative and significant effect of oil rents in 1990 on the long-run growth rates, supporting the resource curse hypothesis. Only in Model 7, when also controlling for fertility, the negative significance of oil rents vanishes. However, the statistical significance of oil rents in growth returns in Model 8 where we exclude the insignificant variables of Model 7 (see Table 13).

Table 13 Resource curse: oil rents and long-run growth rates across countries
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2.2 Long-run growth, education spending and education quality

We investigate the effects of government spending on education and of education quality [using the measure by Kaarsen (2014)] on long-run growth (Table 14). There is a positive effect of education quality and a negative effect of education spending on growth.

Table 14 Education spending, education quality, and long-run growth rates
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In the following table (Table 15), we use the logarithm of government spending per student at the secondary level schooling (PPP adjusted) instead of government spending on education (percentage of GDP). The overall results do not change.

Table 15 Education spending per student (secondary level), education quality, and long-run growth rates
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2.3 Using International database on human capital quality (Altinok and Murseli 2007)

See Table 16.

Table 16 Education quality and oil rents
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2.4 Robust regression: examining the effect of outliers or influential observations

See Tables 17 and 18.

Table 17 Robust regression: re-examining Model 3.8 from Table 3
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Table 18 Robust regression: re-examining Model 4.9 from Table 4
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Farzanegan, M.R., Thum, M. Does oil rents dependency reduce the quality of education?. Empir Econ 58, 1863–1911 (2020). https://doi.org/10.1007/s00181-018-1548-y

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