How Does Foreign Aid Affect Total Fertility Rate? Panel Data Evidence

ABSTRACT

We explore the relationship between aid and the total fertility rate in recipient countries, which is closely linked to the literature on aid and economic development. Using data on official development assistance in 86 developing countries over 1970–2015 and controlling for potential endogeneity issues, we find that foreign aid helps to lower the total fertility rate in recipient countries in general. In addition, our results suggest that development assistance is most effective in lower-income countries or countries with a lower level of human capital. We also observe considerable regional heterogeneities regarding the effect of aid on the total fertility rate.

KEYWORDS:

• Official development assistance
• foreign aid
• total fertility rate
• aid effectiveness
• economic development

JEL CLASSIFICATIONS:

• F35
• J11

Disclosure statement

No potential conflict of interest was reported by the authors.   

Notes

1 In addition to child health as a proxy for child quality in Neanidis (2012), we consider child education another important dimension of child quality. An increase in schooling of each child increases the cost of child quantity. When foreign aid aims to improve child education, we might see a substitution effect between child quality and child quantity – fewer children, but each child has a higher level of education.

2 The macroeconomic conditions in donor countries are correlated with foreign aid an individual country receives, but are not correlated with the error term in Equation (1). For example, real GDP per capita of the U.S. can affect how much aid U.S. provides to Argentina, which in turn may affect the fertility rate in Argentina. But it is unlikely that the fertility rate in Argentina would influence the U.S.’ per capita GDP.

3 The sample average fertility is 3.65 births per woman. Hence, a 0.14% reduction in fertility would yield 0.0014*3.65=0.00511 (fewer birth per woman), which implies 1.022 fewer births per 200 women.

4 See Table 2 of Neanidis (2012).

5 We obtain similar results when interacting aid with the total education in the recipient countries. The results are available upon request.

6 In regression 5.3, one of the instruments is weighted by the common official language, which is a binary indicator. We also use as the weight a common spoken language indicator, which is the probability that two random speakers understand each other based on the common spoken language. We find that the results are qualitatively similar. The estimated results are available upon request.

Additional information

Funding

This research is supported by the Center for Global and Economic Studies, Marquette University and the Miles Research Grant from the College of Business Administration, Marquette University.

Notes on contributors

Miao Wang

Miao Wang is a Professor of Economics in the College of Business Administration at Marquette University. Her area of expertise is in applied international economics, primarily related to international trade and foreign direct investment (FDI) issues.

Hong Zhuang

Hong Zhuang is an Associate Professor of Economics and Director of the Bureau of Business and Economic Research in the Judd Leighton School of Business and Economics at Indiana University South Bend. Her research areas are international economics and development economics.