Do Women Benefit from Women Education Aid? Evidence from Panel Data

Abstract

Generally, the literature on aid focuses on the potential growth effects of aggregate aid. Due to the fact that donors have consistently asserted the multidimensionality of their purposes, it is necessary to conduct a much more disaggregated analysis of aid effectiveness. In this study, the effect of women education aid on 72 developing countries is examined empirically over the period 1990–2016. Using cross-country regression, this study examines the effectiveness of aid targeted at women’s education. Based on the fact that donors provide a large amount of women’s education aid to countries whose voting positions in the UN General Assembly are similar, this analysis exploits an instrumental variable. This study shows that women’s education aid has a significantly positive effect on women’s education. The results of this study are robust when different sensitivity checks are performed. The findings have significant policy implications for donor countries and international aid organizations, as they assist in identifying the most effective types of foreign aid flow to the various sectors of the recipient country’s economy.

KEYWORDS:

• Women’s education aid
• women’s education
• panel data
• developing countries

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Source AidData database. Link: http://aiddata.org/.

2 This analysis considers only bilaterally committed aid provided for educating women rather than the disbursed amount. Theoretically, examining the effect of the disbursed aid on recipients’ outcome variables might give compelling findings since the recipient countries have already received the aid capital. However, the aid literature shows some limitations in the use of disbursed aid. First, in many cases, the data for disbursed aid is missing as it is ‘spotty’ in most of the aid data sources. Second, aid disbursement is unpredictable compared to commitments because the amount of aid could be disbursed mainly in periods when output or domestic revenue are high and held back when domestic economic activity is shrinking (see Bulíř & Hamann, 2008). Hence, the analysis incorporates recipient and time-fixed effects in all models to consider any bias from systematic divergences between commitments and disbursements.

3 I only use bilaterally committed aid because the instruments of women's education aid (i.e. donor and recipient countries voting similarity in the United Nations General Assembly) directly affect bilateral aid but not multilateral aid. However, in other unreported results, I regress overall women's education aid (i.e. bilaterally plus multilaterally committed aids) on measures of women's education using OLS and GMM estimations. Nevertheless, the qualitative nature of the results stays similar to the baseline findings (the results are available upon request).

4 It is common to use this scaling procedure in the aid literature (see, e.g. Wilson, 2011; d’Aiglepierre & Wagner, 2013). Further, following Arndt et al. (2010), I treat zero-valued aid observations as zeroes rather than missing.

5 Section 4.2 presents more discussion on the instrumental variables.

6 I also run the model using fixed effect estimation and the qualitatively nature of the results remains the same with the findings from OLS estimations (results are available upon request).

7 Using the lagged values of foreign aid is broadly accepted in the literature (see e.g. Mishra & Newhouse, 2009). I also regress women’s education on different lagged values of the women education aid and the finding are qualitatively the same with the baseline results (results are available upon request).

8 To control the endogeneity issue, I use one-year lagged values of growth of per capita income, ICRG index, under-5 mortality, percentage of female children ages 7–14 in children employment and government expenditure on education as a percentage of GDP. 

9 It important to note that Djankov et al. (2008) examine the impact of overall aid on growth.

10 It is important to note that averaging of data over a certain period need not always capture the steady-state equilibrium while smoothing out time series data removes variation from the data, which could help to estimate the parameters of interest with more accuracy (see Baltagi et al., 2009). 

11 In their aid and growth regressions, Hansen and Tarp (2001) argue that aid has decreasing returns and find a significantly negative coefficient of the squared-aid in all models.

12 The presence of aid elements in the error term of Equation (1) may violate one of the Gauss-Markov assumptions, such as the expected value of women’s education aid and the error term may not be zero, and thereby create an endogeneity issue (see Cragg & Donald, 1993).

13 I also ran regressions using the affinity index of other bilateral donors, including Australia, France, Germany, Italy, Japan, Korea, the Netherlands, Norway, Spain, and Sweden. These are instrumental variables of the aid variable. The results are qualitatively similar to the results reported in 2SLS estimations in Table 2 (results available upon request). These donors have missing affinity index data when compared to USA, Canada, and UK, which may lead to ambiguous conclusions. Therefore, my analysis mainly focuses on the findings obtained from using the affinity indices of the USA, Canada, and the UK as instruments of the aid variable.

14 Appendix 1 shows the amount of women's education aid provided by the largest bilateral donors.

15 I also re-estimate Equation (1) by excluding outliers using annual and averaged data. Dropping these observations leaves the qualitative nature of the baseline results intact. Finally, it is worth mentioning that I used the Hadi (1992) procedure to identify outliers in the sample.

16 It is important to note that I also consider females’ average years of primary, secondary, tertiary, and total schooling for different age categories as indicators of women’s education by using a new dataset of educational attainment from Barro and Lee (2013). Since the data for these variables are incomplete for most of the countries considered in this analysis, I did not report the results in this section. However, the results are available upon request.

17 I find qualitatively similar results with the baseline findings when I use annual data to examine the effect of women education aid on females’ primary school completion rate, adults’ female literacy rate as a percentage of females ages 15 and above and females’ effective progression rate to secondary school (results are available upon requests). 

Additional information

Notes on contributors

Admasu Asfaw Maruta

Admasu Asfaw Maruta has published on foreign aid, economic growth, financial development, and trade in top-ranked journals. Maruta holds a Ph.D. in Applied Economics from the University of South Australia; MSc in Agriculture and Resource Economics from the University of Alberta, Canada; MA in Business Economics from Unity University, Ethiopia; and BA in Economics from Haramaya University, Ethiopia. Maruta has previously worked in various international institutions, including the World Bank, the London School of Economics and Political Science, and RMIT University in Melbourne.