Foreign Aid and Female Empowerment

Abstract

We estimate the community-level impact of foreign aid projects on women’s empowerment in the country with the most complete recent record of geo-coded aid project placement, Malawi. Our estimates can thus be interpreted as the average impact of aid from many different donors and diverse projects. We find that aid in general has a positive impact, in particular on an index of female agency and women’s sexual and fertility preferences. Gender-targeted aid has a further positive impact on women’s sexual and fertility preferences, and more tentatively on an index focusing on gender-based violence. However, the positive impact of gender-targeted aid disappears in patrilineal communities, and men’s attitudes towards female agency in the areas of sexuality and fertility are even negatively affected. This suggests that donors need to consider that the impact of aid on female empowerment can depend on the community context when they decide on aid project design and placement.

Keywords:

• foreign aid
• gender
• development
• geo-coded data
• impact analysis

Acknowledgements

The authors are grateful for comments from Markus Goldstein, Masayuki Kudamatsu, Ann-Sofie Isaksson, Jonathan Lehne, Nathan Nunn, Martina Björkman Nyqvist, Alex Trew and Maiting Zhuang. Previous versions of the paper have been presented at CSAE 2017, Stockholm School of Economics, Namur University, Pennsylvania State University and the ASWEDE 2021 conference. This paper originated within the UNU-WIDER project on ‘Gender and Development’. All remaining errors are our own. Research funding from the Swedish Research Council, grant number 2018-01342, is greatly appreciated.

Disclosure statement

All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

Data availability statement

Datasets generated for and analyzed in the current study are available at the Dataverse Project repository, https://doi.org/10.7910/DVN/KTLSQT.

Notes

1 A list of the resulting project types can be found in the Supplemental Material.

2 The level of female empowerment is stronger in matrilineal societies as we show in the Supplemental Material, so it is possible that preexisting gender norms affect the impact. But since these communities may differ also in other aspects, we cannot attribute causal impact on preexisting gender norms alone.

3 As measured through DHS surveys by respondent’s agreement with at least one of five given reasons whether a husband is justified to beat his wife, i.e. if she burns the food, argues with him, goes out without telling him, neglects the children, or refuses to have sexual intercourse with him.

4 The AIMS was developed and introduced by the State Committee on Investments and State Property Management (SCISPM) with technical support from the UNDP and funded by the Department for International Development (DFID). It is a web-based Aid Management Platform (AMP) that allows governments of developing countries and their donors to share and analyze aid information. The data and information collected include the number of implemented projects and agreements, their cost, terms, and duration, and executing and implementing agencies.

5 In the main specification we use as treatment an indicator variable. In robustness checks, we replicate results using instead the number of projects each household is exposed to, within the same spatial area. Projects do also differ in financial size, but unfortunately we do not have information on aid commitments at the level of aid project placements so we cannot explore that variation in intensity of exposure.

6 An alternative definition of gender-specific aid could be based on the Gender Equality Policy (GEP) marker developed by the OECD-DAC, that distinguishes between projects with gender equality as: (i) the primary objective, (ii) a significant objective, (iii) no stated objective. This indicator has some limitations that makes it less useful for our purpose, though. In particular, the use of this marker is voluntary for the donors, and as a consequence marked projects are relatively few. Out of our 906 projects, only 363 are marked, and out of these only 61 are marked into category (i) or (ii). This raises concerns of selection bias, as donors that make the effort to mark their projects may be particularly concerned about gender equality and may be more than average diligent in making sure gender-targeted aid delivers results. It is also possible that donors choose to mark only the projects they most strongly believe will indeed generate positive impacts on gender equality.

7 Figure 2 in the Appendix and Figure 4 in the Supplementary materials show the grid visually.

8 An alternative approach used in the literature (e.g. Isaksson & Kotsadam, 2018; Knutsen & Kotsadam, 2020) is to define respondents in areas where projects are not yet implemented but where they will be implemented in the future as controls and respondents in areas with ongoing projects as treated. This is helpful if areas that are never close to aid projects are substantially different from those in project vicinity, but it might lead to a sample where a substantial majority of units classified as treated are surveyed late while the opposite is true for the group of controls. This raises the question of bias from overall changes in outcomes over time, that may be hard to control for. Our approach avoids the latter concern, but relies on comparability of treatment and control groups and parallel trends prior to the implementation of the projects, discussed further below.

9 Our control variables are generally exogenous or pre-determined, with the possible exception of household size, literacy, and years of education, which might be directly targeted and affected by aid interventions. Results are however minimally affected by excluding these variables as compared to specifications including the full list of controls, which are reported in tables throughout the paper.

10 Results are slightly stronger, as expected, but all in all do not change much using the full sample.

11 With the exception of the Lomwe group, this classification is the same as in the Ethnographic Atlas (Murdock et al., 1999).

12 In the Supplemental Material we show tables using the median rather than the mean to differentiate matrilineal from patrilineal communities. This increases the number of communities defined as patrilineal quite substantially, adding more evenly mixed communities. As expected, this leads to a moderation of the difference between the two communities, though the pattern is largely the same. In particular, the impact on men’s attitudes is still negative in patrilinear societies but smaller. We also show a table testing for the difference in pre-trends between matrilineal and patrilineal communities. For the HS index results suggest that there is a pre-trend towards more favourable outcomes in matrilineal communities, which suggests that results for this index should be interpreted with care. For the other indices, coefficients are all negative suggesting that the pre-trend goes in the opposite direction if anything.

13 The impact of foreign aid in general on female empowerment is a question that has received surprisingly little attention in the quantitative academic literature. In a special issue, the Journal of International Development (Volume 28, 2016) published several papers on the topic, some with a quantitative component. Most closely related to this paper, Pickbourn and Ndikumana (2016) use cross-country data to estimate the correlations between foreign aid inflows and the United Nations Development Program’s Gender Inequality Index (GII), finding no significant correlation. The authors do, however, find positive correlations between maternal mortality rates and youth gender literacy gaps and aid targeted towards the health and education sector respectively.

14 While t-statistics test the null hypothesis of no difference in means between treated and control units, the normalized differences ``provide a scale- and sample size-free way of assessing overlap’' (Imbens, 2015).

Additional information

Funding

This work was supported by Swedish Research Council, grant number [2018-01342].