Over decades much attention has been devoted to the relationship between foreign aid and economic growth, while few studies have focused on the effects of foreign aid on female empowerment. This despite the fact that empowerment of girls and women is a key driver of development, and often an explicit objective of foreign aid. Using geo-coded data on aid project placement and household-level survey responses, Perrotta Berlin, Bonnier and Olofsgård (2023), show that foreign aid has a modest but robust effect on several dimensions of female empowerment. This is the case for both aid in general and gender-targeted aid, highlighting the potential of foreign aid to reduce gender inequalities. It is also found, though, that the impact is contingent on the context, and that there can even be a backlash in male attitudes towards female empowerment in more traditional communities.
The donor community has long been invested in the empowerment of women and girls, and the 2030 Agenda for Sustainable Development also includes gender equality as an explicit goal. Yet surprisingly little quantitative research has tried to make a broader assessment of the effect of foreign aid on gender equality measures.
This policy brief summarises a study by Perrotta Berlin, Bonnier and Olofsgård (2023) which addresses this question by matching the location of aid projects with geo-coded household surveys in Malawi between 2004 and 2010. Analysing the community-level impact on five different female empowerment indices, the study finds foreign aid to affect positively women’s empowerment across several dimensions. Furthermore, the authors find that gender-targeted aid has an additional impact on an index measuring women’s control over sexuality and fertility-related decisions and an index focusing on violence against women.
When considering areas with patrilineal land inheritance traditions, the results however partly shift, especially in relation to men’s attitudes. This implies that the success of foreign aid and gender-targeted aid in reducing gender inequalities may be conditional on the community context.
Gender Equality and Foreign Aid in Malawi
Malawi is highly dependent on foreign aid. Net official development assistance (ODA) has exceeded 10 percent of gross national income yearly since 1975, reaching as high as 23.5 percent in 2016 (World Bank, WDI database).
In recent years, reforms have been undertaken by the Malawian government to improve gender equality. The minimum legal age of marriage was raised from 15 to 18 through the 2015 Marriage, Divorce and Family Relations Bill, and the 2013 Gender Equality Act strengthened the legislation concerning gender-based violence and included a universal condemnation of all types of gender-based discrimination. Yet, in 2020, Malawi was ranked 116 out of 153 in the World Economic Forum Gender Gap Report and 172 out of 189 in UNDP’s Gender Inequality Index. An area of concern regards the high rates of child marriage, with 9 percent of girls already married at age 15 and 42 percent by the age of 18. Alongside these numbers, 31 percent of women report to have given birth by the age 18.
Another aspect potentially influencing gender equality is the prevalence of matrilinear land tenure systems, particularly in the southern and central parts of the country (as depicted in Figure 1). While previous research has shown that land ownership empowers women and suggested that property rights affect decision power over key decisions, fertility preferences, age of marriage etc., less research has been devoted to analysing the effects on women’s empowerment outcomes in a matrilinear kinship setting. Some recent literature however suggests women in matrilinear societies have greater say in household decisions – including financial ones – and are less accepting of, as well as exposed to, domestic violence (Lowes, 2021; Djurfeldt et al., 2018).
Figure 1. Intensity of matrilineal tenure in Malawi.
Methodology and Data
For the analysis, the authors make use of geo-coded data on aid projects from the Government of Malawi’s Aid Management Platform (AMP) and match it to household-level data from the Malawi Demographic and Health Survey (DHS). The country of Malawi and the period 2004-2010 were chosen in order to maximize data coverage on aid disbursement. Malawi’s AMP covers 80 percent of all aid entering the country during those years, which gives a much more complete picture compared to only focusing on one specific donor.
To identify causal impact, the authors apply a difference-in-differences specification on survey clusters in proximity to aid projects implemented between 2004 and 2010. Proximity was identified as within a 10-kilometer radius from an aid project. Among those, households interviewed in 2004, i.e., prior to the implementation date of any aid project, were considered the control group, and households interviewed in 2010 formed the treatment group. The underlying assumption of parallel pre-treatment trends was confirmed with the use of earlier DHS surveys. The model specification includes individual-level controls (age, ethnicity, household size, a Muslim dummy, years of education and literacy) and also a geographic fixed-effect based on a grid of coordinates.
The analysis distinguishes between the impact of aid in general, and the additional impact of gender-targeted aid. Gender-targeted projects are defined as projects that have any of the words woman, girl, bride, maternal, gender, genital or child, in the title, description or activity list. When estimating the effect of gender-targeted aid the authors control for overall aid intensity in the household’s vicinity. The estimated effect should therefore be interpreted as the additional effect from being exposed to a gender-targeted aid project while keeping the general number of aid projects in the area constant.
Figure 2. Map of aid projects and household clusters from 2004 and 2010 survey waves in Malawi.
To capture female empowerment, the authors make use of thousands of responses to DHS survey waves from 2004 and 2010. From these responses, the authors construct four different indices. Two of these are modelled on indices used in different contexts by Haushofer and Shapiro (2016) and Jayachandran et al. (2023). The former captures experiences of violence together with men’s and women’s attitudes towards violence, and some measures of decision making and control over household resources. The more recent index by Jayachandran et al. (2023) focuses on female agency and includes questions on women’s participation in decisions on large household purchases and daily expenditures, decisions on family visits, and decisions concerning their own healthcare.
To also capture questions related to sexual and fertility preferences, often regarded as measures of female empowerment, the authors construct two additional indices. The women’s attitudes index is based on responses to questions about whether the respondent is able to refuse sexual intercourse with her husband and ask him to use a condom, age at first marriage, and age at first childbirth, among others. The men’s attitudes index is based on questions about whether the respondent thinks it is justified to use violence to force intercourse, if a woman is justified to refuse intercourse, as well as fertility and child spacing preferences. In addition, all four indices are weighted and combined into an aggregated general index.
Considering all aid projects, the authors find that being exposed to an aid project in the 2004 to 2010 window has a significant positive impact on the agency index, the female attitude index and the combined general index (12, 11 and 31 percent of their respective means). When considering gender-targeted aid, the authors found the exposure to at least one such project to increase the women’s attitude index by 7 percent and the general index by 17 percent of their respective means. The impact is present for both a narrower and a wider exposure area, and quite persistent over time.
When breaking down the analysis for areas with matrilineal versus patrilineal land tenure systems the results diverge. In communities where the share of matrilineal ethnic groups exceeds the mean of 73 percent, the results are largely in line with those in the full sample. In patrilineal communities (< 73 percent matrilineal households), the results are however vastly different. Aid projects in general, and gender-targeted aid in particular, affect negatively the men’s attitudes index. In addition, gender-targeted aid seems to have no additional impact on the other indices.
In the paper underlying this brief, the authors study the effect of foreign aid on female empowerment, a frequent but understudied objective often set by donors. Looking at geo-coded aid projects in Malawi, the authors estimated such projects to positively impact girl’s and women’s empowerment across several indices. This is true for aid in general, and for some indices even more so when considering gender-targeted aid. Some of the positive results disappear or even change sign, though, in patrilineal communities, displaying the significance of pre-existing community norms for the effectiveness of development investments. Aid even generates a backlash when it comes to men’s attitudes towards women’s sexual and fertility preferences in these communities.
The takeaway from the study lies in foreign aid’s potential to empower women in targeted communities. This however hinges on pre-existing norms in recipient communities – something that aid donors should be aware of.
The authors emphasize the need for more research to better understand the role of pre-existing norms in the uptake of aid, to distinguish direct effects from aid from potential spillovers, and to understand what type of aid projects deliver the best outcomes in terms of female empowerment.
- Djurfeldt, A. A., E. Hillbom, W. O. Mulwafu, P. Mvula, and G. Djurfeldt. (2018). “The family farms together, the decisions, however are made by the man” -Matrilineal land tenure systems, welfare and decision making in rural Malawi. Land use policy 70, 601-610.
- Haushofer, J. and J. Shapiro. (2016). The short-term impact of unconditional cash transfers to the poor: experimental evidence from Kenya. The Quarterly Journal of Economics, 131(4), 1973-2042.
- Jayachandran, S., M. Biradavolu, and J. Cooper. (2023). Using machine learning and qualitative interviews to design a five-question survey module for women’s agency. World Development 161, 106076.
- Lowes, S. (2021). Kinship structure, stress, and the gender gap in competition. Journal of Economic Behavior & Organization 192, 36-57.
- Perrotta Berlin, M., Bonnier, E., and A. Olofsgård. (2023). Foreign Aid and Female Empowerment. SITE Working Paper Series, No. 62.
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
Poor and fragile countries are particularly vulnerable to adverse economic shocks (see OECD’s States of Fragility 2020 report). Historically, Somalia has been struggling with poverty and conflict and it is now also dealing with the added burden of negative socio-economic impacts from the COVID-19 crisis. This is in addition to decreasing agricultural yields from flooding and swarms of desert locusts.
Under such circumstances, the international community becomes crucial in sustaining livelihoods and avoiding further setbacks to reaching the 2030 Agenda for Sustainable Development. This includes direct development assistance from multilateral and bilateral donors, support for debt restructuring and debt forgiveness but also facilitation of private capital flows, including remittances.
In this webinar Somalia’s finance minister, Dr. Abdirahman Dualeh Beileh and Staffan Tillander, Swedish ambassador to Somalia, will discuss the critical role of fruitful and constructive cooperation between the Government of Somalia and the international community on continuing the path of economic reforms.
In this webinar, we will first hear about the current situation in Somalia from the country’s finance minister, Dr. Abdirahman Dualeh Beileh. Dr. Beileh will discuss the critical role of fruitful and constructive cooperation between the Government of Somalia and the international community on continuing the path of economic reforms. Given the present economic shock and its impact on both government revenues and the livelihoods of the Somali population, international support becomes particularly vital for maintaining the positive trajectory of economic and institutional reforms necessary in order to, for example, strengthen the Central Bank or implement legislation against corruption.
Following Dr. Beileh, Ambassador Staffan Tillander will talk about recent developments in Somalia from the Swedish government perspective, and discuss solutions being implemented to support the Somali government and people in the challenging current situation.
A discussion moderated by Anders Olofsgård with the two speakers will follow, with the final 15 minutes open to questions from the audience. You will be invited to submit your questions during the webinar via the chat function.
The event is organised by Stockholm Institute of Transition Economics (SITE) and Mistra Center for Sustainable Markets (Misum) at Stockholm School of Economics.
Poor and developing countries are now starting to be affected by the Covid-19 pandemic. Important differences in the setting need to be considered when thinking about their prospects, and the role richer countries may play in helping them face the challenge.
Most of the focus in current analyses of the policy response to the Covid-19 crisis center on Western and East Asian countries that were hit first and hardest. Some initiatives are tracking the situation in transition countries of Eastern Europe (e.g., the FREE Network initiative and the Vienna Institute for International Economic Studies tracker).
However, poor and developing countries start also being affected by the pandemic, and richer countries have an important role in helping them face the challenge. Besides the moral obligation, in the presence of a global externality it would be extremely myopic not to do so. When thinking about this, it is important to reflect on the differences that will be relevant in these settings.
What is Happening? The Spread of the Virus
Currently, the spread of the contagion is still at substantially lower levels in low income countries (LIC) as compared to high income countries (HIC). There is not enough evidence yet to either support or reject the hypothesis that a lower spread could be due to differences in climatic zones (warmer temperatures and humidity). Younger populations might account for both a lower (observed) spread and lower mortality, but on the other hand the denser and multigenerational living arrangements with poorer hygienic conditions should be pushing in the opposite direction. Observing lower spread and lower mortality could also be put down to lower testing (and more generally, data availability and quality of information systems). Finally, we can’t exclude that this is simply a matter of timing. Many LIC are relatively less connected to global routes, and moreover were fast to close their borders: many opted for early lockdown. If this is the case, they are merely postponing the sharp increases in infections and fatalities observed in other countries. (At the time of writing, worrisome reports of a severe outbreak in Somalia are emerging.)
Figure 1: Total confirmed Covid-19 deaths.
Figure 2: Total Covid-19 tests per 1,000 vs. GDP per capita.
A number of factors related to the demographic structure as well as the public health systems are relevant as a base for our expectations on how the situation is going to evolve in these countries. Since age plays an important role on how severely Covid-19 patients are affected by symptoms, the demographic structure of the population has consequences for the demands that will be placed on the health care system by an outbreak. This plays in favor of LICs, where only 3% of the population is above 65 years of age on average. The corresponding share is 18% in OECD countries. The state of the health care system is intuitively crucial once there is an outbreak. In Table 1, the Global Health Security Index (GHS) “Health Security Score” paints a dismal picture in terms of overall capacity “to treat the sick and protect health”, where the group of LICs (as defined by the World Bank) scores an average of 14,5 out of 100 (HIC average is 51,9).
Table 1: Public health.
This is clearly related to how wealthy a country is. The wealthier countries have better health care systems in general, and will do better if they experience an outbreak, while the poorer countries will do worse. Even if the average 6% of GDP devoted to health care spending in LICs looks comparable to the HIC average share (8,8%), these translate into very different figures in terms of per capita dollar spending: 40 USD per capita in the first group, to be compared to over 4,000 USD in the second. Even if costs do differ as well, a ventilator is unlikely to be two orders of magnitudes cheaper in Liberia than in Italy. Nevertheless, the “Health security – response capability” index, which includes things as emergency response plans and existing links between health and security authorities, averages 30,9 in LICs against 45,8 for HICs. The difference across income levels is much smaller in this case, reflecting both the more general lack of preparedness in this particular domain, but also the familiarity and experience of poorer countries with infectious diseases outbreaks, which might give an edge in an emergency. The World Health Organization reports over one hundred “public health events of varying magnitude and socio-economic effects” annually in Africa, for example. After the 2014-15 Ebola outbreak, an Africa Centre for Disease Control and Prevention was set up in 2017, which might have contributed to an upgrade in the index. The Centre has been quick to react in the present case, as discussed later in the policy response section.
What is Happening? Economic Impacts
It is hard for HIC to put numbers on forecasts of economic activity. For LIC, the challenge of forecasting is further compounded by the normally poor array of statistical systems and the larger informal sectors. Better indicators of economic activity and income distribution normally rely on surveys, and while surveys are still being conducted these days (see for example the relentless work of IPA affiliates the focus at the moment is naturally on the health emergency and related behavior, rather than incomes and investments.
Even without exact numbers, we can nevertheless expect that LICs’ economies are going to be hit harder, for two main reasons:
- They are more sensitive to the global shock(s), through commodity prices and exports, and also because of the limited access to international financial markets
- They start from worse structural conditions, in terms of fiscal capacity and governance capacity, which makes them less resilient.
Again, a number of fiscal and macro factors are relevant for our expectations on how the situation is going to evolve, such as the trade and fiscal balance, and the composition of exports. Besides concerns for long-term growth prospects, the most immediate threat is that to people’s livelihoods, in particular poor people’s, due to the slowdown of economic activity. While this can’t be fully avoided due to the dependence on international linkages, it is made radically worse in case of domestic lockdown. The combination of large populations living below or at the margin of the poverty threshold and the slim fiscal capacity for compensation and redistribution results in much sharper trade-offs associated to different policy measures.
Some of these countries, heavily dependent on external trade and in particular on commodity exports, are at the moment facing a double shock, due to the collapse of commodity prices and the disruptions to global value chains, on top of the epidemic itself. This is dramatically reducing the fiscal space for response, which was already limited to start with. Therefore, even though a number of LICs have formulated response plans, as will be discussed in the next section, the question remains how to finance them.
Table 2: Macro factors.
What is Happening? Policy Response
With few exceptions, most countries in this group were quick to react in at least two dimensions: closing borders and closing schools. While the first was probably a very wise choice and might have delayed significantly the entry of the virus in the countries, not enough thought has been given to the consequences of school closures. Less than one in four countries is providing some form of distance learning; and even where this is available, access will be very unequal, for a number of reasons: access to internet and suitable devices, need to compensate for parent’s lost income, responsibility for younger siblings are just some of the factors, in addition to the inequality in parental socioeconomic and educational background which is common also to HICs. Based on experiences from the Ebola epidemic in 2014-15 in West Africa, the protracted lack of schooling is liable to leave deep long-lasting consequences.
A quarter of the countries (8 out of 31) entered lockdown or very strict social distancing. Few of them, with help from the international community, support the enforcement of a lockdown with food distribution (for example Liberia and Uganda). This is not possible everywhere, due to financing and logistic issues, and in its absence, livelihoods are put at risk. Because of this, in many areas people defy the rules, in some cases notwithstanding enforcement by the military. Another quarter of countries opted for curfews rather than lockdown, to limit the frequency of interactions without halting completely economic activity. Very few countries explicitly chose much more limited interventions in terms of social distancing (Burundi, Mozambique, Tanzania), while most of the rest do not have the governance capacity for intervention, in some cases due to other preexisting crises (Yemen, Mali, Guinea-Bissau).
The quality of the country’s health care system and the resources that can be invested in testing will determine for how long containment measures will be needed. Two thirds of the countries have already enacted emergency interventions in the health sector, meant to strengthen the general capacity for care and in particular the infrastructure for testing. All in all, though, half of the countries have opted for either strict public order measures or fiscal interventions. Most of the remaining half have neither, while very few have both. In most cases, the health-related emergency measures are financed by small reallocations of current spending that amount to few per-mille points of GDP. With fewer resources to cure and test, countries will need to maintain longer containment measures to avoid the spread, once the contagion reaches them. However, as mentioned above, the cost of lockdown is very different in these countries, where almost half of the population (48% on average) lives below the international poverty line. Stricter and longer lockdowns will call for broader fiscal interventions in support of households’ (food) consumption and SMEs. The few countries that planned such interventions, and/or to increase health sector spending by more than 1% of GDP, are counting on donor financing. At the same time, all are suffering contractions in their fiscal space, as noticed above, and the same can be said of most donor countries too. The question of how to finance this gap looms therefore large.
A Role for Rich Countries
In normal times, the relative importance of different financial flows entering developing countries could be phrased as follows: foreign aid is small, remittances bigger, trade and investments biggest. ODA receipt accounts for 12% of GDP in the average LIC. While almost all donor countries fall short of the pledge to give 0,7% of their annual GDP, even if they did, thus trebling the current aid bill (152,8 billion USD in 2019), this would still not reach the level of remittances flows, estimated at 551 billion USD in 2019. The FDI flows, estimated at 671 billion USD (in 2018) are more important in the aggregate, although their distributional implications are very different. The importance of trade is also substantial, as shown in Table 2.
Given the situation, though, with a global recession looming, we can expect substantial contractions in trade and FDIs at least in the short run, but more likely for a protracted period. The limitations to international mobility will also imply severe reductions in remittances flows, as migrant workers have either returned to their countries, or are more likely to lose employment in the host countries even if they stay. Clearly this implies a continued role for international support.
Without going in the merit of an optimal policy mix recommendation to developing country governments, which others have done (for example, the International Growth Centre COVID-19 guidance note), rich countries that want to play a role in this should keep in mind a few points. Aid budgets should at the very minimum not be reduced, notwithstanding the domestic fiscal squeezes. More than ever, the same amount of money has a much larger life-saving potential in a poor country than domestically. Besides quantity, the type of support will be important. During the health crisis, the priority needs to be to finance emergency expansion of health care spending, but for this to be sustainable it needs to be paired with a strong effort to limit the spread. This includes two elements: i) testing and tracing, or in absence of tests at least keeping track of the geographic spread of symptomatic outbreaks; and ii) supporting livelihoods to enable social distance or lockdown. The first includes, besides the medical material and infrastructure for the testing itself, which might not be the most cost-effective way of using resources, enabling safe and reliable public communication, which needs to go two-ways: from authorities to citizens, avoiding fake news and potential stigma attached to the contagion, and from citizens to the authorities to collect policy relevant data. Since internet is not widespread enough, and the radio only allows for one-way communication, the best shot at this is leveraging mobile telephone networks. Technical assistance in this could be valuable, as well as analytical capacity for the processing of the data.
It goes without saying that all the progress happening in rich countries, in terms of understanding of the virus spread, efficacy of different policies and behaviors, development of treatments and in due time vaccine should be promptly shared.
When it comes to consumption support, it is debatable whether cash transfers or in-kind distributions should be the preferred option. This will of course vary depending on the situation: cash is logistically easier and more flexible – but it will not help if and where the markets shut down.
In the aftermath, it is important to keep in mind that poor countries will not be able to borrow (in particular, issue domestic public debt) to finance fiscal stimuli and other recovery measures. There will be again an important role for international lenders. At the same time, a swift recovery of global economic activity must be considered as the all-over superior solution.
- GHS Index, 2020. “Global Health Security Index, 2019”.
- Our World in Data, 2020. “Total confirmed Covid-19 cases by country”.
- The World Bank, 2020. “World Bank Open Data”.
- UNCTAD, 2020. “Commodity exports (as a share of total merchandise exports) in 2017”.
- WHO, 2017. “Acute Public Health Events Assessed by WHO Regional Offices for Africa, the Americas, and Europe under the International Health Regulations (2005) – 2017 Report”.
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
As school enrolment in low- and middle-income countries has increased substantially in the last couple of decades, attention has instead turned to the poor quality of education. This ”learning crisis” (UNESCO 2013) manifests itself in primary school students without basic skills in language and mathematics, and high school students being vastly outperformed by their peers in high-income countries (World Bank 2018). In this policy brief, I give a very brief background to the learning crisis and report on a research project we have implemented and evaluated in the Democratic Republic of Congo (DRC) with the aim of improving student learning in primary education. The intervention consisted of an incentivized program to stimulate more usage of existing textbooks for self-study, and the impact was evaluated through a randomized experiment (Falisse, Huysentruyt and Olofsgård 2019).
Education systems in many low- and middle-income countries fail to deliver actual learning at the level necessary for people and societies to thrive. According to leading international assessments of literacy and numeracy, the average student in low-income countries performs worse than 95 percent of the students in high-income countries. According to an assessment of second-grade students in India, more than 80 % could not read a single word from a short text or conduct two-digit subtraction. Students perform poorly also in some European middle-income countries; more than 75 % of students in Kosovo and the Republic of North Macedonia perform worse than the 25th percentile in the average OECD country (World Bank 2018). The reasons behind the learning crisis are of course many, ranging from poorly trained and absent teachers, lack of financial resources for infrastructure and learning material, malnutrition and lacking early childhood development, and sometimes weak demand.
Textbooks for Self-Study in the DRC
The learning crisis is particularly evident in fragile, low income countries. This is also where the major challenge to achieve the 2030 Sustainable Development Goal 4 of quality education to all lies (World Bank, 2018). Yet, very few interventions targeting student achievement have been evaluated in the most fragile countries of the world (Glewwe and Muralidharan 2016). This is a concern, since interventions that work in poor but stable environments may not be feasible or effective in even more resource constrained and violent environments (Burde and Linden 2013). In particular, there is an extra value in identifying interventions that are not only cost efficient, but also low cost in absolute terms and simple and transparent.
Projects focusing on school inputs have often yielded surprisingly disappointing results (Glewwe and Muralidharan 2016). One example is interventions focusing on textbook distribution despite belief in their effectiveness and investments from donors and governments (Glewwe, Kremer and Moulin 2009; Sabarwal et al. 2014). One major challenge with textbooks is that they for different reasons are often not used by teachers or pupils, and certainly not to their potential (e.g. Sabarwal et al. 2014). This raises the question of whether the potential of textbooks can be leveraged through incentives on their usage. A couple of recent papers have found that it is indeed the combination of inputs (including textbooks) and incentives that is critical to yield a significant impact on student test scores (Mbiti et al. 2019; Gilligan et al. 2018).
Following up on this idea we collaborated with the Dutch NGO Cordaid that is running a program in primary education in South Kivu, in eastern DRC, in 90 schools. We designed an intervention that encouraged 5th and 6th grade students from 45 randomly selected schools to regularly take home textbooks and use them for self-study. We used a mix of financial and non-financial incentives focused on the students, such as a public display of stars assigned to each student that brought math and French textbooks home and back in good condition, and an in-kind gift of pens and pencils for all students in classes regularly participating in the routine. We also offered participating schools a small flat compensation to compensate for lost and damaged books. The main goals of the intervention were to increase student achievement and to affect their aspirations for further study and more qualified careers.
To measure student achievement, we rely on self-conducted tests in the French language and math, but also high stakes national exam scores that determine eligibility to secondary education. Following the literature, we analyze test results using a model that assumes that baseline test scores capture student learning up to that point, so once this is controlled for end line results capture cleanly the added value of the intervention introduced. We also carefully address potential statistical problems due to slight unbalance between treatment and control groups, students from baseline not present at end line and poor compliance with the intervention in a small set of schools. The results are generally robust across different specifications of the details of the model.
We emphasize three main sets of results. First, we find that the students in the treatment schools (those selected to receive the books) scored significantly better than those in control schools on the French language tests. The estimated improvement was 1/3 of a standard deviation, which compares favourably with other interventions in developing countries targeting student test scores (Kremer et al. 2013). On the other hand, we found no significant impact on math scores. We cannot tell for sure why we observe this difference between French and math, but it should be noted that both textbooks were in French, suggesting that language could be learned from both books. It has also been suggested that math requires more supervision than language and that math is more ”vertical” in terms of skills progression while language is more ”horizontal”. That is, if students are far behind the curriculum in the textbook, they don’t have the necessary basic building blocks to understand the math problems. But for language, this matters less, as progress can be made in different areas more independently.
Secondly, students in treatment schools were more likely to sit and pass the national exam. This is important as this is a requirement for the continuation of schooling at a higher level. More qualified jobs, and jobs that require more French language skills, typically require at least secondary schooling. This is also consistent with the finding that students exposed to the intervention were more likely to aspire to non-manual jobs. Finally, the intervention was low cost and cost-efficient. In particular in fragile environments with very limited resources, this is essential. The intervention is also easy to implement and transparent and does not give raise to incentives to cheat as has been the case in some interventions linking incentives directly to student test performance.
The current key challenge in education policy in low- and middle-income countries is to improve student achievement while continuing the successful increase in enrolment despite often serious constraints in complementary inputs in the education production function. Financial resources for school infrastructure and material are limited, competent and motivated teachers are in short supply, and weak parental support and little early childhood development leaves children unprepared for sometimes too ambitious curricula. In such circumstances simple and low-cost interventions that make better use of existing resources are particularly valuable. In this project we designed and evaluated such an intervention, using incentives to stimulate more usage of existing textbooks, in a particularly challenging environment, Eastern DRC. We find a positive impact on French language skills and higher student aspirations as shown through greater participation in national exams required for continued education. On the other hand, we find no impact on math test scores. Serious sustainable improvement in student learning in a country like the DRC requires wholesale reforms to the education sector and substantially increased financial resources. Realistically, this is a long-run ambition. In the meanwhile, small low-cost interventions that match incentives with existing resources can significantly increase student achievement also in the short run.
- Burde, Dana and Leigh L. Linden, 2013. “Bringing Education to Afghan Girls: A Randomized Controlled Trial of Village-Based Schools.” American Economic Journal: Applied Economics, 5(3), 27-40.
- Falisse, Jean-Benoit, Marieke Huysentruyt and Anders Olofsgård, 2019. “Incentivizing Textbooks for Self-Study: Experimental Evidence on Student Learning from the Democratic Republic of Congo”, Working Paper.
- Gilligan, Daniel O., Naureen Karachiwalla, Ibrahim Kasirye, Adrienne M. Lucas, Derek Neal, 2018. “Educator Incentives and Educational Triage in Rural Primary Schools.” NBER WP 24911.
- Glewwe, Paul, Michael Kremer, and Sylvie Moulin, 2009. “Many Children Left Behind? Textbooks and Test Scores in Kenya.” American Economic Journal: Applied Economics, 1(1): 112-35.
- Glewwe, Paul and Karthik Muralidharan, 2016. “Improving Education Outcomes in Developing Countries: Evidence, Knowledge Gaps, and Policy Implications”, in Handbook of the Economics of Education, pp. 653-743. Elsevier.
- Kremer, Michael, Conner Brannen, and Rachel Glennerster, 2013. “The Challenge of Education and
- Learning in the Developing World.” Science 340, 297-300.
- Mbiti, Isaac, Karthik Muralidharan, Mauricio Romero, Youdi Schipper, Constantine Manda, Rakesh Rajani, 2019. “Inputs, Incentives, and Complementarities in Education: Experimental Evidence from Tanzania.” NBER WP 24876.
- Sabarwal, Shwetlena, David K. Evans, and Anastasia Marshak, 2014. “The permanent input hypothesis: the case of textbooks and (no) student learning in Sierra Leone”, Policy Research working paper, no. WPS 7021. Washington, DC: World Bank Group.
- UNESCO, 2013. “The Global Learning Crisis: Why every child deserves a quality education”, UNESCO, Paris.
- World Bank, 2018. “World Development Report 2018: Learning to Realize Education’s Promise”, Washington DC: World Bank.
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
Recent economic research suggests that childhood environments in part determine success in life. So far, most of this evidence comes from rich countries. In a new paper, we use education data to measure intergenerational mobility across 26 African countries and find large differences across space. Results using data on migrants suggest that regions have causal effects on social mobility of Africans.
Why do people “make it” in life? Few of us can claim, as Robert Strauss, former U.S. ambassador to the Soviet Union and Russia, once quipped, that we were born in a log cabin we built ourselves. One chunk of individual success in climbing the social ladder is determined by our parents – be it through their genes (Sacerdote 2002, 2004), their parenting style (Doepke and Zilibotti 2017), or their income and connections. Another chunk is individual effort. Companies like Apple or Google were started in garages. That leaves our surroundings. Can the places we grow up in raise us up or pull us down? A growing body of research suggests that they can.
Growing evidence that “places matter” for individual mobility
At the forefront of these efforts, Chetty and Hendren (2018a, 2018b) have compared the incomes of American children to those of their parents. They link parents to kids through social security numbers in tax returns. Among families that moved, they find that children exposed to places with higher average social mobility for longer during childhood do better than children exposed to places with lower average mobility. Importantly, this holds when comparing the kids of parents with the same income and other observable characteristics, i.e. holding the “starting line” constant for everyone. Their findings have been reproduced for the U.S. (Chetty, Hendren, and Katz 2016), (Chyn 2018), Canada (Laliberte 2018), Australia (Deutscher 2018), and Denmark (Eriksen 2018). By contrast, studies identifying the causal effects of places on individual mobility in developing countries are still rare (a recent contribution by Asher, Novosad, and Rafkin (2018) on India is a notable exception).
New evidence from Africa
In a new paper (Alesina et al. 2018) we fill part of this gap by examining intergenerational mobility in Africa. After decades of stagnation, there is optimism about Africa’s future. Growth has returned (Young 2012), and some now see Africa as a continent of “1.2 billion opportunities” (Economist 2016). At the same time, anecdotal evidence suggests large inequalities, indicating that the recent aggregate gains may not be broadly shared, and that social mobility remains limited.
Measuring intergenerational mobility using education data
Measuring intergenerational mobility in Africa is difficult because of patchy data. Economists typically think of mobility in terms of income or wealth. In Africa, we lack tax records as well as administrative information linking children to parents. Instead we rely on censuses from 26 African countries and measure mobility using education data on children that share a household with their parents. [Card, Domnisoru, and Taylor 2018; Azam and Bhatt 2015; Narayan et al. 2018; Black and Devereux 2011 also study intergenerational mobility using education data.]
We measure upward mobility as the likelihood that kids of parents with less than primary education complete at least primary school. Similarly, we call an individual downwardly mobile if her parents have completed at least primary education and she has failed to do so. We compute these measures among children aged 14-18. This gives them enough time to complete primary school if they were ever going to do so. At the same time, most children at that age still live with their parents, which limits potential bias from co-residence selection.
Using education to measure social mobility has five advantages. First, education is a broad measure of living standards, reflecting not just income, but also aspirations and capabilities. Second, unlike income, much of which is informal and therefore under-reported in poor countries, schooling can be easily measured. Third, education, once completed, remains fixed and so intergenerational mobility can be assessed early in life. Fourth, “Mincerian returns” – how much extra income one more year of schooling commands in the labor market – seem to be especially high in Africa (Young 2012; Psacharopoulos 1994; Caselli, Ponticelli, and Rossi 2014), suggesting that education is a meaningful proxy of income. Finally, more schooling is correlated with many positive outcomes: household asset ownership, lower fertility, and even support for democracy. These correlations hold strongly comparing two individuals living in the same place, which means that education “quantity” is a useful stand-in-measure of living standards, even if the quality of schooling differs from place to place.
Main data patterns
The census data give us millions of individual observations to accurately measure intergenerational mobility over time (birth-cohorts) and in small geographic areas. First and most prominently, the descriptive analysis reveals differences in mobility both across and within countries. Figure 1 shows the geography of upward mobility across the 26 countries. Darker regions indicate places with lower mobility – children of illiterate parents are less likely to finish primary school.
Figure 1. Upward mobility across Africa
Source: Alesina et al. 2018
Country-differences are clearly important – South Africa is more mobile than Mozambique. Still, even within countries, there are vast differences as figure 2, which zooms in on Ghana, illustrates.
Figure 2. Upward mobility in Ghana
Source: (Alesina et al. 2018)
In some regions in Northern Ghana, average mobility is below .2 while it exceeds .8 in Accra, the capital. Second, while mobility does increase over time, these increases are modest and most pronounced in the most recent decades. This is still consistent with overall rising education, since average schooling in Africa was low until recently. Taking patterns one and two together, the persistent variation in mobility between places is more important than changes in mobility over time.
What accounts for differences in mobility across space? By far the strongest correlate of intergenerational mobility is the average literacy in the same place in the generation of the parents. This means that, comparing two individuals that grew up as children of illiterate parents in different regions, the individual that grew up in the region that has higher literacy in her parents’ generation has a greater chance of completing at least primary school. Several explanations might account for this pattern. Most simply, some regions have more schools than others, and can educate more individuals “per period”. One alternative story are peer effects: even though my parents are uneducated, I learn by example from the people around me that going to school is possible and desirable.
Beyond the correlation with initial education, we find that geography, colonial history, and at-independence development matter for intergenerational mobility. There are two important caveats to these results. First, pinning down the mechanism of why initial literacy and mobility are related remains a challenge. Second, these results represent correlations and not causally identified effects.
Causal effects of regions
To make causal inferences, we use data on families that have moved between two regions within a country in two ways. First, we compare siblings from migrant households, one child born in the origin of migration, the other in the destination. Figure 3 shows a (binned) scatter plot of the association between average birth-region upward mobility (computed among non-migrants) on the horizontal and individual likelihood of upward mobility on the vertical axis, conditional on household as well as birth-cohort effects. The slope indicates that kids born in a region with a ten percent higher mobility are 2.65 percent more likely to complete primary school compared to their siblings born in a different region with lower mobility.
Figure 3. Migrant vs non-migrant siblings
Source: (Alesina et al. 2018)
Second, we compare migrants that moved at different ages during childhood. Figure 4 plots the effects on individual outcomes of moving from a place of on average zero mobility to a place where all children of uneducated parents become educated against the age of the child at which the move occurred, once again comparing individuals within the same household. As intuition would suggest, earlier moves to better regions have larger positive effects than later moves, and effects turn insignificant towards the end of the period relevant for primary school.
For both empirical strategies, the sibling comparisons (enabled by household fixed effects) are crucial to separate treatment effects of regions from sorting whereby illiterate parents that may be more motivated/capable in educating their children move to regions with greater average opportunities.
Figure 4. Migration exposure effects
Source: (Alesina et al. 2018)
New research points to the importance of “places” in shaping individual social mobility. Complementing several recent works on developed economies, we document that opportunities for educational advancement vary widely within and across African countries. The strongest correlate of differences in mobility between places are differences in the initial education level in the generation of the parents, with more educated places showing higher mobility. Using information on migrants, we find that regions have a causal impact on individual outcomes. Taken together, our results suggest that initial conditions have persistent effects on the transmission of human capital between generations and that overall regional differences in human capital transmission in turn matter for who “makes it” in Africa.
- Alesina, Alberto, Sebastian Hohmann, Stelios Michalopoulos, and Elias Papaioannou. 2018. “Intergenerational Mobility in Africa.” Centre for Economic Policy Research Discussion Paper 13497 https://cepr.org/active/publications/discussion_papers/dp.php?dpno=13497
- Asher, Sam, Paul Novosad, and Charlie Rafkin. 2018. “Intergenerational Mobility in India: Estimates from New Methods and Administrative Data.” Mimeo, Dartmouth College.
- Azam, Mehtabul, and Vipul Bhatt. 2015. “Like Father, Like Son? Intergenerational Educational Mobility in India.” Demography 52 (6): 1929–59. https://doi.org/10.1007/s13524-015-0428-8.
- Black, Sandra E., and Paul J. Devereux. 2011. “Recent Developments in Intergenerational Mobility.” In Handbook of Labor Economics, 4B:1487–1541. Elsevier.
- Card, David, Ciprian Domnisoru, and Lowell Taylor. 2018. “The Intergenerational Transmission of Human Capital: Evidence from the Golden Age of Upward Mobility,” 102.
- Caselli, Francesco, Jacopo Ponticelli, and Federico Rossi. 2014. “A New Dataset on Mincerian Returns.” Unpublished.
- Chetty, Raj, and Nathaniel Hendren. 2018a. “The Impacts of Neighborhoods on Intergenerational Mobility I: Childhood Exposure Effects.” The Quarterly Journal of Economics 133 (3): 1107–62. https://doi.org/10.1093/qje/qjy007.
- ———. 2018b. “The Impacts of Neighborhoods on Intergenerational Mobility II: County-Level Estimates.” The Quarterly Journal of Economics 133 (3): 1163–1228. https://doi.org/10.1093/qje/qjy006.
- Chetty, Raj, Nathaniel Hendren, and Lawrence F. Katz. 2016. “The Effects of Exposure to Better Neighborhoods on Children: New Evidence from the Moving to Opportunity Experiment.” American Economic Review 106 (4): 855–902. https://doi.org/10.1257/aer.20150572.
- Chyn, Eric. 2018. “Moved to Opportunity: The Long-Run Effects of Public Housing Demolition on Children.” American Economic Review 108 (10): 3028–56. https://doi.org/10.1257/aer.20161352.
- Deutscher, Nathan. 2018. “Place, Jobs, Peers and the Teenage Years: Exposure Effects and Intergenerational Mobility.” Mimeo.
- Doepke, Matthias, and Fabrizio Zilibotti. 2017. “Parenting With Style: Altruism and Paternalism in Intergenerational Preference Transmission.” Econometrica 85 (5): 1331–71. https://doi.org/10.3982/ECTA14634.
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- Eriksen, Jesper. 2018. “Finding the Land of Opportunity Intergenerational Mobility in Denmark.” Mimeo.
- Laliberte, Jean-William. 2018. “Long-Term Contextual Effects in Education: Schools and Neighborhoods.” Mimeo.
- Narayan, Ambar, Roy Van der Weide, Alexandru Cojocaru, Silvia Redaelli, Christoph Lakner, Daniel Gerszon Mahler, Rakesh Ramasubbaiah, and Stefan Thewissen. 2018. Fair Progress?: Economic Mobility Across Generations Around the World. World Bank Publications.
- Psacharopoulos, George. 1994. “Returns to Investment in Education: A Global Update.” World Development 22 (9): 1325–43. https://doi.org/10.1016/0305-750X(94)90007-8.
- Sacerdote, Bruce. 2002. “The Nature and Nurture of Economic Outcomes.” American Economic Review 92 (2): 344–48. https://doi.org/10.1257/000282802320191589.
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Gender inequality goes beyond discrimination and sexism. It is also a matter of efficiency and development, and therefore, the socioeconomic losses that result from such inequality must be acknowledged and tackled. This policy brief summarizes the presentations held during the 6th SITE Academic Conference at the Stockholm School of Economics on December 17-18 2018. The event brought together scholars from around the world to examine existing forms of gender inequality, its causes, consequences, and policy interventions through a series of keynote speeches, research presentations and panel discussions.
Gender and survival
The reality of gender inequality is diverse throughout the world. The extent to which women and men face different opportunities and reach different outcomes vary substantially across countries and regions, and the forms of inequality that women face also vary geographically.
While richer countries have mostly closed their gender gaps in health and education, in other parts of the globe women are still struggling to survive, to make their marriage and reproductive choices freely, and to achieve the same educational opportunities as men. This is exactly where modern economic research can facilitate the understanding of the roots of such inequalities in each society, as well as the most likely drivers of change.
Corno, Hildebrandt and Voena (2017) show that in Sub-Saharan Africa and India, the age of marriage is a result of short-term changes in economic conditions (such as a reduction in crop yields due to droughts). Therefore, through for instance insurance mechanisms and temporary transfers, economic policy can influence marriage markets and the age of marriage. Relatedly, according to Ashraf, Bau, Nunn and Voena (2018), a girl in Indonesia or Zambia has a higher probability of being educated if she belongs to a group practicing bride price, defined as the “price” paid by a groom or his family to the bride’s family. This means that marriage markets could be a driver of educational investment. Cousin marriage is another issue within this context. Edlund (2018) suggests that this system serves as a barrier for economic growth by favoring men over women, the old over the young, and the collective over the individual. In general, challenging these marriage systems and improving female economic opportunities require a deeper understanding of the economic role of traditional cultural norms and institutions.
Some groups of women struggle for survival even in the so called “developed world”, being victims of gender violence. Sex workers in the United States are a particularly vulnerable population in this matter. Cunningham, DeAngelo and Tripp (2017) point out that, given that prostitution in most cities of the US isn’t only illegal, but also very dangerous (recording the highest homicide rate of any female occupation), it is critical to improve sex workers’ safety. Craigslist Erotic Services (CES) seemed to have contributed to it, by reducing female homicide rates by 17.4%. Apparently, this was a result of street prostitutes moving indoors and being able to filter clients to be safer. It is, therefore, suggested that the closure of such a platform put sex workers in an even more vulnerable position. Similarly, when it comes to adult entertainment establishments and its relation to sex crimes, Ciacci and Sviatschi (2018) argue that this type of businesses helps decrease daily sex crimes between 7-13% in the precinct where they are located.
When discussing approaches to prostitution, the “Nordic Model” has been highly praised and adopted by several countries. The term refers to a reform initiated in Sweden that considers buying sex a criminal offense, while decriminalizing those who are prostituted. However, preliminary results from Perrotta Berlin, Spagnolo, Immordino and Russo (2018) suggest that intimate partner violence and violence against women might have increased because of its enactment in Sweden.
Gender violence, however, isn’t only domestic or affecting sex workers. Borker (2018) claims that, in India, female college students are willing to choose less prestigious universities, to make additional expenses and to spend more time on transportation than their male counterparts only to avoid harassment on the street or public transportation. Street harassment, therefore, perpetuates gender inequality in both education and potentially the labor market.
Challenging social norms
As already seen, even the most gender-equal countries still suffer from persistent forms of inequality that need to be acknowledged and tackled. Doing so will result both in fairer societies and in more efficient economies, because it will make full use of both halves of the world’s skills and knowledge.
Friebel, Auriol and Wilhelm (2018) state that, in Europe, it is harder for women to make a career in economics. The representation of women in academics is low, and the higher ranked the university, the lower is the representation. This could be a consequence of several issues, one of them being the “glass ceiling”.
The glass ceiling, according to Bertrand (2017), is the phenomenon by which women remain underrepresented in high-level occupations, and earn less. Even in countries such as Denmark and Sweden, women still receive less for the same jobs. There are many potential explanations for this. One of them refers to the gender differences in psychological attributes in work, such as the idea of women performing worse under pressure or being unwilling to compete. This interpretation ultimately falls under the nature vs nurture discussion and only accounts for up to 10% of the pay gap. Another reason states that women suffer the penalties associated with demanding more flexibility. Such demand comes from the need to perform non-market work, like domestic work and, especially, caring for children. This means that women, especially the more educated ones, are paying a disproportionate price in the labor market for raising a couple’s children. Giving women more flexibility won’t crack the glass ceiling, au contraire, it will backfire because flexibility is negatively priced in the market. Besides, it doesn’t address the earning gaps. A more compelling proposal is to shift the focus from increasing flexibility to changing social norms and gender role attitudes. Normalizing and encouraging paternal child care in workplaces, for example, could be a way to do so.
Social norms based on traditional gender stereotypes also seem to be the reason why in Sweden, promotions to top jobs dramatically increase women’s probability of divorce but do not affect men’s marriages, as reported by Folke and Rickne (2018). In this case, promoting norms and policies with a more gender-equal approach to couple formation could increase the share of women in top jobs.
Given the importance of social norms, understanding how they can change is crucial. In Saudi Arabia, two studies were conducted on the influence of misperceived social norms. Both showed that the low-cost intervention of simply providing information could make a big difference. In one case, Bursztyn, González and Yanagizawa-Drott (2018) have evidenced that most young married men privately support female labor force participation (FLFP) outside of home. Nevertheless, they tend to underestimate the level of support for FLFP by other men. When correcting those misperceptions, the men’s willingness to let their wives join the labor force increases. Comparably, Ganguli and Zafar (2018) have shown that there is an increased likelihood of working full-time for female students when they, along with their close circles, receive information about the labor market and the aspirations of other women peers.
Challenging social norms isn’t only beneficial when discussing the glass ceiling and FLFP, it also has the potential to improve public health. In fact, Milazzo (2018) argues that women’s increased mortality rate in India can be an unintended consequence of son preference. Son preference induces women with a first-born daughter to adopt behaviors that increase the risk of maternal morbidity and mortality. Therefore, interventions to change deeply rooted social norms such as the boy preference could significantly reduce maternal mortality risk.
Bridging research and policy
In Malawi, research by Perrotta Berlin, Bonnier and Olofsgård (2017) on aid project location suggests that proximity to aid has a positive effect on the lives of women and children. Likewise, Goldstein (2018) reports that the World Bank’s Empowerment and Livelihoods for Adolescents (ELA) program in Uganda has also led to positive reproductive outcomes and income effects. These results illustrate the importance of reducing the divide between research and policy. Research has the potential of serving as an instrument for informed policy-making and aid intervention.
The Organization for Economic Cooperation and Development (OECD), for instance, applies research to create tools that help improve economic and social well-being. Two of those tools are the Social Institutions and Gender Index (SIGI) and the Development Assistance Committee (DAC) Gender Equality Policy Markers. On one hand, Missika (2018) explains that the SIGI is a cross-country measure of discriminatory social institutions against women and girls. Though the progress is slow (it might take around 200 years to close the gender gaps), its use gradually promotes the creation of locally designed solutions that, combined with adequate legislation, could enhance gender equality. On the other hand, Williams (2018) states that the DAC Gender Equality Policy Markers are meant to ensure that women have access to and benefit from finance.
Consistently , for the Swedish International Development Agency (SIDA), which works on behalf of the Swedish government, gender equality is a priority that permeates its interventions. In this context, the Feminist Foreign Policy has strengthened Sweden’s commitment in the topic.
Prior to finalizing the conference, representatives of the FROGEE Network (Forum for Research on Gender Economics in Eastern Europe and Emerging Economies) made a short presentation about the key challenges for achieving gender equality in their countries and the research opportunities available.
Conference material, including presentations, can be found here.
Speakers at the conference
Marianne Bertrand, University of Chicago
Alessandra Voena, University of Chicago
Alessandra González, University of Chicago
Anders Olofsgård, SITE
Annamaria Milazzo, World Bank
Bathylle Missika, OECD Development Centre
Eva Johansson, SIDA
Girija Borker, World Bank
Guido Friebel, Goethe University Frankfurt
Ina Ganguli, University of Massachusetts
Amherst Johanna Rickne, Stockholm University
Lena Edlund, Columbia University
Lisa Williams-Katz, OECD
Maria Perrotta Berlin, SITE
Markus Goldstein, World Bank
Michal Myck, CenEA
Riccardo Ciacci, The University Loyola Andalucía
Scott Cunningham, Baylor University
- Ashraf, Nava; Natalie Bau, Nathan Nunn, and Alessandra Voena. 2018. “Bride Price and Female Education”. The National Bureau of Economic Research Working Paper No. 22417.
- Bertrand, Marianne. 2017. “The Glass Ceiling”. Becker Friedman Institute for Research in Economics Working Paper No. 2018-38.
- Borker, Girija. 2018. “Safety First: Perceived Risk of Street Harassment and Educational Choices of Women”. Job market paper.
- Bursztyn, Leonardo; Alessandra González, and David Yanagizawa-Drott. 2018. “Misperceived Social Norms: Female Labor Force Participation in Saudi Arabia”.
- Ciacci, Riccardo; and Maria Micaela Sviatschi. 2018. “The Effect of Adult Entertainment Establishments on Sex Crime: Evidence from New York City”.
- Corno, Lucia; Nicole Hildebrandt, and Alessandra Voena. 2017. “Age of Marriage, Weather Shocks, and the Direction of Marriage Payments”. The National Bureau of Economic Research Working Paper No. 23604.
- Cunningham, Scott; Gregory DeAngelo, and John Tripp. 2017. “Craigslist’s Effect on Violence Against Women”.
- Edlund, Lena. 2018. “Cousin Marriage Is Not Choice: Muslim Marriage and Underdevelopment”. American Economic Association Papers and Proceedings, Volume 108, pages 353- 57.
- Folke, Olle; and Johanna Rickne. 2018. “All the Single Ladies: Job Promotions and the Durability of Marriage”.
- Friebel, Guido; Emmanuelle Auriol, and Sascha Wilhelm. 2018. “Women in Europen Economics”. [Mimeo]
- Ganguli, Ina; and Basit Zafar. 2018. “Information and Social Norms: Experimental Evidence on Labor Market Aspirations of Saudi Women”. [Mimeo]
- Goldstein, Markus. 2018. “Evidence on adolescent empowerment programs from four countries”. [Mimeo]
- Milazzo, Annamaria. 2018. “Why are adult women missing? Son preference and maternal survival in India”. Journal of Development Economics, Volume 134, pages 467-484.
- Missika, Bathylle. 2018. “Are laws and social norms still an obstacle to gender equality? Lessons from the SIGI 2019”. [Mimeo]
- Perrotta Berlin, Maria; Evelina Bonnier, and Anders Olofsgård. 2017. “The donor footprint and gender gaps”. WIDER Working Paper 2017/130, United Nations University World Institute for Development Economics Research.
- Perrotta Berlin, Maria; Giancarlo Spagnolo, Giovanni Immordino, and Francesco Flaviano Russo. 2018. “Prostitution and Violence: Empirical Evidence from Sweden”. [Mimeo]
- Williams, Lisa E. 2018. “Financing for gender equality beyong ODA”. [Mimeo]