Development economics in a developed country: how do poor Americans save?

This recent episode of NPR’s planet money talks about the financial lives of poor Americans. A few practices mentioned in the article, such as group lending, private lenders, and high interest rates for loans, are strikingly similar with what poor people do in developing countries in Africa and Southeast Asia.

This resemblance leads me to think whether ideas and methodologies in development economics can be (more extensively) applied in developed country settings. Classic models in development economics such as health-based poverty trap and credit constraint can be easily applied to study the causes of poverty in a developed country setting. Comparative studies of poor individuals in developed vs. developing countries can shed light on the impact of institutions, governance, and infrastructure on addressing poverty.

The data mentioned in this episode are pretty amazing — 235 poor households across America tracked over a year with high frequency financial diaries. I bet interesting research based on this data is on the way.


My Two Cents on Randomized Controlled Trials

Randomized control trials (RCTs) have been at the forefront of development economic research in recent years. How well do these inform us of policy alternatives to reduce poverty?

On the bright side, RCTs allow us to identify the causal impact of policy interventions, and a lot of studies provide evidence that some simple nudging can make a big difference on behavior (see Esther Duflo’s work on encouraging Kenyan farmers to use fertilizers). However, there are also a few caveats in interpreting RCT results:

Publication Bias: only significant results — either positive or negative — get published. Are we learning about the truth or the truth we WANT to know? For instance, microfinance has been applauded as an innovative and effective way to increase savings and investments, encourage entrepreneurship, and reduce poverty. But a recent working paper has found zero effects of access to microfinance on long term development outcomes.

Pre-analysis plan vs. manual selection after study is initiated: Here is a philosophical discussion in the Journal of Economic Perspectives by Ben Olken.

Heterogeneous treatment effects: the magnitude of the effects of policy varies a great deal. External validity is often a concern. Here is a thought-provoking paper by Eva Vivalt, the founder of AidGrade, a database on impact evaluations.

Experimental arms race: Are we simply adding more technical details into the same experiments without shedding light on fundamental channels of how they change behavior? Here is an article by David McKenzie on the tpoic. More specifically, Rachel Glennerster writes about what this implies for RCTs involving governments.

Your thoughts and comments are welcome.

New Directions for Migration Research

If you are interested in economic research on migration, I highly recommend this presentation at the Center of African Economies 2015 conference. A great deal of fresh, puzzling recently discovered data patterns and exciting directions for future research from a group of young promising economists. The following are some quick notes. I encourage you to watch the whole presentation for your own inspiration.

Michael Clemens on Skilled Migration

– Future research questions: 1) look at surveys with details about migrants: what do they learn? How well do they assimilate into the local labor markets? 2) How does participation in the global labor market affect education systems in the origin country? 3) How should we model human capital externality (this is hard!)?

– Incorrect language can mislead our discussion on migration. “Brain drain”? Think about calling women’s labor force participation rate “family abandonment rate”. How would that feel?

Melanie Morten on Migration in Sub-Saharan Africa

– Facts: much migration in Sub-Saharan Africa in rural-urban, rural-rural, and urban-urban; there are large regional differences in migration flows.

– Apart from migration across sectors, we should think about labor flows within sector across areas as well.

– Potential reasons for the lack of migration in the presence of large wage gaps: 1) selection; 2) people care more than wages (amenities/quality of life, cost of living, congestion, etc), which is the compensating differentials argument; 3) it’s costly to migrate.

– There’s evidence for all three channels mentioned above. This points to the need for further understanding of the costs of migration and potential roles of policy to foster a better match between skill and location.

– Policies that are space based (e.g. subsidies) might prevent people from migration by making it easier to stay in low-productivity places. (I found this particularly interesting).

Clement Imbert on Seasonal Migration

– Seasonal migration is especially important for risk coping in certain areas with agricultural lean seasons (India, Bangladesh, etc); but the contexts are similar.

– Data: NSS data, REDS data, RICE survey.

– Two sectors: construction (spot market) and manufacturing (network/referral matters).

– Need to consider the general equilibrium effects of seasonal migration (on urban wages, amenities, etc).

– Little data and work done on seasonal migration in Africa.


1. How to study forced migration due to climate change or conflicts?

Morten: This is challenging to model because migration is not a choice. Current research has focused on the impact of large migrant inflows on the local economy. See Ran Abramitzky‘s work. In the case of climate change, which is a permanent shock, political economy might matter and the distributional impacts are important. See Esteban Rossi-Hansberg‘s recent work (he presented at Duke two weeks ago and it was fascinating!).

2. Are migrant non-migrant wage differences only seen in developing countries?

Morten: No. It seems people value amenities differently. In developing countries, the benefits can be similar with those in developed countries, but the costs can be very high. See my paper on nonparametric estimation of migration costs (my comment: gravity model in trade?). Thinking about differences between developing and developed countries can be useful for policy intervention.

Clemens: There is a broader literature on “Why don’t people make profitable investments?” There’s a lot to explore in developed countries — why do people stay in the Appalachia when they can move to Miami?

3. How do property rights affect models on migration?

Morten: This is a first-order consideration. See paper in AER: as people get formal property rights, they migrate more. Part of the lack of migration might be due to property rights.

4. What’s the role of information on migration?

Morten: Experiments. Need to find high quality data on wages that people can trust. There are many open questions and possibilities to extend existing models.

Imbert: Information seems to be more easily available for lower-skilled jobs.

New Data Repository on Randomized Controlled Trials

Data from Randomized Controlled Trials conducted by IPA/J-PAL are available from Harvard:

The availability of more data on RCTs will hopefully spur more meta analysis across studies and shed light on the design of effective development policies in different contexts.

Assorted Links

1. Eva Vivalt at NYU looks at how much do impact evaluations generalize in her job market paper. Her guest post at the World Bank Development Impact blog summarizes the paper well. The following paragraph caught my attention:

In the greater paper, I also find that when academics or NGOs implement a project, the project tends to yield higher effect sizes than when a government implements it; worrisome if the smaller, academic/NGO-implemented projects are intended to estimate the effects of the program were the government to implement it on a larger scale.

The generalizability of impact evaluation is very important for policy implementation, and more work needs to be done on this.

2. Brookings report on the rationale of encouraging higher education in STEM fields to meet the demand of employers. This made me wonder what forces are driving the location decision of different types of firms and how local policies interact with those. Sounds like there’s some spatial equilibrium there.

3. New York Times interview with executive women on finding and owning their voice. The whole article is worth reading. but these two pieces of advice stand out:

One of the things I see sometimes is that women mistake words for voice. They feel that because they have a seat at the table and they say something, that’s good. But it’s important for women to know that having a voice really means having a track record of success and accomplishments, so that people want to listen to what you have to say, because you’re saying something of value. So use your voice, but use it strategically.

Sometimes when you’re the only woman in a meeting, or one of just a few women in the group, you can feel like you almost have to say something. I think there are women who just want to make sure that they present at a meeting and that people are hearing them. But I think it’s just as important that you listen, because when you listen you get more out of the meeting. Sometimes you’re waiting to talk, and then you’re not listening. You have to balance listening and speaking. Then it becomes more natural.

Pascaline Dupas on Decentralization and Efficient Targeting of Subsidies

Pascaline Dupas, a development economist at Stanford University, gave a talk in our department this afternoon on how decentralization affects the targeting of subsidies in Malawi.

The allocation of public subsidies to targeted beneficiaries is an important task of governments in developing countries. Three conditions need to be met to maximize the effectiveness of these subsidies:

1. They should be assigned to people with the highest returns.

2. There should be limited leakage on the way to assigned beneficiaries.

3. Beneficiaries should put subsidies in appropriate (intended) use.

The second and the third conditions have been explored extensively in the development economics literature. For example, economists have examined the effectiveness of conditional cash transfer (CCT) programs on school enrollment of children in poor families. By making school enrollment a precondition for cash transfers, the government hopes to nudge parents to send their children to school, which they probably wouldn’t do if the transfers were unconditional.

The purpose of this paper is to investigate the first condition. More specifically, they look at the effectiveness of centralized (proxy means tested, or PMT-based) vs. decentralized (through chiefs) allocation of subsidies in Malawi. Dupas and her coauthors ask the following questions:

To what extent does the poor performance of local authorities in the aggregate come from inefficient allocation of resources or subsidies? How well do chiefs target? Could a centralized (PMT-based) system do better?

In Malawi, like in many other African countries, chiefs play an important role in allocating resources within local communities. There are advantages and disadvantages of letting the chiefs assign the subsidies instead of the government. PMT-based systems assign subsidies based on asset owned by individuals/families, but asset is a poor predictor for consumption levels of the poor, and the latter is a much more reasonable measure of poverty. Chiefs, on the other hand, are likely to have more local information, especially regarding the relative characteristics of the households within the community. Therefore, they might do a better job assigning the subsidies to targeted beneficiaries. However, chiefs might also favor their kins and dampen the intended equalizing effects of the subsidies.

The authors assess the efficiency of subsidy allocation along two margins: poverty targeting and productivity efficiency. The former is giving subsidies to people who are eligible based on their consumption of perishable food, while the latter assigns subsidies to people where the returns are the highest. If chiefs know that there will be income pooling between community members after subsidies are put to use, they will have more incentive to allocate subsidies to the most productive households in order to maximize efficiency. Such an outcome will also be more likely the more heterogeneity there exists in productivity.

Dupas showed us the error rates of three systems of subsidy allocation: a hypothetical PMT-based system, an allocation through chiefs, and a random allocation. There are two types of errors: type I error where an eligible individual is not given a subsidy, and type II error where an ineligible individual is given one. Surprisingly, chiefs seem to do as well as a PMT-based system at identifying the targeted beneficiaries.

How to incorporate productivity and equality considerations into the decision process of the chiefs? The authors proposed a general framework where the chief maximizes a weighted sum of households’ benefits from the subsidy (in terms of expected additional profit) within the community. They divide households into different classes based on their relationship with the chief, poverty status, and returns to the subsidy. The relative weights of the chief towards different classes can be recovered through comparing the allocation patterns of food versus input subsidy, with the assumption that the former doesn’t involve productivity considerations while the latter does.

Although the question they are investigating is interesting and important, I wish the speaker were clearer about what alternative allocation systems could generate similar effects as allocating through the chiefs. In particular, if the only advantage of the chief relative to a centralized agency is that he (or she, in the rare case) has updated local information, would it be possible to create a self-monitoring system within the community such that people divide the subsidy optimally among themselves? Another related concern is that the chief is likely to base their judgment on self-reported characteristics of the household when allocating resources. If they don’t observe the true productivity of households in the community, how might that affect the findings about their productivity efficiency consideration? I look forward to reading their paper when it comes out.

Economics of the Family (5): Investment in Children


Here comes the long-awaited discussion on the economic modeling of parental investment in children. It took us two classes to cover the classic quality-quantity tradeoff theory and its recent empirical tests.

Becker and Tomes (1976) model the parents’ utility as a function of the number of children, the quality of each child (assumed to be equal), and other goods produced in the family (“Z goods”). Costs of raising children is multiplicative in the quantity and quality of children because of “equal concern”. The resulting conditions indicate that shadow price of children is endogenous because the number of children is a choice variable. While this model provided a basic framework to think about fertility decisions, it has two important flaws. First, counter-factual questions are hard to make sense because “n” and “q” are jointly determined. Second, they assume costless transfers between children in terms of income and other outcomes to achieve equality. This assumption might be plausible for outcomes like welfare or happiness, which are difficult to take from one person to another. By contrast, Behrman, Pollak and Taubman (1982) addressed equal concern in aspects other than income. They incorporated endowment as complementary to education (i.e. parental investment) and argued that equal concern would not necessarily lead to equal investments.

For empirical results on the topic, Schultz (2001) provides a comprehensive account of the opportunity costs of having children. A clever strategy mentioned in the paper is to use labor market conditions that affect the career prospects of the male but not the female to identify the costs of having children. It is also important that opportunity cost may not be the whole story: the increasing bargaining power in the family might allow women to have their desired number of children, which could be fewer than what their husbands want.

Researchers have used data from developed and developing countries to test the quantity-quality tradeoff and differential investment by gender. In class we touched upon a few papers on “If parents’ income expands, how do they allocate the additional money towards investments in their children?” Paxson and Shady (2010) assessed the “intent to treat” effects of cash transfers on the health of children; De Brauw and Hodinott (2011) investigated whether taking away the school enrollment conditions hurt the effectiveness of the cash transfer program in Mexico. But it is important to bear in mind that the benefits and costs of enrollment are not examined in their paper. The goal of the policy is to increase enrollment.

I think there should be more research on fertility and child investment decisions in a dynamic framework. Parents might time their births to reap the most benefits from scale economies, and the gender of firstborn might affect subsequent childbearing decisions. There is a lot to be learned.


Becker, G., & Tomes, N. (1976). Child endowments, and the quantity and quality of children.
Behrman, J. R., Pollack, R. A., & Taubman, P. (1982). Parental preferences and provision for progeny. Journal of Political Economy, 90(11), 52-73.
De Brauw, A., & Hoddinott, J. (2011). Must conditional cash transfer programs be conditioned to be effective? The impact of conditioning transfers on school enrollment in Mexico. Journal of Development Economics, 96(2), 359-370.
Paxson, C., & Schady, N. (2010). Does money matter? The effects of cash transfers on child development in rural Ecuador. Economic Development and Cultural Change, 59(1), 187-229.
Schultz, T. P. (2001). The fertility transition: Economic explanations. Economic Growth Center Discussion Paper, (833).