Published on Development Impact

Weekly links November 4: financial education 9 years later, beating up on macro models & meta-analysis, job market advice, and more…

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·       On All about Finance, Bruhn, Garber, Koyama and Zia summarize their work on the long-term impacts of high school financial education in Brazil. “we use administrative data to follow 16,000 students in the original sample for nine years after graduating high school. Based on students’ name and age, we obtained their taxpayer identification number (CPF). We then used the CPF to consult administrative data housed at the Central Bank of Brazil. These data include bank account ownership (but not account balances), use of various credit products, as well as information on formal employment status and formal microenterprise ownership. We follow young adults from when they finished high school in 2011, until February 2020, just before the COVID-19 pandemic hit Brazil…the financial education program had no effect on long-run bank account ownership, but a high percentage of students (85 percent) have a bank account after graduating high school…Treatment students are 1.4 percentage points less likely to have credit card debt and 0.9 percentage point less likely to use overdrafts…the program led to a lower likelihood of having loans with repayment delays , by about 0.9 percentage points”.

·       Your favorite DSGE model sucks is the great title of this blogpost on statistical problems with macro modelling using dynamic stochastic general equilibrium models, along with this fun description of what happens if you take these models literally.

·       People with mobile phones in Nigeria, Kenya and Tanzania have lots of internal mobility within these countries: On VoxDev, Blanchard, Gollin and Kirchberger summarize insights from mobile phone location data on internal mobility. “Between 10% and 20% of the days when we observe our users, they are away from their home locations…a sizeable fraction of users across all densities are found to make visits to one or more cities, and residents in more remote locations are more likely to visit at least one city. We also find that users are making multiple visits to non-home cities…The spatial detail of our data also allows us to obtain insights into the precise locations that visitors to cities are seen at. … individuals visit a vast array of amenities including shopping areas, entertainment locations, airports, public offices, and health facilities….The substantial mobility we observe also suggests that variable costs of travel (e.g. bus tickets) are unlikely to be the main factors constraining the allocation of people across space – although that may not be true for poorer households.  As people travel, information is likely to travel with them, which also casts doubt on the idea that information frictions sustain large spatial gaps in outcomes. Instead, our results point toward other types of frictions, such as loss of social connections, informal insurance, or claims to land or other possessions.”

·       “nearly everybody in the audience will forget what the discussant said within a couple of weeks, but the authors will remember forever. This is not to say that you shouldn’t criticize the paper’s shortcomings. But try to criticize in the way that you would criticize a close friend’s work.” – part of James Choi’s advice on how to give a good paper discussion.

·       The Data Colada blog covers some of the issues plaguing meta-analyses. Part 1 notes a common problem is that some studies are more valid than others, and that often meta-analyses “attempt to be comprehensive, actively seeking out and incorporating studies that would otherwise – and deservedly – have gone unnoticed or unpublished. Nobly intentioned, this procedure will lead to the inclusion of studies that have been evaluated to a lower standard, never evaluated at all, or evaluated and found to be invalid. In other words, many meta-analysts act as though 100% of studies conducted anywhere, by anyone, are valid, and thus that the average of any set of located studies is valid. But that is probably not true” and a second problem of combining interventions and outcomes that are not exactly the same, making the average effect meaningless “A notable example is that meta-analysts frequently average the effect of interventions while ignoring the size of those interventions. For example, they might average the effects of a blatant reminder with the effects of a subtle reminder, they might report that average as d = .25, and they might summarize that as indicating that reminders work but the effect isn’t big. As sensible as that might seem, it is meaningless and potentially misleading, as blatant reminders may have large effects and subtle reminders may not work at all.” Part 2 digs deeper into this second issue of how often “the average is meaningless” because it is averaging over not that similar interventions, taking the example of meta-analysis of nudge treatments. A quite-terrifying look inside the sausage of how meta-analysis is often done – and the conclusion is “In sum, we believe that many nudges undoubtedly exert real and meaningful effects on behavior. But you won’t learn that – or which ones – by computing a bunch of averages, or adjusting those averages for publication bias. Instead, you have to read the studies and do some thinking.”

·       On the World Bank’s data blog, looking back on two decades of experience with poverty mapping.

·       Just in time for the job market: Tia Palermo, Thoai Ngo, Valerie Mueller, and Amber Peterman offer advice of things to consider when applying for non-academic jobs. See also our series of interviews on using a PhD in development economics outside of a research university, including advice on applying to liberal arts colleges.

·       Marc Bellemare offers guidance on how to write a diversity, equity and inclusion statement well and links to an example of his own. Really useful, especially for those applying to universities that require these, in some cases applying a first filter or screen on the DEI statement before the rest of the application is considered. Apparently these are also increasingly needed as part of promotion packets as well at some universities.

·       NEUDC 2022’s plenary panel discussion on “Lessons Learned on Poverty Measurement through Household Panel Surveys” being held on Saturday November 5 from 5-6pm ET at Yale University will be simulcast. You can register to join this one-hour webinar for free here - .

·       Conference call for papers: Inclusion Economics at Yale University, the Yale Economic Growth Center, and the Asian Development Bank Institute invite submissions from academic researchers of original, rigorous, empirical and/or theoretical papers in economics or related fields for an upcoming conference on “Gender-Sensitive Economic Recovery and Resilience in Asia” in Tokyo, Japan, March 9-10, 2023.  Read more about the call for papers and how to submit here. Paper submission deadline is December 2, 2022.


David McKenzie

Lead Economist, Development Research Group, World Bank

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