Review of AEA sessions in Atlanta (Jan 4-5)

I took last month off from this blog (and most other productive activities) because I was on holiday for three weeks in the Bay Area. I hope all of you had a great holiday season 2018 with friends and family and a refreshing start to the new year 2019. The first topic I wanted to come back to is a review of the webcast sessions from the American Economic Association's annual meetings held in Atlanta from Jan 4-5, 2019. Several of the sessions are webcast here and you can access lectures on various topics including growth in the developing world, automation and the future of work, public debt, and - returning from last year with an extremely compelling panel - the gender problem in economics and what steps the profession can take to address it. In this post, I discuss two of the panels with an eye to discussing Autor's lecture on the future of work in the next post.

Growth challenges in the developing world 

The AEA convened a "World Bank economists" session consisting of three former World Bank Chief Economists (Justin Lin, Francois Bourguignon, Kaushik Basu), current Chief Economist Pinelopi Goldberg, and moderated by former Acting Chief Economist Shanta Devarajan. The purpose of the panel was to deliberate on the challenges facing the developing world. Given the very broad - arguably too broad - scope of the topic, it is natural that the panelists settled on a narrower topic over the course of the conversation: industrialization and the informality trap facing Africa.

Historically, industrialization and the rapid job creation in the formal wage sector that accompanies it have been seen as the most effective ways to raise wages and lower the poverty rate in developing countries. Lin cited historical examples of low-income countries' growth trajectories after capturing manufacturing jobs moving from the U.S. to Japan in the aftermath of WWII, Japan to Southeast Asia in 1960s and 1970s, and from Southeast Asia to China in 1980s and 1990s. Now that wages are increasing in China, many of these manufacturing jobs will be looking for a new home. How can Africa capitalize on these opportunities in coming decades was the question most of these economists were trying to answer. Chapter 2 of this policy report from the African Development Bank does a good job of summarizing these issues including evidence of what some economists call "de-industrialization" and the obstacles to small business growth. Given the demographic changes that will add 2 billion to the working age population in the African continent in this century, the creation of jobs in the formal wage sector will be important not only for economic but social and political stability. 

1. The primary point of contention is that it is not clear that "de-industrialized" countries will capture these manufacturing opportunities without concerted policies. E.g. automation is a real threat to manufacturing jobs in certain industries and less so in others (retail incl. clothing, shoes, and furniture). Furthermore, the trade environment is rapidly changing with advanced economies looking to be less hospitable to imports from low-income countries. The second half of the panel asked panelists to comment on different ways of approaching this issue wherein I think the issue of too broad a topic came to light. I think it would have been more useful to showcase specific examples and evidence from recent research. 
2. It wasn't discussed in the panel but it is relevant discuss the impact of a shift from self-employment and agriculture to industrial employment on working populations and whether there is desire on the part of working populations to hold these types of jobs in the first place. Specifically, J-PAL poses the issue in preface to a 2017 paper from Chris Blattman and Stefan Dercon that studied the effects of industrial employment on Ethiopian workers: "Industrial sector development to boost mass hiring is seen as important to poverty alleviation at the macroeconomic level. But how those jobs, particularly in early stages of industrial sector development, affect the workers themselves and what the workers prefer are less well-understood." The findings from this paper are summarized in this New York Times article with the bottom line being: workers are initially unaware but quickly become aware of the safety hazards and poor wages paid in sweatshop conditions leading to a high turnover rate in these early-stage manufacturing firms. The authors find that particularly when the constraints to self-employment were addressed through cash grants the workers preferred self-employment. 
1. Does this mean that industrialization is not the best way to raise wages and lower the poverty rate in low-income countries? No. But it indicates that there may be a more efficient equilibria where a set of regulations providing a baseline level of safety for workers that address the issues identified in this study (chemical fumes, repetitive stress injuries, and probability of serious injury) can be beneficial to both employers via a lower turnover rate and to workers who would more likely work there if these health concerns were addressed. Such a set of regulations need not be so stringent that they reduce the comparative advantage of setting up shop in sub-Saharan Africa given the low wages on the continent but they will provide better standards of living for workers expected to drive these changes. 

Gender in the profession

On the panel on gender in the economics profession. The community by now is well aware of statistics indicating the low proportion of women who study economics as undergraduates, the lower proportion who study it as PhD candidates, and the even lower proportion who are tenured faculty at universities. The primary questions now, in my opinion, are (1) whether members of the community believe that these statistics are indicative of gender bias (as opposed to differences in ability or preference between the genders); and (2) whether members of the community believe that they can and should take action to address this bias, particularly when it is implicit and particularly where it requires the buy-in of economists who are neither part of the problem nor the solution.

Several of the questions posed in the panel revolve around these ideas. First is the need for data and evidence that is reflective of implicit bias to indicate to said economists that there is a problem at hand. Erin Hengel's paper on publication records of male and female economists that I discussed last year and Alice Wu's paper on sexism within the Econ Job Market Rumors website which is informal but commonly used among academic economists for job postings and career advice (see this interview with Wu on this paper) are two examples of this type of evidence. This webpage put together by the UC Berkeley Women in Economics group offers other useful information.

From my own anecdotes and research experience within the Gender Innovation Lab at the World Bank, there are a few issues that I think are actionable to address:

1. Role models and social networks among women 
2. Gender gap in perceived abilities in STEM fields 
3. Culture and implicit bias within the profession

Given that the third issue is probably the one that is most difficult to address I think it requires first the buy-in from the community that I mentioned above. Being aware of implicit bias and its effects on the community are important because they are needed to take the next steps. For example, one issue that was talked about in the panel is aggression in economics seminars. It likely impacts women more than men because women tend to do better in collaborative and non-aggressive environments and the aggression tends to be more often directed towards women than it does towards other men (e.g. see Wu's paper on EJMR). But suffice it to say, I think we would all do better - men and women alike - if we were all a bit kinder to one another without compromising the rigor of our work. Specifically, to both acknowledge that we can and should be able to communicate questions and criticisms without resorting to aggression and be willing to learn the techniques to do so. Same with being willing to learn the techniques to recognize and address implicit bias.  

I have been supported in my efforts by peers and role model figures - mostly male - that have been enthusiastic about my ability to succeed in this profession. I have been blessed in not only role models in professional and academic life but also partners in my personal life that have been the most influential factors in my decision to undertake graduate studies. My thoughts on this issue are - in addition to addressing systematic issues within the field - if you can support a young person and believe in their abilities it is probably a determining factor in their decision to pursue higher studies. Whether we have the data or not as of yet (and there is more empirical research being conducted on role model figures and mentoring), we can't underestimate the value of empathy in how people decide whether or not they want to be in a particular location, field, university, firm. 

In-depth look at income and wealth data (pt. 1.5 of 3): Non-traditional data and machine learning approaches

While this was originally meant to be a three-part series on income and wealth data, it would have been an oversight to not include some discussion of the non-traditional data and machine learning approaches to collecting information on poverty. These data are particularly relevant in developing countries where traditional sources of data - administrative data and survey data - are not collected as widely, regularly, or thoroughly. This can be for several reasons: nationally representative surveys are expensive and the costs of data collection too high, challenges associated with data collection in conflict-affected areas (discussed in greater detail in a previous publication I worked on), and large proportions of the population are employed in the informal economy meaning there is little by way of administrative tax records at the lower end of the income distribution.

Yet, information on poverty is still needed in these countries to inform evidence-based policymaking by governments, international organizations, and non-profits. A brief article by researcher Joshua Blumenstock published a few years ago in Science, "Fighting Poverty with Data", discusses the frontier of research in this area that aims to supplement the traditional sources of data on wealth and inequality with machine learning approaches. Blumenstock discusses, for example, the rise in use of nightlight data to track economic productivity and growth citing one paper which utilizes nightlight based measures to study the impact of sanctions on North Korea. In fact, a paper that I reviewed earlier in the year on the impact of Chinese aid projects on local corruption used nightlight data to proxy for local economic activity in areas around active and inactive Chinese aid sites.

More novel and more interestingly, the author cites research in machine learning that uses satellite imagery in conjunction with nightlight data to identify the visual features of relatively wealthier areas (which have brighter nightlight) that would allow researchers to leverage daytime satellite images to better track poverty in developing countries. There are limitations to this approach for example that nightlight is not an ideal measure of activity at the lower end of the income distribution - where all is dark - but with further research these approaches could be very useful in the developing country context.

Mobile phone data - which was discussed in part in the above article and in greater detail in this other Science piece also by Blumenstock - is also promising. Using mobile phone logs, researchers extract statistics including volume, intensity, and timing of phone calls, the structure of the individual's network of contacts, and mobility and migration information based on geospatial markers and whittle down to the statistics that can be used to predict socioeconomic status. In the case cited in this article, the researchers paired consenting individuals' mobile phone data with survey data that they collected on individual income and wealth in order to train the model. It should be noted that mobile phone data is subject to greater ethical and privacy concerns than publicly available data. While the research cited here aimed to obtain macro level statistics to inform policymaking it is clear that attempting to obtain a more granular understanding for specific demographics will be challenging. ICT access and use is far from universal and, often, those who are excluded from its access are the most vulnerable. This is similar to the challenges with using conflict data wherein the data on those who are the most vulnerable and impacted by conflict is the data that is the most challenging to collect and to collect accurately. This is not, however, meant to generalize, given that some of the poorest regions of the world have reasonably high mobile phone penetration but rather a cautionary note when assessing whether data are representative with respect to specific populations.

For example, with respect to a recent project that I've worked on, there is high mobile phone penetration in sub-Saharan Africa despite low income. Yet, while its neighbors in East Africa have experienced fast growth in mobile phone ownership and usage in the past five years, Ethiopia has fallen behind largely due to government ownership of the nation's telecom monopoly which has limited expansion and service. Further, analyzing the distributional data on mobile phone usage indicates that women are far less likely to own and use mobile phones than men - consistent with the findings in many developing countries - and that any data collected from these devices in a hypothetical scenario would only be representative of a specific demographic.

And yet, despite the challenges, non-traditional sources of data offer promise particularly in geographic areas where recent, traditional data on wealth and poverty are not available. Research in this interdisciplinary area will be interesting to watch in the near future.

"Intelligent evolution of humanity"

First of all, I'm sorry for the long hiatus in posts: midterms, a bout of the flu, and starting a new work project have all taken away from the time that I normally spend on this blog.

Given that the project that I've started working on focuses on the gender gap in agricultural productivity levels in sub-Saharan Africa, I visited a number of seminal papers in agricultural economics and in the study of firm and individual-level productivity and efficiency. In this survey of the literature, I stumbled on Theodore Schultz's Nobel Prize lecture, "The Economics of Being Poor", after having read his work on smallholder farmer efficiency ("poor-but-efficient" hypothesis which states that farmers are calculating economic agents who are highly efficient in a traditional agricultural environment (Schultz, 1964)) and his consequent focus on human capital and the gains to labor productivity and entrepreneurial ability as the key to improving the well-being of the agricultural population.

In his lecture he discussed the entrepreneurship inherent in agriculture and criticized the government price distortions in developing countries: "experts fail to recognize how efficient they [small farmers] are... This allocative ability is supplied by millions of men and women on smallscale producing units; agriculture is in general a highly decentralized sector of the economy.... The allocative roles of farmers and of farm women are important and their economic opportunities really matter (Schultz, 1978b)." Yet he maintained that despite their efficiency in a traditional agricultural setting, small farmers needed further investments in human capital and skills to be just as entrepreneurial and efficient in a dynamic setting (i.e. one with constant technological and economic change made more dynamic by increased globalization in past decades).

He also gave the following hopeful exposition on humanity addressing natural resource constraints (economic growth models to come such as Nordhaus (1992) viewed natural resources including land and energy as lags on economic growth): "It is ironic that economics, long labelled the dismal science, is capable of showing that the bleak natural earth view for food is not compatible with economic history; that history demonstrates that we can augment resources by advances in knowledge. I agree with Margaret Mead: 'The future of mankind is open ended.' Mankind's future is not foreordained by space, energy, and cropland. It will be determined by the intelligent evolution of humanity."

In other words, that land is a fixed resource and that traditional sources of energy are a depleting resource do not preclude the fact that investments in human capital can modify the production function and the relationship between the traditional inputs and output.

Look no further than the growth and takeoff of alternative, renewable energy sources and the expansive yields from sustainable farming as examples. As much as Schultz critiqued government for distorting agricultural systems that he viewed as otherwise efficient, he may not have appropriately appreciated government's unique role in incentivizing the resource allocation decisions of firms towards that "intelligent evolution of humanity" that he spoke of. For example, we've seen in the last decade the role of government in incentivizing alternative, renewable energy sources through large-scale investments and subsidies for the research and development and market viability of those energy sources.

The interesting and challenging piece of the lecture is connecting those two goals: improving the human capital and skills of much of the population (key to improving well-being) and moving towards a more sustainable future that is not tied to traditional, fixed or depleting natural resources. While positing these ideas, he also provided advice to economists that is still relevant today:

"We all know that most of the world's people are poor, that they earn a pittance for their labor, that half and more of their meager income is spent on food, that they reside predominantly in low income countries and that most of them are earning their livelihood in agriculture. What many economists fail to understand is that poor people are no less concerned about improving their lot and that of their children than rich people are."

If you want a more humorous speech, try this one that he gave at the Nobel banquet. 

China and the future of development aid

At the end of last year, research organization Aid Data published the first extensive data set documenting Chinese aid flows around the world. The Chinese government is notoriously secretive about its development aid outflows: it does not publish any project-level or country-specific data on its own nor does it work with international organizations that attempt to quantify and release this information. To create the data set, researchers at Aid Data scoured publicly available news reports, official embassy documents, and aid/debt information from other countries for the past five years. The Tracking Underreported Financial Flows methodology that they rely on is detailed here.

China's lack of transparency has been cited as a growing issue given its increasing role on the international stage. In the past few years, China surpassed the U.S. in terms of annual spend on development aid and has established itself as one of the key development players in Africa. Critics - such as Moises Naim in this opinion post years ago in the New York Times - have raised concerns that in competing with Western donors and international organizations such as the World Bank, China is seen as the "no strings attached" donor likely to give to undemocratic regimes and countries with poor institutions that would be subject to higher scrutiny under traditional Western giving and lending practices. 

It is unsurprising, then, that researchers have already taken to the new Aid Data data set to answer a myriad of questions about the impact of Chinese aid on local economies. In this post I look specifically at the paper, "Chinese aid and local corruption" published last month in the Journal of Public Economics. Authors Isakkson and Kotsadam (2018) employed Aid Data's Chinese Official Finance to Africa data set to identify locations with: (1) ongoing Chinese aid projects, and (2) those selected for future Chinese aid projects. They then connected this data with Afrobarometer survey data eliciting survey respondents' experiences with corruption (whether they "had to pay a bribe, give a gift, or do a favor to government officials") in order to estimate the effect of an ongoing Chinese aid project in a given location on the level of local corruption. Because they geocode both data sets and restrict their sample to only those aid projects for which they can identify a granular location, the authors are able to identify respondents within 50 or 25 km of aid project locations to analyze their experiences with localized corruption. 

The authors find that Chinese aid projects have a statistically significant effect on local corruption with point estimates of a 3.5% (bribes given to "avoid a problem with the police") or 2.7% (bribes given to "get a document or permit") increase in bribery in locations with ongoing Chinese projects relative to locations selected for future Chinese projects. They speculate that Chinese aid increases local corruption through two potential mechanisms: first, that presence of the donor changes the cost-benefit structure of engaging in corruption (i.e. if the donor is indifferent as to the "means" by which a project is completed and is willing to reward for the "ends" of completing it then this raises the benefits associated with corruption). Second, they posit that the donor is in a position of power to influence social norms and create institutional change. A donor's acceptance or propagation of corrupt activity could worsen norms (noting that norms are easier to change for the worse than the better). Finally, they employ the same strategy around World Bank aid project locations and do not find any effect of these projects on local corruption. 

Estimation strategy

The paper employs a model similar to a difference-in-differences model wherein the responses of individuals who live near a site that is currently developed by the Chinese are compared to the responses of individuals who live near a site that will be developed by the Chinese in the future. In the following regression model, the authors employ the difference between the coefficients on "active" and "inactive" as the key parameter of interest. Individuals located within the radius of an ongoing Chinese aid project are "active" ("active" = 1). Those located within the radius of a future Chinese aid project are "inactive" ("inactive" = 1). And those that are outside the radii of any current or future Chinese aid projects are neither active nor inactive ("active" = 0; "inactive" = 0). 

(1) Yit = β1 activeit + β2 inactiveit + αs + δt +y Xit +εivt 

Isakkson and Kotsadam employ this method rather than interpreting the coefficient on the "active" dummy to avoid the ex-ante assumption that "there is no relationship between project localization and the pre-existing institutional characteristics of project sites." In other words, if the locations for Chinese aid projects were selected based on certain institutional characteristics it is very possible that those institutional characteristics are correlated with corruption levels and that, as a result, interpreting the coefficient on "active" alone erroneously captures pre-existing differences in corruption levels between locations with Chinese aid projects and those without. 

To control for the geographic and time-based variation in the data set - which includes data from across the African continent and spanning 2000-2013 - the authors include spatial fixed effects, year fixed effects, and a set of individual controls. While the baseline results indicate that Chinese aid projects led to an increase in local corruption, the various iterations do lead to questions:

1. The authors point to two statistics from the regression output to determine whether the parameter of interest is significant: coefficient on the "active" dummy variable (if there is an effect this should be positive and statistically significant) and the statistic for an F-test testing the hypothesis "active - inactive = 0" (if the effect on corruption is a result of a Chinese aid project this hypothesis should be rejected). The baseline results indicate that both with respect to police bribes and permit bribes the coefficient on "active" is positive and highly statistically significant and the F-test hypothesis can be rejected at the 5 percent level. See Table 1 for details.
2. However, the sensitivities indicate that the coefficient on "active" is statistically significant across most but not all iterations and the F-test cannot be rejected at the 5 percent level in at least one of the iterations. The results indicate that the effects are stronger for police bribes than they are for permit bribes which leads to questions about the mechanism that leads to increased corruption and why it impacts police bribes more so than permit bribes. See Table 2 for details.
3. Furthermore, the authors conclude that World Bank aid projects do not similarly lead to an increase in local corruption not because the coefficient on "active" in that sample set is not positive or statistically significant (in fact it is significant in several iterations) but because the F-test results indicate that it cannot be rejected that the "active" and "inactive" coefficients are equal. But why is it that both "active" and "inactive" locations with World Bank projects see a higher level of local corruption than those without World Bank projects (where Chinese aid projects don't, because the "inactive" coefficient is not significant in most Chinese projects)? It's not a question that this paper seeks to answer but there should be a reasonable hypothesis for why locations selected for World Bank vs. Chinese aid projects differ in this way.
4. My main question reading this paper was whether the implementation of an aid project leads to a change in the demographic population of a locality. Given that the Afrobarometer survey is not a panel data set, the same individuals are not necessarily interviewed pre- and post-implementation of an aid project. 
1. While it is possible that the implementation of a project leads to the corruption of existing actors it also seemed possible that it led to inflows of new actors into the locality due to a possible increase in local economic growth and activity. If the increase in local corruption is due to the influx and changing composition of the locality this is distinct from an increase due to corruption of the existing population. 
2. The authors attempt to address this question by analyzing whether there are more police stations in active aid areas vs. inactive aid areas (to address the claim that more bribery is a result of more police stations rather than more corruption), stating: "Neither do we find any evidence that the results are driven by increased resource flows making the project areas into 'honey pots' attracting corrupt actors." However, the empirical investigation does not seem to answer the original question of whether aid projects lead to an influx of corrupt actors. 
3. It boils down to how the effect is interpreted: in the case of this paper, the parameter of interest does not distinguish or isolate the two effects presumably because both lead to an increase in local corruption whether by migration or by impacting existing populations. 

Implications for the future of development aid

Overall, the implications of the paper that Chinese aid projects lead to local corruption are the first step in understanding how different forms of aid (and specifically "no strings attached" aid) can create institutional change and impact social norms. While the quantification of this impact is important, the paper does not explicate the mechanism by which these projects increase corruption and without that linkage it is difficult to prescribe appropriate policy solutions to improve local governance and reduce corruption. But the paper reaffirms questions about China's development strategy to work within existing entrenched systems to create economic growth vs. the traditional Western approach to attempt to improve governance and create institutional change at the same time. The broader takeaways for the field of development aid:

• It is clear that conceptualizing Chinese vs. Western aid as a competition is not the most effective way to improve growth and development in these economies, rather, assuming Chinese aid will continue at its current rate how can each set of aid practices complement and supplement one another? Transparency and data availability make it easier to answer these questions. 
• Given the importance of international coordination in aid, this new availability of data on Chinese aid offers a novel opportunity for other donors to provide a value-add to these economies in sectors and projects that the Chinese are not investing in and to advocate more strongly for better governance and effective democratic institutions given the apparent worsening of certain aspects of local governance as a result of Chinese aid projects. 
• China's increasing role in development and aid places places a need for further introspection on the part of international organizations such as the World Bank, specifically: how should the organization continue to advocate for good governance and effective democratic institutions while simultaneously recognizing the need to work with one of the largest unilateral donors that may not be interested in propagating those norms? Will the World Bank's role and priorities change as funds from China and from private investors play an increasing role in the growth of developing economies? How can it position itself most effectively in this rapidly changing space and provide a distinct value-add?

Sources

1. Isaksson, A. and Kostadam, A. (2018). Chinese aid and local corruption. Journal of Public Economics. 

Unintended consequences of tax reform in developing countries

Corporate tax reform has been a big topic of conversation among economists here in the U.S. with the passage of the recent tax bill on Capitol Hill. Proponents argued that lowering the corporate tax rate from 35 to 20 percent would increase U.S. competitiveness and increase after-tax profits for corporations and in turn increase investments and returns to labor.

A large component of the discussion surrounding the corporate tax rate in developed, wealthy nations revolves around international tax competition and large multinational firms with the capacity to move their operations across borders to take advantage of lower tax structures (see Devereux and Loretz, 2012, for a review of the relevant literature on tax competition). Much of the study of corporate taxes has been based in these high enforcement settings and has been a study of corporate firms.

Setting appropriate tax rates is arguably more challenging in developing countries with low enforcement and large informal sectors. In these settings firms (noncorporate firms in particular) have two additional methods of responding to changes in the tax rate: they can move into the large existing informal sector, thereby reducing the overall tax base, or they can engage in tax evasion, given the low enforcement capabilities of the government. Evidence from these settings can be informative as to the elasticity of noncorporate firms to tax rates in a setting that is distinct from that of developed countries where there are fewer tax evasion opportunities. Waseem (2017) recently published a paper in the Journal of Public Economics ("Taxes, Informality, and Income Shifting: Evidence from a Recent Pakistani Tax Reform") analyzing firm responses to an increase in the tax rate for noncorporate firms in Pakistan that looks at this issue.

He finds that the responses by firms to the tax reform were so large that the Pakistani government was collecting less in in revenue three years after the reform than it was prior to the reform. On the intensive margin, firms impacted by the increase in tax rate were reporting significantly smaller earnings. On the extensive margin, fewer firms were registered with the government and reporting positive profits, indicating a shrinking of the formal tax base.

The policy in question was enacted by the Pakistani government in 2009 and raised the tax rate on partnerships from a bracketed system with average tax rates that varied progressively from 0% to 25% (a tax rate that continued to be applied to sole proprietorships) to a flat tax rate of 25%. This change was enacted in order to reduce the variation in tax rate between partnerships and corporate firms, the latter of which was taxed at and remained taxed at 35% after the tax reform, and to reduce the disincentives for incorporation of new firms.

The implications of this study for developing countries are stark. Policies such as this one need to account for the "unintended" effects of tax reform on formalization and tax evasion as well as the intended effects on incentives to incorporate. High variation in the tax rates across noncorporate firms in this particular case (specifically between partnerships and sole proprietorships) led to significant levels of tax evasion, income shifting, and movement into informality that counteracted higher tax revenues collected from the partnership firms still remaining in the formal tax base and from incorporated firms.

Estimation strategy

The author employs a difference-in-differences method to take advantage of the tax reform in Pakistan in 2009. He uses administrative data of income tax returns filed from 2006-2011 to compare the outcomes of partnership and non-partnership firms (corporations and sole proprietorships): log change in reported earnings and log change in the number of filers. The former represents the intensive margin response of firms that remained in the tax base and the latter the extensive margin response of firms that exited the tax base.

The model estimated for the intensive margin elasticity is:

Δlog zit =α+β Partnershipi +ε Δlog(1−τit)+Xi δ+λt +uit

This regresses the log change in the reported earnings of a firm i at time t on its status as a partnership, the log change in its net-of-tax rate, a set of control variables, and year fixed effects.

To account for potential endogeneity between the change in the tax rate and change in reported earnings (i.e. not only does the change in the tax rate impact the change in reported earnings but reported earnings also impacts the tax rate), the author uses an instrumental variables strategy regressing the change in the tax rate on a dummy variable for whether the observation is a partnership firm in the post-reform period in a first-stage regression that allows him to isolate the variation in the tax rate change that is due to the tax reform for partnerships.

Insurance products for increasingly risky livelihoods

As climate change increases the frequency and severity of extreme weather in developing countries, a key question is whether agricultural insurance can play a larger role in mitigating the household level losses associated with catastrophic droughts.

Insurance products are particularly relevant in regions where agriculture or pastoralism constitute the primary source of income. In these regions indemnity payouts from insurance products can provide significant consumption smoothing benefits to insurance policyholders in the event of drought and improve material well-being.

A paper published last week by the World Bank's Development Research Group ("Insuring Well-being? Buyer's Remorse and Peace of Mind Effects from Insurance") finds that index-based livestock insurance (IBLI) also improves non-material well being for a sample of pastoralists in southern Ethiopia. Specifically, Tafere et al. (2017) find that a "peace of mind" effect associated with purchasing insurance that is positive and statistically significant. They find this effect outweighs a negative "buyer's remorse" effect on well-being, which arises when households purchase insurance and realize after the uncertainty period ends that they did not need the insurance after all.

They note:

"The implication is that, despite premiums set above actuarially fair rates, IBLI improves buyers' SWB [subjective well-being] even over a period when pastoralists in southern Ethiopia lose money on the policy. The ex ante peace of mind effect dominates any ex post buyer's remorse. In other words, even an insurance policy that does not pay out still improves people's perceptions of their well-being."

Estimation strategy

To estimate the "peace of mind" effect, the authors randomize the provision of 10-80 percent discount coupons and comic book or audio tape information interventions to households in the communities in southern Ethiopia. These incentives act as instruments which increase uptake of IBLI among households in an instrumental variables model. In the reduced form  stage, the measure of well-being is regressed on the probability of IBLI uptake predicted in the first stage.

The authors measure SWB using the question: "On which step do you place your present economic conditions?" with possible responses ranging in the Likert scale from very bad (1) to very good (5). They employ a vignette-based adjustment of the SWB scores by asking respondents to rank their own circumstances with respect to a set of individuals described in short vignettes to improve the comparability of the subjective welfare measurements to one another.

In both of the years in which insurance policies were active there were no indemnity payouts. This allowed the authors to disentangle the ex ante peace of mind effect from the ex post buyer's remorse effect on SWB. It also ensured that the effects measured were not material or payout based.

There are two issues to consider with respect to the empirical strategy:

1. Depending on how well-being is assessed it is possible that well-being could be positively affected by discounts for the sole reason that it is a discount and is saving people money. If true this would invalidate the instrumental variables assumption that the instrument be exogenous from the reduced form model. Though I considered this possibility, I dismissed it since SWB is assessed in the period after the period in which the discount is applied and insurance purchased, it seems unlikely that said discount would have such a lasting impact on SWB.