An all-in-one post for the past three months

Instead of doing a deep dive into one topic today, I have a few different points of discussion. First, thank you to Intelligent Economist for including me again this year in the top economics blog list. Second, I'll be joining a PhD program in Economics this fall and I can share my thoughts on the application procedure and offer whatever limited advice I have and hope/encouragement to those thinking about applying. This is particularly for those who have been out of school for more than a few years in job/grad school and those who found economics a little later in life (both of these apply to me). If I had one general piece of advice about PhD preparation, it is that I've found many people shy away from math and believe that only a few "select" individuals with innate abilities can be good at it (if I had a dollar for every economist I ran into while solving problems in a coffee shop who told me about the one genius in their college real analysis class) but - like anything else in life - I think those who are driven, purposeful, and work hard at it are well-rewarded.

One of the previous posts on this blog had discussed minimum wage policy. There wasn't enough time to cover all of the implications of minimum wage in that post, but I recently came across an interesting implication that I had not read about before. Specifically, a paper by Dettling and Hsu (2018) finds that higher minimum wages have significant effects on consumer credit markets (supply of unsecured credit, payday lending, and delinquency on credit payments). Higher minimum wages lead to lower borrowing costs for low income borrowers because they increase the number and favorability of credit card offers and they increase credit limits and decrease delinquencies. As noted in the paper, "labor market outcomes... are just one part of a household's finances. Interactions with consumer credit markets also play a crucial role in many families' economic wellbeing..."

Ethiopia gender diagnostic

The World Bank's Gender Innovation Lab - the team that I work for within the Office of the Chief Economist for Africa - has published a gender diagnostic report for Ethiopia. In this section, these views and interpretations are my own not that of the WB. The report does a few things: it provides evidence of gender gaps in agriculture, self-employment, and wage sectors in Ethiopia based on the Ethiopia Socioeconomic Surveys; it utilizes an Oaxaca-Blinder decomposition method to connect these gaps to gender gaps in the levels and returns to resources (e.g. fertilizer); and it provides concrete ideas to address the challenges that Ethiopian women face in the labor market. Oaxaca-Blinder decomposition decomposes the gender gap into observable differences in factors of production (endowment effect) and unexplained differences in returns to the same observed factors of production (structural effect). It allows us to determine to what extent differences in productivity are due to differences in the levels of resources versus the impact of those resources on productivity. It should be noted that the report is policy-oriented rather than academic in nature.

One example of a finding from this report is that the evidenced gender gap in agricultural productivity in Ethiopia is by and large due to unequal levels of productive factors such as land size and quality, fertilizer and other production inputs, formal credit, and farmer extension services (which can serve as a proxy for agricultural knowledge). When these - and other individual- and household-level observable characteristics - are controlled for, the gender gap in agricultural productivity drops from 36 percent to 6 percent. This is not necessarily the case in other countries in Sub-Saharan Africa, where giving female farmers access to the same level of productive factors as male farmers will not close the gender gap. For Ethiopia, we can assess how to close the gaps in factors of production.

For example, access to formal credit is an issue for not only female farmers but male farmers as well. The report on myths in African agriculture that I cited in an earlier post indicated that across the African continent only 6 percent of households used credit - formal or informal - to purchase agricultural inputs. It notes that "rural credit markets need to be deepened to serve farmers better, especially with respect to modern input use." On the other hand, the proliferation of farmer extension services is much greater with nearly 40 percent of male plot managers in Ethiopia having attended extension services recently (but only 23 percent of female plot managers having attended). There is a gender gap in both of these resources but while one has high take-up among male farmers, the other does not and therefore may require broader solutions.

If we focus on women's attendance of extension, we hypothesize based on existing literature and data that there are institutional factors that impact women's attendance and their level of agricultural knowledge more broadly. Namely, women are more time-constrained due to greater responsibilities in the home and are not as mobile due to costs of travel and to safety considerations. Both of these factors - time poverty and more limited mobility - can limit women's access to knowledge because they are not necessarily able to be in a particular place at a particular time to learn.

It is interesting because this underlying theme runs through many discussions about gender gaps in both the developed and developing worlds. A better understanding of how our existing systems are structured around the needs of specific subsets of our population can allow us to devise solutions that can better suit the needs of the others. For example, we posit that access to mobile phone technology can dramatically improve agricultural knowledge among female farmers because - conditional on their access to the technology - they will be able to access information at the time and place that is convenient for them (in Ethiopia, this is particularly challenging due to the limited competition in the telecommunications sector that has hindered mobile phone and internet penetration). Similarly, as this article in the Harvard Business Review illustrates, women in the developed world are advocating for more flexible working arrangements not to reduce hours but to manage workload at their own time and place where possible.

However, there are other important solutions as well. For example, investments into technologies that can alleviate the time poverty that women face in the first place. The tasks of collecting firewood, other fuel, and water for household energy consumption often fall on the women of the household and can take several hours per day in rural areas. Yet, there are interesting companies engaged in East Africa that are focused on addressing these energy consumption needs (some of which are highlighted in this report as examples). They provide alternatives to wood-fuel stoves in the form of solar energy or biodegradable biomass. This is just one example of how an evidence-based finding from the report can be developed to identify areas for future academic research, e.g. how successful are these alternative fuel companies and how effective are their alternatives at addressing women's time poverty? For more on these ideas, do check out the report.

Recession 

There has been an upsurge in talk of recession recently in the popular media. It's not a topic that I've had much experience working on but I'll do my best to point out a few resources and start the conversation.

This article from the Fed lists the points of concern that have gotten analysts, investors, and economists talking in the first place. It lists four important housing market indicators, notes the significance of the housing market has in predicting economic downturns ("based on its forecasting track record - where a housing downturn is necessary but not sufficient for a recession to occur - the risk of broad-based economic recession certainly would be higher if the housing market were to weaken further"), and illustrates that recent trends are consistent with other pre-recessionary periods in 2001 and 2008.

The key is that current estimates of the four indicators listed - 30-year fixed mortgage rate, home sales rate, home-price change, and residential investment - are compared to their averages over the past three years to determine whether there is significant deviation from the average. For example, the below Fed chart shows the deviation in percentage points of the mortgage rate from the preceding three-year average. In the run up to the recessionary periods in 2001 and 2008 there was a rising deviation of the mortgage rate from the three-year average. The green trend for 2019 indicates the same pattern today.



It is unclear in my opinion that the other three indicators track the 2001 and 2008 trendlines as closely as they do here for the mortgage rate but either way I think this is one part of a larger picture. The larger picture is that in 2001 and 2008 these housing market indicators worked in conjunction with a private sector financial deficit (financial deficit from households and firms). This is discussed in an episode of the Exchanges at Goldman Sachs podcast - which I highly recommend - on five areas of credit market risk and how they impact the likelihood of recession. It is just one section from a GS report, "Learning from a Century of US Recessions."

The report itself provides a high-level summary of the various risk areas that lead to economic downturns but does not provide much detail on the individual risk areas. The podcast does a better job of discussing in detail the primary risk area: financial risk and asset bubbles. The GS viewpoint is that the current private sector surplus differentiates the current situation from 2001 and 2008 where the housing market may have been heating up - as it is today - but at the same time the private sector was running a large deficit. These two deficit periods are indicated in the GS figure below.



In the podcast, it is also mentioned that debt growth for households in the mortgage market is in decline - 16 percent inflation adjusted decline - that is unprecedented in the past 60 years. I ran a quick chart using the Fed's data to see the trendlines for all mortgage holders (in blue) and one- to four- family residences (in orange) and they do indicate a slow growth in recent years. How much slower than in the rest of the 30-year period shown here (1990-2018) is not clear since the trendline is still rising. However, it is unsurprising that mortgage debt is rising more slowly now than in the pre-2008 period given tighter credit standards in the aftermath of the Great Recession.

It is possible that an overheating of the housing market and relatively slow mortgage debt growth for households are consistent with one another if mortgage debt and home-ownership are more concentrated today than they were pre-2008. It would be interesting to see whether a smaller segment of the population is driving the uptick in the housing indicators being measured by comparing mortgage debt and home-ownership across the income distribution today and pre-2008. Tighter credit standards and more sluggish recovery among lower- and middle-income households after the 2008 economic downturn, in addition to rising income inequality in the aftermath of the downturn, may explain greater concentration in debt and home-ownership today. This could explain trends in the housing market as well as the private sector financial balance.   

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.