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.   

In-depth look at income and wealth data (pt. 2 of 3): Wealth

First, to preface with why wealth as distinct from income is relevant to economists and to policymakers at large. Kopczuk (2014) discusses the importance of understanding the wealth distribution: "the extent to which the well-off are going to rely on work vs. return to their wealth in the future is clearly important for assessing the extent to which a society will view itself in some way a meritocracy." Wealth is an important determinant of labor force participation and therefore impacts productivity and economic growth. It also has important implications for inequality, intergenerational mobility, and, consequently, implications for democratic institutions whose stability is reliant on a meritocratic society or at least the verisimilitude of a meritocratic society.

It should be noted that estimates of wealth inequality and the top wealth shares are not as widely agreed upon as estimates of income inequality and labor income shares. There are a few main data sources for estimating wealth inequality that are aptly summarized in Alvaredo, Atkinson, and Morelli (2018):

1. Household surveys including the U.K. Wealth and Assets Survey and the U.S. Survey of Consumer Finances;
2. Administrative data on individual estates at death; 
3. Administrative data on wealth of living from annual wealth taxes; 
4. Administrative data on investment income that are capitalized; and 
5. Lists of large wealth-holders (e.g. Forbes).

These data sources are discussed in great detail in Kopczuk (2014)'s "What Do We Know About the Evolution of Top Wealth Shares in the United States?" which specifically discusses the U.S. Survey of Consumer Finances (1), the mortality multiplier method with individual estate data (2), and investment income data (4). Each of these data sources is subject to different concerns. Household surveys and list of the wealthiest individuals are recent phenomena and cannot be used for estimates prior to the 1950s when the household surveys on wealth were first implemented. Administrative data on wealth of the living based on wealth taxes cannot be recouped in most developed countries because only a few developed countries, most notably France and Norway, have a wealth tax to begin with. Therefore, most researchers rely on estate tax records on individual estates at death or on reported taxable capital income.

The primary concern with estate taxes is that the distribution of estates of the deceased must be projected to the population at large: i.e. a multiplier method must be used in order to answer the question, how does the distribution of wealth among the deceased reflect the distribution of wealth among the living? Mortality multipliers are inverses of mortality rates based on various criteria, for example, wealthy individuals tend to have lower mortality rates and increased longevity compared to less wealthy individuals and therefore a higher mortality multiplier would be applied to the upper estate ranges meaning there are relatively more individuals living within those ranges than lower ones. For more on recent discussions of the relative longevity of the wealthy see Saez and Zucman (2016) and Chetty et al. (2016).

Kopczuk presents a few interesting stylized facts about wealth that provide a good introduction to the wealth distribution and methods of estimating it:

• Wealth is highly concentrated (top 10 percent holds between 65 and 85 percent of the total wealth, top 1 percent holds between 20 and 45 percent of total wealth based on time period); 
• While the methods of estimating the wealth distribution disagree on the timing it is clear that wealth concentration hit its apex prior to the Great Depression and declined after that; 
• Different methods lead to varying estimates for the top 1% for several reasons: one is that the estate tax multiplier method uses the individual as the unit of observation, surveys use the household, and the capitalization method uses tax units; another is that tax evasion impacts the administrative tax-based methods (estate tax and capitalization) but not the survey-based methods. Some capture debt (estate tax returns) whereas others do not (capitalization). 

In a recent issue of the Journal of Public Economics commemorating Tony Atkinson's work, Alvaredo, Atkinson, and Morelli (2018) provides new evidence on the evolution of top wealth shares in the U.K. To choose one of the most interesting facets of the discussion of wealth that they present in the article, it is enlightening to view the top wealth shares compared to the wealth shares excluding housing.



The top 1%'s total wealth share and wealth share excluding housing tracked each other for much of the late 20th century but the authors note the divergence between the two trends in the 21st century, wherein the share of the top 1% of wealth holders of total wealth increased much more rapidly than its share of wealth excluding housing. In other words, the growth of wealth excluding housing is likely to be a more significant contributor to rising inequality than is the growth of housing wealth. In fact, they even mention that increases in housing prices serve an equalizing effect for the top 1%:

"It appears that housing wealth has moderated a definite tendency for there to be a rise in recent years in top shares in total wealth apart from housing. When people talk about rising wealth concentration in the U.K., then it is probably the latter that they have in mind... The results show how the impact of a general rise in house prices has changed over the period but it is always equalizing for the top 1%. At the beginning of the period a rise of 25% led to a reduction of some 1 percentage point in the share of the top 1% but the effect became smaller over time."

It should be noted, however, that trends in the housing market - particularly the resurgence in the private landlord and "buy to let" over the past three decades - likely have impacts on other areas of the wealth distribution apart from the top 1% of wealth owners (though these impacts are not addressed in this paper). This New York Times article from last year, for example, is a news feature that discusses the role that homeownership plays in propagating existing wealth and income inequalities. These topics and the lower rungs of the wealth distribution more broadly are areas for further investigation, but for the time being Alvaredo, Atkinson, and Morelli (2018) highlight how granularity in wealth data can be used to better identify the causes of growing wealth inequality over the past few decades and, while they utilize estate data and the mortality multiplier method in their analysis, can also be triangulated with other methods and data sources to form a more comprehensive understanding of the wealth distribution.

Sources

1. Alvaredo, F., Atkinson, A., Morelli, S. (2018). Top wealth shares in the UK over more than a century. Journal of Public Economics.
2. Kopczuk, W. (2014). What do we know about the evolution of top wealth shares in the United States? NBER Working Paper 20734.
3. Chetty, R., Stepner, M., Abraham, S., Lin, S., Scuderi, B., Turner, N., Bergeron, A., Cutler, D. (2016) The association between income and life expectancy in the United States 2001-2014. Journal of American Medical Association.
4. Saez, E., Zucman, G. (2016) The distribution of US wealth, capital income, and returns since 1913. Quarterly Journal of Economics. 

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.