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Wednesday, May 29, 2019

Jobs in developed economies

The Economist recently published an opinion piece on the status of work and jobs in developed economies that caught my attention. The topic of the piece - the success of today's labor market - is important due to its increased politicization and its implications for democracies in developed countries. Therefore this brief post will fact-check a few of the statements made in the article and provide a more comprehensive and accurate perspective on the labor market in developed economies. First, how well does the data support each of the following statements that were made in the article:
  1. Very low unemployment rates in developed economies - TRUE BUT MISLEADING. 
    • It is true that many developed economies have very low unemployment rates today. For an interactive look at the unemployment rate in OECD countries over the past several decades see here. Below is a screenshot from the interactive chart. This data shows that for many OECD countries, the unemployment rate today is lower today than at any point in the past thirty years. However, France and Southern European countries including Greece, Spain, and Italy are notable exceptions.
    • However, an analysis of the labor market that focuses on the unemployment rate without considering the labor force participation rate can be very misleading. I.e. is the unemployment rate very low because a significant proportion of the population has taken itself out of the labor market? Would these people accept a job if they were given one, despite not actively looking? In the U.S., labor force participation is lower than it was thirty years ago and it took a particularly significant hit in the aftermath of the Great Recession. In 1987, the labor force participation rate was 75 percent and it was 73 percent in 2017. The inactivity rate among working-aged men has risen from 14 to 20 percent over the same time period. Below is a screenshot from the interactive chart for labor force participation. This article explains why a recent rise in labor force participation this past year reflects a composition effect - meaning fewer people are leaving the labor force - rather than bringing people out of labor force back into it.          
    • The unemployment rate today is low by historical standards in many OECD countries - again the exceptions should be pointed out and the diversity within these developed economies should be acknowledged - but as an indicator it must be used in conjunction with the employment and labor force participation rates rather than as a standalone metric. 
  2. "Ever more women work" and women account for almost all the growth in the rich-world employment rate since 2007 - FALSE.
    • In the U.S., a decline in women's labor force participation is in part driving the overall decline mentioned above. This statistic adds a distributional dimension to this analysis that the article overlooks. In the U.S., specifically, labor force participation among women is lower today than in 1999 (which was the high point for women's labor force participation in the U.S.). For more on the declining labor force participation rate in the U.S., see this blog post on the topic. The below graph is taken from a BLS interactive page on labor force data. 
  3. "As for precariousness, in America traditional full-time jobs made up the same proportion of employment in 2017 as they did in 2005." - FALSE.
    • According to the OECD, full-time employment was 79 percent for men and 59 percent for women in 2005. The same figures for 2017 were 76 percent and 59 percent. 
    • Furthermore, just as unemployment rate was only one part of the picture to understand the trends in employment status, the full-time employment rate is only one part of the picture to understand labor market insecurity and the degree of job formalization. While this is not a statistic, this New York Times article from a few years ago exemplifies this point. It discusses the situation of two janitors: one who worked at Kodak in the 1980s and became a CTO of the company and the other who works at Apple today through a contracting company. The article details the aspects of job quality that are not captured by a simple full-time vs. part-time metric. In fact, OECD and ILO among other organizations have worked on measuring job quality. See Table 1 in this document that details the some of the measures of job quality: lifelong learning and career development, safety, ethics, working conditions, collective interest representation, and the stability and security of work. 
Not only do these statements provide a narrow and therefore misleading perspective on the labor market, building a case for a labor market that works for everyone requires research and evidence. The larger problem with this piece is that it does not put in the research and evidence required. Rather, it claims that now that the unemployment issue has been "settled" in developed countries (a questionable conclusion in and of itself), the public has moved on to a "series of complaints about the quality and direction of work" which are "less tangible and harder to judge than employment statistics." Most of our jobs as economists are to study things that are "less tangible and harder to judge".

George Akerlof has a forthcoming article in the Journal of Economic Literature entitled "Sins of Omission and the Practice of Economics" that discusses this bias against "important topics and problems when they are difficult to approach in a 'hard' way" with "hard" being defined as the ease or difficulty of producing precise work on a given topic. He argues that this bias within the discipline often leads to important topics being neglected at the expense of topics that can be more precisely studied. He provides several examples of situations in which the neglect of those topics that are more difficult to study precisely has led to an oversight in understanding. For example, he writes that in the lead-up to the Great Recession there were incentives to study the individual pieces of the recession puzzle but not to study them jointly.

On the theory side: "Following Caballero (2010), regarding theory, a model with all the pieces could not have been published; it would have been considered too far from precise, simple ideas (such as those that motivate simple new Keynesian or DSGE models); and, in this way, too Soft to merit publication." On the empirical side: "Regarding predictions from empirical evidence, the crucial data would have been of the wrong form... Even if she had uncovered, for instance, AIG's 533 billion dollars of commitments to insure securities such as CDS's, she would have still needed to turn it into the basis for a publishable paper. Those 533 billion dollars indicated tail risk of sufficient size to threaten a gigantic crash of the financial system; but it was only a single number. It was not the statistical evidence that typically underlies empirical papers in economics."

Akerlof's discussion has interesting parallels to this conversation on jobs in developed economies. Unemployment statistics are telling but they only tell one part of the story. The other part of the story - job quality, wages, and the working-poor rate - will likely better explain the dissatisfaction within developed economies that is driving the political shifts that we've seen in recent years. At the same time this other part of the story may be more complicated to study than unemployment statistics and, importantly for politicians, it may also be more complicated to discuss in the political arena. However, as the Times article poignantly illustrates, it should not be assumed that job quality has improved over the past few decades and the hypothesis that it has deteriorated - particularly for low-wage jobs - is a valid one that should be given its due consideration.

Tuesday, May 21, 2019

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.

Percentage-Point Deviation of 30-Year Mortgage Rate from 12-Quarter Average

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.   














Tuesday, February 5, 2019

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. 

Thursday, December 6, 2018

In-depth look at income and wealth data (pt. 3 of 3): Minimum wage policy and the income distribution

In this final part of this series of posts on income and wealth, I originally intended to discuss the data used to analyze income inequality but I will introduce a more specific topic within income inequality: minimum wage policy and its impact on the wage and income distributions. Given the ongoing public debate over stagnating real wages despite a strong labor market (see this piece by the Pew Research Center for a concise description of the trends and a few of the reasons given by economists for the wage stagnation for workers at the lower end of the earnings distribution), it is particularly relevant to revisit the evidence on minimum wage policy and its impact on income inequality.

In this post, I focus on two papers - Autor, Manning, and Smith (2016) and Dube (2018) both in American Economic Journal: Applied Economics - that discuss the distributional implications of minimum wage policy. These papers have significant differences in both methodology and level of analysis. While AMS (2016) focus on individual wage inequality, Dube (2018) focuses on household income inequality with two iterations on how income is defined. The first is the conventional definition of income that includes both earnings and cash transfers. The second is a broader definition that also includes tax credits and non-cash transfers that enables the author to assess the substitutability between minimum wage earnings and government benefits to derive results that are closer to general equilibrium. The choice and level of the outcome variables measured in these two papers - earnings versus income and at the individual versus household level - are important to treat as distinct and independently informative. As I discussed in an earlier post on household income inequality in the U.S. and Britain, the distributions of individual labor market outcomes and household incomes do not necessarily track one another closely.

Context

In the U.S., minimum wage policy is determined at several levels of government: federal, state, and as of late even citywide minimum wages exist (in San Francisco, San Jose, Albuquerque, Santa Fe, and Washington, DC). This piece on minimum wage policy outlines the basics. In part because the U.S. federal minimum wage declined in real value almost continuously for thirty years between 1979 and 2007 (it was fixed in nominal terms between 1981-1990 and 1997-2007), more than thirty states enacted legislation over the same time period to raise their state minimum wages above the federally mandated level. The below graph tracks the real value of the federal minimum wage (in part indicating the motivation for state and city-level legislation on the issue):

Source: UC Davis Center for Poverty Research (2018)
The minimum wage has therefore seen significant state and time-based variation within the U.S. over the past several decades that continues to this day. This variation has been utilized by many empirical studies of the minimum wage including AMS (2016) and Dube (2018).  It should be noted that the ratio of real minimum wage to median wage provides some quick information about lower-tail inequality that will be discussed in greater detail below. For example, between 1950-1970 this ratio fluctuated between 45-55 percent but by 1989 it had fallen to 36 percent.

Estimation strategy


The challenge with estimates of the impact of minimum wage policy are that both minimum wage legislation and wage inequality levels are impacted by several other unobserved characteristics. For more details on these challenges and how research designs can overcome them, see Allegretto et al. (2013). These two papers take two very different approaches: while AMS (2016) employ an instrumental variables strategy, Dube (2018) employs a series of sensitivities and falsification tests to indicate the robustness of his results. The takeaway is that identification of the employment and inequality effects of the minimum wage is challenging and can result in complicated empirical strategies.

As Dube (2018) indicates of the initial fixed effects model that he presents: "A problem with the two-way fixed effects model [state and time fixed effects] is that there are many potential time varying confounders when it comes to the distribution of family incomes. As shown in Allegretto et al. (2013), high- versus low-minimum wage states over this period are highly spatially clustered, and tend to be differ in terms of growth in income inequality and job polarization, and the severity of business cycles." In other words, the legislation of minimum wage and wage inequality are both impacted by a number of factors that are not controlled for in an OLS or even two-way fixed effects model.

AMS (2016) also reference potential biases when they present their initial OLS model. They cite confounding evidence that the effective minimum wage is found to be equally significant on both the lower-tail and upper-tail inequality (where it is expected to only have a significant effect on lower-tail inequality since minimum wage policy is only binding for at most the 15th/20th percentile of the wage distribution). The initial OLS model that they present estimates the impact of the "bindingness" of the minimum wage at the state-year level (a variable initially employed in Lee (1999)) - the log difference between the effective minimum wage and the median wage - on the difference between the log real wage at a specific percentile and the log real wage at the median. The former variable on the "bindingness" of the minimum wage is included as a quadratic term because minimum wage is expected to have a larger effect on the part of the wage distribution where it is more binding (i.e. at the lower-tail of the wage distribution) rather than a linear effect. To address potential biases, they employ an instrumental variable strategy and instrument for the observed effective minimum wage.

Because they include the effective minimum wage as a non-linear term, AMS (2016) utilize a set of three instruments as opposed to just the first one: (1) log of the real statutory minimum wage; (2) square of the log of the real minimum wage; and (3) interaction between log minimum wage and average log median real wage for the state across all periods. While it is not discussed in great detail in the paper, this instrument (1) I would assume is the legislated federal minimum wage and it clearly impacts the state-level effective minimum wage (either the federal or state minimum whichever is higher) but does not impact wage inequality at the state-year level through any channel other than the state-level effective minimum wage.

Dube (2018) does not employ an IV strategy in his paper on household income distribution but he includes several sensitivities and falsification tests of his original model to indicate the robustness of his results. His original model is the two-way fixed effects model that he critiques in the section above. In his "most saturated" specification he includes in addition to his original controls, division-specific year effects (to capture the effects of regional shocks on minimum wage legislation that may be driving some of the spatial heterogeneity that we see in minimum wage levels), state-specific recession-year dummies (to address the concern that minimum wage legislation is correlated with state business cycle fluctuations), and state-specific linear trends (to capture long-run trend differences across states). It is challenging to assess the effectiveness of these sensitivities and tests at obtaining causal estimates in comparison to quasi-experimental methods (seminal example is Card and Krueger (1993) in their study of the employment effects of minimum wage).

Findings


Based on their instrumental variables empirical strategy, AMS (2016) find that a 10 log points increase in the effective minimum wage leads to a reduction in the 50/10 inequality (inequality between the 50th percentile and 10th percentile in the wage distribution) by 2 log points for women, 0.5 log points for men, and 1.5 log points for the pooled sample. For a better understanding of why this paper uses log points as opposed to percentages, see this post on the topic. Women see a larger effect because a greater share of women work at minimum wage (6 percent of women in 2012 compared to 3 percent of men).

Source: Bureau of Labor Statistics (2013)
They are also able to state that the decline in the real minimum wage explains less than 50 percent of the rise in 50/10 inequality between 1979 and 2012 indicating that the majority of the rise in inequality over this period is due to changes in underlying wage structure (contradicting earlier findings that the decline in the real minimum wage contributed to around 60 percent of the rise in wage inequality over this period). This study indicates that erosion of the real minimum wage played a significant role in the growth of wage inequality over the past several decades but other factors, including skill-biased technological change and increased import competition from low-income countries which compose the "changes in underlying wage structure" that AMS (2016) reference, played a larger role than the decline in the real minimum wage.

What then of the related outcome of household income inequality? Dube (2018) finds that the poverty rate elasticity with respect to the minimum wage is between -.220 and -.552 indicating that a ten percent increase in the minimum wage yields between a 2.2 and 5.5 percent decrease in the poverty rate. He finds a first order effect of the minimum wage on the family income distribution of a ten percent increase in the minimum wage yielding between 1.5 and 4.9 percent increase in the pretax cash incomes of the 10th and 15th quantiles. This finding is indicated in the graphic below with the green line for cash income. The more interesting finding in my opinion is how much these minimum wage gains are offset by reductions in other government benefits due to ineligibility based on the higher income. He finds that for the bottom fifth of the income distribution, 30 percent of the gains to income resulting from minimum wage are offset by reductions in non-cash transfers and tax credits indicating that the relationship between wage and income (in its broader definition) is not nearly 1:1 for the poorest Americans. This is indicated in the graphic below with the orange line for cash income, tax credits, and non-cash transfers.

Source: Washington Center for Equitable Growth (2017)
Implications
  • These two papers' findings are consistent with one another though they measure different outcomes - individual wage inequality versus household income inequality. They both indicate that minimum wage has a significant impact on inequality (as well as absolute indicators such as the share of Americans living below the poverty line measured in Dube). 
  • It should be noted that the policy debate over minimum wage consists not only of discussion on the benefits of minimum wage policy on inequality, poverty, and other outcomes of interest but the costs in terms of the employment effects (i.e. hiring and firing decisions of firms, indicators of the quality of work, etc...). Economists are far from agreement on the employment effects of minimum wage (not discussed in this post). The extent to which minimum wage gains are offset by public assistance is also an important consideration and Dube's contributions to this question form part of a broader literature on the linkages between minimum wage and the existing social safety net. 
  • An important note about both of these papers is that by construction they reflect not only the mechanical effect of minimum wage (in increasing wages for individuals working at or below minimum wage) but may also reflect spillover effects on those who already work above it. This is an area for future research, however, since AMS (2016) attribute these additional effects not as spillovers but as measurement error of wages for low-wage workers. Specifically, they find that these spillovers do not "represent a true wage effect for workers initially earning above the minimum" rather accepting the null hypothesis that "all of the apparent effect of the minimum wage on percentiles above the minimum is the consequence of measurement error."
  • Increased trade and automation have yielded dramatic changes to the underlying wage structure of the economy. These changes may not be reflected in the share of workers who work at federal minimum wage (this share declined prior to 2000 and has remained roughly constant between 2000 and 2018 excepting a significant increase at the recession) but perhaps it has increased the share of those in the lower percentiles of the wage distribution. 
    • While these jobs in the lower percentiles may not be "minimum wage" jobs they are certainly part of a broader pattern of low-paying jobs in the U.S. These jobs - and the people who hold them - are illustrated in this article in the New York Times. The article indicates that nearly one-third of workers in the U.S. earn at or below $12/hour with 7.6 million Americans designated as "working poor" meaning they spent at least half of the year in question working or searching for work and were below the poverty line. 
  • Minimum wage policy will not - in a mechanical sense - impact these other low-paying jobs nor will it address factors that impact the underlying wage structure of the economy. However, in an environment in which trade and automation have changed the wage structure dramatically, it has been evidenced to address both wage and household income inequality. 
Sources
  1. Autor, D., Manning, A., Smith, C.L. (2016). The Contribution of the Minimum Wage to US Wage Inequality over Three Decades: A Reassessment. American Economic Journal: Applied Economics. 
  2. Dube, A. (2018). Minimum wages and the distribution of family incomes in the United States. Forthcoming in American Economic Journal: Applied Economics
  3. Allegretto, S., Dube, A., Reich, M., Zipperer, B. (2013). Credible Research Designs for Minimum Wage Studies. IRLE Working Paper #148-13. 
  4. Card, D., Krueger, A. (1993). Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania. American Economic Review

Friday, October 5, 2018

In-depth look at income and wealth data (pt. 2.5 of 3): A small note on wealth inequality from the archaeologist's perspective

I've been working on fellowship applications these past few weeks so naturally I began my research in a germane area of literature and ended up somewhere completely random. And by completely random I mean not even within the field of economics anymore and at best tangential to my original topic of investigation, but fascinating. I stumbled upon Ten Thousand Years of Inequality: The Archaeology of Wealth Differences, a volume on the archaeological studies of wealth inequality, and given its relevance to the posts I've been writing on inequality I figured I would make a small note on how inequality is being measured for a society that lived nearly two thousand years ago.

I haven't written a formal post on the Gini coefficient but given that this article uses it extensively in the archaeological context I preface by stating a few things: (1) the Gini coefficient is notably a simple measure of inequalities (most commonly income inequality) therefore it has its limitations that are well summarized in this Wikipedia post; (2) it has also been subject to revision and extensive debate as well as the creation of alternative measures of inequality including the Atkinson Index that may be more informative if certain contexts; (3) given my lack of knowledge of archaeology (and my lack of knowledge more broadly on the range of applications that the Gini coefficient has had in diverse fields within social science) I don't assess whether or how the Gini coefficient was applied and rather introduce it as a thought-provoking application outside of the realm of economics.

Feinman, Faulseit, and Nicholas (2018) provide estimates of wealth inequality for the Classic period in the history of the pre-Hispanic Valley of Oaxaca, Mexico based on archaeological house excavations at six pre-Hispanic settlements. They rely principally on architectural constructions and space to proxy for wealth and apply the Gini coefficient to three architectural variables: terrace area, house size, and patio area. They also utilize distribution of artifacts such as obsidian and other rare items.

The Lorenz curve in the figure below shows the Gini coefficient constructed based on the house sizes for all of the houses in the sample (a total of 36 excavated houses across all six sites in the Valley of Oaxaca) with a coefficient of 0.35 and a 95 percent confidence interval between 0.31 and 0.39. A Gini coefficient of 0 represents perfect equality whereas 1 represents perfect inequality.

The Gini coefficients from all samples (houses, patios, and terraces) and excavation sites are indicated in the figure below. They range from 0.35 to 0.43.


The authors find based on their analysis that wealth inequality during this time was low compared to other urbanized and preindustrial settings (confirming extant evidence).

While there was notable variation between the periods that the authors link to changes in the socio-political structures of the time, they specifically note that "[t]he consistently low Gini values... are informative, especially as indicators of wealth inequality, because they challenge the long-term notion that archaic states were always starkly divisible into the rulers and the ruled, with dramatic differences in resources and quality of life between the two. This coercive/despotic vantage on archaic states is well ensconced in the historical/social sciences for preindustrial times (e.g. Mann 1977; Wittfogel 1957) but now is being challenged as not uniformly applicable (Blanton 2016; Blanton and Fargher 2008), with some historical polities seen as having had a more collective institutional orientations and lower degrees of wealth inequity (e.g. Mann 2016, for a change from his earlier perspective)."

A few comments and questions came to mind about the application of Gini coefficient in this context:
  1. The sets of data used in these analyses of the Classic period in particular are considered by the authors to be large and representative (likely given the difficulties involved in excavations and the number of houses, patios, and terraces that are still intact after thousands of years) but they are still subject to the well-described small sample size bias associated with the Gini coefficient. Smaller samples are biased towards having smaller Gini coefficients which may make it difficult to compare excavated sites in the graphic below (from the Smithsonian Magazine article about this book) with the United States today, which is not based on excavated evidence and has a much, much larger sample size. 
  2. Comparisons across past civilizations, though, if they are based on similar sample sizes may be more informative than comparisons between past civilizations that were excavated and modern day societies. Similarly, I would find variation within a given civilization over time (as evidenced in the article) to be informative especially in conjunction with changes in socio-political structures. This is hinted at by the authors when they quote from Piketty (2015) on the impacts of these structures on inequality: "[O]ne should be wary of any economic determinism in regard to inequalities of wealth and income... The history of the distribution of wealth has always been deeply political... How this history plays out depends on how societies view inequalities and what kinds of policies and institutions they adopt." 

Wednesday, October 3, 2018

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

Tuesday, August 14, 2018

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.

Tuesday, July 3, 2018

In-depth look at income and wealth data (pt. 1 of 3): Background

For some time now I have been interested in writing an in-depth post on income and wealth data in order to discuss how the study of inequality - in conjunction with the data and methods that enable this study - has progressed over time. While this was initially intended to be a single post, it quickly became evident that there was too much to discuss within too short a space. In this first post of a three-part series, then, I focus on providing the background for a more granular discussion of wealth and income in the next two parts.

Given the topic at hand, it is noteworthy that several articles on inequality and tax and redistribution policy were published in a recent special issue of the Journal of Public Economics honoring the late Tony Atkinson. For an introduction to that series of papers see here. My previous post on individual and household level inequality is based on a paper within this special issue. Additionally, a recent issue of the Quarterly Journal of Economics features an article that combines national accounts data with micro data to produce estimates of inequality in the U.S. that are consistent at the macro level.

In light of expanding research on inequality, its growing presence in policy debates in developed countries, and the evolution of both data and methods that enable its rigorous study, it is useful to take stock of the existing data sets and methods used by researchers to answer some of the most pressing questions in public economics today: those that deal with the distribution of wealth and income in our societies and the reasons for widening or stagnant inequality levels. We can also assess what types of questions we are now able to answer and how our answers to these and other - yet unasked - questions can become more accurate through improved data collection and methods and how future data collection can fill existing gaps in our knowledge.

To begin, the World Inequality Database - a database of global wealth and income inequality data co-founded by Tony Atkinson - provides a concise description of data and research in this field over the past twenty years. Two important trends:
  1. Most studies on inequality have until very recently focused on income rather than wealth. The key reason is the greater availability of micro data to study income, which is taxed and therefore observable in administrative data, as opposed to wealth, which in most developed countries is not taxed apart from an estate tax upon death. A secondary reason is that it has not been made evident until recently - likely for similar data reasons - that wealth concentration plays a large role in the inequality we see within developed countries. Piketty (2014)'s Capital in the Twenty-First Century was not the first but perhaps the most prominent description of the growing role of capital in widening divisions between haves and have-nots.
  2. Current efforts are aimed at producing distributed national accounts that combine administrative micro data with national accounts macro data - ledgers of assets and liabilities at the national level - in order to reconcile inequality estimates that are created based on micro data with the national accounting. This publication from the founders of the WID discusses the motivation and methodology for the creation of these "distributed national accounts." It notes the historical background, "[by] combining the macro and micro dimensions of economic measurement, we are of course following a very long tradition. In particular, it is worth recalling that Kuznets was both of the founders of the U.S. national accounts and the author of the first national income series and also the first scholar to combine national income series and income tax data in order to estimate the evolution of the share of total income going to top fractiles in the U.S. over the 1913-1948 period (see Kuznets, 1953)." The article cited above from the QJE, Piketty, Saez, and Zucman (2018), presents "distributed national accounts" for the U.S., which they note is distinct from government statistical agencies' work in this area.
Discussion of the main data types and their roles in inequality studies

Administrative micro data

To preface a discussion on administrative tax data for wealth and income studies, I provide context for use of this data for social sciences research more broadly. Administrative data are collected for the purposes of registration, transaction and record keeping, and are often linked to public service delivery. They are typically collected by public sector agencies and can be used in administration systems in education, health, and taxation, among other departments of the public sector. It should be noted that these data are "found" data and are not collected for the purposes of research as survey data are. The social sciences, and economics in particular, have shifted to using administrative data over survey data sources in recent years for several reasons.

Specifically as noted in this white paper to the National Science Foundation: "Administrative data are highly preferable to survey data along three key dimensions. First, since full population files are generally available, administrative records offer much larger sample sizes... Second, administrative files have an inherent longitudinal structure that enables researchers to follow individuals over time and address many critical policy questions, such as the long term effects of job loss (von Wachter, Song, and Manchester, 2009) or the degree of earnings mobility over the life cycle (Kopczuk, Saez, and Song, 2010). Third administrative data provide much higher quality information than is typically available for survey sources, which suffer from high and rising rates of non-response, attrition, and under-reporting."

Access to this data is not without its challenges in many developed countries. Nordic countries have been leaders in enabling researchers to access de-identified administrative or "register" data but other countries, such as the U.S., have been relatively slow to follow. Given the central role that administrative data has come to play in social sciences and economics research in particular (see the two charts on the number of publications in leading economics journals that employed administrative data in this presentation from researcher Raj Chetty, who also co-authored the white paper cited above), it is clear that access to these data has important implications that are outlined in an article published in the Economist last month on the topic.

Administrative tax data are widely used in income and wealth inequality studies. For example, wealth inequality is largely studied through either estate tax records - in order to create wealth distributions of wealth at death and to extrapolate from those records the distribution of wealth among the living using the mortality multiplier method - or through taxable capital income (it should be noted that only one-third of total capital income is reported on tax returns which is why it is challenging to estimate wealth based on this quantity). Similarly, income inequality is studied through income tax records. Given the socioeconomic and demographic data contained in these records we are able to answer (or attempt to answer) a wide range of social science research questions based on micro data. Yet, the missing piece is information on movements in the economy at large over time (e.g. increase in fraction of retired individuals or declines in household size) which could have implications for inequality.

As noted by Piketty, Saez, and Zucman (2018), studies that use micro data exclusively are unable to answer questions such as: (1) what fraction of economic growth accrues to different parts of the income distribution, (2) what fraction of the increase in income inequality is due to changes in share of labor vs. capital in national income as opposed to changes in the distribution within labor or capital earnings, (3) how does government redistribution impact inequality (i.e. we are only able to observe pretax income using micro data series which does not allow us to observe the changes in the income distribution between pre- and posttax). To answer these questions, they argue, merging micro data with national accounts data at the macro level is valuable.

National accounts macro data

On the macro side side, national accounts data aggregate output, expenditure, and income activities of each sector of the economy. While income and consumption measures are important for evaluating standards of living they offer only a static picture of well-being. Specifically, income and consumption reflect current well-being: how much a household or an economy is producing and consuming at present, but they do not provide much insight into a household or economy's long-term or future well-being (beyond making assumptions that current well-being and consumption are highly correlated over time). This is where national accounts data can be useful: data on a household or economy's ownership of marketable assets and contraction of debts can provide insight into long-term or future well-being though it may be cross-sectional rather than longitudinal.

For a valuable introduction to balance sheets and the national accounts data see here for a discussion from the French National Institute of Statistics and Economic Studies (INSEE). It should be noted that the definitions of "assets" and whether or not they provide "economic advantages" refer specifically to those items that have market values. This would exclude, as stated by INSEE, "items that one might expect to see in the accounts (human capital, natural heritage, natural State property, household durables, pension entitlements linked to the allocation system, etc.)" They note as a rule of thumb that only items that are featured in the capital and financial accounts are included as assets in order to maintain internal consistency. The capital account and financial account link the opening and closing balance sheets to one another: they specify what happened to the accumulation of capital based on capital consumption, assets sold and acquired, discoveries and inventions, and nominal holding gains as a result of price fluctuations.

These data, and specifically the national income measures in these data, may be relied upon to fill the gaps in our knowledge from the tax data. Specifically, there are gaps between the reported income and the national income that are not captured in micro studies: imputed rents of homeowners and taxes on top of unreported and untaxed labor income in the form of tax-exempt fringe benefit. Piketty, Saez, and Zucman (2018) estimate that the fraction of national income reported on tax returns in the U.S. has declined from 70 percent in the late 1970s to roughly 60 percent today which indicates that micro data alone may underestimate the level and growth of income in this country and perhaps more so for certain parts of the income distribution than others depending on what exactly is being excluded from the tax data that is present in the national income.

For a more in-depth description of the methods and the process by which these two data are being combined, I would look to the article. The authors effectively illustrate both the motivation and the methods for incorporating national income macro data into inequality studies. In the next part of this three-part series, I will discuss the data and research on wealth inequality specifically to provide greater detail on wealth estimates using estate tax data compared to those using capital income.

Sources
  1. Piketty, T., Saez, E., Zucman, G. (2018). Distributional National Accounts: Methods and Estimates for the United States. Quarterly Journal of Economics.
  2. Kleven, H., Luttmer, E. (2018). A Special Issue of the Journal of Public Economics: Honoring the Work of Sir Anthony B. Atkinson (1944-2017). Journal of Public Economics. 
  3. Blundell, R., Joyce, R., Keiller, A.N., Ziliak, J.P. (2017). Income inequality and the labour market in Britain and the US. Journal of Public Economics.