Sexism in the National Accounts, QOTD

Since 1953, the United Nations has published an influential set of standards for national income statistics called the System of National Accounts (SNA). In the 1970s and 1980s, these statistics came under assault for ignoring women’s work, culminating in the influential critique of Marilyn Waring, If Women Counted published in 1988. (Edit: Specifically, the statistics were criticized for treating differently unpaid/non-market work performed by men from that of women. Much of men’s work was argued to be worth trying to estimate a value for even though no price was directly paid for it, while women’s work was simply left out.) What I would not have guessed is how much those critiques had already been internalized by the experts working in the UN. Among the papers of Richard Stone (lead author of the original 1953 SNA), I found this gem from a 1982 United Nations expert paper laying out an agenda for revising the SNA:

To a considerable degree, the [UN] Blue Book’s borderline between subsistence output, to be included in production and consumption, and household activity, to be excluded, reflects a sexist view that is gradually changing. Subsistence activities, for the most part, are male activities; household activities are female ones. Thus winemaking is included, cooking is not; caring for animals is, caring for children is not; and communal volunteer projects like road building and similar activities are, but those of women’s groups running volunteer community service programs like libraries health services, and school services are not. This disparity in treatment should be remedied.

And yet to present, 30 years later, not that much has changed. What went wrong? The report’s next paragraph offers some insight:

The problems of valuation are more difficult, however, for the kinds of non-market activity not now included in the SNA. In order to value household activity, for instance, it is necessary to decide whether to use the opportunity cost of the housewife in the labour market or the cost of hiring comparable household services o the market. For analyzing resource allocation an opportunity cost valuation might be appropriate, but for measuring consumption of household services, a market valuation might be better.

As I’ve argued elsewhere, from the 1920s to present, the objections to including housework have very rarely been on the principled ground that housework shouldn’t count but rather on the (seemingly) practical objection that valuing it is very hard. And yet, the end result is the same – housework remains (largely) uncounted (in official national accounts).

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What is the Largest Economy?

Ok, pop quiz: What’s the world’s largest economy, as defined by GDP?

The answer is “it depends on who you ask.”* If you want internationally comparable GDP data, there are three main places to look: The Penn World Tables, The World Bank, and Angus Maddison’s database. According to inequality researcher Branko Milanovic, right now, the last database puts China in the lead, while the first two give it to the US. New revisions of the Penn tables put them closer to the US, but not all the way there. But the Penn World Tables are constantly in flux, and Milanovic predicts that the next round of revisions might put China in the lead. Here’s how he put it, in confusing reverse order because Twitter:

Screen Shot 2013-07-06 at 10.17.40 AM

The range of the three is quite large: from China being 10% higher than the US to 25% lower. As Morten Jerven has shown, this problem (of large variation between the different data sets) is pervasive, and especially problematic for African countries.

* It also depends on *how* you ask (which PPP data you use, etc.), which amounts to something very similar.

Debating the Wrong Reinhart + Rogoff

On April 15, three UMass Amherst economists (a graduate student Thomas Herndon, and his advisors Ash and Pollin) published a critique of an influential paper, “Growth in a Time of Debt,” written by Harvard economists Carmen Reinhart and Kenneth Rogoff (R+R 2010 for short). Since then, basically everyone has weighed in on the debate – even Stephen Colbert, in this hilarious bit (and a follow-up interview with Herndon). Here’s one of many summaries of the affair.

Much of the subsequent debate has attempted to assess the original paper and the critique. I want to argue that, in some sense, this is the wrong debate. There was nothing wrong with the original R+R 2010 piece. Oh, sure, there were Excel coding errors and a questionable weighting scheme, but as many commenters have since noted, these are common in lots of early-stage research. You try things out, make mistakes, show it to your colleagues, go back and improve your methods, and science progresses. This is the argument advanced by defenders of R+R on all sides, from Greg Mankiw to Betsey Stevenson and Justin Wolfers to Jeff Smith (and even some sociologists). On a purely academic level, I agree with this argument.

But Reinhart and Rogoff didn’t just write a short conference paper. They wrote op-eds in prominent places. They spoke to policymakers. They argued that because of what they’d found in that short, not peer-reviewed piece, policymakers should fear a 90% debt/GDP threshold or cliff. They drew on their academic credibility – both personal, and in the research they had published – to try to influence policy quite directly. And for that reason I disagree strongly with Jeff Smith when he argues that Herndon, Ash, and Pollin should have shared their critique with Reinhart and Rogoff before going public and given them a chance to respond. And similarly, in some sense I agree with Greg Mankiw when he (and many others) write that the spreadsheet errors have gotten too much attention. At least, I would agree if this were purely an academic debate. But if this is a political fight, then Reinart and Rogoff’s credibility as policy experts is exactly what’s at stake. They are tenured economists at Harvard, so they start off with a lot of credibility. The spreadsheet error is important because it shows just how sloppy was the research they were shilling in 2010-2011. At the time, Krugman wrote of R+R 2010, “this just isn’t careful work.” The Excel spreadsheet error is the smoking gun proof of that.

So, yes, Herndon, Ash, and Pollin (HAP, for short) were explicitly critiquing R+R 2010, but the critique matters because of the editorials and policy tracts that R+R wrote in 2010-2012 that explicitly invoked R+R 2010 to argue for cuts in government spending or at least more attention paid to rising debt. These editorials, and similar advice given by R+R themselves directly to policymakers, shifted the terrain of the debate: this is not just an academic dispute that can take place on the usual academic time scales and with the usual academic norms, but rather an explicitly political fight about government spending in a time of recession. Here’s one example that will hopefully serve as a case-in-point, a 2011 editorial published by Bloomberg titled Too Much Debt Means the Economy Can’t Grow. Here are the key invocations of the 2010 paper:

Our empirical research on the history of financial crises and the relationship between growth and public liabilities supports the view that current debt trajectories are a risk to long-term growth and stability, with many advanced economies already reaching or exceeding the important marker of 90 percent of GDP.

And:

In our study “Growth in a Time of Debt,” we found relatively little association between public liabilities and growth for debt levels of less than 90 percent of GDP. But burdens above 90 percent are associated with 1 percent lower median growth. Our results are based on a data set of public debt covering 44 countries for up to 200 years. The annual data set incorporates more than 3,700 observations spanning a wide range of political and historical circumstances, legal structures and monetary regimes.

We aren’t suggesting there is a bright red line at 90 percent; our results don’t imply that 89 percent is a safe debt level, or that 91 percent is necessarily catastrophic. Anyone familiar with doing empirical research understands that vulnerability to crises and anemic growth seldom depends on a single factor such as public debt. However, our study of crises shows that public obligations are often hidden and significantly larger than official figures suggest.

“Growth in a Time of Debt” was a brief, not peer-reviewed paper. That’s their evidentiary basis. And yes, they back somewhat away from the strong threshold claim – but in terms of sensitivity, as in, we know high debt somewhere around here kills growth, but we aren’t sure it’s exactly 90%. And, as they later note in defense of themselves, they do emphasize the median claim (which is robust to the Excel and weighting critiques of HAP). But the causal claim is right in the friggin’ title* and it had already been disputed by many prominent critics who read the original paper (e.g. Krugman here). Suppose you’re a Harvard professor – would you publish an op-ed which relied this heavily on a not peer-reviewed study that had already received substantial critiques for overstating its causal claims? If you did, would you expect the same kind of courtesy from your colleagues as if you’d just posted a paper on SSRN or NBER?

To me, Herndon, Ash, and Pollin are responding to this op-ed (and others like it) as much or more than they are responding to R+R 2010 itself. Responding to an academic working paper may have some norms of fair warning associated with it. Responding to a series of hackish op-eds drawing legitimacy from a working paper that looks like a publication doesn’t have the same norms or goals. It’s about destroying credibility, not improving flawed methods. Social science always has an element of politics – we are, after all, making knowledge about people. But R+R, much like the equally contentious Regnerus affair, was politicized in a more transparent, more partisan way. Regnerus at least somehow managed to get his study through the normal peer-review process, and left it to others to make fools of themselves citing it in public for political effect by playing up a (questionably derived) correlation into a strong causal statement, leaving himself in a somewhat defensible position of never having taken a position on the political issue in public, nor of having made the bad causal claim (later undermined somewhat by behind the scenes evidence). Reinhart and Rogoff did no such thing – they took their preliminary results straight into the heart of an important political debate and made (academic) fools of themselves.

There’s always something messy and frustrating around these explicit interweavings of high-stakes politics and “normal” social science that twists everything up. But in the end, I think Mankiw, Stevenson and Wolfers, Smith, and other academics, give Reinhart and Rogoff too much credit by treating this incident as a purely academic, normal science debate. The error wasn’t (just) in the spreadsheet, it was in the attempt to claim policy relevant expertise based on the spreadsheet.

* As Mike argues in the comments, op-eds receive their final titles from editors rather than authors, so it’s hard to know who picked that title out. That said, R+R make the causal version of the claim in various places in this piece and elsewhere. As O’Brien notes, “R-R whisper “correlation” to other economists, but say “causation” to everyone else.” (This footnote was added after Mike’s comment.)

“Empirical” in Economics

As part of reframing a paper on the history of swaps and Glass-Steagall,* I’ve been reading a lot of papers in economics journals about financial innovation. In 2004, Frame and White published a very useful survey in Journal of Economic Literature of empirical studies of financial innovation. F&W argue that despite a massive wave of innovation starting in the 1970s-1980s, there has been very little empirical research on the topic (unlike other forms of innovation). What’s interesting to me is how they define empirical for purposes of their survey:

Empirical: The article must have formally presented data and tested hypotheses. As a result, a necessary condition for inclusion in the survey was that a standard error (and/or a t-test) appeared some where in the article. (124)

Ok, I know that F&W are mostly trying to distinguish empirical articles from theory articles, and that in economics itself such a rule-of-thumb (does it have a t-test?) probably comes very close to catching all relevant papers, but I still find the collapsing of “empirical” to “amenable to a t-test” to be quite confining. Do case studies never add to our knowledge? If they do, aren’t they “empirical”? This definition excludes all of the work from the social studies of finance tradition, for example. Even if you only believe such case studies are useful for generating, and not testing, hypotheses, they would still seem of interest to scholars of financial innovation.

I’m guessing this definition of empirical is reasonably widely held in economics, but I wonder how pervasive it is (if less audibly so) among quantitative sociologists.

*Earlier version here, but note that the next version will look very different.

Visualizing Inequality in the US, 1947-2011

How can we best understand trends in postwar income inequality in the United States? What data are available for understanding these trends? What is the best way to represent these trends visually? In this post, I want to argue that the basic facts of income inequality over the last 65 years require a minimum of two graphs drawing on two data sources. First, I’m going to say a bit about the data, then a bit about the trends, and finally I’m going to show a few possible graphs which cover parts of the story (but none of which is perfect on its own).

Data on Income Distribution

The United States has surprisingly poor historical data about income distribution (and thus, income inequality). More recent years are covered by comprehensive survey datasets like the Panel Study on Income Dynamics. But the crucial period from the end of World War II to the 1960s is covered in only two big datasets[1]: first, the now famous Piketty and Saez data on top incomes which goes back to 1913 [2], and second, Current Population Survey data limited to measurements of family rather than household income that go back to 1947. For whatever reason, the Census historical data on household incomes only start in 1967, presumably reflecting some change in the methodology of the CPS’s annual income supplement.[3]

My favorite dataset for understanding income distribution, the CBO’s post-tax and transfer data, only go back to 1979. These data combine survey and income tax data in a way that is very difficult for researchers outside the government, along with estimates of government transfers, and they also attempt to adjust for household size and the nonlinear relationship between expenses and number of people in the household. Thus, the data are probable the best available for looking at real economic outcomes from the bottom of the distribution to the top 1%. As such, these data are the base for Lane Kenworthy’s excellent “best inequality graph.” I recommend his extensive analysis and defense of the graph (the updated version of which is below). I agree that it (or something very similar) is the best graph to cover the post-1970s period, but I will argue that at least two graphs are needed to show what happened to the whole distribution from 1947 to present.

Kenworthy (2010) Best Inequality Graph Updated

Stylized Facts of Inequality, 1947-2011
As suggested by the above graph, one of the most important (and recently discovered [4]) facts about inequality in the 20th century is the dramatic growth of incomes at the very top combined with the stagnation of real income for most of the distribution. The stagnation in wages for the middle of the distribution starts in the late 1970s/early 1980s, and persists until present. The top 20% or 10% do a bit better, and the very top (.01%) do incredibly well (as I will show in a moment). But what happened before, in the crucial postwar golden years of 1947-1978(ish)?

To me, the most salient feature of the 1940s-1970s income distribution is how every part of the distribution rose relatively equally. Specifically, between 1945 and 1978, the income threshold to be in the 20th, 40th, 60th, 80th, and 95th percentile all doubled. Fascinatingly, during this time, the incomes at the very top stagnated. These trends diverged in the 1980s – top incomes kept going up, and the very top skyrocketed, while most income stagnated.

Alright, so now we have a sense of the basic facts and the best available data. How can we best visualize them?

Two Possible Graphs

The brilliant thing about Kenworthy’s graph is that it manages to portray so viscerally the stagnation at the bottom alongside the growth at the top while using actual dollar magnitudes. When we switch to telling the whole postwar income distribution story, however, I’m not sure we can do it cleanly with actual dollar amounts. At least, the best things I’ve come up with so far involve normalizations instead. If you’d like to take your hand at it, I’m happy to provide the spreadsheet from which these graphs were generated.[5] So, the first graph tries to show the equality of gains across the distribution followed by the rupture in the late 1970s.

Source: Census.

This graph shows the threshold for the real (inflation-adjusted) 20th, 50th (median), 80th, and 95th percentiles of family income from 1947 to 2011, with 1947 set to 100. This graph shows the unified growth of incomes up through the late 1970s, and then the divergence as the median and 20th percentiles stagnate while the top quintile continue to increase. This increase levels off for both the 80th and 95th percentiles in late 1990s, and over the last decade incomes have basically been flat at all levels. But this paints a distorted picture of the very top of the income distribution. While the 95th percentile tripled since 1947, and increased by about 50% since 1980, the very top have done a lot better. So, here comes the Piketty and Saez data, mixed with a dash of not quite commensurable census data:

Sources: Census, Piketty and Saez.

We borrow the median income data from the previous graph and combine it with the top income thresholds from the Piketty and Saez dataset, all inflation-adjusted, all normalized to set 1947=100. Also, these are the Piketty and Saez data excluding capital gains (which would make the picture look even more extreme, but also less comparable as the Current Population Survey doesn’t capture capital gains well). What do we see? Median income still rises and then flattens out post-1980. The 90th percentile follows much the same trend, but flattens out a bit less. In contrast, the 99 and 99.99 percentiles behave quite differently, staying relatively flat in the 1950s-1960s, and skyrocketing in the 1980s. The trend at the very top (the 99.99th percentile) is particularly striking. These very elite, top incomes didn’t budge from 1947 to 1978. Then, they take off like gangbusters, increasing by a factor of 6 in just 30 years. The 99th percentile follows the same trend, but much less sharply.

So, together, what do these graphs show? The postwar golden era was one of rising incomes for everyone but the superrich. The 1980s-2000s saw stagnating incomes in the middle of the distribution, small gains at the top, and massive gains at the very top.

Other Possibilities

There are lots of other ways you could graph this data. You can show actual income on regular or logged scales, you can look at simple ratios (90/50) that more directly capture our understanding of inequality, and so on. I like these because they show trends very nicely, and they highlight the stylized facts that I think most usefully characterize the income distribution in this period [6]. What do you think? Suggest an alternative, or ping me for the data and plot it yourself!

Update
Kevin Bryan, of A Fine Theorem, published a nice detailed paper on this topic in 2008. Bryan and co-author Martinez use data from the CPS, Piketty and Saez, and Social Security data which I had missed in my discussions. That paper also has some nice examples of what you can do with 90/50 and 50/10 ratios, and logged graphs. Here’s one example:

Bryan and Martinez 2008

This figures shows and then decomposes the 90/10 gap: “Figure 2 presents the evolution of log income ratios. It shows that from 1961 to 2002, the CPS March log 90-10 ratio increased from 1.23 to 1.61. The ratios computed using the CPS ORG data set behave similarly. Figure 2 also shows that the vast majority of the increase in the log 90-10 ratio is due to an increase in the 90-50 ratio.”

End Notes

[1] That I know of! Experts on income data, please come forward and let me know of any that I’ve missed!
[2] When the US first started collecting income taxes, and thus generated good data on top income earners.
[3] What’s difference between a household and a family, according to the Census? Glad you asked: “A family consists of two or more people (one of whom is the householder) related by birth, marriage, or adoption residing in the same housing unit. A household consists of all people who occupy a housing unit regardless of relationship. A household may consist of a person living alone or multiple unrelated individuals or families living together.”
[4] I am currently working on a paper / dissertation chapter on the history of income distribution data which tries to understand why it took so long for the growth in top incomes in the 1980s to become widely discussed (e.g. “the 1%” that became such a topic of academic and political interest in the 2000s). Send me an email if you’d like to read a (very) preliminary version, or attend my presentations at SASE or ASA this summer.
[5] This is the part where data vis folks can make fun of me for using Excel. I know, I’m sorry. One of these days I plan to do more than just tinker with R and actually get it to do what I want. Until then, we can all suffer through.
[6] It’s worth putting in a reminder that the thing being graphed here is the distribution of income, more specifically, the threshold needed to be in a certain part of the income distribution in different years. Individuals follow income trajectories, and don’t stay in exactly the same place over time. Questions around the stability of income within-individuals and across generations are exactly what panel studies like the PSID are designed to show. Unfortunately, as far as I know, they don’t go back much before the late 1960s. In response to criticisms along these lines, Statistics Canada has recently published some data on stability in the very top income earners (using confidential tax data) which suggests that “Four-fifths of Canadians in the top five income percentile have consistently been there in the past five years, the statistics show, and the proportion of people remaining in the upper echelons has been growing since the early 1980s.” So, the 1%, in Canada at least, is a consistent group of individuals, not simply a statistical artifact as individuals rotate in and out. The US does not publish similar official income data, nor similar data on mobility into and out of the 1%.

Update:

Kopczuk, Saez, and Song (2010) also have a nice paper on the US using Social Security data which tries to determine how much of the increase in inequality is due to transitory vs. permanent dynamics, and thus they conclude: “the evolution of annual earnings inequality over time is very close to the evolution of inequality of longer term earnings.” (94-95) Kopczuk et al. also find that those who are in the top 1% of earners in one year are 80% likely to be in the top 1% the following year, and 60% likely five years later, again suggesting that the top 1% is a meaningful group. More broadly It seems like Social Security data have real promise for producing income inequality measures and graphs going back to World War II, but have thus far been used only a handful of scholars due to their lack of public availability. One nice feature of the Social Security data is the inclusion of (a few) demographic variables including gender and race. For example, this nice graph shows that women make up only about 14% of the top 1% of income earners, and only about 22% of the top 10% of income earners, even as they make up about 44% of all workers (all data through 2004).

Kopczuk Saez and Song (2010)

What do economists do all day?

Economics is a pretty varied field. And yet, external impressions of the field still paint it as quite homogenous, often focused primarily on macroeconomics (in the case of the general public), or on a narrow, dogmatic neoclassical microeconomics (in the case of some sociologists). So, in the spirit of trying to explore the heterogeneity of modern economics, here are short descriptions of three papers from the most recent issue of the American Economic Review that caught my eye as showing off the diversity of interesting things you might not expect to see in economics. Note, even more than usual, reference here is not endorsement.

First up, we have a paper based on the Sapir-Whorf hypothesis! Rather than posting the abstract, I’ll post the first paragraph because it much more clearly articulates the argument of the paper:

The Effect of Language on Economic Behavior: Evidence from Savings Rates, Health Behaviors, and Retirement Assets
Languages differ in whether or not they require speakers to grammatically mark future events. For example, a German speaker predicting rain can naturally do so in the present tense, saying: Morgen regnet es which translates to “It rains tomorrow.” In contrast, English would require the use of a future marker like “will” or “is going to,” as in: “It will rain tomorrow.” In this way, English requires speakers to encode a distinction between present and future events, while German does not. Could this characteristic of language influence speakers’ intertemporal choices?

The paper uses survey data on cultural traits (the World Values Survey!), cross-country regressions, cites linguists (including explicitly invoking the Sapir-Whorf hypothesis), and so on. Cool right? And in a top econ journal!

Next up, a paper on the meeting point of post-WWII US economic and political imperialism:

Commercial Imperialism? Political Influence and Trade during the Cold War
We provide evidence that increased political influence, arising from CIA interventions during the Cold War, was used to create a larger foreign market for American products. Following CIA interventions, imports from the US increased dramatically, while total exports to the US were unaffected. The surge in imports was concentrated in industries in which the US had a comparative disadvantage, not a comparative advantage. Our analysis is able to rule out decreased trade costs, changing political ideology, and an increase in US loans and grants as alternative explanations. We provide evidence that the increased imports arose through direct purchases of American products by foreign governments.

The paper relies on recently released CIA documents to identify exactly when CIA influence was exercised. According to the authors’ analysis, following a CIA intervention, the foreign government would increase its purchases of US goods. Even better, the goods bought by the new government tended to be things which the US was comparatively bad at producing, the opposite of what is predicted by purely economic theories of trade (that is, even if you expected an increase in trade following the installation of a new regime that was pro-trade, in the absence of some political machinations, that trade would be in the things the US has a comparative advantage in and vice versa). So, score one for political power and imperialism driving economic behavior!

Finally, a shorter paper by the ever-controversial Steven Levitt (and co-author Fryer) on race and intelligence:

Testing for Racial Differences in the Mental Ability of Young Children
Using a new nationally representative dataset, we find minor differences in test outcomes between black and white infants that disappear with a limited set of controls. However, relative to whites, all other races lose substantial ground by age two. Combining our estimates with results in prior literature, we show that a simple model with assortative mating fits our data well, implying that differences in children’s environments between racial groups can fully explain gaps in intelligence. If parental ability influences a child’s test scores both genetically and through environment, then our findings are less informative and can be reconciled with a wide range of racial differences in inherited intelligence.

Rather than the large, approximately one standard deviation, differences found in intelligence tests among older children, Fryer and Levitt find a minuscule .0055 standard deviation difference among children between 8 and 12 months old. That is, all of the difference in intelligence pops up after the first year. The results for different models after that are somewhat inconclusive, but the data do suggest that you don’t need any genetic difference between racial groups to explain racial gaps in intelligence tests at older ages.

So, three papers, about the role of language in shaping economic behavior, about the role of political and military imperialism in shaping trade, and on the (non)role of genetics in shaping racial disparities. Welcome to economics, 2013?*

*Ok, to be fair, there’s plenty of stuff that looks exactly like what you’d expect (asset pricing, job search over the business cycle, fiscal and monetary policy models, etc.). My point is just that there’s a lot more than that too!

On the Origins of the Kuznets Curve

Today, I’m blogging from the Harvard University Archives. I’m five boxes deep into the Simon Kuznets collection, for my second visit related to my dissertation research. I just came across a letter that, while not especially important to my dissertation, seemed like it might be of wider use to anyone interested in the history of the “Kuznets Curve.” Simon Kuznets, in addition to spearheading the first official US national income statistics in 1932-1934, is probably most famous for this curve/hypothesis.* In his presidential address to the American Economics Association in December of 1954, later published as Economic Growth and Income Inequality, Kuznets argued that income inequality and economic growth might have an inverted-U shaped relationship. That is, as economic growth/development increases, inequality first goes up and then comes back down.

Kuznets based this empirical story on a few decades worth of data from the US and UK, and asserted in the conclusion that: “The paper is perhaps 5 per cent empirical information and 95 per cent speculation, some of it possibly tainted by wishful thinking.” (1955: 26) Since 1955, the paper has generated an entire literature asking whether or not the curve is real, whether it’s reversed in the past 30 years to create a new pattern, and so on. Much of the early research supporting the Kuznets curve came from cross-national studies which mapped inequality against current GDP/capita and showed the expected inverted-U shaped relationship. These studies, like Kuznets’, lacked longitudinal data. The recent consensus seems to be that the Kuznets curve does not hold within countries (e.g. Gallup 2012). That is, within a particular country, the relationship between economic growth and income inequality does not follow the expected path. Here are two graphs from Gallup 2012 that help sell the argument. The first is the relationship between log income and inequality, which looks like a Kuznets curve (though as Gallup notes, the fit is not quadratic for unlogged data):
Gallup 2012 Figure 3

The second is the author’s estimates of within-country income inequality trajectories (each line is one country). Note the chaos, and the absence of any coherent story, with some tendency towards a U-shape (not a upside-down U!):
Gallup 2012 Figure 8

Gallup further notes that Kuznets’ original data only had half of the original story. Yes, Kuznets shows a decrease in inequality in the US and UK in the late 19th century to post-WWII period. But Kuznets has no data on the initial upsurge in inequality! That is, Kuznets only has the falling half of the curve. Gallup graphs Kuznets’ income inequality data against historical gdp data now available from the work of Angus Madison to give some sense of what Kuznets was seeing:
Gallup 2012 Figure 1
Gallup (2012: 6) summarizes:

Kuznets showed that inequality fell in two high-income countries as they grew richer after World War I, but he had no evidence of rising inequality at low income levels. Ironically, Kuznets’ prediction that inequality will rise during the early stages of development, for which he had no evidence, is better remembered than his prediction that inequality will fall at higher incomes.

What did Kuznets have, if not data? He had a model. Specifically, Kuznets motivated his proposed empirical story (rising then falling inequality) with a two sector model of income inequality. Kuznets argued that agricultural incomes were likely as or more equal than industrial/urban incomes, but industrial incomes were much higher on average. Thus, as workers moved from agricultural to industrial labor, inequality would increase (low agricultural wages to higher industrial wages), until so few workers were in agriculture that a tipping point was reached and inequality came back down. This story falls apart, however, if agricultural incomes in the pre-development period are actually more unequal than industrial ones, because “if agricultural incomes are more unequal than industrial wages, perhaps due to unequal land ownership, movement out of agriculture into industry could reduce inequality right away.” (Gallup 2012: 7)

Ok, so why did Kuznets think that agricultural incomes were more equal than industrial ones? An archival document I just perused before starting this post sheds some light on the answer: Kuznets had some intuitions backed by absolutely no data. Data on income distribution are still poor now, and were even worse back in the 1950s, with historical data being extremely limited to a handful of studies (some conducted by Kuznets himself). These studies did not readily break incomes down by urban/rural or industrial/agricultural divides. Kuznets wrote to one of his collaborators, Selma Goldsmith (then at the Office of Business Economics, what is now the Bureau of Economic Analysis) to ask about the data she had been working on to produce official income distribution data for the United States (see, e.g., Goldsmith et al. 1954). In a letter dated August 15, 1954, Kuznets writes about his current work on economic growth and income inequality:

In my model, I somehow began with the assumption that inequality within the ag. sector is lesser than within the non-ag—although I had no evidence whatsoever. … the distributions that are available (in your D. of C. publication and elsewhere) show inequality among the ag. group (i.e. farm operator families) to be greater than among the non-ag—certainly in recent years. … Yet one would expect that with the much wider range of income opportunities in the non-ag sector, the inequality in size distribution would be appreciably wider in the latter. … Do you have any explanation for it?**

Let’s just stress that again: Kuznets had absolutely no evidence that inequality within the agricultural sector was less than the industrial one. Kuznets guesses that this discrepancy between his theoretical intuition and the data has something to do with the data not quite matching what they want – there’s higher year-to-year variability in farm incomes, but in the long-run the inequality within families might be less than appears in the data, or something like that. He’s not sure. And that uncertainty, among many others, is reflected in his strong caveats at the end of presidential address.

Years later, Kuznets himself would abandon the curve and caution against looking for historical laws of development, especially in cross-sectional data (Moran 2005). And yet the debate over it lives on, as does the search for laws of economic growth and development.

*In 1971, Kuznets won the Nobel Prize in economics, “for his empirically founded interpretation of economic growth which has led to new and deepened insight into the economic and social structure and process of development.”
**Papers of Simon Kuznets, Harvard University Archives, HUGFP88.10 Misc. Correspondence, Box 4.

Economists of the Union

Last night’s State of the Union address by President Obama was not his most memorable speech, although it was not devoid of heart-wrenching moments and reasonable (if unlikely to succeed) policy proposals. Sadly, what caught my ear the most* was Obama’s explicit and implicit invocations of economists. The implicit was the more consequential: the call for universal pre-school. Nobel-prize winning economist James Heckman has been pushing the universal pre-school argument hard for years now, based in part on evaluations of the Perry preschool experiment conducted in Ypsilanti, Michigan. While economists are not the only ones pushing for universal preschool, the language President Obama choose suggests the influence of social scientists and especially economists, rather than a moral or purely educational argument:

.Study after study shows that the sooner a child begins learning, the better he or she does down the road. But today, fewer than 3 in 10 four year-olds are enrolled in a high-quality preschool program. Most middle-class parents can’t afford a few hundred bucks a week for a private preschool. And for poor kids who need help the most, this lack of access to preschool education can shadow them for the rest of their lives.

Every dollar we invest in high-quality early childhood education can save more than seven dollars later on — by boosting graduation rates, reducing teen pregnancy, even reducing violent crime.

Elsewhere in the speech, in reference to the deficit, Obama explicitly invoked “economists”:

Over the last few years, both parties have worked together to reduce the deficit by more than $2.5 trillion — mostly through spending cuts, but also by raising tax rates on the wealthiest 1 percent of Americans. As a result, we are more than halfway towards the goal of $4 trillion in deficit reduction that economists say we need to stabilize our finances.

In 2011, Congress passed a law saying that if both parties couldn’t agree on a plan to reach our deficit goal, about a trillion dollars’ worth of budget cuts would automatically go into effect this year. … They would certainly slow our recovery, and cost us hundreds of thousands of jobs. That’s why Democrats, Republicans, business leaders, and economists have already said that these cuts, known here in Washington as the sequester, are a really bad idea.

President Obama is not the first president to invoke “economists” in the State of the Union, though he is the first to do so multiple times, and he has done so the most of any President. A quick search of UCSB’s Presidency Project database of State of the Union addresses shows that Cleveland in 1895, Harding in 1921, and FDR in 1938 mentioned economists, and then the term went unused until Obama’s 2010 and 2011 addresses, both of which mentioned economists (in reference to the stimulus bill and health care respectively).**

I’m not sure what to take away from this exercise, except perhaps to say congratulations economists, President Obama seems to be paying attention.***

* Ok, second-most. The bit about 102-year old Desiline Victor waiting in line six hours to vote was amazingly touching. But seriously, she deserves more than a non-partisan commission to improve the voting experience.
** The State of the Union was not always been given as a speech, especially throughout most of the 1800s. Notably, in the 1860s, Andrew Johnson mentioned “political economists” (the predecessors of modern economists) three times, in reference to questions of public debt and currency.
*** It probably goes without saying but “sociologist” has never been used in a State of the Union address. A broader search of documents in the USCB archive reveals a few references to sociologists by presidents, but not many. Perhaps most amusingly President Clinton referred specifically to Max Weber in 2000, in a discussion with Reverend Bill Hybels: “In 1918 the German sociologist Max Weber wrote an essay. You and I never talked about this before; I just thought about it while you asked me the question. It’s called “Politics as a Vocation.” And Weber was a Christian Democrat, a devout Catholic. And he said politics is a long and slow boring of hard boards. And anyone who seeks to do it must risk his own soul.” Clinton accounts for most of the “sociologist” usages, including other references to that essay, and references to Margaret Mead (oddly enough).

Indirect Taxes as Factor Payments to Government

Warning: This post may be one of my more “wonkish”/boring efforts to anyone not interested in national income statistics.

I’ve been studying macroeconomics on and off for the past four years. These studies have mostly consisted of trying to make headway through some of the main textbooks (e.g. Mankiw’s intermediate book), along with reading some of the classic papers and, perhaps most usefully, carefully following all of the macro debates on the major econ blogs. This approach has been successful enough, but the one big gap has been anyone actually holding my feet to the fire to make me walk through the math underlying the basic models (IS-LM, Solow Growth, etc.). So, this term I’m sitting in on a Master’s level macro class. We’ll see how it goes.

As is traditional*, the first macro class covered national income accounting. The professor walked through the three standard methods for calculating GDP: sum of the value of final production (plus changes in inventories), sum of value added by industry/sector (plus changes in inventories), and the sum of factor payments plus indirect taxes. The first two involve adding up the value of the stuff that is sold, the last involves tracking the payments to workers and owners (of land and capital). Listening to the third method explained though, something bothered me a bit: government had mysteriously appeared. The first two approaches were explained without any reference to the government sector, but the third added in the proviso about indirect taxes. The problem, as it was framed, is that the observed incomes of individuals and businesses don’t quite sum up to the value of the goods produced because of “indirect” taxes. Think here of a sales tax: a business sells something for $1, but only receives $.94 (in Michigan) because 6% goes to the State. So we have to account for that somehow.

What was notable to me was that at that point, we hadn’t mentioned government as a part of the (simple, model) economy at all. But, of course, many of the goods and services used to produce and distribute goods and services are made by the government. Roads are a pretty obvious example, but there are many others. So, why not just think of Government as another, kind of weird factor of production? In that economic ontology, indirect taxes are no longer an exception added to the sum of factor payments – they are one of the factor payments. And your production function is no longer dependent on just land, labor and capital, but also on government (Y = F(K,L,G)). Ok, now all sorts of wacky things would follow from this set of definitions. But it would be a nice opportunity to think through exactly how and why government outputs are different from other kinds of products, and why payments to government (taxes) are different from other kinds of payment. And it would open up the long closed debate about whether or not some government output should count as intermediate production – like the portion of the value of roads** used up by businesses distributing goods – rather than final production.

All this to say, the basic idea that government is fundamentally not productive works its way into many parts of our economic statistics and economic theories, even as we explicitly acknowledge its importance for production in other ways.

* Judging by the typical placement of a chapter about national income data in a macro textbook – often chapter 2, the first substantive chapter after a short introduction.
** Roads might be a bad example because they are durable, and thus should maybe count as a capital good/investment. On the other hand, I live in Michigan, and here the roads do not appear to be that durable at all…

It Is 2012, Right? Economists Propose a New Journal: “Man and the Economy”

Marginal Revolution just posted a link to an interview with the venerable Nobel Prize-winning economist Ronald Coase where he (and a collaborator) announce that they are trying to start a new journal to reorient the field towards the study of actual economies. Here’s the excerpt:

We are now working with the University of Chicago Press to launch a new journal, Man and the Economy. We chose our title carefully to signal the mission of the new journal, which is to restore economics to a study of man as he is and of the economy as it actually exists. We hope this new journal will provide a platform to encourage scholars all over the world to study how the economy works in their countries. We believe this is the only way to make progress in economics.

Sigh. As much as I like the idea of a new economics journal focused on “the economy as it actually exists,” it’s hard to believe that a prominent academic would propose naming a journal “Man and the Economy” in 2012, let alone that they would assert that the name was chosen “carefully.”

Also, as a warning, the comments on the Marginal Revolution post go in a predictable direction of “PC” bashing.