Month: March 2019

What Are the Effects of Economic Freedom at the State Level?

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A brand new paper from the Mission Foods Texas-Mexico Center at SMU:

In this paper, we examine the relationship between institutional quality and bilateral trade patterns between Mexican states and U.S. states. We are contributing to the small, but growing, literature which uses gravity models to examine economic exchange at the subnational level (see Havranek and Irsova 2017 for a recent review of this literature). We are the first to explicitly incorporate institutional quality into a model of trade between the U.S. states and Mexican states, and the first to examine these sorts of relationships between the U.S. and Mexican states more generally. Poor institutions can be viewed as a cost for potential trading partners, and economic theory tells us that when an action becomes more costly, less of that action will be undertaken. Conversely, when an action becomes less costly, more of that action will be undertaken. We find that states with better institutional environments as measured by the Economic Freedom of North America index do, indeed, realize higher levels of trade. We also contribute to the literature examining trade border effects (Hillberry and Hummels 2002; Chen 2004; Head and Ries 2001) by examining the impact the border has on trade between the U.S. states and Mexican states. Finally, we use our dataset to examine the relationship between trade volume and three measures of economic prosperity (pg. 6).

The authors lay out their key findings and policy recommendations:

Economic institutions matter.

Minimum Wage & Low-Skilled Workers: More Evidence

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Image result for minimum wage

Ready for yet another post on the minimum wage? From a recent paper in the Journal of Public Economics:

Our empirical analysis uses the fact that the 2007 through 2009 increases in the federal minimum wage were differentially binding across states. We base our “bound” designation on whether a state’s January 2008 minimum wage was below $6.55, rendering it bound by the entirety of the July 2009 increase. In the states we describe as “unbound,” the effective minimum wage rose, on average, by $1.42 between 2006 and 2012. In the states we describe as “bound,” the effective minimum wage rose, on average, by $2.04. Of the long-run differential, $0.58 took effect on July 24, 2009.

We use monthly, individual-level panel data from the 2008 panel of the Survey of Income and Program Participation (SIPP) to implement a combination of difference-in-differences and triple difference research designs. Because we use longitudinal employment records with data on wage rates, our implementation of these research designs has two key advantages. First, we are able to pinpoint “target” groups more intensely affected by minimum wage increases than the analysis groups in many studies. Second, we are able to pinpoint workers who were not directly affected yet, as evidenced by their wage rates, were only moderately more skilled than the “target” workers. We incorporate this second group of workers into our analysis as a “within-state control” group. That is, we use this group to construct a set of counterfactuals that proxy for otherwise unobserved shocks to the low-skilled labor market (pg. 53).

What do they find?:

  • “We find that increases in the minimum wage significantly reduced the employment of low-skilled workers. By the second year following the $7.25 minimum wage’s implementation, we estimate that targeted individuals’ employment rates had fallen by 6.6 percentage points (9%) more in bound states than in unbound states. The implied elasticity of our target group’s employment with respect to the minimum wage is −1, which is large within the context of the existing literature” (pg. 54).
  • The average monthly incomes of low-skilled individuals decreased. “Relative to low-skilled workers in unbound states, targeted individuals’ average monthly incomes fell by $90 over the first year and by an additional $50 over the following 2 years. While surprising at first glance, we show that these estimates can be straightforwardly explained through our estimated effects on employment, the likelihood of working without pay, and subsequent lost wage growth associated with lost experience. We estimate, for example, that targeted workers experienced a 5 percentage point decline in their medium-run probability of reaching earnings greater than $1500 per month” (pg. 54).

The researchers conclude,

We use data from the SIPP to investigate the effects of the 2007 to 2009 increases in the federal minimum wage on the employment and income trajectories of low-skilled workers. We estimate that the minimum wage increases enacted during the Great Recession had negative effects on affected individuals’ employment, income, and income growth. The SIPP data suggest that this period’s minimum wage increases reduced aggregate employment rates by at least half of a percentage point in states that were fully bound by the federal minimum wage’s rise from $5.15 to $7.25 (pg. 67).

Stuff I Say at School – Part VI: Economic Freedom & Corruption

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This is part of the Stuff I Say at School series.

The Assignment

Response to a group’s summary of Jakob Svensson’s “Eight Questions About Corruption.”

The Stuff I Said

The Fraser Institute’s Economic Freedom of the World (EFW) Index, published in its annual Economic Freedom of the World reports, defines economic freedom based on five major areas: (1) size of the central government, (2) legal system and the security of property rights, (3) stability of the currency, (4) freedom to trade internationally, and (5) regulation of labour, credit, and business. According to its 2018 report (which looks at data from 2016), countries with more economic freedom have substantially higher per-capita incomes, greater economic growth, and lower rates of poverty. Drawing on the EFW Index, Georgetown political philosophers Jason Brennan and Peter Jaworski point to a strong positive correlation between a country’s degree of economic freedom and its lack of public sector corruption.

Granted, a lack of corruption could very well give rise to market reforms and increased economic freedom instead of the other way around. However, recent research on China’s anti-corruption reforms suggests that markets may actually pave the way for anti-corruption reforms. Summarizing the implications of this research, Lin et al. explain,

Reducing corruption creates more value where market reforms are already more fully implemented. If officials, rather than markets, allocate resources, bribes can be essential to grease bureaucratic gears to get anything done. Thus, non-[state owned enterprises’] stocks actually decline in China’s least liberalised provinces – e.g. Tibet and Tsinghai – on news of reduced expected corruption. These very real costs of reducing corruption can stymie reforms, and may explain why anticorruption reforms often have little traction in low-income countries where markets also work poorly. China has shown the world something interesting: prior market reforms clear away the defensible part of opposition to anticorruption reforms.Once market forces are functioning, bribe-soliciting officials become a nuisance rather than tools for getting things done. Eliminating pests is more popular than taking tools away … A virtuous cycle ensues – persistent anticorruption efforts encourage market-oriented behaviour, which makes anticorruption reforms more effective, which further encourages market oriented behaviour.

Interesting enough, there is some evidence that suggests that more government hands in the pies increases corruption. For example, a 2017 study found that larger municipality councils in Sweden result in more corruption problems. A 2009 study found that more government tiers and more public employees lead to more bribery. Finally, a 2015 study showed that high levels of regulation are associated with higher levels of corruption (likely because of regulatory capture).

Do Most Americans Really Want What They Say They Want?

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I hear a lot about how “most Americans” are in favor of “Policy XYZ.” The problem is that the social science shows that most Americans don’t know what they’re talking about. Do opinions change with more information or when costs are introduced? Two surveys from the Cato Institute seem to answer this in the affirmative.

The first is on federal paid leave. Seventy-four percent of the 1,700 Americans surveyed “a new federal government program to provide 12 weeks of paid leave to new parents or to people to deal with their own or a family member’s serious medical condition…Support slips and consensus fractures for a federal paid leave program, however, after costs are considered.” A 20 percentage point drop in support occurs when a $200 price tag is attached. Less than half are willing to pay $450 more in taxes for the program. When other potential costs are introduced (e.g., smaller future raises, reduction in other benefits, women less likely to be promoted,[ref]Ekins writes, “Research has found that government-provided paid leave programs may slow the pace of women’s career trajectories. Studies have found that government-provided paid leave may lead to fewer women getting promoted and becoming managers because they take longer leaves than they otherwise would. Other studies have noted that employers, particularly smaller companies that have difficulty accommodating workers taking leave, may be less willing to hire female employees to begin with. Some argue that American women’s corporate success is due to the fact that the United States does not provide generous family leave policies. Consistent with these findings, American women are more likely to rise up the corporate ladder than their European counterparts who have access to generous family social welfare programs. An analysis of OECD countries reveals that American women are 3 to 14 times as likely as Scandinavian women to be employed as managers with 14.6% of American women who are managers compared to 4.6% of Norwegian, 4.2% of Swedish, and 1% of Danish women. Americans women are also more likely than women in France (5.1%), the United Kingdom (7.8%), Germany (2.7%), and the Netherlands (3.6%) to hold managerial positions. The 20-First’s 2018 Global Gender Balance Scorecard finds that 53% of American companies compared to 14% of European companies have three or more women on company executive committees-the individuals who report directly to the CEO.”[/ref] cut funding to other government programs), the majority of Americans find themselves opposing the program.

Less than half of men would be willing to pay even $200 more, while 55% of women would still be willing to pay $450 more. Support for the program drops across all political parties as costs are introduced, with 60% of Democrats still willing to fork over $1,200 a year to implement it (but only 22% of Republicans and 45% of Independents). “In sum,” writes Cato researcher Emily Ekins, “Democrats have a much higher tolerance threshold for taxes than the average American.”

Another survey looked at support for the Affordable Care Act’s pre-existing condition regulation. Out of the 2,498 Americans questioned, 65% support this aspect of the ACA. However, when costs are introduced, support drops. Furthermore, wealthier Americans are more willing to entertain trade-offs than lower-income ones.

Thomas Sowell has written, “There are no solutions; there are only trade-offs.”[ref]Sowell, The Vision of the Anointed: Self-Congratulation as a Basis for Social Policy (New York: Basic Books, 1995), 142.[/ref] What “most Americans” want depends on whether or not trade-offs are kept in the dark.

The Economic Consequences of Tariffs

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From a recent IMF working paper:

We use impulse response functions from local projections on a panel of annual data spanning 151 countries over 1963-2014. The main analysis on aggregate data is complemented with industry-level data.

Our results suggest that tariff increases have an adverse impact on output and productivity; these effects are economically and statistically significant. They are magnified when tariffs are used during expansions, for advanced economies, and when tariffs go up. We also find that tariff increases lead to more unemployment and higher inequality, further adding to the deadweight losses of tariffs. Tariffs have only small effects on the trade balance though, in part because they induce offsetting exchange rate appreciations. Finally, protectionism also leads to a decline in consumption; this, together with our findings, suggests that tariffs are bad for welfare.

All this seems eminently sensible and bolsters the arguments that mainstream economists make against tariffs; our results can be regarded as strong empirical evidence for the benefits of liberal trade. And given the current global context, we take special note of the negative consequences when advanced economies increase tariffs during cyclical upturns (pg. 25-26).

Does Populism Reduce Economic Inequality?

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The above comes from a recent study of The New Populism project. This reduction in economic inequality may lead some populist supporters to feel vindicated. However, the study continues by pointing out that “the fiscal policies of populists are less progressive than non-populists. This is what we might have expected; they are not reducing inequality as a result of government taxation or welfare structures.” The mechanism remains unknown, “maybe minimum wage policies, maybe moves towards formalization of the labour force, or limits on income generation of the very wealthy (or even possibly in the case of Venezuela, the very wealthy leaving, thereby reducing overall levels of market inequality). But they do reduce overall levels of market inequality” (pg. 5).

However, this isn’t the only effect of populists:

  • Populist leaders increase indirect (regressive) taxation.
  • Populism has no real impact on corruption, despite corruption often bringing populists to power.
  • “[P]opulist chief executives are more likely to infringe on the freedom and fairness of the electoral process than their non-populist counterparts” (pg. 14).
  • “[B]oth right and left populist chief executives seem more likely to embark on a mission to cut back on civil liberties” (pg. 15).
  • “We confirm a strong, negative effect of populism on press freedom. Not every decline can be attributed to populists, but almost every strong or moderate populist registers some decline” (pg. 17).
  • “Finally, populism in government is often associated with the centralization of power under the chief executive” and the erosion of executive constraints (pg. 18-19).


So giving power over to populist authoritarians who undermine democratic institutions and civil liberties is one successful avenue to economic equality. The others, according to historian Walter Scheidel, are “mass-mobilization warfare, violent and transformative revolutions, state collapse, and catastrophic epidemics. Hundreds of millions perished in their wake, and by the time these crises had passed, the gap between rich and poor had shrunk.”

What Was the Cost of Trump’s Trade War in 2018?

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A new working paper confirms what economists have been saying about tariffs all along:

Economists have long argued that there are real income losses from import protection. Using the evidence to date from the 2018 trade war, we find empirical support for these arguments. We estimate the cumulative deadweight welfare cost (reduction in real income) from the U.S. tariffs to be around $6.9 billion during the first 11 months of 2018, with an additional cost of $12.3 billion to domestic consumers and importers in the form of tariff revenue transferred to the government. The deadweight welfare costs alone reached $1.4 billion per month by November of 2018. The trade war also caused dramatic adjustments in international supply chains, as approximately $165 billion dollars of trade ($136 billion of imports and $29 billion of exports) is lost or redirected in order to avoid the tariffs. We find that the U.S. tariffs were almost completely passed through into U.S. domestic prices, so that the entire incidence of the tariffs fell on domestic consumers and importers up to now, with no impact so far on the prices received by foreign exporters. We also find that U.S. producers responded to reduced import competition by raising their prices.

Our estimates, while concerning, omit other potentially large costs such as policy uncertainty as emphasized by Handley and Limão (2017) and Pierce and Schott (2016). While these effects of greater trade policy uncertainty are beyond the scope of this study, they are likely to be considerable, and may be reflected in the substantial falls in U.S. and Chinese equity markets around the time of some of the most important trade policy announcements (pg. 22-23).

What Drives Racial and Ethnic Inequality Today?

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A brand new study offers some interesting insights into the question. Kay Hymowitz summarizes,

Using Census and ACS data, [John Iceland] shows that whites were the least likely of all groups to be poor throughout the decades studied (though, notably, their poverty rates inched up after 1980.) Although blacks and American Indians have become markedly less poor since 1959, they remain the groups with the highest— and fairly similar—odds of living in poverty. Hispanics never had poverty rates as high as those for American Indians and Blacks in the years studied, but their rates today, at 22.5%, are only marginally lower than those poorer groups (26.1 and 25%, respectively).

Iceland’s calculations also confirm that we are a much richer nation than we were in 1959. Affluence, defined as family income-to-poverty ratios five times the poverty threshold (or $120,180 as of 2015), has grown for all demographic categories, though at a faster pace for whites and Asians than others. (This “affluence” may strike New Yorkers and renters in other expensive cities as dubious, though the author checked his findings against alternative measures of poverty and affluence; they all showed the same basic trends.) 


Despite the massive declines in poverty, what are the main factors behind continuing inequality?

Taking the groups as a whole, he finds immigrant status to be the characteristic that best correlates with poverty, and education the trait most associated with affluence. However, the features most closely related to poverty and affluence differ among groups in fascinating ways. At a time of renewed concerns about racial inequality, the most striking story is for blacks. African Americans are more than three times as likely to be poor than whites without controls. With controls, the gap declines considerably—to 1.71.  Iceland estimates that female-headed households can now explain about one-third of the black-white poverty difference, age comes in second at 16%, and education at 15%; all-in-all, the three characteristics can explain two-thirds of the poverty gap between blacks and whites. 

Iceland’s findings on trends in minority poverty and affluence are consistent with a narrative of progress in racial relations. In 1959, family structure, education, and age explained less than half of the poverty and affluence gap between blacks and whites, for example; most of the divide was due to “unobservables” like discrimination, neighborhood, and social networks. Iceland confirms earlier research showing black and American Indian poverty plummeting in the 1960s; 57% of African-Americans and 60.3% of Indians started that decade poor. By the 1970s the number was 35.5% and 35.5%. Because the strong economy of the 1960’s lifted all ethnic and racial boats during the 1960s, black, Hispanic, Asian, American Indian, and white, however, the decade ended with inequality between the various groups more or less unchanged. In sum, between civil rights laws and economic growth, minority groups were able to make substantial economic progress in the 1960s, though not enough to catch up with whites.

But as discriminatory barriers fell, individual and family characteristics became more crucial for economic mobility. The author shows that “observables,” including family structure, age, and education, have considerably more explanatory power for poverty and affluence gaps today than they did in 1959, while unobservable factors, like discrimination, though still significant for blacks and American Indians, have nevertheless become less so.

What about other groups?

Education differences have the largest effect on the Hispanic and white poverty gap and that effect has grown over time; age and immigrant status play strong supporting roles. Education has been the prime mover for affluent Hispanics and American Indians; intact families, fewer children, and relocation to metropolitan areas also helped the latter group improve their outcomes.

Asians are the most educated of any group as well as the most stably married. These traits help explain the 35.8% of Asians who are affluent (vs. 32.9% of second-place whites) as well as why, though they have higher poverty rates than whites mostly due to immigration, they are still somewhat “protected” against poverty.

Some limitations and cautions:

It could be that family structure itself is partly a proxy for discrimination. If black men have trouble finding jobs because of prejudice, they are inevitably less “marriageable.” He notes as well that the data available has serious limitations. “Asian” was not a Census category until 1980; before that, people checked the Chinese, Japanese, Filipino, or Hawaiian box. It’s worth noting that Asian remains an awkward grouping, encompassing people of very different histories and cultures; in measuring affluence, Iceland shows, the Vietnamese don’t look at all like other Asians. “Hispanic” is similarly problematic. With controls, Cubans are as likely as whites to be affluent; that’s far from the case with other Hispanic subgroups.

Google, the Gender Pay Gap, and Markets

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So you’ve probably seen this article making the rounds: Google Finds It’s Underpaying Many Men as It Addresses Wage Equity. It’s not hard to see why. The idea that a socially-aware megacorp tried to equalize women’s pay and ended up handing out raises is not only intrinsically funny, but offers a dose of schadenfreude for all the folks who still think James Damore was fundamentally right about the tech giants ideological echo chamber. Fair enough. But I want to talk about something different, and the real reason I’m deeply skeptical of the whole idea of a gender pay gap.

The first thing to realize is that the entire concept of a pay gap is actually philosophically tricky to define. From the NYT article:

When Google conducted a study recently to determine whether the company was underpaying women and members of minority groups, it found, to the surprise of just about everyone, that men were paid less money than women for doing similar work.

OK, but how does Google define “similar work”? Probably–I’m guessing, but a guess is good enough in this case–by looking at stuff like job title. Do you think everyone who works at your company with the same job title as you is working as hard / getting as much done as you do? No? Then this isn’t a very good basis for assessing “similar work” is it?

In fact, the problem is really bad because–even if a company paid men and women equally given that they had the same job title (in this case Google appears to have paid women more) they could still discriminate at an earlier stage in the process. Thus (another quote from the NYT article):

Critics said the results of the pay study could give a false impression. Company officials acknowledged that it did not address whether women were hired at a lower pay grade than men with similar qualifications.

In other words, maybe Google pays senior developers the same (or even pays female senior developers more), but at the same time it also stacks the deck against new hires so that female applicants are more likely to get hired as regular developers and then men are more likely to get hired as senior developers. In that case, it could be true that Google is biased towards paying women more within one job title, but also that it’s biased towards paying women less overall.

Not so simple, eh?

Now, I don’t actually know if Google used job title to define “similar work” and I made the bold claim that I didn’t really care if they did or not. The reason for that is that there is no good way to measure how much work a person does. If they used job title, then that’s a bad proxy. But if they used something else, then I am confident that they used another bad proxy. Because there’s absolutely no practical way that Google could have spent the time and resources required to actually assess all of their workers. There’s a name for this in economics, for the ides that it’s basically impossible to measure how much work an employee is doing. It’s called the principle-agent problem. And, believe it or not, that’s actually the easy part. Even if you could accurately, easily, and cheaply quantify how much work your employees do (you can’t), there’s still no accepted methodology for assessing how much value that work contributed to the company. If you’re the sales guy who closes a deal that earns your company $1,000,000 in revenue you might think the answer is simple: your effort just got the company a cool million. But you didn’t do that alone. You were selling a product that you didn’t make, for one thing. So the designers, the marketing guys, and the folks on the assembly line building the widgets all need a cut. How do you attribute the value you made–$1,000,000–among all the complex, networked, interconnected contributors? Good luck with that.

So far, all I’ve really said is that trying to detect a wage gap is going to be really, really hard because assessing “similar work” is basically impossible. But there’s good news! If you understand the way markets work, you will understand that you have very, very good reason to be skeptical that men and women are really being paid different amounts for similar work.

Now, before I explain this, let me just point out that there are a lot of people who will tell you that economic models of markets are over-simplified, flawed, and misleading. They’re right, but those criticisms don’t really apply. There’s this whole controversial literature over concepts like the efficient market hypothesis that, luckily, we don’t need to get into here and now. In a nutshell, economists like to pretend (for the sake of tractable theories) that humans are perfectly rational and statistical geniuses who take all possible information into account when making purchasing decisions. If that were true, then things like market bubbles would (probably) not be possible. (It depends on the specific of your model.) So let me just say: yeah, I concede all that. Precise, mathematical models of markets are basically all wrong. We can quibble about whether they are “perpetual motion machine”-wrong or just “spherical chicken”-wrong, but whatever.

Here’s the point: in a market (even a fairly messed-up, realistic one) you’ve got a lot of companies who are all competing. Although there’s a lot going on, one vital way that one of these companies can get a leg up over its competitors is if it finds a way to offer the same good or the same service for less cost. This isn’t rocket science, this is really, really obvious. If company A and company B are both selling more or less interchangeable widgets, but company A can make them for $1.00 / each and company B can make them for $0.90 / each, then company B has a huge advantage.

So here’s the thing: if there were any real indication that you could hire a woman, pay her 70% of what you pay a man, and get “similar work”, then what you’re saying is that there’s an easy, obvious way to go out there and make your widgets for $0.70 when everyone else has to pay $1.00 to make theirs.

We don’t need to take any derivatives here. We don’t need advanced theory. We don’t need to assume that human beings are perfectly rational, hyper-calculating machines. We just have to assume that companies generally want to find ways to reduce the cost of the goods and/or services they sell. If that humble, uncontroversial assumption is true, then any perceptible evidence of a real gender pay gap would immediately be identified and exploited by the market.

If anyone could find a real gender pay gap, it would be the mother of all arbitrage opportunities. And look, folks, if there’s one thing that every red-blooded capitalist wants to find, it’s an arbitrage opportunity. This isn’t hypothetical, by the way. You look at an industry like currency trading, and companies invest huge amounts of money hiring geniuses, buying them super-computers, and paying for access to network cables that give them millisecond advantages so that they can find and identify arbitrage opportunities before the market erases them.

Because that’s what markets do. They look for chances to make free money and then they exploit them until they disappear. If you find out that you can trade your dollars for yen, your yen for rubels, your rubels for pesos, and then your pesos back to dollars and end up with more than you started with: that’s arbitrage. And you will immediately pump as much money as you can into running through that cycle. As a result, the prices will go up and the arbitrage opportunity will close. This is what markets do.

And so if there is a way out there to hire women to do men’s work for 70% (or whatever) of their pay, companies would do that instantly. And the result? Well, the first company would offer women $0.70 on the dollar, but then a competitor would offer them $0.71, and then another competitor would offer them $0.72… and pretty soon no more arbitrage.

So what’s my point?

Trying to find out if there actually is an real wage-gap is very, very hard because measuring “similar work” is difficult. But, if there is ever a whiff of a reliable, objective, solid gender pay gap it will disappear as quickly as it is spotted as the market rushes to exploit the arbitrage opportunity.

Here’s what it all comes down to: if you believe in the gender pay gap, you believe that a bunch of cold-blooded, selfish capitalists are staring at a pile of money left on the table, and not one of them is trying to get their hands on it. This isn’t a completely open-and-shut case, but it’s a very, very strongly suggestive argument that capitalism and wage inequality–of any kind: gender-based, race-based, sexual orientation-based, etc–are fundamentally incompatible in the long run. It doesn’t mean that we shouldn’t have laws against discrimination, because individual business owners might make stupid, bigoted decisions and we might decide not to wait around to let the market fix them. But it does mean that the idea of a real, persistent, ongoing gender pay-gap is like UFOs or Bigfoot or–even rarer than anything else–a free lunch.

It’s just probably not there.