Are Immigrants a Threat?

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From a new working paper:

The empirical evidence comes down decidedly on the side of immigrants being less likely to commit crimes. A large body of empirical research concludes that immigrants are less likely than similar US natives to commit crimes, and the incarceration rate is lower among the foreign-born than among the native-born (see, for example, Butcher and Piehl 1998a, 1998b, 2007; Hagan and Palloni 1999; Rumbaut et al. 2006). Among men ages 18 to 39—prime ages for engaging in criminal behavior—the incarceration rate among immigrants is one-fourth the rate among US natives (National Academies of Sciences, Engineering, and Medicine 2015).

…There is some evidence that the lower propensity of immigrants to commit crimes does not carry over to immigrants’ children. The US-born children of immigrants—often called the “second generation”— appear to engage in criminal behavior at rates similar to other US natives (Bersani 2014a, 2014b). This 4 “downward assimilation” may be surprising, since the second generation tends to considerably outperform their immigrant parents in terms of education and labor-market outcomes and therefore might be expected to have even lower rates of criminal behavior (National Academies of Sciences, Engineering, and Medicine 2015). Instead, immigrants’ children are much like their peers in terms of criminal behavior. This evidence mirrors findings that the immigrant advantage over US natives in terms of health tends to not carry over to the second generation (e.g., Acevedo-Garcia et al. 2010).

Although immigrants are less likely to commit crimes than similar US natives, they are disproportionately male and relatively young—characteristics associated with crime. Does this difference in demographic composition mean that the average immigrant is more likely than the average US native to commit crimes? Studies comparing immigrants’ and US natives’ criminal behavior and incarceration rates tend to focus on relatively young men, leaving the broader question unanswered. However, indirect evidence is available from looking at the relationship between immigration and crime rates. If the average immigrant is more likely than the average US native to commit crimes, areas with more immigrants should have higher crime rates than areas with fewer immigrants. The evidence here is clear: crime rates are no higher, and are perhaps lower, in areas with more immigrants. An extensive body of research examines how changes in the foreign-born share of the population affect changes in crime rates. Focusing on changes allows researchers to control for unobservable differences across areas. The finding of either a null relationship or a small negative relationship holds in raw comparisons, in studies that control for other variables that could underlie the results from raw comparisons, and in studies that use instrumental variables to identify immigrant inflows that are independent of factors that also affect crime rates, such as underlying economic conditions (see, for example, Butcher and Piehl 1998b; Lee, Martinez, and Rosenfeld 2001; Reid et al. 2005; Graif and Sampson 2009; Ousey and Kubrin 2009; Stowell et al. 2009; Wadsworth 2010; MacDonald, Hipp, and Gill 2013; Adelman et al. 2017). The lack of a positive relationship is generally robust to using different measures of immigration, looking at different types of crimes, and examining different geographic levels.2 Further, the lack of a positive relationship suggests that immigration does not cause US natives to commit more crimes. This might occur if immigration worsens natives’ labor market opportunities, for example.

The few studies that examine crime among unauthorized immigrants have findings that are consistent with the broader pattern among immigrants—namely, unauthorized immigrants are less likely to commit crimes than similar US natives (apart from immigration-related offenses).4 Likewise, studies that examine the link between the estimated number of unauthorized immigrants as a share of an area’s population and crime rates in that area typically find evidence of null or negative effects (pg. 3-5).

Comparatively, the effects of border control on crime is mixed. The authors conclude,

A crucial fact seems to have been forgotten by some policy makers as they have ramped up immigration enforcement over the last two decades: immigrants are less likely to commit crimes than similar US natives. This is not to say that immigrants never commit crimes. But the evidence is clear that they are not more likely to do so than US natives. The comprehensive 2015 National Academies of Sciences, Engineering, and Medicine report on immigration integration concludes that the finding that immigrants are less likely to commit crimes than US natives “seems to apply to all racial and ethnic groups of immigrants, as well as applying over different decades and across varying historical contexts” (328). Unauthorized immigrants may be slightly more likely than legal immigrants to commit crimes, but they are still less likely than their US-born peers to do so. Further, areas with more immigrants tend to have lower rates of violent and property crimes. In the face of such evidence, policies aimed at reducing the number of immigrants, including unauthorized immigrants, seem unlikely to reduce crime and increase public safety (pg. 11).

Does Female Autonomy Lead to Long-Term Economic Growth?

From a new study:

A number of development economists have found higher gender inequality to be associated with slower development. Amartya Sen (1990) estimated a large number of ‘missing women’, which resulted in skewed sex ratios, and argued that this has been one of history’s crucial development hurdles. Stephan Klasen, with various co-authors, used macroeconomic regressions to show that gender inequality has usually been associated with lower GDP growth in developing countries during the last few decades (Klasen and Lamanna 2009, Gruen and Klasen 2008). This resulted in development policies targeted specifically at women. In 2005, for example, UN Secretary General Kofi Annan stated that gender equality is a prerequisite for eliminating poverty, reducing infant mortality, and reaching universal education (UN 2005). In recent periods, however, a number of doubts have been made public by development economists. Esther Duflo (2012) suggested that there is no automatic effect of gender equality on poverty reduction, citing a number of studies. The causal direction from poverty to gender inequality might be at least as strong as in the opposite direction, according to this view.

…In a new study, we directly assess the growth effects of female autonomy in a dynamic historical context (Baten and de Pleijt 2018). Given the obviously crucial role of endogeneity issues in this debate, we carefully consider the causal nature of the relationship. More specifically, we exploit relatively exogenous variation of (migration-adjusted) lactose tolerance and pasture suitability as instrumental variables for female autonomy. The idea is that high lactose tolerance increased the demand for dairy farming, whereas similarly, a high share of land suitable for pasture farming allowed more supply. In dairy farming, women traditionally had a strong role, which allowed them to participate substantially in income generation during the late medieval and early modern period (Voigtländer and Voth 2013). In contrast, female participation was limited in grain farming, as it requires substantial upper-body strength (Alesina et al. 2013). Hence, the genetic factor of lactose tolerance and pasture suitability influences long-term differences in gender-specific agricultural specialisation. In instrumental variable regressions, we show that the relationship between female autonomy and human capital is likely to be causal (and also address additional econometric issues, such as the exclusion restriction, using Oster ratios, etc.). 

Age-heaping-based numeracy estimates reflect a crucial component of human capital formation. Recent evidence documents that numerical skills are the ones that matter most for economic growth. Hanushek and Woessmann (2012) argued that maths and science skills were crucial for economic success in the 20th century. They observed that these kinds of skills outperform simple measures of school enrolment in explaining economic development. Hence, in the new study we focus on math-related indicators of basic numeracy. We use two different datasets: first, we use a panel dataset of European countries from 1500 to 1850, which covers a long time horizon; second we study 268 regions in Europe, stretching from the Ural mountains in the east to Spain in the southwest and the UK in the northwest. 

Average age at marriage is used as a proxy for female autonomy. Low age at marriage is usually associated with low female autonomy. Age at marriage is highly correlated with other indicators of female autonomy, such as the share of female household heads or the share of couples in which the wife was older than the husband. Age at marriage is particularly interesting because of the microeconomic channel that runs from labour experience to an increase in women’s human capital. After marriage, women typically dropped out of the labour market, and switched to work in the household economy (Diebolt and Perrin 2013). Consequently, after early marriage women provided less teaching and self-learning encouragement to their children, including numeracy and other skills. Early-married women sometimes also valued these skills less because they did not ‘belong to their sphere’, i.e. these skills did not allow identification (Baten et al. 2017).

What do they find?

Figure 3 depicts a strong and positive relationship between average age at marriage and numeracy for the two half centuries following 1700 and 1800. Most countries are close to the regression line. Denmark, the Netherlands, Germany, Sweden, and other countries had high values of female autonomy and numeracy – interestingly, many of the countries of the Second Industrial Revolution of the late 19th century, rather than the UK, the first industrial nation. In contrast, Russia, Poland, Slovakia, Italy, Spain, and Ireland had low values in both periods.

In our regression analyses, we include a large number of control variables, such as religion, serfdom, international trade, and political institutions. We find that the relationship between female autonomy and numeracy is very robust.

We also study the relationship between female autonomy and human capital formation at the regional level in the 19th century. Numeracy and age at marriage (after controlling for country-fixed effects and other control variables) yield an upward sloping regression line (Figure 4). 

…In sum, the empirical results suggest that economies with more female autonomy became (or remained) superstars in economic development. The female part of the population needed to contribute to overall human capital formation and prosperity, otherwise the competition with other economies was lost. Institutions that excluded women from developing human capital – such as being married early, and hence, often dropping out of independent, skill-demanding economic activities – prevented many economies from being successful in human history.


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

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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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

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).

The Economic Consequences of Tariffs

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?

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?

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?

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.