Abstract
Concerns about the institutional impact of immigration, particularly in the United States, are not new. We can trace them back to Benjamin Franklin, Thomas Jefferson, and Alexander Hamilton. More recently, in response to a literature that questions the desirability of current immigration restrictions, Borjas (J Econ Lit 53:961–974, 2015) speculates that immigrants coming from countries with poor institutions could reduce substantially the institutional quality in the United States to a point where it could negate all economic gains associated with immigration in terms of GDP and income. Using the Economic Freedom of North America index since 1980, we find no evidence to corroborate Borjas’s concerns. However, we find mixed evidence that immigration increases minimum wages and union density.
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Notes
Additional examples of studies evaluating the efficiency losses associated with immigration restrictions include Moses and Letnes (2004), Walmsley and Winters (2005), Clemens (2011), Kennan (2013), di Giovanni et al. (2015), and Docquier et al. (2015). Docquier et al. (2015) seem to offer the most conservative estimates of gains resulting from liberalizing migration. First, they find that, contrary to most other studies, there are “high ‘incompressible’ migration costs” and therefore “the number of people in the world who have a desire to migrate is around 400 million” (Docquier et al. 2015, p. 326). Second, when accounting for these “incompressible” migration costs, Docquier et al. (2015, p. 335) show that the efficiency gains fall in the range of 7–18% of world GDP in the medium term, “with a focal effect around 12 percent of the world GDP.” Specifically, they show that two possible effects can significantly impact the efficiency gains from liberalizing migration. They show that “schooling externalities” are the most significant factor that can reduce these efficiency gains. They find that these gains fall to 7% in the presence of “schooling externalities” (p. 331). Yet, migration “network externalities” have the most positive impact on efficiency gains. In the presence of migration “network externalities,” the efficiency gains resulting from “a complete liberalization of migration with network effects” climb to 17.9% of GDP (p. 332).
Borjas’s concerns about the effects of immigration on US institutions are not new. They can be traced as far back as prerevolutionary times. For example, Franklin ([1753] 1904, pp. 408–416), in a letter addressed to Peter Collinson dated May 9, 1753, expresses his strong reservations about the ability of German immigrants to assimilate and his concerns about the likely impact a massive immigration of Germans would have on the country’s ability to preserve its unique institutions. Henry George (1899, p. 409), in a series of articles from 1869 to 1899, expressed his concerns about “the immigration of Chinese in considerable numbers, which began shortly after the discovery of gold in California.” George (1899, p. 411) believed that “Chinese immigration differs from European immigration in being practically non-assimilable.” As a result, he thought that this massive settlement in the United States of nonassimilable immigrants, the Chinese, “would be to the degradation of the superior civilization without any commensurate improvement of the lower” (p. 413). Finally, after Thomas Jefferson proposed, in his first address to Congress on December 7, 1801, to revise the Alien and Sedition Acts of 1798 that required immigrants to establish a residence of 14 years before they could be naturalized and allowed to vote, Alexander Hamilton wrote a series of essays criticizing Jefferson’s proposal using Jefferson’s own Notes on the State of Virginia, in which he “cautioned against ‘great importations of foreigners’—carrying with them, he feared, their monarchist views” (quoted in Population Council 2010, pp. 177–178). Hamilton feared that granting citizenship to foreigners unconditionally would lead to the deterioration of American political institutions. Hamilton concludes in his last essay dated January 12, 1802, that “to admit foreigners indiscriminately to the rights of the citizens, the moment they put foot in our country, as recommended in the message, would be nothing less than to admit the Grecian horse into the citadel of our liberty and sovereignty” (quoted in Population Council 2010, p. 182).
See also Gwartney et al. (2004) and Easton and Walker (1997), who also find strong positive relationships between the level of economic freedom, economic growth, and income. Hall and Lawson (2014, p. 1) survey the economic literature using the economic freedom of the world as an independent variable and find that “over two-thirds of these studies found economic freedom to correspond to a ‘good’ outcome such as faster growth, better living standards, more happiness, etc. Less than 4% of the sample found economic freedom to be associated with a ‘bad’ outcome such as increased income inequality. The balance of evidence is overwhelming that economic freedom corresponds with a wide variety of positive outcomes with almost no negative tradeoffs.”.
Stansel and Patrick Tuszynski (2017) survey the literature using the EFNA as an independent variable and find results similar to Hall and Lawson (2014). They show that out of 155 papers that empirically assess the impact of economic freedom in North American states and provinces, “two-thirds of these found economic freedom to be associated with ‘good’ outcomes (such as faster economic growth), and only one found economic freedom to be associated with a ‘bad’ outcome” (p. 1).
See also Gochenour and Nowrasteh (2014), who show that increased immigration stocks and flows don’t appear to lead to a larger welfare state.
A possible explanation for this absence of such implication is that these authors’ research has more to do with the emergence of specific economic-growth-conducive institutions and their stickiness resulting from migrants bringing institutions conducive to economic growth into countries almost devoid of any of such institutions, leading to higher income today; it has less to do with migrants bringing institutions harmful to economic growth to supplant existing institutions that support economic growth. Even though this literature is related to Borjas’s concerns about the institutional impact of immigration, the authors didn’t find in Borjas’s work any reference to Putterman and Weil’s or Ang’s works as evidence supporting his concerns about the institutional impact of immigration.
See Müeller et al. (2012) for a critique of Algan and Cahuc’s (2010) results showing a much weaker relationship between inherited trust in 1935 and trust in the home country. They also argue that the overall measure of inherited trust in 1935 is to be interpreted with caution because of the limitations of the General Social Survey, particularly with regard to the sample size for most ethnic heritage groups.
Algan and Cahuc (2013) do discuss some policy implications of the research on the impact of trust and its various measures on development and growth. They show that while “a large part of the literature considers trust to be a cultural component hardly malleable. . . recent studies looking at immigrants show that their level of trust converges gradually to the average level of trust in their country of destination” (Algan and Cahuc 2013, p. 39). When discussing ethnic fractionalization and trust, Algan and Cahuc (2013, p. 47) point to Uslaner’s (2012) work, which “challenges Putnam’s thesis and argues that residential segregation, rather than ethnic diversity per se, drives down trust.” Algan and Cahuc (2013, p. 47) argue that “one conclusion is that immigration and urbanization policy should avoid ethnic ghettos to maintain trust.” Algan and Cahuc’s (2013, p. 50) survey also shows that “trust and institutions strongly interact, with causality running in both directions. These findings set new avenues of research to identify the policies that could promote social capital and cooperation, from rule of law and democracy to education policies.” In their discussion of the policy implications, Algan and Cahuc (2013) do not explore the idea that restrictions should be put on immigrants coming from countries where trust is low.
We should note that EFNA goes back to 1981, while our period of analysis starts in 1980. However, because we are dealing with institutional change and institutional change takes time, we believe that the scores that each state received in 1981 are unlikely to be much different from the scores each state would have received in 1980. It’s unlikely as well that the variation in scores between 1980 and 1981 would be the result of the immigration stock in 1980 or the change of that stock in the preceding 12 months.
A potential problem with our equation is that the unobserved determinants of economic freedom, represented in \(\varepsilon_{it}\), likely are correlated with the share of immigrants, leading to an upward-biased estimate of \(\gamma\). To some extent, our first estimation method circumvents this problem. In Sect. 3, we discuss further how we attempt to address this problem.
We should note that in the authors’ conception of the liberal-conservative continuum, they refer to operational ideology [or what Stimson (1999) calls policy mood] rather than symbolic ideology (or self-identification) (Berry et al. 1998, pp. 327–328; Berry et al. 2012, p. 178n2). Such a measure has an advantage over the more standard variable “percent of legislature controlled by democrats” because it mitigates the effects that “important ideological distinctions among parties with the same label, e.g., the difference between Southern and Nonsouthern democrats” have in terms of policy choice (Berry et al. 1998, p. 329).
To eliminate the Nickell bias resulting from a correlation between regressor \(ef_{it - 1}\) and the error term \(\varepsilon_{it}\), we also run baseline fixed effects regressions without the lagged dependent variable since fixed effects control for any time-invariant unobserved heterogeneity between the states, which among other things includes the initial level of economic freedom. The authors thank the referee for suggesting this additional robustness test.
The “shift-share” instrument is often used in the immigration literature. See, for example, Basso and Peri (2015) and Mayda et al. (2015). The main reason for not using an external instrument such as the shift-share instrument is that one important assumption behind such an instrument is that the size of the past settlement of immigrants from an origin country is the sole determinant of migration to a specific state by immigrants from the same origin country. However, as noted by Mayda et al. (2015, p. 6), it’s likely that “the past location of immigrants across destinations might be correlated with past local economic and political conditions. To the extent that these conditions are persistent and hence correlated over time, this would invalidate the exclusion restriction of shift-share instruments à la Card (2001).” In previous iterations of this paper, we did use the shift-share instrument, and Hansen’s J tests confirm that it performed poorly.
Stansel et al. (2017, p. 13) define union density as "the percentage of unionized workers in a state".
In addition, to address possible issues with omitted variables, we perform additional robustness checks by estimating our basic model in first differences to remove all possible state-specific effects. The results of our robustness checks are available in the Appendix.
An objection to using the share of age-eligible-to-vote naturalized citizens in the age-eligible-to-vote population as a variable of interest to tease out the direct impact of immigrants on economic freedom is that not all naturalized US citizen immigrants who are eligible to vote are registered to vote and actually vote. Therefore, using the share of naturalized US citizens is a poor proxy, but we cannot ignore that US citizen immigrants might influence the political process through lobbying as well. In addition, not all native-born individuals who are eligible to vote are registered to vote and actually vote. To the authors’ knowledge, no state-level data are available on the share of naturalized immigrants eligible to vote who actually vote; data exist only on the share of US citizens eligible to vote who actually vote. However, data are available at the US national level looking at the share of naturalized US citizens and native-born citizens eligible to vote who are registered and who voted in past presidential or congressional elections. See Crissey and File (2012), who examine the voting behavior of naturalized citizens from 1996 to 2010 using the US Census Bureau’s Current Population Survey’s November Voting Supplements. For example, data show that in 2010, about 54.2% of naturalized US citizens eligible to register to vote were registered voters and approximately 37% of all naturalized US citizens eligible to vote actually voted. Data also show that naturalized US citizens register and vote at a lower rate than native citizens do. In 2010, 66.1% of eligible native citizens were registered voters, and 46.3% of eligible native citizens voted (Crissey and File 2012, p. 14, Table 1).
Only in our fixed effects regression (3) including the lagged dependent variable is the effect negative and statistically significant at the 10% level. The results in our fixed effects regression (1) without the lagged dependent variable show that, while the relationship between the share of naturalized citizen immigrant population and the overall economic freedom score is negative, it is not statistically significant. Therefore, some of the results in our fixed effects regression (3) may be driven by the Nickell bias.
As we did when we examined the relationship between immigration and economic freedom, in an effort to address possible issues with omitted variables when examining the relationship between naturalized US citizens and economic freedom, we performed additional robustness checks by estimating our model in first differences to remove all possible state-specific effects. The results of our robustness checks are available in the Appendix.
As we explain above, weak statistical significance appears only in the fixed effects regressions, but no statistical significance is found in the system GMM regressions.
Much of the empirical literature on wage effects finds that new waves of immigrants are close substitutes for earlier waves of immigrants and have a significant negative impact on the wages of those previous immigrants (National Research Council 1997, p. 223). See also Card (2001) and Ottaviano and Peri (2008).
Mayda et al. (2015) address this issue when looking at the impact of US immigration on the share of votes that Republicans and Democrats receive during the elections. They show that immigrants are more likely to vote for Democrats, but at the same time native-born individuals are more likely to vote for Republicans, if they believe that immigrants are voting for Democrats.
Stansel et al. (2017, p. 69) define the general consumption expenditure as “total expenditures minus transfers to persons, transfers to businesses, transfers to other governments, and interest on public debt.” Stansel et al. (2017, p. 70) define insurance and retirement payments as a percentage of income as “payments by employment insurance, workers compensation, and various pension plans.”.
Stansel et al. (2017, p. 73) adopt the following definition: “Property and Other Tax revenue consists of total tax revenue minus income and sales tax revenues.”.
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Acknowledgements
We thank the participants from the Free Market Institute at Texas Tech University research workshop for their comments. We owe a great deal of gratitude to Andrew T. Young, Jamie Bologna, and Audrey Redford for their comments and assistance in helping us improve on previous drafts of this paper. We also thank the anonymous referees, whose comments and suggestions have helped us to improve our article. The usual caveats apply. Padilla gratefully acknowledges financial support from the John Templeton Foundation.
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Appendix
Appendix
1.1 Robustness checks
This appendix discusses additional robustness checks to address possible issues with omitted variables when attempting to measure the impact of immigration on economic freedom.
1.1.1 Immigration and economic freedom: first differences
To address possible issues with omitted variables when evaluating the impact of immigration and economic freedom, we follow Spilimbergo’s (2009, pp. 535–536) empirical strategy and estimate our basic model in first differences to remove all possible state-specific effects in two ways. We control for state fixed effects by taking first differences; we control for state-specific trends with fixed effects in differences. To address simultaneity bias, our model in first differences enters all explanatory variables with lags of 10 years. In other words, we observe the change in economic freedom from 2000 to 2010, and our explanatory variables are for the period 1990–2000. Table 16 presents the results of our regressions on the impact of the change in the share of immigrants. Only in the subarea of taxes does an increase in the share of foreign-born citizens have a statistically significant negative impact on the change in economic freedom at the 10% level. It likewise is negative for government spending, but the results are not statistically significant. And while it is not statistically significant, the impact is positive on labor market freedom.
1.1.2 Naturalized citizens and economic freedom: first differences
We again follow Spilimbergo’s (2009, pp. 535–536) empirical strategy to mitigate possible issues of omitted variables when assessing the impact of naturalized citizens on economic freedom. We estimate our basic model in first differences to remove all possible state-specific effects in two ways. First, we control for state fixed effects by taking first differences. Second, we control for state-specific trends with fixed effects in differences. To address the possibility of simultaneity bias, our model in first differences enters all explanatory variables lagged 10 years; that is, we look at the effect of the change in the share of naturalized US citizen immigrants on the change in economic freedom or one of its dimensions in the following decade.
Table 17 shows that the change in the share of naturalized US citizens doesn’t have a statistically significant impact on subsequent changes in economic freedom overall. But our fixed-effects regressions show that an increase in the share of naturalized US citizens has a negative impact on future changes in economic freedom for government spending (Area 1) and taxes (Area 2), while it has a positive impact on future changes in economic freedom for labor market freedom (Area 3). These results are statistically significant at the 5% level. Because each area of economic freedom has several components, we investigate these results further.
Table 18 displays the results of our regressions investigating the relationship between the change in the share of naturalized US citizens and government spending and its components. Our results show that an increase in the population share of naturalized US citizens is negatively correlated with economic freedom in the areas of general consumption expenditures by government as a percentage of income and for insurance and retirement payments as a percentage of income.Footnote 22 Those results are statistically significant at the 1 and 5% levels, respectively. However, while an increase in the share of naturalized US citizens is negatively correlated with transfers and subsidies, it is not statistically significant.
Table 19 shows the negative correlation between the change in the share of naturalized US citizens and taxes. Our results show that the negative correlation comes from revenues from property and other taxes.Footnote 23 These results are statistically significant at the 1% level: a standard deviation increase in the change of the share of naturalized US citizens leads to a reduction in change in economic freedom for property and other taxes by about half of a standard deviation.
We investigate the positive correlation between the share of naturalized US citizen immigrants and labor market freedom and present the results in Table 20. We run regressions with subcategories of the labor market freedom score as our dependent variables: minimum wage legislation, government employment as a percentage of total state employment, and union density. Our pooled and fixed-effects regressions show that the positive impact of an increase in the share of naturalized US citizen immigrants on future changes in labor market freedom comes from minimum wage legislation. A standard deviation increase in the change of the share of naturalized immigrants produces an increase of about 0.16 of a standard deviation of the change in economic freedom score for minimum wage legislation. On other dimensions of labor market freedom, namely government employment and union density, the results are not statistically significant.
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Padilla, A., Cachanosky, N. The Grecian horse: does immigration lead to the deterioration of American institutions?. Public Choice 174, 351–405 (2018). https://doi.org/10.1007/s11127-018-0509-5
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DOI: https://doi.org/10.1007/s11127-018-0509-5