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- Appendix Table 11: Growth Models and Population Change Difference in Patenting per IHS of Patenting 100,000 People Post 1980 Post 1975 (1) (2) (3) Immigrationt d 0.115*** 0.598*** (0.040) (0.105) sq(Immigrationt d) -0.001*** (0.000) IHS(Immigrationt d) 1.723*** (0.111) N 18,846 18,846 21,987 First Stage F-Stat (first coefficient) 911 95 94 First Stage F-Stat (second coefficient) 11231.347 AR Wald F-Test p-value 0.010 0.000 0.000 Geography FE State State State Time FE Yes Yes Yes Notes: This table reports the results of our IV specification, described in equation (1), for changes in patenting per 100,000 people (columns 1 and 2) and the inverse hyperbolic sine (IHS) of patenting (column 3), where patenting is defined as the number of patents filed in the five-year period. Column 1 repeats our main specification while column 2 adds as a second endogenous variable the square of non-European immigration (1,000s) to d in t.
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- Construction of ancestry stock For the years 1980, 1990, 2000, and 2010, we take from the respective census all individuals in a US county or county group that list as their primary ancestry a foreign nationality or area.
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- Construction of education data for migrants For the five-year migration periods from 1975 to 2010, whose construction is previously described, we also identify the total number of years of education for each set of immigrants. Specifically, we take the set of individuals that make up each five-year immigration flow and limit to those individuals who are aged 25 years or older at the time of each respective census.
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- Construction of pre-1880 immigration stock From the 1880 census, we count all individuals who were born in a foreign origin country o and reside in a historic US geography d, regardless of the date of arrival to the US. We then add to this count all individuals residing in d who were born in the US but whose parents were born in origin country o (if an individualâs parents were born in different countries, the individual is assigned a count of one half for each parentâs origin country o). We then transform the given birthplace to 1990 foreign countries and the pre-1880 US geography to 1990 US counties following the transition method outlined in Burchardi et al. (2019).
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- Finally, columns 6-9 display the first-stage regressions for column 4 of Table 8. For each specification, we report the first-stage F -statistic for the IV estimation in Table 8, utilizing the F -statistic described in Angrist and Pischke (2009, p. 217-218) in the case of multiple endogenous variables. Standard errors are clustered by state for all specifications, and *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.
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- For each 1990 US county d, we then sum the number of years each individual is reported to have over all immigrants in this set, assigning the midpoint when a range of years of education is provided instead of an exact number of years. We then generate the average years of education for immigrants to county d in each period t and demean these values. Finally, we take the demeaned average years of education and multiply by the count of immigrants aged 25 or older to generate the (demeaned) total years of education. We construct this variable for total years of education as well as for years of college education.
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- Online Appendix âImmigration, Innovation, and Growthâ Konrad B. Burchardi Thomas Chaney Tarek A. Hassan Lisa Tarquinio Stephen J. Terry A Data Appendix A.1 Details on the construction of migration and ethnicity data To construct county-level data on migration, ancestry, and ethnicity, we follow the approach of Burchardi et al. (2019). We utilize data from each available IPUMS wave from 1880 to 2010.
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- Percent Output per Person 1970 1980 1990 2000 2010 Year -10 -5 0 5
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- Percent Patenting per Person 1970 1980 1990 2000 2010 Year -10 -5 0 5
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Peters, M. and C. Walsh (2021): âPopulation Growth and Firm Dynamics,â Working Paper 29424, National Bureau of Economic Research.
- Repeating the regression in column 2 of the second panel for the 10-year difference in average annual wages ($100s, at 2010 prices) of native non-movers (US-born working individuals who have not moved outside of the county within the past 5 years) on 10-year migration and corresponding education results in coefficients of 0.246 (0.057) and 0.142 (0.040) on immigration and average years of education times immigration, respectively.
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- Specifically, we use the 10% sample of the 1880 Census, the 5% sample of the 1900 Census, the 1% sample of the 1910 Census, the 1% sample of the 1920 Census, the 5% sample of the 1930 Census, 1% Form 1 Metro sample of the 1970 Census, 5% State sample of the 1980 Census, 5% State sample of the 1990 Census, 5% sample of the 2000 Census, and the American Community Service 5-Year sample of the 2010 Census. The following section summarizes this approach, highlighting any difference in data construction made in this paper.
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Stuen, E. T., A. M. Mobarak, and K. E. Maskus (2012): âSkilled Immigration and Innovation: Evidence from Enrolment Fluctuations in US Doctoral Programmes,â The Economic Journal, 122, 1143â1176.
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- Table 3: County-Level Panel Regressions of Difference in Patenting on Immigration 5-Year Difference in Patenting per 100,000 People Post 1980 (1) (2) (3) Panel A: OLS Immigrationt d 0.196** 0.186* 0.342 (0.097) (0.093) (0.225) N 18,846 18,840 18,846 R2 0.023 0.044 0.155 Panel B: IV Immigrationt d 0.115*** 0.111*** 0.116*** (0.040) (0.041) (0.026) N 18,846 18,840 18,846 First Stage F-Stat 911 807 85 AR Wald F-Test p-value 0.010 0.016 0.000 Panel C: First Stage Immigrationt d
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- Terry, S. J., T. M. Whited, and A. Zakolyukina (2020): âInformation versus Investment, â Working paper.
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- The dashed dark blue line labelled High γ uses γ = 0.95. The dash-dot light blue line labelled Low γ uses γ = 0.75. The responses are in percentage point deviations from the balanced growth path. 1970 1980 1990 2000 2010 Year -10 -5 0 5
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- To simulate the model, we draw immigration supply shocks νot, regional productivity shocks d,t, and bilateral immigration cost shocks Ïo,d,t for a large number of periods T = 1000, O = 10 origins, and D = 9 destination regions. Given a parametrization of the model, the exogenous shock draws together with the nonlinear policy functions obtained in our solution step allow for unconditional simulation of the model. This unconditionally simulated data can be processed to produce a range of moments for structural estimation of the model, which is detailed below.
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- To understand the rough quantitative implications of a shift in foreign-born population growth of the magnitude observed in the post-1970 US data, we compute the counterfactual evolution over time of macroeconomic aggregates in our model without the increased foreignborn share of population growth. In particular, we impose a string of negative exogenous shocks to immigration supply, symmetric across origins, which reduce population growth by 16% relative to the BGP growth rate. The resulting cumulative declines in per-capita aggregate output, patenting, and wages, together with the cumulative decline in the aggregate labor force, resulting from this experiment are plotted in the main text.
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- US Census Bureau (2014): âThe âSecond Great Waveâ of Immigration: Growth of the Foreign-Born Population Since 1970,â Web posting.
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- Walsh, C. (2019): âFirm Creation and Local Growth,â Working paper.
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- We also can produce impulse response functions following the simulation approach to computing nonlinear generalized impulsed response functions from Koop et al. (1996). This involves simulating pairs of economies with and without a particular shock present, and comparing subsequent dynamics over a large number of simulations. We implement all of these numerical model steps, i.e., solution, unconditional simulation, and impulse response calculations, using Dynare within a MATLAB environment. Given the smooth nature of our equilibrium conditions, the well behaved non-stochastic BGP, and the large number of equilibrium conditions, the Dynare package is a natural choice for numerical analysis in this context.
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- We also utilize information on education from the census to construct county-level demographic controls for the share of the countyâs population that has a specified level of education in a baseline year, 1970. Using data from the 1970 census, we calculate the share of all individuals, regardless of birthplace, residing in a historic US county d who report having at least a Grade 12 education (share of high-school educated) and those who report having at least four years of college education (share of college educated). These values are then transformed from 1970 US counties to 1990 US counties, again using the transition matrices described by Burchardi et al. (2019).
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- We compute the Kelly Skewness of employment growth rates across 4-digit sectors, and then transition this measure from 2010 to 1990 US counties.
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- We then estimate the ancestry stock in each midyear (1975, 1985, 1995, and 2005) by taking the individuals identified in each census year as belonging to a given ancestry and removing all individuals who either were born or migrated to the US after the midyear. Ideally, we would also remove all individuals who moved to the county after the midyear, but data is not available for all census years; thus, for consistency, we do not remove these individuals. Again, we follow Burchardi et al. (2019) in transforming ancestries to 1990 countries and US geographies to 1990 US counties. As with the data on foreign birthplaces, some ancestries do not correspond directly to a modern (1990) country; again, we follow the weighting scheme outlined in Burchardi et al. (2019) for transitioning stated ancestries to 1990 foreign countries.
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Young, A. (1998): âGrowth without scale effects,â Journal of Political Economy, 106, 41â63.