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3. Did Timing Matter? Life Cycle Differences in Effects of Exposure to the Great Recession. (2019). Rinz, Kevin.
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1. (A.4) E[y] = exp( + σ2 /2), (A.5) where p1 is the share of establishments (with at least 1000 employees) with 1000-1499 employees, 48 When including the 526 counties with at least 5 percent of 1976 employment in mining, log median family income evolves similarly from 1950-1970, but declines by less after 1980. Because relatively few people live in high mining counties, my estimates of long-run effects on children more closely reflect the persistence seen in Figure A.2, which excludes high mining counties.
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2. 1 + y2), instead of ln(y) throughout (Burbridge, Magee and Robb, 1988). The log and inverse hyperbolic sine yield very similar coefficients in linear regression models when y is sufficiently large. 57 I downloaded these data from the NBER website, with thanks to Michael Greenstone for making them available.
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3. “The Local Economic and Welfare Consequences of Hydraulic Fracturing.” Bartik, Timothy J. 1991. Who Benefits from State and Local Economic Development Policies? Kalamazoo, Michigan: W.E. Upjohn Institute for Employment Research.

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8. All monetary variables are in 2014 dollars.
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9. all strings that are matched in the probabilistic step, disagree with the match found in the Isen, Rossin-Slater and Walker (2017) algorithm but were not hand checked by them, and have at least 50 occurrences in the NUMIDENT file for the 1950-1985 cohorts.
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10. Almond, Douglas, and Janet Currie. 2011. “Human Capital Development before Age Five.” In Handbook of Labor Economics. Vol. 4B, ed. David Card and Orley Ashenfelter, 1315–1486. Elsevier.
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11. Almond, Douglas, Janet Currie, and Valentina Duque. 2018. “Childhood Circumstances and Adult Outcomes: Act II.” Journal of Economic Literature, 56(4): 1360–1446.
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12. Altonji, Joseph G., Lisa B. Kahn, and Jamin D. Speer. 2016. “Cashier or Consultant? Entry Labor Market Conditions, Field of Study, and Career Success.” Journal of Labor Economics, 34(1): S361–S401.

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14. Appendix Figure A.17 shows that there is no evidence of a relationship between the evolution of infant mortality from 1950-1979 and the severity of the 1980-1982 recession. The point estimates are generally small in magnitude and indistinguishable from zero (p = 0.91). When including counties with a high mining employment share, there is also no evidence of a significant relationship (p = 0.71). This rules out changes in infant health as an explanation for the long-run effects of the 1980-1982 recession.
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15. Appendix Table A.1 summarizes match rates for individuals observed in the 2000 Census and 2001-2013 ACS. I limit the sample to individuals who were born from 1950-1980 and were age 25-64 at the time of the survey. I also limit the sample to individuals with non-allocated values of gender, age, race, and state of birth, and who report being born in the U.S on the census survey. 95.9 percent of the sample has a non-missing protected identification key (PIK), which is the anonymous identifier used to link Census/ACS and SSA data. Of these individuals, 99.6 percent have a PIK that is not duplicated within a survey year. We identify a unique birth county for 93.6 percent of the individuals with non-duplicated PIKs. Ultimately, these restrictions leave 89.4 percent of the initial sample. The majority of matches, 80.4 percent, are exact matches, while 11.0 percent are duplicates, 5.1 percent are matched probabilistically, and 3.5 percent are hand matches.
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16. Appendix Table A.13 presents similar results for labor market outcomes. The results are again quite robust, with the 1960 covariates having the largest impact. The robustness of the labor market outcomes to controls for subsequent recession severity is particularly notable, as later recessions could have substantially impacted income and wages; the insensitivity to these controls is consistent with the small correlations shown in Appendix Table A.10.
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17. Appendix Table A.14 shows that results are similar when replacing the 1979-1982 decrease in log earnings per capita with other measures of recession severity: the decrease in log earnings, the decrease in log income per capita, the decrease in log employment, and the decrease in earnings per capita. The magnitude of estimates are similar when measuring the change in earnings per capita in logs or levels.55 H Effects on Local Government Expenditures and Revenues This section examines the effects of the 1980-1982 recession on local government expenditures and revenues, which could affect human capital development in childhood. I find that expenditures per capita fell starting in 1992 in counties that experienced a more severe recession, but there is little evidence of a decrease before then, likely due to higher federal transfers. The decline in expenditures is driven by spending on welfare and health, and not education.
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18. Appendix Table A.16 provides suggestive evidence that intergovernmental transfers initially offset the decrease in tax revenues after the recession. As seen in column 1, there is a significant decrease in general direct revenues from 1992-forward.60 Underlying this is an immediate decrease in tax revenue (column 2), possibly offset by an increase in intergovernmental transfers in 1982 and 1987 (column 4). Column 5 shows that property taxes, which account for 33 percent of general direct revenue and 89 percent of tax revenue, drive the decrease in total tax revenues. Columns 6-8 suggest that offsetting intergovernmental transfers came from federal and local, as opposed to state, governments.
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22. B Recession Severity and the Evolution of Median Family Income from 1950-2010 The BEA earnings data used in Figure 2 are only available starting in 1969. In this section, I examine the relationship between the evolution of median family income from 1950-2010 and the severity of the recession predicted by pre-existing industrial specialization.
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32. Both instrumental variables use county-by-industry employment shares from 1976. See notes to Table 2.
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39. c Na,c(R79−82 c − RCF c )π̂a, where Na,c is the number of births of individuals residing in county c net of infant mortality, R79−82 c is the observed decrease in log real earnings per capita from 1979-1982 in county c, RCF c is the counterfactual decrease in log real earnings per capita, and π̂a is the difference-in-differences estimate. In counterfactual 1, I set RCF c = 0 and in counterfactual 2, RCF c = −0.055, which corresponds to the average annual growth in earnings per capita from 1969-1979 of 1.84 percent. Column 1 reports the total number of births for each age group, net of infant mortality ( P c Na,c). Columns 2 and 5 use difference-in-differences estimates from column 4 of Table 2. Columns 3 and 6 use estimates from column 2 of Table 3. Columns 4 and 7 use estimates from column 5 of Table 3. Numbers are rounded.
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54. Column 7 controls for the county-level severity of post-1982 recessions. Column 8 returns to the baseline specification, but replaces birth state by age in 1979 fixed effects with birth region by age in 1979 fixed effects. Column 9 adds to the baseline specification controls for the log number of births in a county; I include this specification because Appendix Figure A.18 displays an increase in birth rates from 1960-1972 followed by a decline, and this fluctuation in birth rates could threaten my empirical strategy if different types of children were born at different times.
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55. Consequently, p̂1 = 1947/4102 ≈ 0.475, p̂2 ≈ 0.293, p̂3 ≈ 0.165 and Ê[y] ≈ 2302. The GMM estimates are ̂ = 7.506 and σ̂ = 0.686. Standard facts about the log-normal distribution imply that the imputed means for the four establishment size groups are 1247, 1952, 3414, and 7055.49 D Addressing Measurement Error in Recession Exposure This section describes measurement error that arises because some individuals’ county of residence in 1979, which is the ideal variable for measuring recession exposure, differs from their county of birth, which is all I observe. I show that this measurement error attenuates estimates of πa in equation (1), and I quantify the size of this attenuation.
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56. Consequently, the estimated effects of the recession will be attenuated if λa ∈ (0, 1), and I can eliminate this attenuation bias with an estimate of λa.51 To quantify the extent of attenuation, the ideal data set is the 1980 Census linked to the NUMIDENT.
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57. convert data on earnings by place of work to earnings by place of residence. To examine this possibility, Panel C shows OLS estimates where the key explanatory variable is the change in log employment from 1979-1982, which is subject to less measurement error (both because employment is easier to measure than earnings and because there is no place of residence adjustment). Consistent with an important role for measurement error in Panel B, there is less attenuation in the OLS estimates in Panel C. Appendix Table A.5 presents reduced-form and OLS estimates for labor market outcomes. The patterns are extremely similar.
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66. F Additional Support for the Empirical Strategy from Birth Certificate Data To provide evidence on the validity of my empirical strategy, I examine whether the pre-recession evolution of infant mortality, parental characteristics, and infant health are correlated with the severity of the 1980-1982 recession. I do not find any evidence that my estimates of the long-run effects of the recession are driven by pre-recession trends in infant health or parental characteristics.
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69. For individuals who were a years old in 1979, I calculate these as P
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82. Hoynes, Hilary, Douglas L. Miller, and Jessamyn Schaller. 2012. “Who Suffers during Recessions ?” Journal of Economic Perspectives, 26(3): 27–48.

83. I estimate the model by 2SLS, using the predicted log employment change from 1979-1982 in all industries as the IV. To remove the countercyclical boom-bust cycle experienced by the mining sector, I exclude the 526 counties with at least 5 percent of 1976 employment in the mining sector. I additionally control for log population and the share of the population age 0-4, 5-19, and 20-64, which could affect the amount and composition of expenditures and revenues.
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84. I examine the evolution of the infant mortality rate (deaths per 1,000 births) by estimating 2SLS regressions similar to equation (A.1). The regression includes county of residence and state of residence by birth year fixed effects, the share of births that are nonwhite, and the pre-recession county covariates. My sample contains individuals born from 1950-1979. I normalize the interaction between the severity of the recession and birth year to equal 0 for individuals born in 1950, and I aggregate the remaining interactions into three-year bins. I use the predicted log employment change as an instrumental variable and initially exclude the 526 counties with at least 5 percent of 1976 employment in the mining sector.
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85. I impute employment at the county by industry level using establishment counts and nationwide information on employment by establishment size. For establishments with fewer than 1000 employees, I impute employment as the number of establishments times average 1977 employment in the establishment size group, where the average comes from nationwide data across all industries.
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86. I use equations (A.2)-(A.5) to estimate (, σ) with GMM, using the identity matrix as the weighting matrix. Using 1977 data across all industries in the U.S., there are 1947 establishments with 1000-1499 employees, 1202 with 1500-2499 employees, 678 with 2500-4999 employees, and 275 with 5000 or more employees. Total employment among these establishments is 9,442,953.
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87. Isen, Adam, Maya Rossin-Slater, and W. Reed Walker. 2017. “Every Breath You Take - Every Dollar You’ll Make: The Long-Term Consequences of the Clean Air Act of 1970.” Journal of Political Economy, 125(3): 848–909.

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90. k pa,c,kR79−82 k , where pa,c,k is the share of individuals age a in 1979 who were born in county c and resided in county k in 1979. Estimates of pa,c,k would allow me to construct R79−82∗ a,c directly. I do not pursue this approach because the measurement error correction for each cohort depends on nearly 9.5 million parameters (≈ 30762 ), and adequate data on county-to-county migration flows are not available.
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99. Migration rate 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 Age Born 1968-1979 Born 1980-1991 Born 1992-2003 Born 2004-2013 Notes: Figure displays the share of individuals living outside of their birth county for different birth cohorts. In the PSID, birth county is identified based on individuals’ county of residence when first observed. I limit the PSID sample to individuals first observed before age 3.
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102. Nationwide CBP data report total employment among establishments with at least 1000 employees, but not by establishment size group. To impute employment for these large establishments, I assume that employment follows a log normal distribution, with mean and standard deviation σ, and estimate (, σ) using the generalized method of moments (GMM), as in Holmes and Stevens (2002). I estimate (, σ) using the following four moments: p1 = Φ ln(1499) − σ
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103. Oreopoulos, Philip, Till von Wachter, and Andrew Heisz. 2012. “The Short- and Long-Term Career Effects of Graduating in a Recession.” American Economic Journal: Applied Economics, 4(1): 1–29.
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104. Our algorithm yields four broad categories of matches. Each step proceeds sequentially and only applies to NUMIDENT strings not previously matched. In a preliminary processing step, we correct for common acronyms and abbreviations by hand for any string that occurs more than 50 times in the NUMIDENT data for the 1950-1985 birth cohorts. First, we obtain exact matches for correctly spelled place names that can be uniquely identified in a birth state with 12 characters.
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105. Overall, these specifications yield quite similar results, especially for high school/GED and four-year college degree attainment. For other outcomes, the most important covariates are the 1960 variables added in column 4. These covariates lead to larger negative impacts on college attendance and attenuate the positive impacts on two-year degree attainment, especially for individuals age 0-10 in 1979. In turn, the attenuated impact on two-year degree attainment leads to more negative impacts on any college degree attainment and years of education. Results are quite similar from the other specifications in the table.
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106. p2 is the share with 1500-2499 employees, p3 is the share with 2500-4999 employees, Φ() is the standard normal CDF, and E[y] is average employment among establishments with at least 1000 employees.
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107. Page, Marianne, Ann Huff Stevens, and Jason M. Lindo. 2007. “Parental Income Shocks and Outcomes of Disadvantaged Youth in the United States.” In An Economic Perspective on the Problems of Disadvantaged Youth. ed. Jonathan Gruber, 213–235. Chicago, IL: University of Chicago Press.

108. Proctor, Bernadette D., Jessica L. Semega, and Melissa A. Kollar. 2016. “Income and Poverty in the United States: 2015.” U.S. Census Bureau, Current Population Reports, P60-256.
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109. Rao, Neel. 2016. “The Impact of Macroeconomic Conditions in Childhood on Adult Labor Market Outcomes.” Economic Inquiry, 54(3): 1425–1444.

110. Rege, Mari, Kjetil Telle, and Mark Votruba. 2011. “Parental Job Loss and Children’s School Performance.” Review of Economic Studies, 78(4): 1462–1489.

111. Relative four-year college degree attainment (age 45 = 1) 20 25 30 35 40 45 Age (b) Share with a Four-Year College Degree, Relative to Age 45 Attainment Notes: Panel A displays the share of individuals with a four-year college degree, for a constant sample of individuals born in the U.S. from 1957-1964. Panel B displays the share of attainment divided by attainment at age 45. I use custom weights from the NLS to account for the fact that these tabulations use multiple survey years.
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112. revenues in Appendix Tables A.15 and A.16 is similar to that used by Bartik et al. (2016). 59 Education expenditure purposes include elementary and secondary education, higher education, and libraries. Public safety expenditure purposes include police, correctional facilities, fire, judicial and legal, and protective inspection and regulation. Welfare and health expenditure purposes include welfare, health and hospital, transit subsidies, and housing and community development. Infrastructure expenditure purposes include airport, total highway, parking, sewerage, solid wage management, and water transport and terminals. Examples of other expenditure purposes are financial administration, central staffing, and parks and recreation. 60 As expected given balanced budget requirements, the change in expenditures in Appendix Table A.15 approximately mirror the change in revenues in Appendix Table A.16.
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113. Ruggles, Steven, J., Katie Genadek, Ronald Goeken, Josiah Grover, and Matthew Sobek. 2015. “Integrated Public Use Microdata Series: Version 6.0 [Machine-readable database].” Ryan, Camille L., and Kurt Bauman. 2016. “Educational Attainment in the United States: 2015.” U.S. Census Bureau, Current Population Reports, P20-578.
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114. Schaller, Jessamyn, and Ann Huff Stevens. 2015. “Short-Run Effects of Job Loss on Health Conditions, Health Insurance, and Health Care Utilization.” Journal of Health Economics, 43(1): 190–203.

115. Schaller, Jessamyn, and Mariana Zerpa. 2015. “Short-Run Effects of Parental Job Loss on Child Health.” NBER Working Paper 21745.

116. Schumpeter, Joseph A. 1939. Business Cycles: A Theoretical, Historical, and Statistical Analysis of the Capitalist Process. New York: McGraw-Hill.
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117. Schumpeter, Joseph A. 1942. Capitalism, Socialism, and Democracy. New York: Harper.
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118. Second, I assume that estimates of λa for individuals born from 1970-2013 accurately characterize the measurement error relationship for individuals born from 1950-1979. Support for this assumption comes from the fact that estimates of λa are stable across the 1968-2013 birth cohorts in the PSID, as shown in Appendix Table A.2.52 Appendix Figure A.5 shows that migration rates are remarkably stable across the 1968-2013 birth cohorts in the PSID. Appendix Figure A.6 shows the consequences of adjusting the effects of the recession on fouryear college degree attainment for attenuation bias. The adjusted effects are larger in magnitude, but they lie within the 95 percent confidence interval of the unadjusted estimates. Appendix Figures A.7-A.11 present the measurement error adjustments for other educational outcomes.
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119. Second, we obtain “duplicate” matches for correctly spelled place names that can, in principle, be identified uniquely in 12 characters. We assign individuals to a single birth county if at least 75 percent of the exact matches are to a single county, and we assign multiple birth counties otherwise.45 Third, we use hand matches from Isen, Rossin-Slater and Walker (2017), described in their Appendix C. Fourth, we use probabilistic matching algorithms.46 Finally, we hand check 43 We restrict attention to geographic features that are plausibly populated (those with a Populated Place, Census, or Civil feature class) or have a federal location code. 44 For example, there are three different Populated Places in North Carolina beginning with “Bells Crossroads” located in different counties. Repeated place names pose less of a problem if the place name has less than 12 characters.
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120. Source: Confidental PSID data Figure A.6: The Long-Run Effects of the 1980-1982 Recession on Four-Year College Degree Attainment, Adjusted for Measurement Error Comparison Group: Age 23-28 in 1979 Exposed to Recession: Age 0-22 in 1979 H0: π24 = π27 = 0 p = 0.774 -.6 -.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 Four-year college degree attainment 0 5 10 15 20 25 30 Age in 1979 Unadjusted for pre-recession migration Adjusted for pre-recession migration Notes: See notes to Figure 3. The dependent variable is an indicator for four-year college degree attainment. The line that adjusts for pre-recession migration divides the unadjusted estimates by the Census/ACS coefficient from Appendix Figure A.4. See Appendix D for details.
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121. Source: National Longitudinal Survey of Youth 1979 (1979-2010) Figure A.4: Relationship between Severity of 1980-1982 Recession in County of Residence and County of Birth 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1
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122. Sources: Bailey et al. (2016), BEA Regional Economic Accounts, Census County Business Patterns, Census County Data Books, Minnesota Population Center (2011)
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123. Sources: BEA Regional Economic Accounts, Census County Business Patterns, Census County Data Books, Minnesota Population Center (2011) Figure A.3: Four-Year College Degree Attainment by Age 0 .05 .1 .15 .2 .25 .3 Share with a four-year college degree 20 25 30 35 40 45 Age (a) Share with a Four-Year College Degree 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1
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124. Sources: BEA Regional Economic Accounts, Census County Business Patterns, Confidential 2000-2013 Census /ACS data linked to the SSA NUMIDENT file, Birth and infant mortality data from Bailey et al. (2016) Figure 1: Change in Log Real Earnings per Capita, 1979-1982 Notes: Figure displays the county-level change in log real earnings per capita from 1979-1982, which I use to measure the severity of the 1980-1982 recession. Categories correspond to unweighted deciles, with darker shades of red representing a larger earnings decrease.
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125. Sources: BEA Regional Economic Accounts, Census County Business Patterns, Confidential 2000-2013 Census/ACS data linked to the SSA NUMIDENT file Figure 4: The Long-Run Effects of the 1980-1982 Recession on Years of Schooling Comparison Group: Age 23-28 in 1979 Exposed to Recession: Age 0-22 in 1979 H0: π24 = π27 = 0 p = 0.932 -3 -2 -1 0 1 2
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126. Sources: BEA Regional Economic Accounts, Census County Business Patterns, Confidential 2000-2013 Census/ACS data linked to the SSA NUMIDENT file Figure A.10: The Long-Run Effects of the 1980-1982 Recession on Any College Degree Attainment Comparison Group: Age 23-28 in 1979 Exposed to Recession: Age 0-22 in 1979 H0: π24 = π27 = 0 p = 0.870 -.6 -.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 Any college degree attainment 0 5 10 15 20 25 30 Age in 1979 Unadjusted for pre-recession migration Adjusted for pre-recession migration Notes: See notes to Figures 3 and A.6. The dependent variable is an indicator for any college degree attainment.
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127. Sources: BEA Regional Economic Accounts, Census County Business Patterns, Confidential 2000-2013 Census/ACS data linked to the SSA NUMIDENT file Figure A.11: The Long-Run Effects of the 1980-1982 Recession on Two-Year College Degree Attainment Comparison Group: Age 23-28 in 1979 Exposed to Recession: Age 0-22 in 1979 H0: π24 = π27 = 0 p = 0.014 -.2 -.1 0 .1 .2 .3 Two-year college degree attainment 0 5 10 15 20 25 30 Age in 1979 Unadjusted for pre-recession migration Adjusted for pre-recession migration Notes: See notes to Figures 3 and A.6. The dependent variable is an indicator for two-year college degree attainment.
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128. Sources: BEA Regional Economic Accounts, Census County Business Patterns, Confidential 2000-2013 Census/ACS data linked to the SSA NUMIDENT file Figure A.12: The Long-Run Effects of the 1980-1982 Recession on Log Personal Income Notes: See notes to Figure 3 and Table 3. The dependent variable is log personal income.
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129. Sources: BEA Regional Economic Accounts, Census County Business Patterns, Confidential 2000-2013 Census/ACS data linked to the SSA NUMIDENT file Figure A.13: The Long-Run Effects of the 1980-1982 Recession on Log Earned Income Notes: See notes to Figure 3 and Table 3. The dependent variable is log earned income.
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130. Sources: BEA Regional Economic Accounts, Census County Business Patterns, Confidential 2000-2013 Census/ACS data linked to the SSA NUMIDENT file Figure A.14: The Long-Run Effects of the 1980-1982 Recession on Log Family Income Notes: See notes to Figure 3 and Table 3. The dependent variable is log family income.
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131. Sources: BEA Regional Economic Accounts, Census County Business Patterns, Confidential 2000-2013 Census/ACS data linked to the SSA NUMIDENT file Figure A.15: The Long-Run Effects of the 1980-1982 Recession on Log Hourly Wage Notes: See notes to Figure 3 and Table 3. The dependent variable is log hourly wage.
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132. Sources: BEA Regional Economic Accounts, Census County Business Patterns, Confidential 2000-2013 Census/ACS data linked to the SSA NUMIDENT file Figure A.16: The Long-Run Effects of the 1980-1982 Recession on Poverty Notes: See notes to Figure 3 and Table 3. The dependent variable is an indicator for being in poverty.
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133. Sources: BEA Regional Economic Accounts, Census County Business Patterns, Confidential 2000-2013 Census/ACS data linked to the SSA NUMIDENT file Figure A.7: The Long-Run Effects of the 1980-1982 Recession on Years of Schooling, Adjusted for Measurement Error Comparison Group: Age 23-28 in 1979 Exposed to Recession: Age 0-22 in 1979 H0: π24 = π27 = 0 p = 0.932 -3 -2 -1 0 1 2
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134. Sources: BEA Regional Economic Accounts, Census County Business Patterns, Confidential 2000-2013 Census/ACS data linked to the SSA NUMIDENT file Figure A.8: The Long-Run Effects of the 1980-1982 Recession on High School or GED Attainment Comparison Group: Age 23-28 in 1979 Exposed to Recession: Age 0-22 in 1979 H0: π24 = π27 = 0 p = 0.965 -.2 -.1 0 .1 .2 .3 High school or GED attainment 0 5 10 15 20 25 30 Age in 1979 Unadjusted for pre-recession migration Adjusted for pre-recession migration Notes: See notes to Figures 3 and A.6. The dependent variable is an indicator for high school or GED attainment.
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135. Sources: BEA Regional Economic Accounts, Census County Business Patterns, Confidential 2000-2013 Census/ACS data linked to the SSA NUMIDENT file Figure A.9: The Long-Run Effects of the 1980-1982 Recession on Any College Attendance Comparison Group: Age 23-28 in 1979 Exposed to Recession: Age 0-22 in 1979 H0: π24 = π27 = 0 p = 0.885 -.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 Any college attendance 0 5 10 15 20 25 30 Age in 1979 Unadjusted for pre-recession migration Adjusted for pre-recession migration Notes: See notes to Figures 3 and A.6. The dependent variable is an indicator for any college attendance.
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136. Sources: BEA Regional Economic Accounts, Census County Business Patterns, Confidential 2000-2013 Census/ACS data linked to the SSA NUMIDENT file Table A.14: The Long-Run Effect of the 1980-1982 Recession on Four-Year College Degree Attainment, Robustness to Measure of Recession Severity Measure of recession severity: 1979-1982 change in Log earnings Log earnings Log income Log Earnings per per capita per capita employment capita, $10k (1) (2) (3) (4) (5) Interaction between measure of recession severity and age in 1979 0-10-0.316-0.228-0.454-0.229-0.150 (0.120) (0.090) (0.176) (0.082) (0.058) 11-19-0.164-0.117-0.240-0.116-0.079 (0.061) (0.045) (0.094) (0.039) (0.031) Notes: The dependent variable is an indicator for four-year college degree attainment. See notes to Table 2.
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137. Sources: BEA Regional Economic Accounts, Census County Business Patterns, Confidential 2000-2013 Census/ACS data linked to the SSA NUMIDENT file Table A.6: The Long-Run Effects of the 1980-1982 Recession on Education, First Difference Estimates Dependent variable: Any Four-year Two-year Any college college college HS/GED college degree degree degree Years of attainment attendance attainment attainment attainment schooling (1) (2) (3) (4) (5) (6) Interaction between 1979-1982 decrease in log real earnings per capita and age in 1979 0-10-0.148-0.626-0.668-0.669 0.001-3.463 (0.072) (0.169) (0.172) (0.174) (0.063) (0.902) 11-19-0.132-0.594-0.594-0.564-0.030-3.068 (0.092) (0.159) (0.132) (0.132) (0.053) (0.769) 20-29-0.166-0.517-0.413-0.381-0.032-2.390 (0.084) (0.116) (0.088) (0.085) (0.040) (0.518) Notes: See notes to Table 2 for details on specification and sample. In this table, I do not include birth county fixed effects.
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138. Sources: BEA Regional Economic Accounts, Census County Business Patterns, Confidential 2000-2013 Census/ACS data linked to the SSA NUMIDENT file Table A.8: The Long-Run Effects of the 1980-1982 Recession on Income, Wages, and Poverty, First Difference Estimates Dependent variable: Log Log Log Log personal earned hourly family In income income wage income poverty (1) (2) (3) (4) (5) Interaction between 1979-1982 decrease in log real earnings per capita and age in 1979 0-10-0.894-0.919-0.611-0.889 0.178 (0.210) (0.215) (0.178) (0.206) (0.054) 11-19-0.836-0.832-0.653-0.874 0.135 (0.191) (0.190) (0.176) (0.189) (0.047) 20-29-0.659-0.631-0.443-0.681 0.098 (0.164) (0.158) (0.147) (0.159) (0.035) Notes: See notes to Table 3 for details on specification and sample. In this table, I do not include birth county fixed effects.
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139. Sources: BEA Regional Economic Accounts, Census County Business Patterns, Confidential 2000-2013 Census/ACS data linked to the SSA NUMIDENT file, Census County Data Book, Grapevine Appropriations Data Table 6: Back of the Envelope Calculations of the Aggregate Long-Run Effects of the 1980-1982 Recession Counterfactual 1: No real earnings Counterfactual 2: Trend real earnings per capita growth, 1979-1982 per capita growth, 1979-1982 Number Four-year Earned Adults Four-year Earned Adults of births, college income, living in college income, living in millions graduates billions $ poverty graduates billions $ poverty (1) (2) (3) (4) (5) (6) (7) Age in 1979 0-10 36.0-567,000-25.7 190,000-1,196,000-54.1 401,000 11-19 34.1-281,000-15.8 68,000-590,000-33.2 144,000 0-19 70.2-848,000-41.5 259,000-1,786,000-87.3 545,000 Notes: Table displays back of the envelope calculations of the aggregate long-run effects of the 1980-1982 recession.

140. Sources: BEA Regional Economic Accounts, Confidential 2000-2013 Census/ACS data linked to the SSA NUMIDENT file, Confidental PSID data Figure A.5: Comparison of Birth County Out-Migration Rates by Cohort 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1
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141. Speer, Jamin D. 2016. “Wages, Hours, and the School-to-Work Transition: The Consequences of Leaving School in a Recession for Less-Educated Men.” B.E. Journal of Economic Analysis & Policy, 16(1): 97–124.

142. Stinebrickner, Ralph, and Todd Stinebrickner. 2008. “The Effect of Credit Constraints on the College Drop-Out Decision: A Direct Approach Using a New Panel Study.” American Economic Review, 98(5): 2163–2184.

143. Taylor, Evan J., Bryan A. Stuart, and Martha J. Bailey. 2016. “Summary of Procedure to Match NUMIDENT Place of Birth County to GNIS Places.” U.S. Census Bureau Technical Report.

144. The dashed lines are pointwise 95 percent confidence intervals based on standard errors clustered at the birth state level. Sample is limited to the 2,550 counties with less than 5 percent of 1976 employment in the mining sector.
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145. The estimates of αt in Appendix Figure A.2 characterize the 1980-1982 recession as a reversal of post-war fortune: counties with a more severe recession saw greater median family income growth from 1950-1970. This pattern arises from estimates of model 1, which contains county 47 I use median family income because it is available at the county-level from decennial Censuses for 1950-2000 (and the ACS for 2010) and is an important measure of local economic conditions. County-level Census data do not consistently report other quantiles of the family income distribution from 1950-2010.
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146. To address this concern, I use confidential Panel Study of Income Dynamics (PSID) data. PSID data allow me to estimate λa for individuals born from 1968-1979 and observed in 1979, but they only contain information on county of residence. I limit the PSID sample to individuals who are first observed before age 3 to make early life county of residence a better proxy for county of birth. As seen in Appendix Figure A.4, point estimates of λa from Census/ACS data range from 0.75 for 0 year olds to 0.40 for 29 year olds. Point estimates from PSID data display a similar age profile, but are larger because county of residence is more strongly related to county of residence in early life than county of birth. Appendix Figure A.4 clearly suggests that my estimates of the recession’s long-run effects are attenuated.
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147. To examine the effect of the recession on local government expenditures and revenues, I estimate event study regressions similar to equation (A.1), where the dependent variable is log real expenditures or revenues.56 I use data from the Census of Governments, which contains information on expenditures and revenues for all government units in years that end in a “2” or “7.”57 I collapse all government units to the county level for years 1972, 1977, 1982, 1987, 1992, and 1997. I normalize the interaction between year 1977 and the severity of the recession to equal zero.
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148. Topel, Robert H. 1986. “Local Labor Markets.” Journal of Political Economy, 94(3): S111–S143.
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149. two assumptions. First, I assume that unobserved measurement error, vi,a,c,t, is uncorrelated with unobserved determinants of long-run outcomes, ε∗ i,a,c,t, conditional on the covariates in equation (A.6). For example, this rules out the possibility that parents of young children with high ε∗ i,a,c,t anticipated the recession and moved to less severe recession counties before 1980. The suddenness of the changes in local economic activity that emerged during the recession support this assumption.
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150. Unfortunately, these files are not currently linked. Instead, I estimate λa using two data sets that provide valuable, but imperfect, information. First, I use 2000-2013 Census/ACS data for individuals born from 1970-2013 who are ages 0 to 29 at the time of the survey. These data contain county of birth from the NUMIDENT, like the data I use to estimate the long-run effects of the 1980-1982 recession, and county of residence at the time of the survey. Because they measure the relationship between county of birth and county of residence after the 1980-1982 recession, they might not accurately characterize measurement error if migration patterns have changed over time.
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151. Voorheis, John. 2017. “Air Quality, Human Capital Formation and the Long-term Effects of Environmental Inequality at Birth.” Census Bureau CARRA Working Paper 2017-05.

152. Wasi, Nada, and Aaron Flaaen. 2015. “Record Linkage Using State: Preprocessing, Linking, and Reviewing Utilities.” The Stata Journal, 15(3): 672–697.

153. When adjusting for this attenuation, I prefer to use the Census/ACS data because they contain the most comparable information on place of birth. The validity of this adjustment depends on 50 These orthogonality conditions imply that ε∗ i,a,c,t is orthogonal to R79−82 c , and hence that εi,a,c,t is orthogonal to R79−82 c . Alternatively, I could assume that εi,a,c,t is orthogonal to R79−82 c , which would imply that ε∗ i,a,c,t is orthogonal to R79−82 c , and hence that ε∗ i,a,c,t is orthogonal to R79−82∗ i,a,c,t . 51 An alternative approach is to define R79−82∗ a,c as the average change in log earnings per capita for individuals age a in 1979 and born in county c, and note that R79−82∗ a,c = P
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154. Wozniak, Abigail. 2010. “Are College Graduates More Responsive to Distant Labor Market Opportunities ?” Journal of Human Resources, 45(4): 944–970.

155. Years of schooling 0 5 10 15 20 25 30 Age in 1979 Notes: Figure plots estimates of the interaction between the 1979-1982 decrease in log real earnings per capita in individuals’ county of birth and indicators for age in 1979. The interaction for individuals age 29 is normalized to equal zero. The dependent variable is years of schooling. See notes to Table 2 and Figure 3 for details on sample and specification.
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156. Years of schooling 0 5 10 15 20 25 30 Age in 1979 Unadjusted for pre-recession migration Adjusted for pre-recession migration Notes: See notes to Figures 3 and A.6. The dependent variable is years of schooling.
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27. Tax competition, spillovers, and subsidies. (2006). Ogawa, Hikaru.
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28. Does immigration reduce imbalances among labor markets or increase them? : evidence from recent migration flows. (2005). Newton, Geoffrey B. ; Keeton, William R..
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29. The Impact of State Corporate Taxes on FDI Location. (2004). Agostini, Claudio.
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31. Local Labor Market Conditions and the Jobless Poor: How Much Does Local Job Growth Help in Rural Areas?. (2003). Davis, Elizabeth ; Weber, Bruce A. ; Connolly, Laura S..
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32. The impact of taxes and public spending on the location of FDI: evidence from FDI-flows within Europe. (2002). Büttner, Thiess.
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34. TAX INCENTIVES: AN EFFECTIVE DEVELOPMENT STRATEGY FOR RURAL COMMUNITIES?. (2002). Rainey, Daniel V. ; McNamara, Kevin T..
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35. A Reconsideration of Environmental Federalism. (2001). Oates, Wallace.
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36. The Rise in Disability Recipiency and the Decline in Unemployment. (2001). Duggan, Mark ; Autor, David.
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37. Measuring the incentive effects of state tax policies toward capital investment. (2001). Tannenwald, Robert ; Plesko, George A..
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38. Solving the Many Problems with Inner City Jobs. (2000). Bartik, Timothy.
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39. The Role of Public Policy in Skills Development of Black Workers in the 21st Century. (2000). Hollenbeck, Kevin ; Bartik, Timothy.
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40. Do Minimum Wage Hikes Raise US Long Term Unemployment? Evidence Using State Minimum Wage Rates. (2000). Partridge, Mark.
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41. The Missing Pollution Haven Effect. (2000). Levinson, Arik.
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42. The Effects of Direct Foreign Investment on Local Communities. (1999). Figlio, David ; Blonigen, Bruce.
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46. Tax and spending incentives and enterprise zones. (1997). Fisher, Peter S..
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47. Turmoil in traditional industry: prospects for nonmetropolitan manufacturing.. (1996). Barkley, David L..
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48. Taxation, imperfect competition, and discontinuities. (1995). Alm, James ; Thorpe, Steven .
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49. The Effects of Metropolitan Job Growth on the Size Distribution of Family Income. (1991). Bartik, Timothy.
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50. The Impact of State Corporate Taxes on FDI Location. (). Agostini, Claudio.
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