- (A.9) E[y] = exp( + Ã2 /2), (A.10) where p1 is the share of establishments (with at least 1000 employees) with 1000-1499 employees, 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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- 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 mation on expenditures and revenues for all government units in years that end in a “2†or “7.â€Â83 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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- A simple way of measuring the persistence of the recession is by relating the 1978-1992 and 1978-1982 changes in log real earnings per capita, ln(Ec,1992) − ln(Ec,1978) = α + β (ln(Ec,1982) − ln(Ec,1978)) + vc, (A.2) where Ec,t is real earnings per capita for county c in year t. In equation (A.2), the average degree of persistence is captured by β, with full persistence represented by β = 1 and no persistence represented by β = 0. However, equation (A.2) has the unattractive property that, even if earnings per capita displays no serial correlation, the model implies a non-zero degree of persistence, β = 0.5. This arises because ln(Ec,1978) appears on both the left and right hand sides of equation (A.2) and occurs even in the absence of measurement error.
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“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.
Aguiar, Mark, Erik Hurst, and Loukas Karabarbounis. 2013. “Time Use during the Great Recession.†American Economic Review, 103(5): 1664–1696.
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Aizer, Anna, Shari Eli, Joseph Ferrie, and Adriana Lleras-Muney. 2016. “The Long Term Impact of Cash Transfers to Poor Families.†American Economic Review, 106(4): 935–971.
Akee, Randall, Emilia Simeonova, E. Jane Costello, and William Copeland. 2015. “How Does Household Income Affect Child Personality Traits and Behaviors?†NBER Working Paper 21562.
Albouy, David Y., and Bryan A. Stuart. 2016. “Urban Population and Amenities: The Neoclassical Model of Location.†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.
- Almond, Douglas, Janet Currie, and Valentina Duque. 2017. “Childhood Circumstances and Adult Outcomes: Act II.†NBER Working Paper 23017.
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- Appendix Figure A.13 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 centered around zero, generally small in magnitude, and indistinguishable from zero (p = 0.93). When including counties with a high mining employment share, there is also no evidence of a significant relationship (p = 0.64).
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- Appendix Table A.17 shows that results are similar when replacing age in 1979 by birth state fixed effects with age by birth division or region fixed effects.80 Appendix Table A.18 shows that results are robust to not controlling for interactions between age in 1979 and the 1950-1970 change in log median family income in individuals’ birth county, to controlling instead for the 1950-1980 change in log median family income, and to controlling for both the 1950-1970 and 1970-1980 change.
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- Appendix Table A.19 shows that results are similar when replacing the 1978-1982 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.81 Appendix Table A.20 shows that results are similar when using all other states in the continental U.S., instead of other states in the same region, to construct the predicted log employment change instrumental variable; estimates are similar but less precise when using the predicted log employment change in manufacturing, which was the largest industry in 1978 and experienced a severe decline in the 1980-1982 recession.
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- Appendix Table A.23 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.86 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. when y is sufficiently large. 83 I downloaded these data from the NBER website, with thanks to Michael Greenstone for making them available.
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- Atkin, David. 2016. “Endogenous Skill Acquisition and Export Manufacturing in Mexico.†American Economic Review, 106(8): 2046–2085.
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- Auto dealers experienced large employment losses from 1978-1982, but gains from 1978-1992. Primary metal manufacturing experienced employment losses over both horizons. 90 In the future, I will report p-values from the test of whether the effects of temporary and persistent earnings decreases are equal. This requires submitting an additional disclosure request to the Census Bureau.
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Autor, David H., David Dorn, and Gordon H. Hanson. 2013. “The China Syndrome: Local Labor Market Effects of Import Competition in the United States.†American Economic Review, 103(6): 2121–2168.
Autor, David, David Figlio, Krzysztof Karbownik, Jeffrey Roth, and Melanie Wasserman. 2016. “Family Disadvantage and the Gender Gap in Behavioral and Educational Outcomes.†NBER Working Paper 22267.
- γd(c), and a county’s Metropolitan Statistical Area (MSA) status.52 FSS limit their sample to counties with at least 10,000 residents in 1977.
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- Bailey, Martha J., and Susan M. Dynarski. 2011. “Inequality in Postsecondary Education.†In An Economic Perspective on the Problems of Disadvantaged Youth. ed. Greg J. Duncan and Richard J. Murnane, 117–131. New York: Russell Sage Foundation.
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Bailey, Martha J., Price Fishback, Michael Haines, Shawn Kantor, Edson Severnini, and Anna Wentz. 2016. “U.S. County-Level Natality and Mortality Data, 1915-2007. Ann Arbor, MI: ICPSR [distributor].†Barnett, Barry J. 2000. “The U.S. Farm Financial Crisis of the 1980s.†Agricultural History, 74(2): 366–380.
Bartik, Timothy J. 1993. “Who Benefits from Local Job Growth: Migrants or the Original Residents ?†Regional Studies, 27(4): 297–311.
Bartik, Timothy J. 1996. “The Distributional Effects of Local Labor Demand and Industrial Mix: Estimates Using Individual Panel Data.†Journal of Urban Economics, 40(2): 150–178.
- Because of this, I control for the 1950-1970 change in log median family income when estimating long-run effects on individuals using equation (1). I also show that controlling for this pre-trend does not change the estimated effects of the recession on post-recession family income.
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- Becker, Gary S. 1962. “Investment in Human Capital: A Theoretical Analysis.†Journal of Political Economy, 70(5): 9–49.
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Belley, Philippe, and Lance Lochner. 2007. “The Changing Role of Family Income and Ability in Determining Educational Achievement.†Journal of Human Capital, 1(1): 37–89.
- Ben-Porath, Yoram. 1967. “The Production of Human Capital and the Life Cycle of Earnings.†Journal of Political Economy, 75(4): 352–365.
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Black, Dan A., Terra G. McKinnish, and Seth G. Sanders. 2005. “Tight Labor Markets and the Demand for Education: Evidence from the Coal Boom and Bust.†Industrial and Labor Relations Review, 59(1): 3–16.
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Bleakley, Hoyt, and Joseph Ferrie. 2016. “Shocking Behavior: Random Wealth in Ante24 bellum Georgia and Human Capital Across Generations.†Quarterly Journal of Economics, 131(3): 1455–1495.
- Bound and Holzer (2000) find that wages decrease by more than local price levels (Table 3 and footnote 28). Notowidigdo (2013) finds that a decrease in labor demand reduces income per adult slightly more than the price of housing, but reduces wages by less than the price of housing (Tables 2 and 4). 49 I aggregate county-level data to 1990 CZ definitions using the crosswalk provided by Autor and Dorn (2013). 50 Using state-level data, Yagan (2016) finds employment-population ratio convergence from the 1980-1982 recession in 8 years (see his Figure A.1.D).
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Bound, John, and Harry J. Holzer. 2000. “Demand Shifts, Population Adjustments, and Labor Market Outcomes during the 1980s.†Journal of Labor Economics, 18(1): 20–54.
- Bound, John, and Sarah Turner. 2007. “Cohort Crowding: How Resources Affect Collegiate Attainment.†Journal of Public Economics, 91(5-6): 877–899.
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- Brand, Jennie E. 2015. “The Far-Reaching Impact of Job Loss and Unemployment.†Annual Review of Economics, 41: 359–375.
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Bulman, George, Robert Fairlie, Sarena Goodman, and Adam Isen. 2016. “Parental Resources and College Attendance: Evidence from Lottery Wins.†NBER Working Paper 22679.
- Burbridge, John B., Lonnie Magee, and A. Leslie Robb. 1988. “Alternative Transformations to Handle Extreme Values of the Dependent Variable.†Journal of the American Statistical Association, 83(401): 123–127.
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- Bureau of Labor Statistics. 1998. “Historical Comparability of Local Area Unemployment Statistics (LAUS) Data.†Caballero, Ricardo J., and Mohamad L. Hammour. 1994. “The Cleansing Effect of Recessions.
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- C Matching NUMIDENT Data to Counties This section describes the procedure used to match the Social Security Administration NUMIDENT file to FIPS county codes. The procedure described here was developed alongside Martha Bailey, Evan Taylor, and Reed Walker. Researchers with access to confidential Census data can read a technical memo with more information on this procedure and will be able to access the code and output from this procedure (Taylor, Stuart and Bailey, 2016).
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- c Na,c(R78−82 c − RCF c )À̂a, where Na,c is the number of individuals born in county c net of infant mortality, R78−82 c is the observed decrease in log real earnings per capita from 1978-1982 in county c, RCF c is the counterfactual decrease in log real earnings per capita from 1978-1982, and À̂a is the difference-in-differences estimate. In counterfactual 1, I set RCF c = 0 and in counterfactual 2, RCF c = −0.076, which corresponds to the average annual growth in earnings per capita from 1969-1978 of 1.9 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.
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Cameron, Stephen V., and Christopher Taber. 2004. “Estimation of Educational Borrowing Constraints Using Returns to Schooling.†Journal of Political Economy, 112(1): 132–182.
- Cameron, Stephen V., and James J. Heckman. 2001. “The Dynamics of Educational Attainment for Black, Hispanic, and White Males.†Journal of Political Economy, 109(3): 455–499.
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Card, David, and A. Abigail Payne. 2002. “School Finance Reform, The Distribution Of School Spending, And The Distribution Of Student Test Scores.†Journal of Public Economics, 83(1): 49–82.
Carrell, Scott E., Mark Hoekstra, and Elira Kuka. 2016. “The Long-Run Effects of Disruptive Peers.†Cascio, Elizabeth U., and Ayushi Narayan. 2015. “Who Needs a Fracking Education? The Educational Response to Low-Skill Biased Technological Change.†NBER Working Paper 21359.
Caucutt, Elizabeth M., and Lance Lochner. 2012. “Early and Late Human Capital Investments, Borrowing Constraints, and the Family.†NBER Working Paper 18493.
- CBP data always report establishment counts by county, industry, and establishment size, but frequently suppress employment at the county-by-industry level. From 1974-forward, the establishment size groups are 1-4, 5-9, 10-19, 20-49, 50-99, 100-249, 250-499, 500-999, 1000-1499, 1500-2499, 2500-4999, and 5000 or more employees.
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Charles, Kerwin Kofi, Erik Hurst, and Matthew J. Notowidigdo. 2015. “Housing Booms and Busts, Labor Market Opportunities, and College Attendance.†Chetty, Raj, and Nathaniel Hendren. 2016a. “The Impacts of Neighborhoods on Intergenerational Mobility I: Childhood Exposure Effects.†Chetty, Raj, and Nathaniel Hendren. 2016b. “The Impacts of Neighborhoods on Intergenerational Mobility II: County-Level Estimates.†Chetty, Raj, Nathaniel Hendren, and Lawrence F. Katz. 2016. “The Effects of Exposure to Better Neighborhoods on Children: New Evidence from the Moving to Opportunity Experiment.†American Economic Review, 106(4): 855–902.
- Chetty, Raj, Nathaniel Hendren, Frina Lin, Jeremy Majerovitz, and Benjamin Scuderi. 2016. “Childhood Environment and Gender Gaps in Adulthood.†American Economic Review Papers and Proceedings, 106(5): 282–288.
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Chyn, Eric. 2016. “Moved to Opportunity: The Long-Run Effect of Public Housing Demolition on Labor Market Outcomes of Children.†Coelli, Michael B. 2011. “Parental Job Loss and the Education Enrollment of Youth.†Labour Economics, 18(1): 25–35.
- Column 4 equals the ratio of column 1 minus column 2 and column 1. Column 5 equals the ratio of column 1 minus column 3 and column 1. The sample contains 15.6 million individuals born from 1950-1979 with a unique birth county, non-imputed variables, and positive values of family income, earned income, personal income, and wage.
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- Cunha, Flavio, James J. Heckman, and Susanne M. Schennach. 2010. “Estimating the Technology of Cognitive and Noncognitive Skill Formation.†Econometrica, 78(3): 883–931.
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Cutler, David M., Wei Huang, and Adriana Lleras-Muney. 2016. “Economic Conditions and Mortality: Evidence from 200 Years of Data.†NBER Working Paper 22690.
- Davis, Steven J., and Til von Wachter. 2011. “Recessions and the Costs of Job Loss.†Brookings Papers on Economic Activity.
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- Davis, Steven J., John C. Haltiwanger, and Scott Schuh. 1996. Job Creation and Destruction. Cambridge, Massachusetts: MIT Press.
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Diamond, Rebecca. 2016. “The Determinants and Welfare Implications of US Workers’ Diverging Location Choices by Skill: 1980-2000.†American Economic Review, 106(3): 479–524.
Dix-Carneiro, Rafael, and Brian K. Kovak. 2016. “Trade Liberalization and Regional Dynamics. †Dynarski, Susan. 2008. “Building the Stock of College-Educated Labor.†Journal of Human Resources, 43(3): 576–610.
Dynarski, Susan, Joshua M. Hyman, and Diane Whitmore Schanzenbach. 2013. “Experimental Evidence on the Effect of Childhood Investments on Postsecondary Attainment and Degree Completion.†Journal of Policy Analysis and Management, 32(4): 692–717.
Feigenbaum, James. 2015. “jarowinkler: Stata module to calculate the Jaro-Winkler distance between strings.†Statistical Software Components S45785, Department of Economics, Boston College.
- Feyrer, James, Bruce Sacerdote, and Ariel Dora Stern. 2007. “Did the Rust Belt Become Shiny? A Study of Cities and Counties That Lost Steel and Auto Jobs in the 1980s.†Brookings-Wharton Papers on Urban Affairs.
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- 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.11). 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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Foote, Andrew, Michel Grosz, and Ann Huff Stevens. 2015. “Locate Your Nearest Exit: Mass Layoffs and Local Labor Market Response.†NBER Working Paper 21618.
- Foote, Christopher L. 1998. “Trend Employment Growth and Bunching of Job Creation and Destruction.†Quarterly Journal of Economics, 113(3): 809–834.
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- For individuals who were a years old in 1979, I calculate these as P
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- For reference, I repeat equation (1), which is the feasible regression of interest: yi,a,c,t = R78−82 c Àa + xi,a,c,tβ + γc + θa,s(c) + δt + εi,a,c,t. (A.11) The explanatory variable of interest in equation (A.11) is R78−82 c , the change in log earnings per capita from 1978-1982 in birth county c.
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Foster, Lucia, Cheryl Grim, and John Haltiwanger. 2016. “Reallocation in the Great Recession: Cleansing or Not?†Journal of Labor Economics, 34(S1): S293–S331.
- Frisch, Ragnar, and Frederick V. Waugh. 1933. “Partial Time Regressions as Compared with Individual Trends.†Econometrica, 1(4): 387–401.
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- Given its importance, I focus on the effect of the recession on four-year college degree 78 Data on infant birth weight are available starting in 1968, and data on maternal education are available starting in 1969. I focus on 1970-forward because the 1969 data handle births to nonresident aliens differently than the 1970forward data. 79 For results on maternal education, I restrict the sample to states that reported education throughout the 1970-1979 period. This results in the exclusion of 13 states (Alabama, Arkansas, California, Connecticut, Delaware, the District of Columbia, Georgia, Idaho, Maryland, New Mexico, Pennsylvania, Texas, and Washington). attainment.
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- Greenstone, Michael, and Adam Looney. 2010. “An Economic Strategy to Renew American Communities.†The Hamilton Project.
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- H 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 detect meaningful relationships, which provides evidence that my estimates of the long-run effects of the recession are not driven by pre-recession trends in infant health or parental characteristics.
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- Hagedorn, Marcus, and Iourii Manovskii. 2013. “Job Selection and Wages over the Business Cycle.†American Economic Review, 103(2): 771–803.
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- Haider, Steven, and Gary Solon. 2006. “Life-Cycle Variation in the Association between Current and Lifetime Earnings.†American Economic Review, 96(4): 1308–1320.
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Hershbein, Brad J. 2012. “Graduating High School in a Recession: Work, Education, and Home Production.†B.E. Journal of Economic Analysis & Policy, 12(1): 1–30.
- High mining counties have at least 5 percent of 1976 employment in the mining sector. Standard errors in parentheses are clustered by state.
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Hilger, Nathaniel G. 2016. “Parental Job Loss and Children’s Long-Term Outcomes: Evidence from 7 Million Fathers’ Layoffs.†American Economic Journal: Applied Economics, 8(3): 247– 283.
- Holmes and Stevens (2002). I believe that FSS measure 1977 total employment from BLS data.
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- Holmes, Thomas J., and John J. Stevens. 2002. “Geographic Concentration and Establishment Scale.†Review of Economics and Statistics, 84(4): 682–690.
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Hoynes, Hilary W. 2000. “Local Labor Markets and Welfare Spells: Do Demand Conditions Matter?†Review of Economics and Statistics, 82(3): 351–368.
Hoynes, Hilary W., Diane Whitmore Schanzenbach, and Douglas Almond. 2016. “Long Run Impacts of Childhood Access to the Safety Net.†American Economic Review, 106(4): 903–934.
- Hoynes, Hilary W., Marianne E. Page, and Ann Huff Stevens. 2006. “Poverty in America: Trends and Explanations.†Journal of Economic Perspectives, 20(1): 47–68.
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Hoynes, Hilary, Douglas L. Miller, and Jessamyn Schaller. 2012. “Who Suffers during Recessions ?†Journal of Economic Perspectives, 26(3): 27–48.
- I estimate the model by 2SLS, using the predicted log employment change from 1978-1982 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 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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- 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 birth year interacted with the 1950-1970 change in log median family income. 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 exclude the 526 counties with at least 5 percent of 1976 employment in the mining sector.
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- I have not been able to replicate the non-shock counties used by FSS. In Table 2, FSS list 66 shock counties (62 of which have non-missing 1977 population) and 1,373 non-shock counties (1,253 of which have non-missing 1977 population). My sample contains 69 shock counties and 2,257 non-shock counties (all of which have non-missing 1977 population).
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- 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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- I The Long-Run Effects of the 1980-1982 Recession on Education: Robustness Checks This section summarizes results that demonstrate the robustness of my estimates to different specifications.
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- I use equations (A.7)-(A.10) to estimate (, Ã) with GMM, using the identity matrix as the weighting matrix.69 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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- I use the predicted log employment change in all industries as an IV and exclude counties with at least 5 percent employment in the mining sector in 1976. All regressions include state fixed effects and control for log real earnings per capita in 1978 and the 1950-1970 change in log real median family income. Standard errors in parentheses are clustered by state.
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- In some specifications, FSS use a binary measure which defines a shock county as one losing at least two percent of initial jobs. Equation (A.6) includes indicator variables for Census division, 51 My Appendix Figure A.3 also resembles their Figure 3. My figures also differ from theirs in that I normalize the more and less severe recession lines to be equal in 1978.
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- indistinguishable from zero. Columns 5-8 add interactions between individuals’ age in 1979 and the 1970-1980 change in log median family income in their county of birth. Columns 5-6, which measure recession severity at the county-level, are similar to columns 1-2, as expected given the lack of a relationship between recession severity and the 1970-1980 change in log median family income (see Appendix Figure A.4). Columns 7-8 measure recession severity at the CZ-level.
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- Isen, Adam, Maya Rossin-Slater, and W. Reed Walker. Forthcoming. “Every Breath You Take - Every Dollar You’ll Make: The Long-Term Consequences of the Clean Air Act of 1970.†Journal of Political Economy.
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- J 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. 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.82 I use data from the Census of Governments, which contains infor82 In a very small number of instances, a county reports 0 expenditures or revenues for the outcomes I examine. To maintain a constant sample, I use the inverse hyperbolic sine, ln(y+ p
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- j0:Dc,j0 ≤100 Nj0 D−1 c,j0 R78−82 j . (A.21) The weight increases in Nj, the 1970 population of county j, and decreases in Dc,j, the distance in miles between counties c and j. These are desirable features because larger counties are likely more popular destinations for migrants or commuters and the cost of migrating or commuting increases in distance. I normalize Dc,c = 1. This approach has the benefit of distinguishing between counties within CZs, while allowing the severity of the recession in nearby counties to influence long-run outcomes. Columns 9-10 are extremely similar to columns 1-2, which provides further support for the robustness of my results.
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- Jacob, Brian A., Max Kapustin, and Jens Ludwig. 2015. “The Impact of Housing Assistance on Child Outcomes: Evidence from a Randomized Housing Lottery.†Quarterly Journal of Economics, 130(1): 465–506.
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Jepsen, Christopher, Kenneth Troske, and Paul Coomes. 2012. “The Labor-Market Returns to Community College Degrees, Diplomas, and Certificates.†IZA Discussion Paper 6902.
- Kahn, Lisa B. 2010. “The Long-Term Labor Market Consequences of Graduating from College in a Bad Economy.†Labour Economics, 17(1): 303–316.
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- Kahn, Lisa B., and Erika McEntarfer. 2015. “Employment Cyclicality and Firm Quality.†NBER Working Paper 20698.
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- Kane, Thomas J., and Cecilia Elena Rouse. 1995. “Labor-Market Returns to Two- and Four-Year College.†American Economic Review, 85(3): 600–614.
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- Leininger, Lindsey Jeanne, and Ariel Kalil. 2014. “Economic Strain and Children’s Behavior in the Aftermath of the Great Recession.†Journal of Marriage and Family, 76(5): 998–1010.
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- Louis, MO as shock counties, but FSS do not. 58 In 1977, 1,144 counties had at least one establishment in the steel industry (SIC 3300), and 861 of these counties (75 percent) had suppressed employment. In the auto industry (SIC 3700), 1,515 counties had at least one establishment, and 1,167 counties (77 percent) had suppressed employment.
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Lovell, Michael C. 1963. “Seasonal Adjustment of Economic Time Series and Multiple Regression.
- Lovenheim, Michael F. 2011. “The Effect of Liquid Housing Wealth on College Enrollment.†Journal of Labor Economics, 29(4): 741–771.
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Malamud, Ofer, and Abigail Wozniak. 2012. “The Impact of College on Migration: Evidence from the Vietnam Generation.†Journal of Human Resources, 47(4): 913–950.
- manufacturing alone, also reveals full persistence. Results are similar when examining the log employment-population ratio (Appendix Table A.2).45 The degree of persistence is similar for years 1987, 1992, and 1997, but economic activity declined further in 2002, 2007, and 2012 in counties which experienced a more severe 1980-1982 recession (Appendix Table A.3).
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- McLaren, John, and Shushanik Hakobyan. 2016. “Looking for Local Labor Market Effects of NAFTA.†Review of Economics and Statistics, 98(4): 728–741.
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- McLoyd, Vonnie C., Toby Epstein Jayaratne, Rosario Ceballo, and Julio Borquez. 1994. “Un27 employment and Work Interruption among African American Single Mothers: Effects on Parenting and Adolescent Socioemotional Functioning.†Child Development, 65(2): 562–589.
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- Migration rate 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 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. The PSID data provide information on county of residence (where the interview took place).
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- Mincer, Jacob. 1958. “Investment in Human Capital and Personal Income Distribution.†Journal of Political Economy, 66(4): 281–302.
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- Minnesota Population Center. 2011. “National Historical Geographic Information System: Version 2.0.†Notowidigdo, Matthew J. 2013. “The Incidence of Local Labor Demand Shocks.†Oreopoulos, Philip, Marianne Page, and Ann Huff Stevens. 2008. “The Intergenerational Effects of Worker Displacement.†Journal of Labor Economics, 26(3): 455–483.
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- 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 Ã, and estimate (, Ã) using the generalized method of moments (GMM), as in Holmes and Stevens (2002). I estimate (, Ã) using the following four moments: p1 = Φ ln(1499) − Ã
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- 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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- 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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- Overall sample contains 3,076 counties in the continental U.S. Low mining counties are the 2,550 counties with less than 5 percent of 1976 employment in the mining sector. Source: Census County Business Patterns
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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.
- Predicted log employment change, 1978-1982 -.2 0 .2 .4 .6 Predicted log employment change, 1978-1982 High mining counties Low mining counties Notes: Predicted log employment change from 1978-1992 and 1978-1982 are constructed using a county’s 1976 industrial structure and the change in industry-level employment from 1978-1992 and 1978-1982 in other states within the same region, as defined in equation (2). The overall R2 includes the variation explained by state fixed effects.
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- 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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Rao, Neel. 2016. “The Impact of Macroeconomic Conditions in Childhood on Adult Labor Market Outcomes.†Economic Inquiry, 54(3): 1425–1444.
- 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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- 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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- Sample limited to counties with at least 10,000 residents in 1977. Heteroskedasticity robust standard errors in parentheses.
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- Sample limited to counties with no more than 5 percent of 1976 employment in the mining sector, and sample excludes 5 counties in New York City. Standard errors in parentheses are clustered by state.
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- Schumpeter, Joseph A. 1939. Business Cycles: A Theoretical, Historical, and Statistical Analysis of the Capitalist Process. New York: McGraw-Hill.
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- Schumpeter, Joseph A. 1942. Capitalism, Socialism, and Democracy. New York: Harper.
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- 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.67 Third, we use hand matches from Isen, Rossin-Slater and Walker (Forthcoming), described in their Appendix C. Fourth, we use probabilistic matching algorithms.68 Finally, we hand check all strings that are matched in the probabilistic step, disagree with the match found in the Isen, Rossin-Slater and Walker (Forthcoming) 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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- Sources: BEA Regional Economic Accounts, Census County Business Patterns, Census County Data Book Figure A.1: Distribution of County-Level Log Real Earnings per Capita Change, 1978-1982 0 .05 .1 .15 .2 .25 Fraction -1 -.5 0 .5 1 Log real earnings per capita change, 1978-1982 Notes: Sample limited to 3,076 counties in the continental U.S. Source: BEA Regional Economic Accounts Figure A.2: Normalized Mean Real Earnings per Capita, by County-Level Severity of the 19801982 Recession, 1969-2013 1978: Before recession 1982: End of recession 20000 25000 30000 35000 40000 Real earnings per capita (2014$) 1970 1974 1978 1982 1986 1990 1994 1998 2002 2006 2010 2014 Year Less severe recession More severe recession Notes: Figure extends the data in Figure 2 from 2002-2013. See notes to Figure 2.
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- Sources: BEA Regional Economic Accounts, Census County Business Patterns, Census County Data Books, Minnesota Population Center (2011) Figure A.5: Log Real Median Family Income Before and After the 1980-1982 Recession, 2SLS Estimates, Including Counties with High Mining Employment Share -1.5 -1 -.5 0 .5 1 1.5 Log real median family income 1950 1960 1970 1980 1990 2000 Year Model 1: County and state-by-year fixed effects Model 2: + year-by-1950-1970 log median income change Notes: See notes to Figure A.4. Sample contains 3,076 counties in the continental U.S.
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- Sources: BEA Regional Economic Accounts, Census County Business Patterns, Confidential 2000-2013 Census /ACS data linked to the SSA NUMIDENT file Table A.11: The Long-Run Effects of the 1980-1982 Recession on Educational Attainment, First Stage Estimates Dependent variable: Interaction between 1978-1982 decrease in log real earnings per capita and age in 1979 20-28 11-19 0-10 (1) (2) (3) Interaction between 1978-1982 predicted log employment decrease and age in 1979 0-10-0.0397*** -0.0336*** 0.552*** (0.00874) (0.00791) (0.0761) 11-19-0.0251*** 0.516*** -0.0138*** (0.00722) (0.0744) (0.00458) 20-28 0.494*** -0.0110*** -0.00699*** (0.0714) (0.00363) (0.00250) F-statistic, all coefficients equal 0 20.20 18.13 19.55 Notes: Table reports first stage estimates of the 2SLS system. See notes to Table 2.
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- 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, 1978-1982 Notes: Figure displays the county-level change in log real earnings per capita from 1978-1982, 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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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 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, 1978-1982 per capita growth, 1978-1982 Number Four-year Earned Adults Four-year Earned Adults of births, college income, living in college income, living in mil. graduates bil. $ poverty graduates bil. $ poverty (1) (2) (3) (4) (5) (6) (7) Age in 1979 0-10 36.0-643,700-27.9 356,903-1,473,351-63.9 816,908 11-19 34.1-324,478-26.9 156,321-737,019-61.0 355,067 20-28 34.6 68,998-9.0 41,038 149,447-19.6 88,887 0-28 104.8-899,180-63.8 554,261-2,060,924-144.5 1,260,861 Notes: Table displays back of the envelope calculations of the aggregate long-run effects of the 1980-1982 recession.
- 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 High School or GED Attainment Comparison Group: Age 23-28 in 1979 Exposed to Recession: Age 0-22 in 1979 H0: À24 = À27 = 0 p = 0.840 -.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.15. The dependent variable is an indicator for high school or GED attainment.
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- Sources: BEA Regional Economic Accounts, Census County Business Patterns, Confidential 2000-2013 Census/ACS data linked to the SSA NUMIDENT file Figure A.17: 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: À24 = À27 = 0 p = 0.604 -.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.15. The dependent variable is an indicator for any college attendance.
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- Sources: BEA Regional Economic Accounts, Census County Business Patterns, Confidential 2000-2013 Census/ACS data linked to the SSA NUMIDENT file Figure A.18: 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: À24 = À27 = 0 p = 0.800 -.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.15. The dependent variable is an indicator for any college degree attainment.
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- Sources: BEA Regional Economic Accounts, Census County Business Patterns, Confidential 2000-2013 Census/ACS data linked to the SSA NUMIDENT file Figure A.19: 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: À24 = À27 = 0 p = 0.553 -.2 -.1 0 .1 .2 .3 .4 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.15. The dependent variable is an indicator for two-year college degree attainment (exactly).
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- Sources: BEA Regional Economic Accounts, Census County Business Patterns, Confidential 2000-2013 Census/ACS data linked to the SSA NUMIDENT file Figure A.20: Predicted Log Employment Change, 1978-1992 and Predicted Log Employment Change, 1978-1982 Overall R2 All counties = 0.452 Low mining = 0.848 High mining = 0.388 Partial R2, 1978-1982 change All counties = 0.076 Low mining = 0.355 High mining = 0.005 -1 -.5 0 .5 1
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- Sources: BEA Regional Economic Accounts, Census County Business Patterns, Confidential 2000-2013 Census/ACS data linked to the SSA NUMIDENT file Table A.20: The Long-Run Effect of the 1980-1982 Recession on Four-Year College Degree Attainment, Robustness to Instrumental Variable Instrumental variable: Predicted log employment decrease from 1978-1982 in All industries All industries Manufacturing All regions Same region Same region (1) (2) (3) Interaction between 1978-1982 decrease in log real earnings per capita and age in 1979 0-10-0.389*** -0.303*** -0.278* (0.112) (0.109) (0.155) 11-19-0.191** -0.159** -0.159* (0.0873) (0.0801) (0.0917) 20-28 0.00831 0.0306 0.0423 (0.0480) (0.0426) (0.0477) Notes: The dependent variable is an indicator for four-year college degree attainment. See notes to Table 2. See equation (2) for definition of instrumental variables.
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- Sources: BEA Regional Economic Accounts, Confidential 2000-2013 Census/ACS data linked to the SSA NUMIDENT file, Confidental PSID data Figure A.12: Comparison of Birth County Out-Migration Rates by Cohort 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1
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- Sources: BEA Regional Economic Accounts, County Business Patterns, Census County Data Books, Minnesota Population Center (2011) Table A.4: The Effect of the 1980-1982 Recession on Log Median Family Income, Rents, and House Values, 2SLS Estimates Dependent variable: 1980-1990 change in Log median Log median Log median family income rent house value (1) (2) (3) Panel A: Excluding high mining counties Change in log real earnings 1.004*** 0.721*** 0.780** per capita, 1978-1982 (0.166) (0.246) (0.349) Observations 2,550 2,550 2,550 Panel B: All counties Change in log real earnings 0.221 0.003 0.220 per capita, 1978-1982 (0.182) (0.156) (0.244) Observations 3,076 3,076 3,076 Notes: I use the predicted log employment change in all industries as an IV. Regressions include state fixed effects and the change in log real median family income from 19501970.
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- Sources: BLS Local Area Statistics, Census County Business Patterns, Census Annual Population Estimates, and BEA Regional Economic Accounts data Table A.8: Sample Construction and Match Statistics Panel A: Number of individuals satisfying sample criteria Meet baseline demographic criteria 27,374,000 and have non-missing PIK 26,253,000 and have non-duplicate PIK 26,147,000 and have unique birth county 24,462,000 Panel B: Birth county match type, as percent of total Exact 76.85 Exact - abbreviation 3.57 Duplicate 10.95 Probabilistic 5.11 Hand check 3.52 Notes: The baseline demographic criteria are having non-imputed values for state of birth, birth year, sex, and race, plus being born in the U.S. according to the Census/ACS survey. A duplicate PIK is one that appears more than once in a survey year. Sample contains individuals born from 1950-1980 who are age 25-64 at the time of the survey.
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- Sources: National Center for Health Statistics (1970-1979), BEA Regional Economic Accounts, County Business Patterns, Census County Data Books, Minnesota Population Center (2011) Table A.17: The Long-Run Effect of the 1980-1982 Recession on Four-Year College Degree Attainment, Robustness to Fixed Effects (1) (2) (3) Interaction between 1978-1982 decrease in log real earnings per capita and age in 1979 0-10-0.303*** -0.244*** -0.223*** (0.109) (0.0830) (0.0789) 11-19-0.159** -0.102* -0.0999* (0.0801) (0.0614) (0.0599) 20-28 0.0306 0.0409 0.0407 (0.0426) (0.0365) (0.0345) Age in 1979 by birth state fixed effects X Age in 1979 by birth division fixed effects X Age in 1979 by birth region fixed effects X Notes: The dependent variable is an indicator for four-year college degree attainment. See notes to Table 2. There are nine divisions and four regions, as defined by the Census Bureau.
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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.
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.
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.
- The analysis also could be extended to the model described in Section 3, where parents purchase traded and nontraded goods for their children and allocate their time between market work, investment in child human capital, and leisure. In this case, the relevant indifference condition is twork(1 − Ä)ŵ = syp̂, where twork ∈ [0, 1] is the share of time allocated to market work and ŵ is the proportional change in the wage. For a given decrease in wages, parents require a smaller non-traded price decrease to remain indifferent because the price of time with children and leisure falls. 48 Bartik (1991) finds similar effects on local prices and wages. Blanchard and Katz (1992) find that median house prices initially decline more than wages, but that both approximately converge within 12 years (Figures 12 and 15).
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- The regression is estimated by 2SLS, using the predicted log employment change from 1978-1982 as an instrumental variable. The dashed lines are pointwise 95 percent confidence intervals based on standard errors clustered by state.
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- The sample in columns 3-11 contains 18.4 million individuals born from 1950-1979 in the continental U.S. with a unique birth county and non-imputed demographic, education, and labor market variables. Information on migration from birth county is not available from publicly available Census/ACS data, and I have not disclosed these statistics from the confidential Census/ACS data.
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- To examine this, I estimate regressions that include the decrease in log real earnings per capita from 1978-1982 and from 1978-1992.88 As instrumental variables, I use the predicted log employment change from 1978-1982 and 1978-1992, based on a county’s 1976 industrial structure.89 While this approach separates the temporary and persistent declines in earnings per capita that emerged at the onset of the 1980-1982 recession, a limitation that should be considered in interpreting these results is that not all of the industry-level employment changes from 1978-1992 are due to the 1980-1982 recession.
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- To increase precision, I combine ages 0-1, 2-4, 5-7, 8-10, 11-13, 14-16, 17-19, 20-22, 23-25, and 26-28. The sample contains 23.5 million individuals born in the continental U.S. from 1950-1979 with a unique birth county and nonimputed variables.
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- To quantify the average degree of persistence, I estimate the regression ln(Ec,1992) = α + β ln(Ec,1982) + γ ln(Ec,1978) + Xcδ + vc. (A.3) Xc includes state fixed effects and the 1950-1970 change in log real median family income in county c, which I include in equation (1) when estimating long-run effects on individuals. Equations (A.2) and (A.3) are equivalent when β+γ = 1 and Xc is included in equation (A.2). However, equation (A.3) eliminates the bias that arises from estimating equation (A.2).
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- Topel, Robert H. 1986. “Local Labor Markets.†Journal of Political Economy, 94(3): S111–S143.
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Wasi, Nada, and Aaron Flaaen. 2015. “Record Linkage Using State: Preprocessing, Linking, and Reviewing Utilities.†The Stata Journal, 15(3): 672–697.
- where N is the total number of establishments and yi is employment at establishment i. Because yi is not observed, y2 i cannot be formed. An alternative would be to use only moment conditions (A.7) - (A.9). that the imputed means for the four establishment size groups are 1247, 1952, 3414, and 7055.70 E 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 likely attenuates estimates of Àa in equation (1), and I quantify the size of this attenuation.
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Wozniak, Abigail. 2010. “Are College Graduates More Responsive to Distant Labor Market Opportunities ?†Journal of Human Resources, 45(4): 944–970.