1. “Masking and Aggregate Employment Changes: Housing Booms and Manufacturing Decline During the 2000s” “Housing Booms, Labor Market Outcomes and Educational Attainment” Kerwin Charles Erik Hurst Matt Notowidigdo

2. Research Design A local labor market approach oIdentify a “manufacturing” labor demand shifter oIdentify a “housing boom” labor demand shifter Some towns experienced larger manufacturing declines than others oDetroit vs. Orlando Some towns experienced larger “housing” demand shocks than others oLas Vegas vs. Dallas Adjust for migration responses

3. Simple Labor Market Model

4. Estimating Equation

5. The Manufacturing “Instrument”: Shift Share (Bartik) Identifying Assumption: oManufacturing composition in location k in period t is orthogonal to local supply shocks and local changes in demand of other sectors. Highly Predictive “First Stage”: oShift share measure strongly predicts actual manufacturing employment changes within the MSA.

6. Predicted vs. Actual Change in Manufacturing

7. Inferring Housing Demand Changes Assuming no local housing supply shocks Housing demand changes are potentially correlated with other labor demand changes and labor supply changes. Need an instrument.

8. Estimating Equations Effects of interest: oβ 1 + δ 1 β 2 (Total effect of predicted manufacturing decline) oβ 2 (Effect of predicted housing demand change) Key Assumption: Housing demand change does not affect predicted manufacturing decline in location (Data strongly support this assumption)

9. Estimating Equations Motivation for using an instrument for housing demand change: oHousing demand change measured with error (e.g., housing supply shocks are possible, measurement error in supply elasticity estimate). oHousing demand change may be result of other labor demand shocks or labor supply shocks (omitted variables bias) Instrument using sharp, structural break in quarterly house price series that occurred in some MSAs during mid-2000s. oIsolate the “Bubble” component of housing demand change (wish test) oLook for “structural breaks” in housing demand series.

10. Identifying Assumptions  Trying to capture housing markets during the 2000s.  Evidence that national/local house prices changed in part because of speculative behavior and changes in lending technology oAs opposed to traditional housing demand factors (e.g., income growth, population growth, etc.) oSpeculative behavior may differ spatially. oLending technology changes may not differ spatially.  Our structural break measure is uncorrelated with all traditional labor market variables (lagged population growth, lagged employment growth, composition of workforce, etc.).  Our structural break measure is highly correlated with changes in Price-to-Rent ratios and share of out-of-town home owners in MSA.

11. Our New Housing “Instrument”: Structural Breaks

12. Relationship Between Instrument and Housing Demand Change

13. Relationship Between Instrument and Lagged Housing Change

14. Relationship Between Instrument and Supply Elasticity

15. Relationship Between Instrument and Lagged Non-Employment

16. Relationship Between Instrument and Lagged Wages

17. Instrument vs. “Out of Town” Buyers (21 MSAs)

19. Effects on Employment: Manufacturing Decline  Manufacturing declines depress employment oA one standard deviation manufacturing decline reduced employment by 0.7 percentage points between 2000 and 2007. oA one standard deviation manufacturing decline between 2000 and 2007 reduced employment by 1.1 percentage points between 2000 and 2011 (suggesting persistence in manufacturing declines).  Manufacturing declines also reduced wage growth 2000-2007 (but not between 2007 and 2011).  Manufacturing declines caused an in migration of workers (but employment propensities of the migrants were similar to natives).  Manufacturing declines hit older workers harder than younger workers (and also resulted in higher disability take ups).

20. Effects on Employment: Housing Boom  Housing boom lifted employment oA one standard deviation housing demand change increased employment by about 1 percentage points between 2000 and 2007. oA one standard deviation housing boom between 2000 and 2007 had essentially no effect on employment between 2000 and 2011 (the booms were followed by busts – different interpretation of Mian and Sufi results.)  Housing booms increased wage growth between 2000-2007 and 2000- 2011 (wags declines during bust didn’t offset the boom).  Housing boom caused an in migration of workers (but employment propensities of the migrants were similar to natives).  For men, employment response concentrated in construction (90%); For women concentrated in FIRE (about 50%). Housing boom hit younger workers more than older workers.

21. Within Individual Masking: Re-Employment Rate

22. A Simple Counterfactual

23. Estimated Effect of Manufacturing Decline on Non-Employment

24. ~42% Explained

25. Estimated Effect of Housing Cycle on Non-Employment Housing Cycle (Construction and Other) Manufacturing Decline

26. The Housing Boom Masked The Manufacturing Decline in 2000s Manufacturing Data Housing + Manufacturing

27. The Housing Boom Masked The Manufacturing Decline in 2000s Manufacturing Data Housing + Manufacturing 34% During Recession

28. Housing Boom and Human Capital Attainment

29. Propensity to Have At Least One Year of College (Age: 18-29)

30. Did Housing Boom Delay College Attendance?  Use same local labor market design to answer this question.  The answer is YES – in both survey and administrative data  Places that had large housing booms had a large reduction in the propensity to attend at least one year of college. oNearly all the action was on two year colleges (community colleges, technical schools, trade schools, etc.). oFound effects for both men and women. oEffect only present among “lost generation”; those who were young in the early 2000s in boom markets.  For this “lost generation”, the effect was persistent through 2013.  Estimates can explain about 40% of the time series change.

31. Summary: Our Interpretation

32. Interpretation  Housing boom “masked” some of the labor market effects of declining manufacturing during the early 2000s. oCross-MSA masking (Detroit vs. Las Vegas) oCross individual masking (Old hurt by manufacturing decline while young lifted by housing boom) oWithin individual masking (Displaced manufacturing workers are more likely to be reemployed in a MSA that experienced a housing boom).  Is the 2007 labor market the right benchmark to assess cyclical fluctuations? oOur results suggest no oLarge temporary housing boom lifted labor markets during early 2000s and then brought them back to trend (particularly for low skilled).

33. Interpretation  We are predicting a period of a “medium run” decline in employment to population decline (relative to pre-recession period) oSome displaced middle age and older workers in manufacturing decline MSAs have taken up disability (Sloane, 2014). oYounger workers will slowly adjust to new labor market conditions (process was delayed because of housing boom).  Is this transition from manufacturing (routine) jobs to non-routine services different from the transition from agriculture to manufacturing? oWe think so. We are working on estimating the transition rate across sectors for different types of workers.

34. Policy Thoughts  Temporary policy stimulus (either monetary or fiscal) will: oOnly have temporary effects on labor market outcomes oPotentially slow down the human capital accumulation process  For example, another temporary housing boom could temporarily improve labor markets and again deter schooling choices.  How do we train workers displaced by manufacturing (routing jobs) to move to non-routine services?. O Are those workers willing/able to work at service job wages? O Will those policies only work for younger workers – or can they lift the employment propensities of older workers. oNot likely something influenced by Fed policy.