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“Masking and Aggregate Employment Changes: Housing Booms and Manufacturing Decline During the 2000s” “Housing Booms, Labor Market Outcomes and Educational Attainment”. Research Design. A local labor market approach o Identify a “manufacturing” labor demand shifter
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“Masking and Aggregate Employment Changes: Housing Booms and Manufacturing Decline During the 2000s” “Housing Booms, Labor Market Outcomes and Educational Attainment”
Research Design • A local labor market approach o Identify a “manufacturing” labor demand shifter o Identify a “housing boom” labor demand shifter • Some towns experienced larger manufacturing declines than others o Detroit vs. Orlando • Some towns experienced larger “housing” demand shocks than others o Las Vegas vs. Dallas • Adjust for migration responses
The Manufacturing “Instrument”: Shift Share (Bartik) • Identifying Assumption: o Manufacturing composition in location k inperiod t is orthogonal to local supply shocks and local changes in demand of other sectors. • Highly Predictive “First Stage”: o Shift share measure strongly predicts actual manufacturing employment changes within the MSA.
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.
Estimating Equations Effects of interest: o β1 + δ1β2 (Total effect of predicted manufacturing decline) o β2 (Effect of predicted housing demand change) Key Assumption: Housing demand change does not affect predicted manufacturing decline in location (Data strongly support this assumption)
Estimating Equations • Motivation for using an instrument for housing demand change: o Housing demand change measured with error (e.g., housing supply shocks are possible, measurement error in supply elasticity estimate). o Housing 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. o Isolate the “Bubble” component of housing demand change (wish test) o Look for “structural breaks” in housing demand series.
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 o As opposed to traditional housing demand factors (e.g., income growth, population growth, etc.) o Speculative behavior may differ spatially. o Lending 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.
Effects on Employment: Manufacturing Decline • Manufacturing declines depress employment o A one standard deviation manufacturing decline reduced employment by 0.7 percentage points between 2000 and 2007. o A 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).
Effects on Employment: Housing Boom • Housing boom lifted employment o A one standard deviation housing demand change increased employment by about 1 percentage points between 2000 and 2007. o A 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.
Estimated Effect of Manufacturing Decline on Non-Employment ~42% Explained
Estimated Effect of Housing Cycle on Non-Employment Manufacturing Decline Housing Cycle (Construction and Other)
The Housing Boom Masked The Manufacturing Decline in 2000s Data Manufacturing Housing + Manufacturing
The Housing Boom Masked The Manufacturing Decline in 2000s Data 34% During Recession Manufacturing Housing + Manufacturing
Propensity to Have At Least One Year of College (Age: 18-29)
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. o Nearly all the action was on two year colleges (community colleges, technical schools, trade schools, etc.). o Found effects for both men and women. o Effect 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.
Interpretation • Housing boom “masked” some of the labor market effects of declining manufacturing during the early 2000s. o Cross-MSA masking (Detroit vs. Las Vegas) o Cross individual masking (Old hurt by manufacturing decline while young lifted by housing boom) o Within 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? o Our results suggest no o Large temporary housing boom lifted labor markets during early 2000s and then brought them back to trend (particularly for low skilled).
Interpretation • We are predicting a period of a “medium run” decline in employment to population decline (relative to pre-recession period) o Some displaced middle age and older workers in manufacturing decline MSAs have taken up disability (Sloane, 2014). o Younger 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? o We think so. We are working on estimating the transition rate across sectors for different types of workers.
Policy Thoughts • Temporary policy stimulus (either monetary or fiscal) will: o Only have temporary effects on labor market outcomes o Potentially 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. o Not likely something influenced by Fed policy.
