Manufacturing Decline, Housing Booms, and Non-Employment Manufacturing Decline, Housing Booms, and Non-Employment

Transcription

1 Manufacturing Decline, Housing Booms, and Non-Employment Manufacturing Decline, Housing Booms, and Non-Employment Kerwin Kofi Charles University of Chicago Harris School of Public Policy And NBER Erik Hurst University of Chicago Booth School of Business and NBER Matthew J. Notowidigdo University of Chicago Booth School of Business and NBER November 2013

2 Manufacturing Decline, Housing Booms, and Non-Employment Housing Booms, Labor Market Outcomes, and College Enrollments Kerwin Kofi Charles University of Chicago Harris School of Public Policy And NBER Erik Hurst University of Chicago Booth School of Business and NBER Matthew J. Notowidigdo University of Chicago Booth School of Business and NBER November 2013

3 This Paper We study how changes in the manufacturing sector and in the housing market affected non-employment since Focus on these two phenomena because since 2000 there have been historically dramatic changes in both sectors - Accelerated manufacturing employment decline starting around Starting in the late 1990s, massive housing boom with housing prices rising by 37% followed by a near total reversal.

4 Total U.S. Manufacturing Employment (in 1,000s): ,000 20,000 ~1 Million Jobs Lost During 1980s and 1990s 18,000 16,000 14,000 ~3.8 Million Jobs Lost During ,000 Even More Jobs Lost After ,000 8,

5 Research Questions What were the separate effects of the decline in manufacturing and the boom and bust in the housing market on non-employment for less-skilled men and other groups? How did these two market wide changes interact to affect labor market outcomes? Use our estimates to assess how labor market outcomes would have evolved had the manufacturing decline and/or housing boom/bust not occurred. Assess whether the housing boom/bust affected college enrollment and education attainment [Preliminary Results] Note: Our analysis focuses on the period and the period.

6 Research Findings Manufacturing decline reduced wages and employment for low skilled workers during the 2000s. Much of this occurred prior to the recession. Manufacturing decline can explain a sizeable portion of the nonemployment increase during the 2000s. The housing boom raised wages and employment during the period. We show that the housing boom masked the manufacturing decline during the early 2000s. Post-recession, manufacturing employment opportunities continued to erode and housing markets returned to normal. The housing boom deterred human capital acquisition during the late 1990s and early 2000s. A substantial portion of the slowdown in educational attainment can be explained by the housing boom.

7 Empirical model: Local Labor Market Approach Changes in Labor Market Outcomes at Local Level ( ΔL k ) Definitions: M H O Δ L = f( ΔD, ΔD, ΔD, Δθ ) k k k k k (1) (2) (3) (4) (1) Effect of manufacturing labor demand change (through all channels) o Use shift share analysis to get predicted changes in manufacturing demand. (1) Effect of housing related labor demand change (2) Effect of other labor demand changes (not captured by first two) (3) Effect of labor supply change/labor supply parameters. Goal is to estimate M dδl / dδ D and dδl / dδd k k k H k

8 Predicted Change in Housing Demand To derive this measure, we start with a simple formulation of housing demand and supply: D D D ln( H ) = ω η ln( P) k k k k S S S ln( H ) = ω + η ln( P) k k k k D S ω, ω : factors that drive local housing demand and supply, respectively. k k D S η, η : price elasticities of housing demand and supply, respectively. k k Denoting Δ as the log difference and imposing equilibrium condition that D S H = H, the shock to housing demand can be expressed as: k k Δ ω = η Δ P +ΔH D D S k k k k Housing demand shock affects both prices and quantities.

9 Predicted Change in Housing Demand Δ ω = η Δ P +ΔH D D S k k k k Price change can affect local labor market outcomes via wealth/liquidity effect which affects demand for local services. Quantity change can affect local labor market outcomes via changes in local construction activity. Assuming no shocks to housing supply, we can express housing demand change in terms of observables. D D S Δ ω = ( η + η ) ΔP k k k k (Key Expression) o Local house price change (from FHFA data) o Local housing supply elasticity (from Saiz, 2010). o Local housing demand elasticity: base case = 0.7 (Polinsky and Elwood 1979). (Key assumption is local housing demand elasticity is uncorrelated with Δω k s, η k s, and other determinants of ΔL k.)

10 Data For Main Results 2000 Census and (pooled) and (pooled) ACS o Compute share non-employed, working in manufacturing, working in construction, population, wages, etc. for each of our 235 MSAs. o Compute separately by sex skill groups. o Focus on individuals aged o Exclude those living in group quarters. Focus on two time periods explicitly: o (before recession started) o (over the entirety of the 2000s) Also explored period (Implicit in some of our analysis given that effects during recession reconcile differences between two periods)

11 Estimating Equations M D O Δ L = β + β Δ D + β Δ ω + αx +Δ D +Δ θ + ε k 0 1 k 2 k k k k k (Unobserved) D M Δ ω = δ + δ Δ D + f( Z ) + γx O + λδ D + ϕδ θ + υ (2) k 0 1 k k k k k k (1) Effects of interest: o β 1 + δ 1 β 2 (Total effect of predicted manufacturing decline) o β 2 (Effect of predicted housing demand change) Key Assumption: Housing demand change does not affect predicted manufacturing decline (Data strongly support this assumption)

12 Estimating Equations M D O Δ L = β + β Δ D + β Δ ω + αx +Δ D +Δ θ + ε k 0 1 k 2 k k k k k (Unobserved) D M Δ ω = δ + δ Δ D + f( Z ) + γx O + λδ D + ϕδ θ + υ (2) k 0 1 k k k k k k (1) Effects of interest: o β 1 + δ 1 β 2 (Total effect of predicted manufacturing decline) o β 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)

13 Estimating Equations M D O Δ L = β + β Δ D + β Δ ω + αx +Δ D +Δ θ + ε k 0 1 k 2 k k k k k (Unobserved) D M Δ ω = δ + δ Δ D + f( Z ) + γx O + λδ D + ϕδ θ + υ (2) k 0 1 k k k k k k (1) 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 o Housing demand change may be result of ΔD k or Δθ k (omitted variables bias) Instrument using sharp, structural break in quarterly house price series that occurred in some MSAs during mid-2000s. o Bubble?

14 Illustration of Instrument

15 Illustration of Instrument

16 Discussion of Instrument We construct instrumental variable as follows: o Using quarterly house price data, we run MSA-specific regression of (residualized) house prices on quadratic and structural break term. o Choose location of structural break to maximize R 2 of above regression. The magnitude of this estimated coefficient for each MSA is the instrumental variable. o Instrument scaled to be an annual growth rate, so a value of 0.1 indicates that annual percentage increase in real house prices jumps discontinuously by 10 pctg pts at the location of the structural break. Instrument strongly predicts predicted housing demand change Uncorrelated with many observable characteristics o Housing supply elasticity, predicted manufacturing decline, lagged non-employment, lagged house price growth, etc.

17 Relationship Between Instrument and Housing Demand Change

18 Relationship Between Instrument and Lagged Housing Change

19 Relationship Between Instrument and Supply Elasticity

20 Relationship Between Instrument and Lagged Non-Employment

21 Relationship Between Instrument and Lagged Wages

22 Instrument vs. Out of Town Buyers (21 MSAs)

23 Housing Booms and Housing Busts

24 Housing Instrument and Housing Busts ( )

25 First Stage

26 Specification: Baseline Non-Employment Results (TSLS) Dependent Variable: Change in Non-Employment Rate, Non-College College Non-College College Men Men Women Women All D Δω k Δ D M k (0.006) (0.002) (0.004) (0.003) (0.003) (0.228) (0.120) (0.155) (0.164) (0.134) Standardized Effects D Δω k Δ D M k (1σ) (1σ) R

27 Specification: Baseline Construction Results (TSLS) Dependent Variable: Change in Construction Employment Share, Non-College College Non-College College Men Men Women Women All D Δω k Δ D M k (0.004) (0.002) (0.001) (0.001) (0.002) (0.197) (0.096) (0.036) (0.035) (0.107) Standardized Effects D Δω k Δ D M k (1σ) (100%) (40%) (75%) (1σ)

28 Additional Results Estimate wage response. Wages respond to declines in manufacturing and housing demand shocks. Implied labor supply elasticity of men ~ Manufacturing declines result in larger non-employment increases for old workers relative to young. Housing demand increases results in larger non-employment responses for immigrants than non-immigrants. Show that declines in manufacturing (increase in housing demand) increases (decreases) participation in public assistance programs. Estimate migration responses. Manufacturing declines (housing demand increases) lead to outmigration (in migration) of prime age individuals.

29 Table 4: Non-Employment Results, Long Run ( ) Specification: D Δω k Δ D M k Changes Defined Non-College All Men (0.013) (0.008) (0.421) (0.261) Standardized Effects D Δω k Δ D M k (1σ) (1σ) R

30 Interpretation Did housing boom mask the deterioration of U.S. labor markets during the period? Masking could occur within and between MSAs and individuals: o Between-MSA Masking: Places that experienced manufacturing decline could be spatially different than places that experienced housing demand boom. (Masks at aggregate level, not local level). o Within-MSA / Between-Individual Masking: People within a given MSA affected by manufacturing shock not the same as those affected by housing demand boom. (Already showed some evidence of this across age groups). o Within-Individual Masking: Some workers who would have been negatively affected by manufacturing decline were able to find work because of temporary increase in housing demand. o Document within-individual masking using DWS.

31 Interpretation Did housing boom mask the deterioration of U.S. labor markets during the period? Masking could occur within and between MSAs and individuals: o Between-MSA Masking: Places that experienced manufacturing decline could be spatially different than places that experienced housing demand boom. (Masks at aggregate level, not local level). o Within-MSA / Between-Individual Masking: People within a given MSA affected by manufacturing shock not the same as those affected by housing demand boom. (Already showed some evidence of this across age groups). o Within-Individual Masking: Some workers who would have been negatively affected by manufacturing decline were able to find work because of temporary increase in housing demand. o Document within-individual masking using DWS.

32 Counterfactuals (With a Grain of Salt) Declining manufacturing contributed about 3 percentage points (40 percent) to the increase in non-employment during the Housing boom raised non-employment rates by about 1 percentage point (30 percent) during the period. Housing booms had little total effect on non-employment during the entire period. Bound some of these results using mobility estimates. Discuss other general equilibrium responses not captured by local labor market analysis.

33 Housing Booms and Human Capital [ WORK IN PROGRESS ]

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

35 Did Housing Boom Reduce College Enrollment? Use same local labor market design to answer this question Data from IPEDS States with MSAs that had large housing booms had a large reduction in first-time, full-year college enrollment o Effects are heavily concentrated in two-year colleges (e.g., community colleges, junior colleges, technical schools, trade schools, etc.) o Similar magnitudes for both men and women o Noticeably larger IV results as compared to OLS (likely due to greater measurement error in housing demand shock in state-level analysis) During the bust, this trend reversed, but only partially, so effects persist

36 Some Broader Conclusion Sectoral declines are important part of understanding the weak labor market in the aggregate. The full effects of these sectoral declines would have shown up earlier in aggregate statistics if not for the temporary housing boom. Individual workers who would have left labor market earlier were kept in market by housing boom. o Important to keep in mind given rising U N transition rate (especially among long-term unemployed). Housing boom deterred college entry and slowed down the adjustment to the sectoral decline.