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Manufacturing Busts, Housing Booms, and Declining Employment September 2012 PowerPoint Presentation
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Manufacturing Busts, Housing Booms, and Declining Employment September 2012

Manufacturing Busts, Housing Booms, and Declining Employment September 2012

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Manufacturing Busts, Housing Booms, and Declining Employment September 2012

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  1. Manufacturing Busts, Housing Booms, and Declining Employment September 2012

  2. This Paper • Try to explain why employment rate changed within the U.S. during the 2000s • Focus on two prominent phenomenon: o Dramatic decline in manufacturing employment (secular decline) o Transitory housing boom followed by housing bust. • Assess how those shocks affected employment rates (and other labor market outcomes) during 2000-2007, 2007-2010, and 2000-2010 periods . • Run counterfactuals “shutting off” the labor market effects of each of the changes. Isolate importance of manufacturing declines. • Look at the effects of two phenomenon on human capital attainment.

  3. Total U.S. Manufacturing Employment (in 1,000s)

  4. Total U.S. Manufacturing Employment (in 1,000s) ~1.5 Million Jobs Lost During 1980s and 1990s

  5. Total U.S. Manufacturing Employment (in 1,000s) ~1.5 Million Jobs Lost During 1980s and 1990s ~3.8 Million Jobs Lost During 2000-2007

  6. Total U.S. Manufacturing Employment (in 1,000s) ~1.5 Million Jobs Lost During 1980s and 1990s ~3.8 Million Jobs Lost During 2000-2007 Even More Jobs Lost After 2007 7/12

  7. This Paper: Estimate Effects on Employment Rate • Causally estimate effects using a local labor market strategy. • Focus on different groups: Primary focus is on non-college men.

  8. This Paper: Estimate Effects on Employment Rate

  9. This Paper: Estimate Effects on Employment Rate

  10. This Paper: Estimate Effects on Employment Rate

  11. This Paper: Estimate Effects on Employment Rate • The housing shock “masked” the labor market effects of the manufacturing shock during the 2000-2007 period.

  12. Summary of Main Findings • Manufacturing decline is important for thinking about changes in non-employment during 2000s. o About 35-40% of increase in non-employment during 2000s can be attributed to the decline in manufacturing. • Labor market was significantly weaker in the 2000-2007 period than we thought. o Housing boom “masked” deterioration of U.S. labor market. o 2000-2007 period marked by secular decline in one sector and a temporary boom in another sector. o Implication: 2007 may not be a good benchmark to evaluate cyclical changes in economic variables of interest.

  13. Summary of Main Findings 3. About one-third of the increase in non-employment during the recession can be attributed to o manufacturing declines during 2007-2010 period, and o manufacturing declines during the 2000-2007 period that were masked by housing boom. • The net effect of housing booms/busts on labor markets was small over the entire decade. o The bust reduced employment but the boom raised employed 5. Housing boom deterred college attainment during 2000-2007 period.

  14. A Word on the “Masking” Effect • Masking occurred both across and within individuals. o Housing booms were often in places that didn’t experience the manufacturing declines. o Type of workers affected differed slightly (by age, skill, and nativity). o However, even for a given individual, evidence that those that were displaced from manufacturing were more likely to find employment in places with a housing boom. • Both types of masking are interesting. o Implies that even though the aggregate employment rate may have been relatively stable during 2000-2007 period, there could still have been distributional effects (across people and locations).

  15. Plausibility of “Masking” Effect? • For our empirical work, we are going to identify effects using cross MSA variation. o Different MSAs received different combinations of manufacturing and “housing” shocks. o For our aggregate calculations, need to discuss the scaling up of local estimates (migration, etc.) • However, the potential plausibility of maskingcan be seen from the time series data.

  16. Employment Trends for Non-College Men (age 21-55)

  17. Employment Trends for Non-College Women (age 21-55) Manufacturing + Construction Share Manufacturing Share

  18. Median Real Wage for Non-College Men (age 21-55)

  19. Long-Run Increase in Non-Employment? • Results do not imply a permanent increase in non-employment o Workers could choose to acquire skills which could increase market wage. o Workers could choose to move to different labor markets. • We think of this is as more of a medium run increase (as opposed to being just do to cyclical fluctuations) – adjustments take time. • Our force is different from traditional mismatch stories. o For us, people are just moving up and down labor supply curve in response to labor demand shocks (market wage < reservation wage). • However, our results suggest that temporary government policies to stimulate labor demand will NOT have lasting effects on employment. o Only implies to the 30-40% of non-employment increase we identify.

  20. Outline • Conceptual model • Empirical model • Main results • Counterfactual estimates • Examine effects on human capital attainment • Conclusion

  21. Conceptual model • Purpose - To provide a simple model which highlights: o the interplay between shocks in different sectors o when those shocks will result in changes in nonemployment. o reasons why the response to nonemployment resulting from a shock may change over time.

  22. Conceptual model • Mass of workers have skill endowment s and reservation wage r, distributed according to F(s,r). • Workers can either choose to be “employed” in either sector A or sector B (which pay wA and wB per efficiency unit, respectively), or they can choose to work in “home” sector H. • Worker of type (s,r) can either supply s efficiency units in A or (1-s) in B. o Therefore, worker chooses employment in AiffswA> (1-s)wBand swA>r • To simplify exposition, assume aggregate production function given by the following: Y = αLA + βLB so that wA = αand wB = β

  23. r α LH β LB LA s s* given by αs*=β(1-s*)

  24. r α LH β A → H LB LA A → B s s* s' given by αs*=β(1-s*)

  25. r α LH β H → B A → H LB LA A→H→B A → A → B A → B s s* s' s'' given by αs*=β(1-s*)

  26. Empirical model • Changes in Labor Market Outcomes at Local Level (1) (2) (3) (4) Definitions: • Effect of Manufacturing Labor Demand Shock (through all channels) • Effect of “Housing Related” Labor Demand Shock • Effect of “Other” Labor Demand Shocks (not proxied by first two) • Effect of Labor Supply Shocks Note: k denotes a local labor market (e.g., MSA) Lk could be employment rate, wages, employment in a sector, etc.

  27. Empirical model • Changes in Labor Market Outcomes at Local Level Our Goal: Estimate: Problems: o We do not observe o We ideally want proxies which are orthogonal to the labor supply shock. Note: We will estimate a causal channel of manufacturing shock on labor market outcomes (housing will be more of a catch all).

  28. Creating a Local Manufacturing Shock • Instrument for the local declines in manufacturing. • Construct predicted change in manufacturing employment following Bartik (1991) ( ). o interact pre-existing cross-sectional variation in industry employment with national industry employment trends. o Key assumption: initial industry variation across MSAs uncorrelated with (local labor supply changes) • Instrument is strongly predictive of actual changes in manufacturing employment.

  29. Creating a “Housing Related” Labor Demand Shock • Use housing price growth in local area ( ) as our measure of housing related demand shock. • Intuition – We have two direct housing related labor demand channels o Wealth Effect Channel: (+) o Construction Demand Channel: (?) • The relationship between construction effect on labor demand and house prices will be positive if variation in house prices is due to variation in housing demand.

  30. Relationship Between Housing Price Growth and Change in Construction Share (Non-College Men, 25-55)

  31. Empirical Model • Note: Housing prices are endogenous • Where Z is some measure of housing supply across locations. • Where is some national change in housing demand. Note: We do not want to take a stand on why house prices changed during the 2000s.

  32. What We Estimate Comment 1: o Effect of manufacturing shock on labor market outcomes includes the direct effect and the indirect effect through house prices - In essence, the house price measure is residualized of manufacturing shock.

  33. What We Estimate Comment 2: o We estimate the above via OLS o We also estimate the above instrumenting for to isolate a more causal channel of house prices on labor market outcomes. - Use variation in Z across places (Saizdevelopable land measure). - Use temporal variation in house price movements within a city (a new instrument). o Not necessarily important for our purposes to estimate a causal relationship. Want to isolate variation orthogonal to θk. o OLS results and IV results are very similar in mostspecifications.

  34. Data For Main Results • 2000 Census and 2005-2007 and 2009-2010 ACS o Most of our analysis comes using Census/ACS data. o All of our analysis starts in 2000 (as a result) o Focus on individuals aged 21-55. • FHFA metro house price indexes • Index of Available Land (Saiz 2010) o Identical results if we use his housing supply elasticity measure.

  35. Time Periods • Base estimation: 2000 – 2007 period o Start in 2000 because of data limitations. o Want to focus on pre-recessionary period to get estimated responses. o Interesting to focus on the boom period (highlights masking). • Follow up with estimation during the 2007-2010 o Can see if the responses change in different periods. • Discuss long changes in outcomes: 2000-2010 o Highlights the role of the temporary effects of housing booms.

  36. A Little More on the Bartik Instruments

  37. Bartik Shock vs. Actual Change in Manufacturing

  38. Bartik Shock and House Price Growth, 2000-2007

  39. Estimates from the Empirical Model: Some Graphical Results

  40. Change in non-employment rate for non-college men, 2000-2007

  41. Change in non-employment rate for non-college men, 2000-2007

  42. Change in average wage for non-college men, 2000-2007

  43. Change in average wage for non-college men, 2000-2007

  44. Change in construction employment share, 2000-2007

  45. Change in construction employment share, 2000-2007

  46. Change in manufacturing employment share, 2000-2007

  47. Change in manufacturing employment share, 2000-2007

  48. Estimates from the Empirical Model:Formal Estimates

  49. Table 4: Baseline Results

  50. Table 4: Baseline Results