Micro Data For Macro Models

1. Micro Data For Macro Models Topic 3: Home Production, Leisure and Labor Supply

2. Home Production and Labor Supply Elasticities

3. Simple Labor Supply Example: No Home Production Look at static model:

4. Simple Labor Supply Example: No Home Production

5. How Do Things Change With Home Production?

6. How Do Things Change With Home Production?

7. Interpretation Home production makes work hours more elastic to changes in wages (holding the marginal utility of wealth constant). Implications: Women’s labor supply more elastic than men (if they do most of the home production) (Mincer 1962) Labor supply is more elastic during temporary wage changes (recessions) with home production. Expenditure (X) is more elastic during temporary wage changes (recessions) with home production. Has business cycle implications….

8. Home Production and The Business Cycle

9. Business Cycle Variation in Hours Standard business cycle models have trouble matching the business cycle patterns of hours worked, consumption, and wages. Wages do not move that much – yet, there are big movements in consumption (measured as expenditures) and hours worked (measured as time spent in the market sector). Trying to reconcile jointly the movements in expenditures, market hours worked and wages has spawned a large literature. o For a recent attempt at reconciliation, see Hall (JPE 2009) “Reconciling Cyclical Movements in the Marginal Value of Time and the Marginal Product of Labor” o Hall (2009) relies on non-separabilities in preferences between consumption and leisure.

10. Earlier Iterations Non-separabilities in preferences (as alluded to in previous lecture) can be thought of as a reduced form for a model with non-market production. Earlier models, tried to reconcile the joint movements of expenditures, hours worked and wages at business cycle frequencies by appealing to models of nonmarket production. o At business cycle frequencies, individuals substitute toward home production when leave labor force. o Small changes in wages can cause substitution of some households from the market sector to home sector. o Big declines in expenditure does not imply big declines in expenditure. o Home production shocks can drive business cycles! See work by Benhabib, Rogerson, and Wright (1991, JPE) and Greenwood and Hercowitz (1991, JPE).

11. Benhabib, Rogerson, Wright Conclusions Business cycle models with home production offer individuals another margin of substitution when wages move: o They can substitute market work hours for nonmarket work hours (when the opportunity cost of time falls). o Even though market work hours fall a lot, the sum of market plus nonmarket work may not fall by as much. Models with home production generate much bigger labor market responses to change in market productivity (wages) at business cycle frequencies. Models with home production generate much bigger declines in market expenditures in response to changes in market productivity at business cycle frequencies. Can pick parameter values for home production technology and shock process for the market and home technologies that can come very close to matching the data.

12. Aguiar, Hurst and Karabarbounis (2013) How does home production actually evolve during recessions? Until recently, that question was not answerable given there were no major data sets that included time use during periods spanning a recession. What we do is use the 2003-2010 ATUS to explore how time use actually evolves during recessions. Potential problem: - Low frequency trends in time use - Need to distinguish business cycle effects from these low frequency trends - Hard to do with short time series

13. Naïve Analysis

14. Look at the Pre-Trends

15. A Cross State Analysis: Home Production (Pooled Years)

16. A Cross State Analysis: Leisure (Pooled Years)

17. A Cross State Analysis: Home Production (Separate Years)

18. A Cross State Analysis: Leisure (Separate Years)

19. Cross State Estimates (Pooled Sample)

20. Implication 2: Are Home Sector Shocks Important? Data only for this recession. No evidence of home sector shocks. Run this on individual level data. Ast is a measure of aggregate labor market conditions in state s during time t (we use unemployment rate as our proxy). Regression asks whether people do more or less home production when aggregate conditions change (at state level) holding their work hours constant. Coefficient on Ast was zero (tightly estimated).

21. Conclusions A non-trivial fraction of the movement of consumption and hours can be explained by movements into home production. Do not have measures of home production output, only measures of home production inputs. The change in home production time during recessions matches well the prediction of business cycle models of labor supply, wages and consumption during recessions with home production. Is the elasticity of substitution between time and goods in home production during recessions the same as during non-recessionary periods? Still need to take a stance on the correlation of shocks between home and market sector at business cycle frequencies. No evidence that home production shocks were important during last recession.

22. “Leisure Luxuries and the Labor Supply of Young Men” Aguiar, Bils, Charles and Hurst Fall 2017

23. How Does Technology Affect Labor Markets? • Labor Demand(Relatively Large Literature): • o Skill biased technological change • o Most real business cycle models • o Automation in manufacturing/routine occupations • o “Robots” • Labor Supply (Much Smaller Literature): • o Home production technology and female labor supply • o Leisure technology and long run trend in hours worked

24. How Does Technology Affect Employment/Hours? • Simplified intuition • o Individuals compare market wage relative to reservation wage. • Technological change and labor markets: • o Market wage effects (perhaps lowering market wages for lower skilled workers in recent periods). • o Reservation wage effects (perhaps raising reservation wages during recent periods). • Note: If technology raises market wages and reservation wages at similar rates, little effect on labor supply.

25. What This Paper is About • Methodological: • Create a methodology (a leisure demand system) which uses data on how time is allocated to different leisure activities to infer innovations to leisure technology. • Application 1: • Use the methodology and detailed time use data to estimate changes in leisure technology for recreational computer activities during the 2000s (video games, social media, etc.). • Application 2: • Use our framework to assess how innovations in recreational computer leisure technologies increased the marginal value of leisure for different demographic groups and contributed to declining market hours during the 2000s.

26. Major Innovations in Computer Leisure Technology Starting in Mid-2000s • Able to engage in leisure activities easily with others at different locations. • Social media – Facebook started in 2004; grew from 12 million to 360 million users between 2006 and 2009. • Video games – Sony, Microsoft and Nintendo all released consoles in 2005/2006 that allowed online capabilities. Video game revenues increased by 50 percent 2006-2009 (were flat between 2000 and 2006). • Large multiplayer online video games developed over same time period. World of Warcraft started around 2005 and had 10 million monthly users by 2009. • iPhone released in 2007. Smart phones take off. • Time series trends in leisure technologies occurred around same time as Great Recession. Need theory to tease the two apart.

27. What We Actually Do: Part 1 • Describe changing nature of broad time use for various groups during 2000s • o Examine trends in schooling, home production, leisure, etc. • o Finding: Change in work ≈ change in leisure for most groups (younger men, older men, etc.) • Document shifts in the nature of leisure for various groups during the 2000s • o Finding: During this time, market work fell and leisure increased by about 130 hours per year in ATUS (from 2004-2015) • o Finding: Young men increased their computer leisure activities by 100 hours per year between 2004 and 2015 • o Finding: No comparable shift for other demographic groups

28. What We Actually Do: Part 2 • Heart of the paper: Introduce a leisure demand system • o Estimate Leisure Engel Curves • o Demonstrate importance of “leisure luxuries” • - Leisure luxuries are not subject to strong diminishing returns • - Very responsive to technological improvements • - Finding: Computer/Gaming is a strong leisure luxury • o Show how Leisure Engel Curves can be used to infer relative changes in leisure technology for different leisure goods. • - Finding: Large estimated increase in computer/video game technology during the 2000s (consistent with BLS price data) • o Key: movement along a Leisure Engel Curve vs. a shift in Leisure Engel Curve

29. What We Actually Do: Part 3 • Use leisure demand system and time use data to estimate change in the marginal return to leisure. • o Findings: Increased computer/video game technology resulted in a shift in of the labor supply curve by about 1.5 to 3.0 percent for young men during 2004-2015. • o Translates to about 20 – 40 hours per year. • o No effect on older men. • o Explains about 38% to 79% of the differential trends in labor supply for young vs older men during the 2004-2015 period. • o Absent the innovations in leisure technology, younger men would have had hours trends that were more similar to older men.

30. Part I:The Changing Allocation of Time

31. Time Use Data • American Time Use Survey (ATUS) • 24-hour recall time diaries, one diary per individual • Samples from the exiting rotation of the CPS • Examine patterns for 4 demographic groups based on age and gender: • o Younger: 21-30 • o Older: 31-55 • Exclude full time students from sample • Examine broad time use categories in Aguiar, Hurst and Karabarbounis (2013). • Pool data across years 2004-2007, 2008-2011, and 2012-2015

32. Changing Time Allocation of Men (Hours Per Week): Changes Between 2004-07 and 2012-15 Younger and older women increased leisure by 1.6 and 1.9 hours per week, respectively, over this time period.

33. Leisure Categories • 1. Adjusted Sleep, Eating and Personal Care • o Subtract off 49 hrs./week for biological needs • o 5th percentile of the distribution • o At state-time cell, no negative values • Computer Usage • o Includes video/computer games, email, time surfing the web, time surfing on smart phones, Facebook, etc. • o Caveat on video/computer games. • 3. TV/Movies/Netflix • o Includes time watching YouTube, streaming services, etc. • Socializing • Other Leisure

34. Hours per Week of Leisure Time, Young Men +2.5 +1.9 (+1.4)

35. Hours per Week of Leisure Time, By Employment Status

36. Distribution of Computer Time, Non-Working Young Men • Only one day of data for each individual

37. Computer Time By Adjusted Leisure Quartile: Men 21-30

38. Time Use (Hours Per Week) from ATUS, By Sex-Age-Skill Group

39. Part II:A Leisure Demand System

40. Static Model • Preferences: • c = spending on consumption good • hi = time input into leisure activity I • I = number of leisure activities • θ = {θi,….θI} = vector of leisure technology shifters • = aggregator over leisure activities • Key assumption 1: Weak separability between consumption and leisure aggregator (conditional on θ).

41. Static Model • Leisure Aggregator: • ηi = governs diminishing returns to spending time in activity i. • Goal: Estimate the ηi’sfrom the ATUS micro data • Allow the ηi’s to differ by demographic groups • Key assumption 2: Linear separability across leisure activities

42. Implication of Linear Separability Assumption, Young Men Note: Engel Curve Adjusted (more details later)

43. Two Stage Budgeting: Allocation of Leisure Across Activities • Given (weak) separability between consumption and leisure, can break the problem in two parts. • 1. Chose time allocation on leisure categories given total leisure • 2. Chose consumption, total leisure (market work), and technology • Given separability: • FOC: • ηi= elasticity of activity iw.r.t. the shadow value of leisure time (μ)

44. Leisure Engel Curves • Leisure Engel Curve: relationship between time spent on leisure activity iand total leisure time (H). • Define βi as the slope of leisure Engel curve: • Define: • βi > 1 is a “leisure luxury” - time is very responsive to technological changes in activity

45. Leisure Engel Curves • Leisure Engel Curve: relationship between time spent on leisure activity iand total leisure time (H). • Define βi as the slope of leisure Engel curve: • Define: • βi > 1 is a “leisure luxury” - time is very responsive to technological changes in activity

46. Overview Using Leisure Engel Curve to Uncover Relative ΔθI ln hI (time spent on leisure good I) Slope = βI (Drawn for a given θI) ln H (total leisure time)

47. Overview Using Leisure Engel Curve to Uncover Relative ΔθI ln hI (time spent on leisure good I) Slope = βI (Drawn for a given θI) ln H (total leisure time) • Estimate the βi’s for different groups using cross-region variation during the • 2004-2015 period. (Discuss this in a few slides). • Estimate βcomputer ≈ 2.0 for young men.

48. Overview Using Leisure Engel Curve to Uncover Relative ΔθI ln hI (time spent on leisure good I) Slope = βI (Drawn for a given θI) ln H2005 ln H (total leisure time)

49. Overview Using Leisure Engel Curve to Uncover Relative ΔθI ln hI (time spent on leisure good I) Slope = βI (Drawn for a given θI) ln H2005 ln H (total leisure time) ln H2015 • Do individuals slide along a given “Leisure Engel Curve”? • Lose a job – take more leisure and spend more time on • computer/video game?

50. Overview Using Leisure Engel Curve to Uncover Relative ΔθI Slope = βI (Drawn for a higher θI) ln hI (time spent on leisure good I) Slope = βI ln H2005 ln H (total leisure time) ln H2015 • Is there a shift up in the Leisure Engel Curve? • Implies a relative increase in leisure technology for good I.