Micro Data For Macro Models

1
Micro Data For Macro Models

• Topic 3 More Home Production

2
What More Do I Want To Do

• We already looked at the importance of home
  production in explaining lifecycle patterns of
  consumption
• What else do I want us to think about?
• 1) How do we estimate the parameters of the home
  production function?
• 2) What are the long run trends in home
  production (and time use more generally)?
• 3) Is home production an important margin of
  substitution at business cycle frequencies?

3

• Part A
• Estimating Parameters of Home Production
  Function
• Using Micro Data

4
Micro Estimates of Home Production Elasticities

• Hard to do.
• Need data on both home production inputs and
  consumption.
• Consistently measured home production data is
  difficulty to find.
• Often missing measures of the opportunity cost of
  time for people who do a lot of the home
  production (those out of labor force, the
  retired, etc.).
• See Rogerson, Rupert and Wright (1995 Economic
  Theory) Estimating Substitution Elasticities in
  Household Production Models
• Use PSID data.
• Estimate the elasticity of substitution between
  time and goods in home production to be about 1.8
  for single women, about 1.0 for single men, and
  about 1.5 for married households.

5

• Aguiar and Hurst (AER 2008)
• Lifecycle Prices and Production

6
Available Margins of Substitution Shopping and
Home Production

• Expenditure is price (p) quantity (q)
• Shopping is time intensive but it may affect
  prices paid (holding quantities constant)
• Given that time is an input into shopping, the
  opportunity cost of ones time should determine
  how much an individual shops.
• Those whose time is less valuable should shop
  more and, all else equal, pay lower prices
  (holding quantities constant)
• Both shopping and home production should respond
  to changes in the opportunity cost of time.

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What We Do in This Paper

• Use new scanner data (on household grocery
  packaged goods) to document
• Prices paid differs across individuals for the
  same good
• Price paid varies with proxies for cost of time.
• Use this micro data to actually estimate
  household shopping functions which relate prices
  paid to shopping intensity.
• This shopping function will give us the implied
  opportunity cost of time for the shopper
• Given margin conditions, we can use the shopping
  function and time use data on home production to
  estimate the home production technology.
• Show empirically that the ratio of consumption to
  expenditure varies over the lifecycle.

8
Scanner Data on Prices

• Note In this data part of the paper, we will
  only be talking directly about food consumptions
  and expenditures (in model, we will extend the
  implications)
• Data is from AC Nielson HomeScan
• Panel of households
• Random sample within the MSA of households
• The survey is designed to be representative of
  the Denver metropolitan statistical area and
  summary demographics line up well with the 1994
  PSID
• Coverage at several types of retail outlets

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Scanner Data (continued)

• Each household is equipped with an electronic
  home scanning unit
• Each household member records every UPC-coded
  food purchase they make by scanning in the UPC
  code
• After each shopping trip, household records
• What was purchased (i.e. scan in UPC code)
• Where purchase was made (specifically)
• Date of purchase
• Discounts/coupons (entered manually)
• AC Nielson collects the price data from all local
  shopping outlets.
• Data has decent demographics (income categories,
  household composition, employment status, sex,
  race, age of members, etc.). Collected annually.

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Sample

• We have access to the Denver data for the years
  1993-1995.
• Short panel
• Sample
• 2,100 households (focus on age of shopper between
  24 and 75)
• 950,000 transactions
• 40,000 household/month observations.

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How We Use the Data

• Derive a price index using the scanner data
• Show some unconditional means of how this price
  index varies across differing income and
  demographic groups
• Think about measurement issues relating to our
  estimate of the price index
• Goal is to get estimate shopping and home
  production functions that I could import into our
  model

12
Potential Measurement Issue 1 Underreporting

• Average monthly expenditure in the data set
  176/month (1993 dollars)
• Average total food at home in the PSID for
  similarly defined sample (1993 dollars) is 320
  (55 coverage rate in the HomeScan Data)
• Differences between the coverage due to
• Omission of certain grocery expenditures due to
  lack of UPC code (some meat, diary, fresh fruit
  and vegetables).
• Omission of expenditures due to household
  self-scanning.
• Explore underreporting by different
  age/education/year cells (forming a ratio by
  comparing homescan data to PSID). The gap does
  not vary with age however, it does vary with
  education levels (only 42 of expenditures for
  high educated vs 55 for low educated).
• Underreporting not a problem for our analysis if
  random.

13
Potential Measurement Issue 2 Attrition

• Cannot observe on the extensive margin (homescan
  only releases data for households who
  participated consistently over the sample)
• Can observe attrition on intensive margin
• Compare average expenditures in Homescan between
  1993, 1994, and 1995
• first quarter of 1994 had 1 less expenditures
  than first quarter of 1993
• first quarter of 1995 had 5 less expenditures
  than first quarter of 1993
• No difference in expenditure declines by age or
  education
• For completeness, we redid our whole analysis
  only including 1993 no differences found

14
Potential Measurement Issue 3 Store Effects

• Price of a good may be associated with better
  (unmeasured) services
• 83.6 of purchases made at grocery stores
• 4.1 at discount stores
• 3.1 at price clubs
• 1.7 at convenient stores
• 1.5 at drug stores
• remainder from vending machines, liquor stores,
  gas stations, pet stores, etc.
• Of the grocery stores, essentially all came from
  Albertsons, King Sooper, Safeway or Cubs Food
• For robustness, we computed everything with store
  chain fixed effects (identify off of price
  differences at a given chain during a given
  period of time)

15
Aggregation over Prices

• We want a summary of the price a household pays
• Relate to cost of time
• Households buy many goods and basket varies over
  time
• Look at one popular good (milk)
• Define an index that answers For its particular
  basket of goods, does this household pay more or
  less than other households?

16
Definition Price IndexHousehold j, good i,
month m, day t

• Expenditure for household j
• Average price for good i
• Average quantity of good i
• Real basket of goods (at average price)

17
Price Index
18
Notes on Price Index

• Controls for quality. Same UPC code.
• Low price does not mean low quality
• Does not reflect bulk purchases (those are a
  different UPC code)
• Brand Switching may occur
• robust to inclusion of control for brand
  switching.
• Like a traditional price index hold quantities
  constant and vary prices.
• Unlike a traditional price index not prices
  over time, but prices in the same market at the
  same time.

19
Simple Hypothesis Tests

• Households with high value of time will pay
  higher prices than households with low value of
  time. We would expect (all else equal
  particularly amounts)
• Higher income households to pay higher prices
  than lower income households
• Households with larger families/children to pay
  higher prices than households with smaller
  families or no children
• Middle aged households (with high wages and lots
  of child commitments) to pay higher prices than
  both younger and older households. ltltLifecycle
  predictiongtgt
• Predictions consistent with data

20
Price and Income (Table 1)
p-value of difference lt 0.01
p-value of difference lt 0.01
21
Price and Household Size (Table 1)
22
Price and Household Composition (Table 1)
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Cost Minimization on Part of Household subject
to Q market expenditures h home
production time s shopping time N some
measure of size of shopping basket
28
First Order Condition From Cost
Minimization Need to estimate shopping
function p(s,N) Use Homescan data to
estimate above equation
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Note PSID data
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Estimation of Home Production Function

• Cost minimization MRT between time and goods in
  shopping MRT between time and goods in home
  production
• Independent of preferences and dynamic
  considerations.
• Caveat assuming that the shopper is the home
  producer
• Note We are allowing shopping functions to
  differ from home production functions

33

• First-order conditions

34

• Home Production Function
• Functional Form
• MRT condition

• s 1/(1-?) elasticity of substitution between
  time and goods
• in home production

35

• RHS variable can be constructed from shopping
  data.
• No measure of h in scanner data set
• Merge in from ATUS using cells based on
• 92 separate cells represented in data
• Run between effects regression over cells

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• We estimate an elasticity of substitution between
  time and goods in home production between 1.5 and
  2.1.
• Less aggregation leads to lower estimates
• With estimated home production parameters, can
  estimate actual consumption given observed
  inputs.
• Consumption/Expenditure varies over lifecycle
• Even if consumption and leisure are separable in
  utility, need to be careful in interpreting
  lifecycle expenditure.

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Conclusions

• Fairly large elasticities between time and money
  due to shopping and home production.
• We find that households can and do alter the
  relationship between expenditures and consumption
  by varying time inputs.
• Household time use, prices, and expenditures vary
  in a way that is consistent with standard
  economic principles and the lifecycle profile of
  the relative price of time.
• Supports growing emphasis on importance of
  non-market sector in understanding households
  interaction in market

41
Long Run Trends in Time Use
42
Aguiar and Hurst (QJE 2007)

• Explore the changing nature of the allocation of
  time over the last 40 years.
• Focus on the aggregate trends.
• Examine the changing nature of leisure
  inequality.
• Ask a related question Can changing educational
  differences in employment status explain changing
  leisure inequality?
• Why is that interesting? In terms of welfare
  implications, it is important to know whether low
  education individuals are taking more leisure
  because they are unable to find employment at
  their reservation wage. (Individuals will be off
  their labor supply curve).
• Help to understand labor supply elasticities and
  how they may evolve over time.

43
The Data (Table 1)

• 1965-1966 Americans Use of Time
• 2,001 individuals Aged 19-65
• One household member must be working in last
  year
• Only one person per household is surveyed
• 24 hour recall of previous day/ Lots of
  additional demographic information
• 1975-1976 Time Use in Economic and Social
  Accounts
• 2,406 adults (1519 households)
• Interviews both husbands and wives (same
  household)
• Interviews them four times (once per quarter)
• Designed to be nationally representative
• 24 hour recall of previous day/ Lots of
  demographic and earnings data
• Note We only use first interview (fall
  1975)

44
The Data (Table 1)

• 1985 Americans Use of Time
• 4,939 adults (over the age of 18)
• One adult per household
• Designed to be nationally representative
• 24 hour recall of previous day
• Limited demographics
• 1992-1994 National Human Activity Pattern
  Survey (sponsored by the EPA)
• 9,386 individuals (7,514 adults over the age of
  18)
• One person per household
• Designed to be nationally representative
• 24 hour recall of previous day
• Limited demographics

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The Data (Table 1)

• 2003 American Time Use Survey (BLS)
• Over 20,000 individuals
• One person per household
• Designed to be nationally representative
• 24 hour recall of previous day
• Very detailed demographics
• Sample is drawing from exiting CPS main sample
  (after survey month 8)
• Only have time use linked to actual wages in
  2003
• Note 2004 data is not available from BLS
  (discuss results throughout the talk)
• Two problems? Much finer time use categories
• One of goals is to create better measures of
  time spent with children.
• Some comfort 1993 data and 2003 data are very
  similar along many dimensions

46
Some Existing Work on Time Use

• Juster and Stafford (1985, 1991) and Robinson and
  Godbey (1997)
• Analyze 1965, 1975, and 1985 time diaries
• Present unconditional means (mostly)
• Robinson and Godbey also analyze a small 1995
  pilot time use survey in their last chapter of
  second edition of their 1997 book
• 1995 sample does not match well with either 85 or
  03 survey.
• Focus on 65 85 trends
• What we do is
• Extend through 03
• Harmonize the data in consistent manner
• Adjust for differences in sample composition
  between surveys
• Also show conditional means.

47
Creating consistent measures of Time Use

• For the 1965, 1975, 1985, and 1993 data, it was
  relatively easy
• Classifying activities in 2003 was a bit harder
• Some codes for 1985 (time spent in)
• Act10 Meal preparation, cooking, and serving
  food
• Act11 Meal cleanup, doing dishes
• Act12 Cleaning house (dusting, vacuuming,
  cleaning bathrooms, etc.)
• Act14 Laundry, Ironing, Clothes Care (sewing,
  mending, etc.)
• Some codes for 1993 (time spent in)
• Act10 Meal preparation, cooking, and serving
  food
• Act11 Meal cleanup, doing dishes
• Act12 Cleaning house (dusting, vacuuming,
  cleaning bathrooms, etc.)
• Act14 Laundry, Ironing, Clothes Care (sewing,
  mending, etc.)

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Sample

• All non-retired individuals between the age of 21
  and 65 (inclusive)
• 1965 time use survey excludes retired households.
• 1965 survey only includes individuals up until
  the age of 65
• Restrict individuals to have a full time use
  report (1440 minutes/day)
• Throughout the talk
• All individuals
• By sex, education, marital status, and employment
  status
• All results are presented in units of Hours per
  Week

49
Are Time Use Samples Representative (Table A1)?

• Compare males in time use data to males in PSID
  (weighting both data sets). Restrict sample
  Age 21 65, non-retired

1965 1965 1975 1975 1985 1985 1993 1993 2003 2003
Time PSID Time PSID Time PSID Time PSID Time PSID
Age 20s 0.25 0.21 0.27 0.30 0.27 0.23 0.25 0.18 0.20 0.16
Age 30s 0.23 0.25 0.28 0.24 0.32 0.33 0.31 0.33 0.26 0.27
Age 40s 0.26 0.27 0.20 0.24 0.20 0.20 0.25 0.30 0.28 0.31
Age 50s 0.19 0.19 0.19 0.18 0.16 0.18 0.15 0.15 0.20 0.21
Age 60s 0.07 0.08 0.06 0.05 0.05 0.05 0.04 0.05 0.06 0.05
Ed gt 12 0.30 0.28 0.30 0.39 0.46 0.49 0.58 0.54 0.55 0.59
Married 0.87 0.89 0.85 0.85 0.69 0.76 ---- 0.71 0.69 0.70
Have Kid 0.65 0.65 0.55 0.60 0.42 0.51 0.32 0.46 0.42 0.45
of Kids Employed 1.57 0.97 1.66 0.96 1.24 0.93 1.30 0.93 0.76 0.88 0.96 0.90 ---- 0.89 0.89 0.91 0.80 0.88 0.86 0.91

• Note 30/40 year olds have increased 1965 to
  2003
• Note Population is becoming more educated
  between 1965 and 2003

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Are Time Use Samples Representative?
Allocation of women with children by day of week
1965 1975 1985 1993 2003
Monday .115 .133 .151 .140 .139 .143 .137 .156 .154 .140 .149 .147 .130 .144 .135 .188 .129 .132 .123 .097 .152 .179 .140 .136 .151 .140 .142 .143 .148
Tuesday .169 .139 .164 .159 .128 .126 .133 .151 .140 .139 .143 .137 .156 .154 .140 .149 .147 .130 .144 .135 .188 .129 .132 .123 .097 .152 .179 .140 .136 .151 .140 .142 .143 .148
Wednesday .169 .139 .164 .159 .128 .126 .133 .151 .140 .139 .143 .137 .156 .154 .140 .149 .147 .130 .144 .135 .188 .129 .132 .123 .097 .152 .179 .140 .136 .151 .140 .142 .143 .148
Thursday .169 .139 .164 .159 .128 .126 .133 .151 .140 .139 .143 .137 .156 .154 .140 .149 .147 .130 .144 .135 .188 .129 .132 .123 .097 .152 .179 .140 .136 .151 .140 .142 .143 .148
Friday .169 .139 .164 .159 .128 .126 .133 .151 .140 .139 .143 .137 .156 .154 .140 .149 .147 .130 .144 .135 .188 .129 .132 .123 .097 .152 .179 .140 .136 .151 .140 .142 .143 .148
Saturday .169 .139 .164 .159 .128 .126 .133 .151 .140 .139 .143 .137 .156 .154 .140 .149 .147 .130 .144 .135 .188 .129 .132 .123 .097 .152 .179 .140 .136 .151 .140 .142 .143 .148
Sunday .169 .139 .164 .159 .128 .126 .133 .151 .140 .139 .143 .137 .156 .154 .140 .149 .147 .130 .144 .135 .188 .129 .132 .123 .097 .152 .179 .140 .136 .151 .140 .142 .143 .148
.169 .139 .164 .159 .128 .126 .133 .151 .140 .139 .143 .137 .156 .154 .140 .149 .147 .130 .144 .135 .188 .129 .132 .123 .097 .152 .179 .140 .136 .151 .140 .142 .143 .148

• Data weighted using survey weights to make the
  sample representative by
• day of the week!
• If random, each cell should have a value equal to
  0.142

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Definitions Time Spent in Market Production
(Table A2)

• 1. Core Market Work Time spent working for
  pay on all jobs
• (Main job, other jobs, overtime)
• Analogous to measure of hours worked in PSID
• Total Market Work - Direct market work, plus
  commuting to work, plus ancillary work
  activities
• Ancillary work activities includes time at work
  off the clock (mandatory breaks, meals at work)

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Time Use Categories (Table A1)

• Market Work Paid work in formal sector
• Paid work in informal sector
• Job search
• Non-Market Work Home and vehicle maintenance
• Shopping/Obtaining goods and services
• All other home production (cooking,
  cleaning, laundry, house work)
• Child Care
• Gardening, Lawn Care, Pet Care
• Note All associated travel time is embedded in
  the time use category

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Time Use Categories (continued)

• Leisure TV watching
• Socializing
• Exercise/Sport
• Reading
• Hobbies/Other Entertainment
• Eating
• Sleeping
• Personal Care
• Other Medical Care
• Care of Other Adults
• Religious/Civic Activities
• Education
• Other

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Trends in the Allocation of Time (Men) Table 1

• Changes Over Time
• (Adjusted for Demographics)
• 05-65 85-65 05-85
• Total Market Work -11.7 -7.7 -4.0
• Non Market Work 3.5 4.3 -0.8
• Child Care 1.8 0.0 1.8
• Leisure 4.7 4.3 0.4

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Trends in the Allocation of Time (Men) Table 1

• Changes Over Time
• (Adjusted for Demographics)
• 05-65 85-65 05-85
• Total Market Work -11.7 -7.7 -4.0
• Non Market Work 3.5 4.3 -0.8
• Child Care 1.8 0.0 1.8
• Leisure 4.7 4.3 0.4

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Trends in the Allocation of Time (Women) Table 1

• Changes
• (Adjusted for Demographics)
• 05-65 85-65 05-85
• Total Market Work 3.4 1.2 2.1
• Non Market Work -10.4 -6.1 -4.3
• Child Care 1.8 -0.8 2.6
• Leisure 3.3 6.4 -3.1

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Trends in Leisure by Sub-Aggregate ALL
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Time Allocation By Education (Leisure
Dispersion) Men

• Changes adjusted for demographics
• 65 85 03-05 05-65 85-65 05-85
• lt 12 104.3 104.9 113.0 8.7 0.5 8.1
• 12 101.2 107.3 107.9 6.7 6.1
  0.6
• 13-15 98.6 104.1 104.4 5.8 5.5
  0.3
• 16 101.9 105.8 99.7 -2.2
  3.9 -6.1
• lt12 vs. 16 2.4 2.1 13.3
• 12 vs. 16 -0.7 1.5 8.2

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Time Allocation By Education (Leisure
Dispersion) Men

• Changes adjusted for demographics
• 65 85 03-05 05-65 85-65 05-85
• lt 12 104.3 104.9 113.0 8.7 0.5 8.1
• 12 101.2 107.3 107.9 6.7 6.1
  0.6
• 13-15 98.6 104.1 104.4 5.8 5.5
  0.3
• 16 101.9 105.8 99.7 -2.2
  3.9 -6.1
• lt12 vs. 16 2.4 2.1 13.3
• 12 vs. 16 -0.7 1.5 8.2 (1)

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Time Allocation By Education (Leisure
Dispersion) Men

• Changes adjusted for demographics
• 65 85 03-05 05-65 85-65 05-85
• lt 12 104.3 104.9 113.0 8.7 0.5 8.1
• 12 101.2 107.3 107.9 6.7 6.1
  0.6
• 13-15 98.6 104.1 104.4 5.8 5.5
  0.3
• 16 101.9 105.8 99.7 -2.2
  3.9 -6.1
• lt12 vs. 16 2.4 2.1 13.3
  (2)
• 12 vs. 16 -0.7 1.5 8.2

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Time Allocation By Education (Leisure
Dispersion) Men

• Changes adjusted for demographics
• 65 85 03-05 05-65 85-65 05-85
• lt 12 104.3 104.9 113.0 8.7 0.5 8.1
• 12 101.2 107.3 107.9 6.7 6.1
  0.6
• 13-15 98.6 104.1 104.4 5.8 5.5
  0.3
• 16 101.9 105.8 99.7 -2.2
  3.9 -6.1
• lt12 vs. 16 2.4 2.1 13.3
  
• 12 vs. 16 -0.7 1.5 8.2

(3)
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Time Allocation By Education (Leisure
Dispersion) Men

• Changes adjusted for demographics
• 65 85 03-05 05-65 85-65 05-85
• lt 12 104.3 104.9 113.0 8.7 0.5 8.1
• 12 101.2 107.3 107.9 6.7 6.1
  0.6
• 13-15 98.6 104.1 104.4 5.8 5.5
  0.3
• 16 101.9 105.8 99.7 -2.2
  3.9 -6.1
• lt12 vs. 16 2.4 2.1 13.3
  
• 12 vs. 16 -0.7 1.5 8.2

(4)
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Time Allocation By Education (Leisure
Dispersion) Men

• Changes adjusted for demographics
• 65 85 03-05 05-65 85-65 05-85
• lt 12 104.3 104.9 113.0 8.7 0.5 8.1
• 12 101.2 107.3 107.9 6.7 6.1
  0.6
• 13-15 98.6 104.1 104.4 5.8 5.5
  0.3
• 16 101.9 105.8 99.7 -2.2
  3.9 -6.1
• lt12 vs. 16 2.4 2.1 13.3
  
• 12 vs. 16 -0.7 1.5 8.2

(5)
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Time Allocation By Education (Leisure
Dispersion) Men

• Changes adjusted for demographics
• 65 85 03-05 05-65 85-65 05-85
• lt 12 104.3 104.9 113.0 8.7 0.5 8.1
• 12 101.2 107.3 107.9 6.7 6.1
  0.6
• 13-15 98.6 104.1 104.4 5.8 5.5
  0.3
• 16 101.9 105.8 99.7 -2.2
  3.9 -6.1
• lt12 vs. 16 2.4 2.1 13.3
  
• 12 vs. 16 -0.7 1.5 8.2
• Question Is the dispersion driven by the
  changing pool of individuals within each
  educational category?

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General Increase in Leisure Dispersion
67
Summary of Trends

• Leisure increased dramatically since 1965 for
  average individual
• Most of the average increase occurred prior to
  the 1990s
• There is a large increase in leisure dispersion
  that also occurred during this period. Most of
  that occurred post 1985 (particularly for men).
• Note The timing of the increase in leisure
  inequality matches the timing of the well
  documented increase in consumption inequality
  and wage inequality.

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Remaining Questions

• Can the increase in leisure for low educated men
  be interpreted as an increase in well being?
• Set out to answer four new questions
• 1. Conditional on working full time, is there an
  educational gap in leisure in either 1985 or
  2003?
• 2. How do men who do not work, regardless of
  education, allocate their foregone market work
  hours?
• 3. Is there an educational gap in leisure for the
  unemployed? the disabled? other non-employed?
• 4. How much of the increased leisure dispersion
  across education groups can be explained by
  changes in employment status by education?

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Employment Status By Education

• Conditional
• Low Ed High Ed
  Difference
• 1985 Share Employed 0.89 0.94 -0.04
• 1985 Share Non-Employed 0.11 0.06 0.04
• Unemployed 0.04 0.02 0.02
• Other Non-employed 0.07 0.04 0.02
• 03-05 Share Employed 0.83 0.92 -0.09
• 03-05 Share Non-Employed 0.17 0.08
  0.09
• Unemployed 0.05 0.04 0.02
• Disabled 0.08 0.02 0.05
• Other Non-employed 0.04 0.03 0.02
• Note From now on, we only focus on two
  education groups (because of small sample
  sizes in some cells).

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Employment Status By Education

• Conditional
• Low Ed High Ed
  Difference
• 1985 Share Employed 0.89 0.94 -0.04
• 1985 Share Non-Employed 0.11 0.06 0.04
• Unemployed 0.04 0.02 0.02
• Other Non-employed 0.07 0.04 0.02
• 03-05 Share Employed 0.83 0.92 -0.09
• 03-05 Share Non-Employed 0.17 0.08
  0.09
• Unemployed 0.05 0.04 0.02
• Disabled 0.08 0.02 0.05
• Other Non-employed 0.04 0.03 0.02
• Note From now on, we only focus on two
  education groups (because of small sample
  sizes in some cells).

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Time Allocation By Education All Men 2003-2005

• Low Ed High Ed Difference
• Total Market Work 36.9 41.9 -4.6
• Total Non-Market Work 10.9
  11.7 -0.7
• Child Care 2.7 3.4 -0.7
• Gardening, Lawn Care, Pet Care 2.2
  2.1 0.2
• Total Leisure 109.8 102.3
  7.1
• T.V. 21.6 15.3
  6.0
• Own Medical Care 0.8 0.7 0.1
• Care of Other Adults 1.7 1.4 0.2
• Religious/Civic Activities 1.5
  1.9 -0.4

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Time Allocation By Education Employed Men
2003-2005

• Low Ed High Ed Difference
• Total Market Work 44.5 45.5 -0.9
• Total Non-Market Work 10.0
  11.1 -1.0
• Child Care 2.6 3.4 -0.7
• Gardening, Lawn Care, Pet Care 2.2
  1.9 0.2
• Total Leisure 104.1 100.1
  3.9
• T.V. 18.4 14.3
  4.0
• Own Medical Care 0.5 0.6
  -0.1
• Care of Other Adults 1.6 1.3 0.3
• Religious/Civic Activities 1.3
  1.8 -0.5
• Conditional on Demographics

73
Time Allocation By Education Employed Men
2003-2005

• Low Ed High Ed Difference
• Total Market Work 44.5 45.5 -0.9
• Total Non-Market Work 10.0
  11.1 -1.0
• Child Care 2.6 3.4 -0.7
• Gardening, Lawn Care, Pet Care 2.2
  1.9 0.2
• Total Leisure 104.1 100.1
  3.9
• T.V. 18.4 14.3
  4.0
• Own Medical Care 0.5 0.6
  -0.1
• Care of Other Adults 1.6 1.3 0.3
• Religious/Civic Activities 1.3
  1.8 -0.5
• Conditional on Demographics

74
Time Allocation By Education Unemployed Men
2003-2005

• Low Ed High Ed Difference
• Total Market Work 3.0 3.8 -0.5
• Job Search 2.4 5.5 -2.9
• Education 0.9 2.1 -1.2
• Total Non-Market Work 18.7
  19.2 -0.1
• Child Care 4.4 4.2 -0.5
• Gardening, Lawn Care, Pet Care 2.3
  4.5 -2.2
• Total Leisure 127.9 121.5 5.5
• T.V. 29.7 22.2 7.5
• Own Medical Care 0.6 0.5
  0.2
• Care of Other Adults 3.0 2.4
  0.8
• Religious/Civic Activities 2.4
  2.6 0.1

75
Time Allocation By Education Unemployed Men
2003-2005

• Low Ed High Ed Difference
• Total Market Work 3.0 3.8 -0.5
• Job Search 2.4 5.5 -2.9 -4.6
• Education 0.9 2.1 -1.2
• Total Non-Market Work 18.7
  19.2 -0.1
• Child Care 4.4 4.2 -0.5
• Gardening, Lawn Care, Pet Care 2.3
  4.5 -2.2
• Total Leisure 127.9 121.5 5.5
• T.V. 29.7 22.2 7.5
• Own Medical Care 0.6 0.5
  0.2
• Care of Other Adults 3.0 2.4
  0.8
• Religious/Civic Activities 2.4
  2.6 0.1

76
Where Did the Foregone Work Hours Go (in percent)?

• Low Ed High Ed
• Total Market Work 6.7 8.4
• Job Search 5.2 11.9
• Education 0.0 4.0
• Total Non-Market Work 19.6 17.8
  
• Child Care 4.0 1.8
• Gardening, Lawn Care, Pet Care 0.2
  5.7
• Total Leisure 53.5 47.0
• T.V. 25.4 17.4
• Socialization 12.6 8.4
• Sleeping 12.6 10.1
• Other Entertainment/Hobbies
  -0.7 8.6

77
Where Did the Foregone Work Hours Go (in percent)?

• Low Ed High Ed
• Total Market Work 6.7 8.4
• Job Search 5.2 11.9
• Education 0.0 4.0
• Total Non-Market Work 19.6 17.8
  
• Child Care 4.0 1.8
• Gardening, Lawn Care, Pet Care 0.2
  5.7
• Total Leisure 53.5 47.0
• T.V. 25.4 17.4
• Socialization 12.6 8.4
• Sleeping 12.6 10.1
• Other Entertainment/Hobbies
  -0.7 8.6

78
Where Did the Foregone Work Hours Go (in percent)?

• Low Ed High Ed
• Total Market Work 6.7 8.4
• Job Search 5.2 11.9
• Education 0.0 4.0
• Total Non-Market Work 19.6 17.8
  
• Child Care 4.0 1.8
• Gardening, Lawn Care, Pet Care 0.2
  5.7
• Total Leisure 53.5 47.0
• T.V. 25.4 17.4
• Socialization 12.6 8.4
• Sleeping 12.6 10.1
• Other Entertainment/Hobbies
  -0.7 8.6

24
79
Time Allocation By Education Disabled Men
2003-2005

• Low Ed High Ed Difference
• Total Market Work 0.0 0.7 -0.7
• Job Search 0.0 0.2 -0.2
• Education 0.2 1.6 -1.7
• Total Non-Market Work 10.6
  12.8 -1.8
• Child Care 2.5 2.0 0.2
• Gardening, Lawn Care, Pet Care 2.2
  1.3 1.0
• Total Leisure 144.1 138.7 5.7
• T.V. 43.2 36.0 7.5
• Own Medical Care 4.3 4.6
  -0.5
• Care of Other Adults 1.5 2.5
  -1.4
• Religious/Civic Activities 2.2
  2.1 0.1

80
Where Did the Foregone Work Hours Go (in percent)?

• Low Ed High Ed
• Total Market Work 0.0 1.5
• Education -1.6 0.2
• Total Non-Market Work -1.6 2.9
• Child Care 1.3 3.7
• Gardening, Lawn Care, Pet Care -0.2
  -3.1
• Total Leisure 89.9 84.8
• T.V. 55.7 47.7
• Socialization 7.9 6.6
• Sleeping 19.1 24.8
• Other Entertainment/Hobbies
  5.6 4.2
• Own Medical Care 8.5
  8.8

81
Time Allocation By Education Other Men 2003-2005

• Low Ed High Ed Difference
• Total Market Work 0.8 2.0 -1.0
• Job Search 0.0 0.3 -0.3
• Education 0.8 0.9 -0.1
• Total Non-Market Work 17.5
  20.1 -3.4
• Child Care 4.0 4.5 -0.4
• Gardening, Lawn Care, Pet Care 3.0
  5.0 -1.4
• Total Leisure 135.2 124.6 9.8
• T.V. 32.9 24.6 8.5
• Own Medical Care 1.4 2.3
  -1.0
• Care of Other Adults 2.2 2.5
  0.0
• Religious/Civic Activities 2.5
  3.6 -0.8

82
Where Did the Foregone Work Hours Go (in percent)?

• Low Ed High Ed
• Total Market Work 1.8 4.4
• Job Search -0.2 0.4
• Education -0.2 1.3
• Total Non-Market Work 16.9 19.8
• Child Care 3.1 2.4
• Gardening, Lawn Care, Pet Care 1.8
  6.8
• Total Leisure 69.9 53.8
• T.V. 32.6 22.6
• Socialization 8.5 9.2
• Sleeping 18.7 14.7
• Other Entertainment/Hobbies
  5.8 2.9

83
2003-2005 Cross Sectional Decomposition

• How much of the difference in leisure between
  high and low educated men in 2003-2005 is due to
  differences in job status?
• Perform a Blinder-Oaxaca Decomposition
• Define Wjk probability of being in job status
  k for educational attainment j
• Xjk hours per week of leisure for individual
  in job status k and educational
  attainment j.
• Conditional Difference 7.5 Hours Per Week
• (WL WH) XH (vectors) 2.4 Hours Per Week
• WL(XL XH) (vectors) 5.1 Hours Per Week
• Roughly 30 of difference in leisure in 2003-2005
  between low and high educated men can be
  attributed to employment status differences.

84
Perform Same Analysis for 1985

• Leisure
• Unconditional
• Low Ed High Ed
  Difference
• All 107.4 105.1
  2.2
• Employed Men 103.9 103.5 0.4
• Non-Employed Men 134.6 130.0 4.6
• Perform a similar Blinder-Oaxaca decomposition
• Roughly 60 of difference in leisure in 1985
  between low and high educated men can be
  attributed to employment status differences.

85
Perform Same Analysis for 1985

• Leisure
• Unconditional
• Low Ed High Ed
  Difference
• All 107.4 105.1
  2.2
• Employed Men 103.9 103.5 0.4
• Non-Employed Men 134.6 130.0 4.6
• Perform a similar Blinder-Oaxaca decomposition
• Roughly 60 of difference in leisure in 1985
  between low and high educated men can be
  attributed to employment status differences.

86
Perform Same Analysis for 1985

• Leisure
• Unconditional
• Low Ed High Ed
  Difference
• All 107.4 105.1
  2.2
• Employed Men 103.9 103.5 0.4
• Non-Employed Men 134.6 130.0 4.6
• Perform a similar Blinder-Oaxaca decomposition
• Roughly 60 of difference in leisure in 1985
  between low and high educated men can be
  attributed to employment status differences.

87
Time Series Decomposition (85-05)

• Percent
• Change (W05-W85)X05
  W85(X05-X85) Explained
• Less Educated 2.5 2.0
  0.4 0.82
• More Educated -2.8 0.6
  -3.4 lt0.00
• How much of the overall dispersion (combining
  cross section and time series) can be explained
  by changing employment status?
• Answer 40
• Conclusion If all non-employment is
  involuntary for low educated men, 60 of the
  documented leisure dispersion remains.
• Low educated men are still choosing to take
  more leisure than high educated men over last 25
  years.

88
Implications for Changing Inequality 1

• How does one value the additional leisure time?
• If individuals are on their labor supply curve,
  we can use their wage to value their increased
  leisure time.
• Back of the envelop calculation
• Approximately 4 to 7 hour increase in leisure
  per week for low educated men relative to high
  educated men since the mid 1980s.
• After tax low educated wage 14 hours per hour.
• Value of the additional leisure time 3,000 -
  5,000 a year.
• Is this large?

89
Implications for Changing Inequality 2

• Provides a caution for interpreting measures of
  consumption inequality.
• Time can be allocated to home production which
  can cause expenditure to diverge from true
  consumption.
• Examples Shopping intensity
• Take advantage of time dependent discounts
• Cooking meals
• Do their own home production
• The unemployed do allocate more time to home
  production/shopping than their employed
  counterparts.
• Changes in employment propensities over time can
  be expected to change the mix of market
  expenditures and time that enter the commodity
  production function. (Aguiar and Hurst 2005,
  2007a, 2007b)

90
Broader Implications

• Why do low educated men choose higher leisure
  relative to higher educated men?
• 1) Do wages differences cause the leisure
  differences?
• Substitution effects are important?
• 2) Or are preference differences driving the
  leisure differences? There are stark
  differences in behavior among the non-employed.
• - Perhaps those with a taste for leisure
  are sorting are the ones
• sorting into the low educated
  category.

91
One Last Point Within Education Dispersion
92
Conclusions (Update)

• The allocation of time has changed dramatically
  over the last 40 years.
• The allocation differed dramatically by
  educational attainment with low educated
  individuals experiencing larger leisure
  increases than high educated individuals.
• Only about 40 of the dispersion can be explained
  by involuntary non-employment.

93
Home Production and The Business Cycle
94
A DiversionLabor Supply and Home Production
95
Simple Labor Supply Example No Home Production

• Look at static model

96
Simple Labor Supply Example No Home Production
97
How Do Things Change With Home Production?
98
How Do Things Change With Home Production?
99
Interpretation

• Home production makes work hours more elastic to
  changes in wages (holding the marginal utility of
  wealth constant).
• Implications
• Womens 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.

100
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.

101
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).

102
Model Consumers
103
Model Production
104
Model Constraints
105
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.

106
Aguiar, Hurst and Karabarbounis (2011)

• How does home production actually evolve during
  recessions?
• Until this year, 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

107
Naïve Analysis
108
Look at the Pre-Trends
109
A Cross State Analysis Home Production (Pooled
Years)
110
A Cross State Analysis Leisure (Pooled Years)
111
A Cross State Analysis Home Production
(Separate Years)
112
A Cross State Analysis Leisure (Separate Years)
113
Cross State Estimates (Pooled Sample)
114
Implication 1 Do Estimates Match The Model?
115
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).

116
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.