Transcript Lecture Notes 3

Micro Data For Macro Models
Topic 3: More Home Production
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?
2
Part A:
Estimating Parameters of Home
Production Function:
Using Micro Data
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.
4
Aguiar and Hurst (AER 2008)
Lifecycle Prices and Production
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 one’s 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.
6
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.
7
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
8
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.
9
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.
10
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
11
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.
12
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
13
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)
14
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?
15
Definition: Price Index
Household 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)
16
Price Index
17
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.
18
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. <<Lifecycle prediction>>
Predictions consistent with data
19
Price and Income (Table 1)
1.04
1.03
1.02
Price Index
1.01
1.00
0.99
0.98
0.97
0.96
0.95
Less than $30,000
$30,000 to $50,000
$50,000 to $70,000
Greater than $70,000
p-value of difference < 0.01
p-value of difference < 0.01
20
Price and Household Size (Table 1)
1.06
1.04
Price Index
1.02
1.00
0.98
0.96
0.94
1
2
3
4
>4
Hosuehold Size
21
Price and Household Composition
(Table 1)
1.06
1.04
Price Index
1.02
1.00
0.98
0.96
0.94
Married with
children
Unmarried female Unmarried male
with children
with children
Married w/o
children
Unmarried female Unmarried male
w/o children
w/o children
22
23
24
25
26
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
27
First Order Condition From Cost Minimization
Need to estimate shopping function: p(s,N)
Use Homescan data to estimate above equation
28
29
30
Figure A1: Wage Rates Over the Life Cycle, Married Males
25.000
Conditional
20.000
Conditional/Fixed Effect
Wage
15.000
10.000
Unconditional
5.000
69
66
63
60
57
54
51
48
45
42
39
36
33
30
27
24
0.000
Age
Note: PSID data
31
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
32
min p(s, Q)Q    s  h 
{s ,Q ,h}
s.t. f (h, Q)  C
•First-order conditions:
p
 Q
s
p
f
Q  p  C
Q
Q
f
  C
h
f
p
Q
h  s
f
p
Q p
Q Q
33
• Home Production Function
– Functional Form:
f (h, Q)   hh  QQ


 

• MRT condition:
 p  p
 h 
ln  h / Q    ln 
   ln   s  Q Q 

Q




p

• σ= 1/(1-ρ) = elasticity of substitution between time and goods
in home production
34
• 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
35
36
37
• 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.
38
39
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
household’s interaction in market
40
Long Run Trends in Time Use
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.
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)
43
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
44
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
45
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.
46
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
Act11
Act12
Act14
Meal preparation, cooking, and serving food
Meal cleanup, doing dishes
Cleaning house (dusting, vacuuming, cleaning bathrooms, etc.)
Laundry, Ironing, Clothes Care (sewing, mending, etc.)
Some codes for 1993 (time spent in):
Act10
Act11
Act12
Act14
Meal preparation, cooking, and serving food
Meal cleanup, doing dishes
Cleaning house (dusting, vacuuming, cleaning bathrooms, etc.)
Laundry, Ironing, Clothes Care (sewing, mending, etc.)
47
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”
48
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
1975
1985
1993
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 > 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
49
Are Time Use Samples Representative?
Allocation of women with children by day of week
1965
1975
1985
1993
2003
Monday
.115
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
Thursday
Friday
Saturday
Sunday
•
•
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
50
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
2.
“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)
51
Figure 1: Comparison of Weekly Core Market Work Hours in PSID and Time Use Surveys:
Sample: All Non-Retired Men Between Ages of 21 and 65
46.00
42.00
40.00
38.00
36.00
20
03
20
01
19
99
19
97
19
95
19
93
19
91
19
89
19
87
19
85
19
83
19
81
19
79
19
77
19
75
19
73
19
71
19
69
19
67
34.00
19
65
Hours Per Week
44.00
Year
PSID Work Hours
Time Use Work Hours
52
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
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
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
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
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
Trends in Leisure by Sub-Aggregate: ALL
10
8
tv
6
leisure 2
Hours per Week
4
sleeping + personal care
2
gardening
sports
entertainment
0
1965
1975
1985
1993
eating
2003
hobbies
-2
reading
-4
-6
socializing
Time Allocation By Education (Leisure Dispersion): Men
Changes adjusted for demographics
65
< 12
12
13-15
16+
104.3
101.2
98.6
101.9
<12 vs. 16+
12 vs. 16+
2.4
-0.7
85
03-05
05-65
104.9
107.3
104.1
105.8
113.0
107.9
104.4
99.7
8.7
6.7
5.8
-2.2
2.1
1.5
13.3
8.2
85-65
05-85
0.5
6.1
5.5
3.9
8.1
0.6
0.3
-6.1
Time Allocation By Education (Leisure Dispersion): Men
Changes adjusted for demographics
65
< 12
12
13-15
16+
104.3
101.2
98.6
101.9
<12 vs. 16+
12 vs. 16+
2.4
-0.7
85
03-05
05-65
104.9
107.3
104.1
105.8
113.0
107.9
104.4
99.7
8.7
6.7
5.8
-2.2
2.1
1.5
13.3
8.2
(1)
85-65
05-85
0.5
6.1
5.5
3.9
8.1
0.6
0.3
-6.1
Time Allocation By Education (Leisure Dispersion): Men
Changes adjusted for demographics
65
< 12
12
13-15
16+
104.3
101.2
98.6
101.9
<12 vs. 16+
12 vs. 16+
2.4
-0.7
85
03-05
05-65
104.9
107.3
104.1
105.8
113.0
107.9
104.4
99.7
8.7
6.7
5.8
-2.2
2.1
1.5
13.3
8.2
(2)
85-65
05-85
0.5
6.1
5.5
3.9
8.1
0.6
0.3
-6.1
Time Allocation By Education (Leisure Dispersion): Men
Changes adjusted for demographics
65
< 12
12
13-15
16+
104.3
101.2
98.6
101.9
85
03-05
05-65
104.9
107.3
104.1
105.8
113.0
107.9
104.4
99.7
8.7
6.7
5.8
-2.2
85-65
05-85
0.5
6.1
5.5
3.9
8.1
0.6
0.3
-6.1
(3)
<12 vs. 16+
12 vs. 16+
2.4
-0.7
2.1
1.5
13.3
8.2
Time Allocation By Education (Leisure Dispersion): Men
Changes adjusted for demographics
65
< 12
12
13-15
16+
104.3
101.2
98.6
101.9
85
03-05
05-65
104.9
107.3
104.1
105.8
113.0
107.9
104.4
99.7
8.7
6.7
5.8
-2.2
85-65
05-85
0.5
6.1
5.5
3.9
8.1
0.6
0.3
-6.1
(4)
<12 vs. 16+
12 vs. 16+
2.4
-0.7
2.1
1.5
13.3
8.2
Time Allocation By Education (Leisure Dispersion): Men
Changes adjusted for demographics
65
< 12
12
13-15
16+
104.3
101.2
98.6
101.9
85
03-05
05-65
104.9
107.3
104.1
105.8
113.0
107.9
104.4
99.7
8.7
6.7
5.8
-2.2
(5)
<12 vs. 16+
12 vs. 16+
2.4
-0.7
2.1
1.5
13.3
8.2
85-65
05-85
0.5
6.1
5.5
3.9
8.1
0.6
0.3
-6.1
Time Allocation By Education (Leisure Dispersion): Men
Changes adjusted for demographics
65
85
03-05
05-65
104.9
107.3
104.1
105.8
113.0
107.9
104.4
99.7
8.7
6.7
5.8
-2.2
2.1
1.5
13.3
8.2
85-65
05-85
0.5
6.1
5.5
3.9
8.1
0.6
0.3
-6.1
< 12
12
13-15
16+
104.3
101.2
98.6
101.9
<12 vs. 16+
12 vs. 16+
2.4
-0.7
Question:
Is the dispersion driven by the changing pool of individuals
within each educational category?
General Increase in Leisure Dispersion
20
15
Hours per Week
90th Percentile
10
75th Percentile
5
50th Percentile
25th Percentile
0
1965
-5
1975
1985
1993
2003
10th Percentile
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.
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?
Employment Status By Education
Low Ed
High Ed
Conditional
Difference
1985 Share Employed
1985 Share Non-Employed
Unemployed
Other Non-employed
0.89
0.11
0.04
0.07
0.94
0.06
0.02
0.04
-0.04
0.04
0.02
0.02
03-05 Share Employed
03-05 Share Non-Employed
Unemployed
Disabled
Other Non-employed
0.83
0.17
0.05
0.08
0.04
0.92
0.08
0.04
0.02
0.03
-0.09
0.09
0.02
0.05
0.02
Note:
From now on, we only focus on two education groups (because of small
sample sizes in some cells).
Employment Status By Education
Low Ed
High Ed
Conditional
Difference
1985 Share Employed
1985 Share Non-Employed
Unemployed
Other Non-employed
0.89
0.11
0.04
0.07
0.94
0.06
0.02
0.04
-0.04
0.04
0.02
0.02
03-05 Share Employed
03-05 Share Non-Employed
Unemployed
Disabled
Other Non-employed
0.83
0.17
0.05
0.08
0.04
0.92
0.08
0.04
0.02
0.03
-0.09
0.09
0.02
0.05
0.02
Note:
From now on, we only focus on two education groups (because of small
sample sizes in some cells).
Time Allocation By Education: All Men 2003-2005
Low Ed
36.9
High Ed
41.9
Total Non-Market Work
10.9
11.7
-0.7
Child Care
Gardening, Lawn Care, Pet Care
2.7
2.2
3.4
2.1
-0.7
0.2
Total Leisure
T.V.
109.8
21.6
102.3
15.3
7.1
6.0
0.8
1.7
1.5
0.7
1.4
1.9
0.1
0.2
-0.4
Total Market Work
Own Medical Care
Care of Other Adults
Religious/Civic Activities
Difference*
-4.6
Time Allocation By Education: Employed Men 2003-2005
Low Ed
44.5
High Ed
45.5
Total Non-Market Work
10.0
11.1
-1.0
Child Care
Gardening, Lawn Care, Pet Care
2.6
2.2
3.4
1.9
-0.7
0.2
Total Leisure
T.V.
104.1
18.4
100.1
14.3
3.9
4.0
0.5
1.6
1.3
0.6
1.3
1.8
-0.1
0.3
-0.5
Total Market Work
Own Medical Care
Care of Other Adults
Religious/Civic Activities
* Conditional on Demographics
Difference*
-0.9
Time Allocation By Education: Employed Men 2003-2005
Low Ed
44.5
High Ed
45.5
Total Non-Market Work
10.0
11.1
-1.0
Child Care
Gardening, Lawn Care, Pet Care
2.6
2.2
3.4
1.9
-0.7
0.2
Total Leisure
T.V.
104.1
18.4
100.1
14.3
3.9
4.0
0.5
1.6
1.3
0.6
1.3
1.8
-0.1
0.3
-0.5
Total Market Work
Own Medical Care
Care of Other Adults
Religious/Civic Activities
* Conditional on Demographics
Difference*
-0.9
Time Allocation By Education: Unemployed Men 2003-2005
Low Ed
High Ed
Difference*
Total Market Work
Job Search
Education
3.0
2.4
0.9
3.8
5.5
2.1
-0.5
-2.9
-1.2
Total Non-Market Work
18.7
19.2
-0.1
Child Care
Gardening, Lawn Care, Pet Care
4.4
2.3
4.2
4.5
-0.5
-2.2
127.9
29.7
121.5
22.2
5.5
7.5
0.6
3.0
2.4
0.5
2.4
2.6
0.2
0.8
0.1
Total Leisure
T.V.
Own Medical Care
Care of Other Adults
Religious/Civic Activities
Time Allocation By Education: Unemployed Men 2003-2005
Low Ed
High Ed
Difference*
Total Market Work
Job Search
Education
3.0
2.4
0.9
3.8
5.5
2.1
-0.5
-2.9 -4.6
-1.2
Total Non-Market Work
18.7
19.2
-0.1
Child Care
Gardening, Lawn Care, Pet Care
4.4
2.3
4.2
4.5
-0.5
-2.2
127.9
29.7
121.5
22.2
5.5
7.5
0.6
3.0
2.4
0.5
2.4
2.6
0.2
0.8
0.1
Total Leisure
T.V.
Own Medical Care
Care of Other Adults
Religious/Civic Activities
Where Did the Foregone Work Hours Go (in percent)?
Low Ed
6.7
5.2
0.0
High Ed
8.4
11.9
4.0
Total Non-Market Work
19.6
17.8
Child Care
Gardening, Lawn Care, Pet Care
4.0
0.2
1.8
5.7
53.5
25.4
12.6
12.6
-0.7
47.0
17.4
8.4
10.1
8.6
Total Market Work
Job Search
Education
Total Leisure
T.V.
Socialization
Sleeping
Other Entertainment/Hobbies
Where Did the Foregone Work Hours Go (in percent)?
Low Ed
6.7
5.2
0.0
High Ed
8.4
11.9
4.0
Total Non-Market Work
19.6
17.8
Child Care
Gardening, Lawn Care, Pet Care
4.0
0.2
1.8
5.7
53.5
25.4
12.6
12.6
-0.7
47.0
17.4
8.4
10.1
8.6
Total Market Work
Job Search
Education
Total Leisure
T.V.
Socialization
Sleeping
Other Entertainment/Hobbies
Where Did the Foregone Work Hours Go (in percent)?
Low Ed
6.7
5.2
0.0
High Ed
8.4
11.9
4.0
Total Non-Market Work
19.6
17.8
Child Care
Gardening, Lawn Care, Pet Care
4.0
0.2
1.8
5.7
53.5
25.4
12.6
12.6
-0.7
47.0
17.4
8.4
10.1
8.6
Total Market Work
Job Search
Education
Total Leisure
T.V.
Socialization
Sleeping
Other Entertainment/Hobbies
24%
Time Allocation By Education: Disabled Men 2003-2005
Low Ed
High Ed
Difference*
Total Market Work
Job Search
Education
0.0
0.0
0.2
0.7
0.2
1.6
-0.7
-0.2
-1.7
Total Non-Market Work
10.6
12.8
-1.8
Child Care
Gardening, Lawn Care, Pet Care
2.5
2.2
2.0
1.3
0.2
1.0
144.1
43.2
138.7
36.0
5.7
7.5
4.3
1.5
2.2
4.6
2.5
2.1
-0.5
-1.4
0.1
Total Leisure
T.V.
Own Medical Care
Care of Other Adults
Religious/Civic Activities
Where Did the Foregone Work Hours Go (in percent)?
Total Market Work
Education
Low Ed
0.0
-1.6
High Ed
1.5
0.2
Total Non-Market Work
-1.6
2.9
Child Care
Gardening, Lawn Care, Pet Care
1.3
-0.2
3.7
-3.1
Total Leisure
T.V.
Socialization
Sleeping
Other Entertainment/Hobbies
89.9
55.7
7.9
19.1
5.6
84.8
47.7
6.6
24.8
4.2
Own Medical Care
8.5
8.8
Time Allocation By Education: Other Men 2003-2005
Low Ed
High Ed
Difference*
Total Market Work
Job Search
Education
0.8
0.0
0.8
2.0
0.3
0.9
-1.0
-0.3
-0.1
Total Non-Market Work
17.5
20.1
-3.4
Child Care
Gardening, Lawn Care, Pet Care
4.0
3.0
4.5
5.0
-0.4
-1.4
135.2
32.9
124.6
24.6
9.8
8.5
2.3
2.5
3.6
-1.0
0.0
-0.8
Total Leisure
T.V.
Own Medical Care
Care of Other Adults
Religious/Civic Activities
1.4
2.2
2.5
Where Did the Foregone Work Hours Go (in percent)?
Total Market Work
Job Search
Education
Low Ed
1.8
-0.2
-0.2
High Ed
4.4
0.4
1.3
Total Non-Market Work
16.9
19.8
Child Care
Gardening, Lawn Care, Pet Care
3.1
1.8
2.4
6.8
Total Leisure
T.V.
Socialization
Sleeping
Other Entertainment/Hobbies
69.9
32.6
8.5
18.7
5.8
53.8
22.6
9.2
14.7
2.9
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
WL(XL – XH)
2.4 Hours Per Week
5.1 Hours Per Week
•
(vectors):
(vectors):
Roughly 30% of difference in leisure in 2003-2005 between low and high
educated men can be attributed to employment status differences.
Perform Same Analysis for 1985
Leisure
Unconditional
Difference
Low Ed
High Ed
All
107.4
105.1
2.2
Employed Men
Non-Employed Men
103.9
134.6
103.5
130.0
0.4
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.
Perform Same Analysis for 1985
Leisure
Unconditional
Difference
Low Ed
High Ed
All
107.4
105.1
2.2
Employed Men
Non-Employed Men
103.9
134.6
103.5
130.0
0.4
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.
Perform Same Analysis for 1985
Leisure
Unconditional
Difference
Low Ed
High Ed
All
107.4
105.1
2.2
Employed Men
Non-Employed Men
103.9
134.6
103.5
130.0
0.4
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.
Time Series Decomposition (85-05)
Change
Less Educated
More Educated
•
2.5
-2.8
(W05-W85)X05
2.0
0.6
W85(X05-X85)
0.4
-3.4
Percent
Explained
0.82
<0.00
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.
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?
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)
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.
One Last Point: Within Education Dispersion
25
<12
12
13-15
16+
20
Change 1965 - 2003
Hours per Week
15
10
5
0
5
15
25
35
45
55
-5
-10
Percentile of Distribution
65
75
85
95
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 nonemployment.
Home Production and
The Business Cycle
A Diversion:
Labor Supply and Home Production
Simple Labor Supply Example: No Home Production
Look at static model:
C 1
N 1
U (C , N ) 
d
1
1 
s.t.
C  wN
F .O.C.
UC :
C   
UN :
dN   w
95
Simple Labor Supply Example: No Home Production
UN :
dN   w
Take Logs:
ln( N ) 
1

ln( w) 
1

ln( ) 
1

ln( d )
Estimate :
ln( N )  A   ln( w)   ln( )  
 = labor supply elasticity with respect to wages (holding  constant)
 = labor supply elasticity with respect to marginal utility of wealth (holding w constant)
96
How Do Things Change With Home Production?
C 1
( N  H )1
U (C , N ) 
d
1
1 
s.t .
X  wN
C  ( H H    X X

)1/ 
F .O.C .
UC :
UH :
UN :
 X C 1   X  1  
 H C 1   H  1  d ( N  H )
d ( N  H )  w
How Do Things Change With Home Production?
Similar in spirit to no-home production model
ln( N  H ) 
1

ln( w) 
1

ln( ) 
1

ln( d )
But, the elasticity of market work changes:
 H 

1
ln( H )  ln( N ) 
ln( w) 
ln 

(1   )
(1   )
 X 
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….
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.
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).
Model: Consumers

E0  U (Ct , Lt )
t 0
C

U (C L ) 
t
t
t
1b
Lt

b 1
1
1 
Ct   1  a  (C )  a(C )
m 
t

h  1/ 
t
Model: Production
Yt  exp( z )( K
m m
t 1
m 1 m
t
C  exp( z )( K
m
h
t 1
h 1 m
t
m
t
h
t
h
t
) (N )
) (N )
K t j  X t j  (1   ) K t j1 ; j=m,h
Zt  [ z , z ]'
m
t
h
t
Z t  RZ t 1   t
 t ~ N (0, )
Model: Constraints
Yt  C  X t
m
t
Xt  X  X
m
t
h
t
C  X t  wN  rt K
m
t
m
t
Lt  N  N  1
m
t
h
t
m
t 1
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.
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
Naïve Analysis
Look at the Pre-Trends
A Cross State Analysis: Home Production (Pooled Years)
A Cross State Analysis: Leisure (Pooled Years)
A Cross State Analysis: Home Production (Separate Years)
A Cross State Analysis: Leisure (Separate Years)
Cross State Estimates (Pooled Sample)
Implication 1: Do Estimates Match The Model?
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).
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.