Transcript Slide 1

Micro Data For Macro Models
Fall 2009
Bad News/Good News
• Bad News
It is hard to get tenured at a top place
It is hard to publish
• Good News
Research productivity increases with effort
No one wins a Nobel Prize for their dissertation
1998 – 2000 Cohort At Top Schools (with likely omissions)
Marianne Bertrand (Chicago)
Esther Duflo (MIT)
Mike Greenstone (MIT)
Ananth Seshadri (Wisconsin)
Amil Petrin (Minnesota)
Muhamet Yildiz (MIT)
Emmanuel Saez (Berkeley)
Jonathan Levin (Stanford)
Sendhil Mullainathan (Harvard)
Chang-Tai Hseih (Chicago)
Markus Bruennermeier (Princeton)
Dmitriy Stoyarov (Michigan)
Monika Piazzesi (Stanford)
Ricardo Reis (Columbia)
Erik Hurst (Chicago)
Enrico Moretti (Berkely)
Dirk Krueger (Penn)
Martin Schneider (Stanford)
Luigi Pistaferri (Stanford)
Mel Stephens (Michigan)
David Autor (MIT)
Emre Ozdenoren (Michigan)
Mark Aguiar (Rochester)
Marc Melitz (Harvard)
~ 900 people got a Ph.D. from top 15
departments during this time.
Victor Chevnozhakov (MIT)
Ted Miguel (Berkeley)
~ 30- 40 people got tenured at top place
Marco Battaglini (Princeton)
~ 4%-5% of students at top departments
David Lee (Princeton)
get tenured at top departments
Publishing?
•
The median Ph.D. from a top 20 department never publishes anything
in a peer reviewed journal
•
The median peer reviewed article has less than 15 citations.
•
See Dan Hamermesh’s web site for:
“Young Economist’s Guide to Professional Etiquette”
http://www.eco.utexas.edu/faculty/Hamermesh/AdviceforEconomists.html
The Good News
•
The creation of research is a skill just like inverting a matrix, solving
DSGE models, computing standard errors, etc.
•
The more you work on it, the better you will become.
•
Read the early work of those recently tenured at top schools. Every
single one of you could have written the same papers!
It is not our technical prowess that distinguishes us throughout our careers,
it is our ability to innovate.
Those who have impact on the profession due so because of their ideas.
Question: Which Style of Research Is Best?
•
Style is not as important as substance.
Structural vs. Reduced Form?
Theory vs. Empirics?
Partial Equilibrium vs. General Equilibrium?
•
Perfect example:
The work of Kevin Murphy
What Skill Are New Ph.D’s Most Deficient?
•
Having the ability to identify interesting research questions
•
The confusion of theoretical or empirical fire power as being an “end”
as opposed to a “means”.
•
Not having the ability to explain why anyone would care about their
research.
The Main Goal of This Class
•
Get you to think about “questions” as opposed to “models”.
•
Caveat:
•
My comparative advantage is in questions.
•
My style should be a complement – not a substitute – to your existing skill
set.
Models are good!
You all have strong skills in this dimension.
Additional Goals
•
Introduce you to a models and literature focusing on household financial
behavior which are of interest to macro economists broadly defined:
consumption, saving, inequality, housing, labor supply, entrepreneurship,
default, etc.
•
Introduce you to micro data sets which can shed light on these questions.
•
Focus on papers that have had big impacts on the respective literatures –
nearly all of which could have been written by anyone in this class (with
your existing skill sets).
Some Housekeeping….
•
Homework
Three components
Expectation for auditors
•
T.A.
•
Papers
•
Slides
•
Timing
Lastly
•
Discussion about the process of research is highly encouraged.
•
I will often ask you how you would attempt to solve certain research
questions.
•
I may not have the answers myself.
•
The dialog is part of the research process.......
Lecture 1:
Consumption
Weeks 1 and 2: Consumption
•
Why is it important?
-
Learn about household preferences broadly
C.E.S. vs. log vs. other / Habits? / Status?
-
Estimate preference parameters
intertemporal elasticity of substitution/ risk aversion/ discount rate
-
Learn about income process
permanent vs. transitory shocks / expected vs. unexpected
-
Learn about financial markets/constraints
liquidity constraints / risk sharing arrangements
-
Learn about policy responses
spending after tax rebates, fiscal multipliers, etc.
Weeks 1 and 2: Consumption
•
The big picture with consumption:
-
Use estimated parameters to calibrate models
-
Understand business cycle volatility
-
Conduct policy experiments (social security reform, health care
reform, tax reform, etc.)
-
Estimate responsiveness to fiscal or monetary policy
-
Broadly understand household behavior
Weeks 1 and 2: Consumption
•
The outline of this lecture:
-
Understand lifecycle consumption movements (this week)
o
Illustrative of how one fact can spawn multiple theories.
o
Show how a little more data can refine the theories
o
Illustrate the empirical importance of the Beckerian consumption
model (i.e, incorporating home production and leisure).
Weeks 1 and 2: Consumption
•
The outline of this lecture:
-
Discuss the importance of precautionary savings (next week)
-
Discuss the estimation of household preference parameters (next
week)
-
Discuss the use of consumption data to estimate the income process
households are facing (next week)
-
Discuss changes in inequality over time (next week)
-
Discuss evidence on household risk sharing (next week)
-
Discuss testing for alternative specifications of household preferences
(next week)
Homework #1
•
Referee reports (on the evolution of household “preferences”)
•
Data work (familiarizing yourself with CEX data AND relating that to
changing variance of consumption over the lifecycle).
•
Big question: “Why did the personal savings rate in the U.S. decline so
dramatically?”
Why start with consumption
•
Much better micro data on the household sector
(Consumption and labor supply)
•
Less good data on the firm side
•
International data is becoming more prevalent (trade data)
Expenditure Data
•
Consumer Expenditure Survey (U.S. data)
Starts in 1980
Broad consumption measures
Some income and demographic data
Repeated cross-sections
•
Panel Study of Income Dynamics (U.S. data)
Starts in late 60s
Only food expenditure consistently
Housing/utilities (most of the time)
Broader measures (recently)
Very good income and demographics
Panel nature
Expenditure Data
•
British Household Panel
•
Family Expenditure Survey
•
Bank of Italy Survey of Household Income and Wealth
•
There are others….
Fact 1: Lifecycle Expenditures
0.30
0.25
Log Difference From Age 25
0.20
0.15
0.10
0.05
0.00
25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75
-0.05
-0.10
Plot: Adjusted for cohort and family size fixed effects
Define Non-Durable Consumption (70% of outlays)
• Use a measure of non-durable consumption + housing services
• Non-durable consumption includes:
Food (food away + food at home)
Alcohol and Tobacco
Non-Durable Transportation
Clothing and Personal Care
Domestic Services
Entertainment Services
Utilities
Charitable Giving
Net Gambling Receipts
Airfare
• Housing services are computed as:
Actual Rent (for renters)
Imputed Rent (for home owners) – Impute rent two ways
• Exclude: Education (2%) , Health (6%), Non Housing Durables (16%),
and Other (5%) <<where % is out of total household expenditures>>
Empirical Strategy: Lifecycle Profile of Expenditure
• Estimate:
ln(Citk )  0  age Ageit  cCohortit  t Dt   fs Familyit  itk
k
where Cit is real expenditure on category k by household i in year t.
Note:
All expenditures deflated by corresponding product-level
NIPA deflators.
Cohortit = year-of-birth (5 year range – i.e., 1926-1930)
Dt = Vector of normalized year dummies (See Hall (1968))
Family Composition Controls:
Household size dummies, Number of Children Dummies
Marital status dummies , Detailed Age of Children Dummies
(1)
Fact 2: Hump Shaped Profile – By Education
From Attanasio and Weber (2009)
Fact 3: Retirement Consumption Dynamics
From Bernheim, Skinner and Weinberg (AER 2001)
The Puzzle? (Friedman, Modigliani, Hall, etc.)
max
Ct
u(Ct , t )  Et
T

s t 1
1
1 
( s t )
u(Cs , s )
X t 1  (1  rt 1 )( X t  Ct )  Yt 1
Yt  PV
t t
Pt  g Pt 1 Nt
{Nt, Vt} are permanent and transitory mean zero shocks to income with underlying
variances equal to σ2N and σ2V
Preferences
Ct1 
u (Ct , t ) 
exp(Θt ),   1
1 
(1/  )  intertemporal elasticity of consumption
r  real interest rate
  time discount rate
  vector of taste shifters
Euler Equation
 ln(1   ) ln(1  rt 1 ) (t 1  t ) *
C t 1 


  t 1





where C t 1  ln Ct 1  ln Ct
if r   (in all periods) or if they are constant and
if the forecast error of future consumption (embedded in  * ) is constant
then consumption growth only depends on changes in tastes ()
or changes in the real interest rate.
What Are Potential Taste Shifters Over Life Cycle
1.
Family Size
o
o
o
2.
Makes some difference
Hump shaped pattern still persists
See Facts 1 and 3 (above) – these were estimated taking out detailed
family size controls.
Other Taste Shifters (that change over the lifecycle – for a given
individual)?
Questions:
What Else Drives the Hump Shaped
Expenditure Profile?
Why Does Expenditures (on food)
Fall Sharply At Retirement?
Explanations
•
Non-Separable Preferences Between Consumption and Leisure Heckman (1974)
•
Liquidity Constraints and Impatience - Gourinchas and Parker (2002)
•
Myopia - Keynes (and others)
•
Time Inconsistent Preferences (with liquidity constraints) - Angeletos et
al (2001)
•
Habits and Impatience
•
Home Production/Work Related Expenses - Aguiar and Hurst (2005,
2008)
Non-Separable Consumption and Leisure
max
Ct , Nt
u (Ct , Nt )  Et
T

s t 1
1
1 
( s t )
u (Cs , N s )
1
u (Ct , Nt ) 
(Ct (1  Nt )1 )1
1


C t 1   0  1 ln(1  rt 1 )  2 (1  Nt 1 )  t*1
Testing Non-Separable Consumption and Leisure
Isolate Spending Changes Around Retirement
Aguiar and Hurst
“Consumption vs. Expenditure” (JPE 2005)
Data: Measuring Consumption Directly
•
Main Data Set: Continuing Survey of Food Intake of Individuals (CSFII)
–
–
–
–
–
–
–
Conducted by Department of Agriculture
Cross Sectional / Household Level Survey
Two recent waves: Wave 1 (1989 -1991) ; Wave 2 (1994-1996)
Nationally Representative
Multi Day Interview
All individuals within the household are interviewed (C at individual level)
Tracks final food intake (not intermediate goods --- think about a cake)
•
Detailed food expenditure, demographic, earnings, employment, and health
measures
•
Large sample sizes:
– 6,700 households in CSFII-91
– 8,100 households in CSFII-96
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Actual Consumption Data (CSFII)
• The key to the data:
24 hour food intake diaries (asked for all days in the survey)
• Diaries are detailed:
–
–
–
–
Amount of food item consumed (detailed 8 digit food codes)
Brand of food item (often unusable by researchers)
Cooking method
Condiments added
• Dept of Agriculture converts the total day’s food intake into several
nutritional measures (calories, protein, saturated fat, total fat, vitamin C,
riboflavin, etc.).
– The conversion is made using all food diary data (i.e., brand, whether
cooked with butter).
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8 digit food codes: Cheese
• Example 18 of the 100 8-digit codes for cheese.
14101010
14102010
14102110
14103020
14104010
14104020
14104200
14104250
14105010
14105200
14106010
14106200
14106500
14107010
14107020
14107030
14107040
14107060
CHEESE, BLUE OR ROQUEFORT
CHEESE, BRICK
CHEESE, BRICK, W/ SALAMI
CHEESE, BRIE
CHEESE, NATURAL, CHEDDAR OR AMERICAN TYPE
CHEESE, CHEDDAR OR AMERICAN TYPE, DRY, GRATED
CHEESE, COLBY
CHEESE, COLBY JACK
CHEESE, GOUDA OR EDAM
CHEESE, GRUYERE
CHEESE, LIMBURGER
CHEESE, MONTEREY
CHEESE, MONTEREY, LOWFAT
CHEESE, MOZZARELLA, NFS (INCLUDE PIZZA CHEESE)
CHEESE, MOZZARELLA, WHOLE MILK
CHEESE, MOZZARELLA, PART SKIM (INCL ""LOWFAT"")
CHEESE, MOZZARELLA, LOW SODIUM
CHEESE, MOZZARELLA, NONFAT OR FAT FREE
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Changes in “Spending” At Retirement
Run: ln(xi) = γ0 + γ1 Retiredi + γ2 Zi + errori
• Retiredi is a dummy variable equal to 1 if the household head is retired.
• Instrument Retiredi status with age dummies (potential endogeneity)
• Z includes: race, sex, health, region, time, family structure controls
• Sample: Relatively “young” older households: Heads aged 57-71
• Total food expenditure (x) falls by 17% for retired households (γ1), p-value
< 0.01
• Other results:
– Food expenditure at home falls by 15%
– Food expenditure away from home falls by 31%
37
Changes in “Consumption” at Retirement
•
How do we turn these food diaries into meaningful measures of consumption?
•
Our approach:
1. Examine Nutritional Quality of Diet (vitamins, cholesterol, fat, calories, etc.)
2.
Examine individual goods with strong income elasticities (hotdogs, fruit,
yogurt, shellfish, wine)
3.
Luxury/Quality goods (e.g. brands vs generics, lean vs. fatty meat)
4.
Use structural model to aggregate food consumption data and perform
formal PIH test.
38
Nutritional Measures
•
•
Regress: ln(ci) = α0 + α1 ln(yperm) + demographics <<sample: heads 25-55>>
Regress: ln(ci) = β0 + β1 Retired + demographics <<sample: heads 57-71>>
Consumption Measure (in logs)
Calories
Protein *
Estimated Elasticity (α1)
-4%
(2%)
-1%
(1%)
Vitamin A *
Vitamin C *
Vitamin E *
Calcium *
44%
34%
18%
10%
(5%)
(5%)
(3%)
(2%)
36% (9%)
33% (9%)
11% (4%)
13% (4%)
- 26%
- 9%
(3%)
(2%)
-9% (5%)
-7% (3%)
Cholesterol *
Saturated Fat *
•
•
•
Retirement Effect (β1)
-2% (4%)
-3% (2%)
* Includes log calories as an additional control ; Include supplements as an additional control.
Instrument for retirement status with age; Examined non-linear specifications (not reported)
No evidence of any deterioration in diet quality
39
Some Specific Consumption Measures
•
•
Regress: ci = α0 + α1 ln(yperm) + demographics <<sample: heads 25-55>>
Regress: ci = β0 + β1 Retired + demographics <<sample: heads 57-71>>
Consumption Measure (Dummy)
Eat Fruit
Eat Yogurt
Eat Shellfish
Drink Wine
Eat Oat/Rye/Multigrain Bread
Estimated Semi-Elasticity
0.25 (0.03) <<59%>
0.14 (0.02) <<8%>>
0.05 (0.01) <<6%>>
0.15 (0.02) <<8%>>
0.10 (0.02) <<9%>>
Retirement Effect
0.14 (0.04)
0.01 (0.03)
-0.02 (0.02)
-0.03 (0.03)
0.06 (0.04)
Eat Hotdog/Sausage
Eat Ground beef
-0.16 (0.03) <<51%>>
-0.10 (0.03) <<22%>>
-0.06 (0.05)
-0.01 (0.04)
•
•
Sample means in << >>
Instrument for retirement status with age
•
•
Drawback: Tastes could differ across income types
Drawback: Categories are broad and do not allow for differences in quality
40
Luxury Goods/Quality: My Favorite….
• Examine some dimensions of quality:
– Eating at restaurants with Table Service
– Eating Branded vs. Generic Goods
– Eating Lean vs. Fattier Cuts of Meat
• Restaurants, Brands, and Eating Lean Meat have very STRONG income
elasticities in the cross section of working households.
• If households are unprepared for retirement, we should see them switching
away from such consumption goods.
• No evidence of that in the data.
41
Creating a Food Intake Aggregate
ln( y perm,i )  0  1c1,i t  ....   J cJi ,t   X ln( X t )  ti  ti
• Where
c1, ….. cJ are quantities of individual consumption categories consumed
X is monthly expenditure on food
θ is a vector of demographic and health controls (including education, sex,
race, family composition, ect.)
yperm is the household’s predicted permanent income
• Estimated on a sample of 40 – 55 year old household heads where the head is
working full time.
42
Thought Experiment
• Permanent income is our numeraire – one unit increase in our consumption
index maps into a one percent increase in permanent income.
– What are we doing: We project permanent income of household i onto
household i’s consumption (controlling for taste shifters).
• Basically, in a statistical sense, if you tell me what you eat, I can predict your
permanent income. Our consumption index is in permanent income
dollars!
• We also did this for households aged 25-55 who are working fulltime (results
did not change).
• We want to ask if households act like their permanent income has
changed once they become retired.
43
Is Our Permanent Income Measure Predictive?
• Projection of income on consumption and expenditure patterns
• How well does consumption forecast income?
– Split sample into odd and even years (again focusing only on prime age
household heads working full time).
– Focus only on odd years of our sample (in sample):
• In sample R-square 0.53
• Food consumption on its own explain 21% of variation in income
• Incremental R-square is 0.12
– Focus on even years (test out of sample):
• Out of sample R-square: 0.42
• Food consumption and expenditure a fairly good predictor of income
44
45
Conclusions
• No “Retirement Consumption Puzzle”
• Technically, preferences between “consumption” and leisure are not
substitutes.
– Leisure goes up dramatically in retirement (we will show this in a few
weeks).
– Consumption (as measured by intake) remains roughly constant (if
anything it increases slightly).
• However, “expenditures” and leisure could still be non-separable.
– Non-separability enters through “home production”
46
Time, Consumption, and Expenditures Over the Lifecycle
Ghez and Becker (1975)
“The Allocation of Time and Goods Over the Life Cycle” (book)
Aguiar and Hurst (2008)
“Deconstructing Life Cycle Expenditure”
A Beckerian Model of Consumption
• Consumption commodities are outputs of production functions using time
(h) and expenditures on market goods (x) as inputs:
u (c1 ,...., c N ), where c n  f n (h n , x n )  ( hn (h n )1 n  xn ( x n )1 n )
Define:
 
n
1

n
1
1 n
where σ > 1 implies x and h are substitutes
• Example Commodity 1: TV Entertainment (σ < 1 – complements)
Time Input:
Market Input:
Time needed to watch the show
T.V., Cable Subscription
• Example Commodity 2: A Meal (σ > 1 – substitutes)
Time Input:
Market Input:
Shop for food, prepare food, eat, clean up
Food, Appliances, Dishes, etc.
Two Margins of Substitution
• Inter-temporal elasticity of substitution: u(c1, ….. , cN)
• Intra-temporal elasticity of substitution: fn(hn,xn)
Model
V (a, w, t )  max u(c1,..., c N )   EtV (a ', w ', t 1)
subject to:
c n  f n (h n , x n ), n  1,..., N
h
n
(assume C.E.S.)
 L 1
n
a '  (1  r )a  wL   p n x n
n
L  0, a '  a.
Let μ, λ, θ, and κ be the respective multipliers on the time budget constraint,
the money budget constraint, the positive hours constraint and the positive
assets constraint.
Assume u(.) is additively separable across time and across goods.
First Order Conditions
x n : un f xn   p n , n
h n : un f hn   , n
L : w   
a ' :  E tVa ' (a ', w ', t  1)     .
If θ = 0 (L > 0), price of time (in permanent income units) (μ/λ = w)
More generally (given L often = 0), μ/λ = ω
First Order Conditions
Intra-period tradeoff between time and goods:
f

 n.
f
p
n
h
n
x
(1)
Marginal rate of transformation between time and goods in
production of n is equated to the relative price of time.
Taking logs and differentiating (1), yields:
 h   .
n
x
d ln
n
d ln  
n
Static First Order Condition
The static F.O.C. pins down expenditure relative to time inputs.
If we know σ and the change in the opportunity cost of time, we
should be able to pin down the relative movement in expenditures
relative to time.
%Δx-%Δh =σ %Δω
Notice, this equation does not require us to make any
assumptions about borrowing or lending, perfect foresight,
etc.
More Intuition (Assume separability in cn’s)
Differentiate FOC for xn with respect to ω holding λ constant. Get:
d ln x n
d ln 
 n
un 
 s   n 
c unn 

n
h
d  0
n n
h
fh
n
sh  n
c
This is just Ghez and Becker (1975)
Need to compare the intra-elasticity of substitution between time and goods to
the inter-temporal elasticity of substitution in consumption.
Note:
Complicates mapping of expenditures into permanent income in
general and the estimation of Engel curves in particular.
Implications
• For given resources (λ):
– As the price of time increases, consumers substitute market goods for
time (xn increases) – depends on σn
– As the price of time increases, consumers substitute to periods in which
consumption is “cheaper” (xn falls) – depends on the inter-temporal
elasticity
• A luxury that is a complement with time should decrease with
ω, while a necessity that is a substitute with time should
increase with ω.
• Prediction: Entertainment should increase as the price of time
decreases, while food should decrease
Different Than Standard Predictions
Differentiate FOC for xn with respect to ω holding λ constant. Get:
n
d ln c
d ln 
d  0
un
 n
c unn
Spending should fall the most (with declines in the marginal value of wealth) for
goods that have high intertemporal elasticities of substitution.
“Entertainment” spending should decline more with shocks to permanent
income than “Food” spending.
Entertainment Spending
1.0
Log Deviation From Age 25
0.8
0.6
0.4
0.2
0.0
25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75
-0.2
All Non Decreasing Categories
3.00
Log Deviation from Age 25
2.50
2.00
1.50
1.00
0.50
0.00
-0.50
25
30
Entertainment
35
Utilities
40
45
50
Housing Services
55
60
Other ND
65
70
Domestic Svcs
75
Decreasing Categories
0.40
Log Deviation from Age 25
0.20
0.00
-0.20
-0.40
-0.60
-0.80
-1.00
-1.20
25
30
35
40
45
50
55
60
65
70
Age
Clothing
Transportation
Food at Home
Food Away
75
Summary (in Log Differences)
Log Change Log Change Log Change
Between
Between
Between
25 and 44
45 and 59
60 and 68
Consumption Category
Share
Decreasing Categories
Food at Home
Transportation
Clothing/Personal Care
Food Away from Home
Alcohol and Tobacco
0.17
0.13
0.08
0.06
0.03
0.24
0.25
0.04
0.13
-1.35
-0.07
-0.20
-0.36
-0.55
-1.69
-0.04
-0.17
-0.20
-0.29
-1.22
Non-Decreasing Categories
Housing Services
Utilities
Entertainment
Other Non-Durable
Domestic Services
0.33
0.11
0.04
0.03
0.02
0.73
0.72
0.80
1.44
1.52
0.23
0.28
0.07
0.16
0.30
0.14
0.11
0.17
0.17
0.32
Food, Transportation and Clothing
• Food is amenable to “Beckerian” home production (see
Aguiar and Hurst 2005, 2007)
No evidence of any decline in food intake over the lifecycle
despite declining food expenditures.
As opportunity cost of time declines later in life, households
substitute towards home production of food (including more
intense shopping for bargains).
Data (and calibrated model) actual show food intake increases
over the back half of the lifecycle
Work Related Expenses
• Transportation, Clothing and Food Away From Home are
work related expenses:
Lazear and Michael (1980) – Net out work related expenses
(clothing and transportation) when making welfare calculations
across people
Banks et al (1998) and Battistin et al (2008) when measuring
consumption changes of retirees
Nelson (1989) and DeWeese and Norton (1991) comprising
models of “clothing demand”
Level of Work Hours Over the Lifecycle
1
45
0.9
40
Fraction Working
Fraction Working
0.7
35
Hours Worked Per Week
0.8
30
0.6
25
0.5
20
0.4
15
0.3
0.2
Hours Per Week
Worked
10
0.1
5
0
0
25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75
Age
New Facts About Food, Clothing, and Transport
• Look at food away patterns at different types of establishments
• Look at changes in different amounts of transportation patterns
using time use data
• Estimate “simple” demand systems and control directly for
work status
Propensity To Eat Away At Home
0.05
Percentage Point Deviation From Age 25
0.00
-0.05
-0.10
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65-69
-0.15
Any Eating
Establishment
-0.20
-0.25
-0.30
-0.35
Age
Propensity To Eat Away At Home
0.05
Restaurants at Lunch
Percentage Point Deviation From Age 25
-3E-16
Restaurants at Dinner
-0.05
-0.1
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65-69
-0.15
Any Eating
Establishment
-0.2
-0.25
-0.3
-0.35
Age
Propensity To Eat Away At Home
0.05
Restaurants at Lunch
Percentage Point Deviation From Age 25
0
Restaurants at Dinner
-0.05
-0.1
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65-69
-0.15
Any Eating
Establishment
-0.2
-0.25
Fast Food and
Cafeteria
-0.3
-0.35
Age
Travel Times and Employment Status
Hours Per Week Deviation From 25-29 Year Olds
1.00
0.50
0.00
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65-69
70-74
-0.50
All Travel Time
-1.00
-1.50
-2.00
-2.50
Travel Times and Employment Status
1.00
Hours Per Week Deviation From 25-29 Year Olds
Non Work Travel Time
0.50
0.00
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65-69
70-74
-0.50
-1.00
-1.50
-2.00
-2.50
All Travel Time
Travel Times and Employment Status
1.00
Hours Per Week Deviation From 25-29 Year Olds
Non Work Travel Time
0.50
0.00
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65-69
70-74
-0.50
-1.00
All Travel Time
-1.50
-2.00
-2.50
Work Travel Time
Control Directly For Work Status
• Estimate a demand system
• Control for labor supply (conditional on total expenditures)
• Estimate:
1)
what consumption categories where spending is positively
associated with market work
2)
to what extent is the decline in spending on clothing,
transportation and food away from home attributable to
employment status.
Estimate Simple Demand System
sitk  0  age Ageit  cCohortit  t Dt   fs Familyit   pk ln Pt k  p ln Pt
k
 X ln X it  L Lit   itk ,
Xit
is total nondurable expenditures (less alcohol and tobacco, plus
housing) for household i in year t.
sitk
is the share of expenditures in consumption category k out of Xit
Ptk
is the price index for consumption category k in year t
Lit
is a vector of work status controls for household i in year t.
Note:
Instrument lnXit with household total income and education controls
1. Simple Demand System Results
• Restrict sample to married households between age 25 and 50
• Use two work status controls: Husband working? Wife working?
Simple Demand System Results
• Restrict sample to married households between age 25 and 50
• Use two work status controls: Husband working? Wife working?
Consumption Category
Husband Work?
Wife Work?
Transportation (0.13)
0.014 (0.002)
0.014 (0.002)
Clothing/P. Care (0.08)
0.003 (0.001)
0.001 (0.001)
Food Away From Home (0.06)
0.008 (0.001)
0.005 (0.001)
Simple Demand System Results
• Restrict sample to married households between age 25 and 50
• Use two work status controls: Husband working? Wife working?
Consumption Category
Transportation
(0.13)
Husband Work?
Wife Work?
0.014 (0.002)
0.014 (0.002)
Clothing/P. Care (0.08)
0.003 (0.001)
0.001 (0.001)
Food Away From Home (0.06)
0.008 (0.001)
0.005 (0.001)
Housing Services (0.34)
-0.009 (0.003)
-0.012 (0.002)
Utilities
(0.12)
-0.005 (0.001)
-0.003 (0.001)
Food At Home (0.18)
-0.016 (0.002)
-0.013 (0.001)
Entertainment (0.04)
0.000 (0.001)
0.000 (0.001)
2. Adding Work Controls To the Lifecycle Profile
• Married Sample, 25 – 75
• Work Status Controls:
7 Dummies for Husband Weeks Worked
7 Dummies for Wife Weeks Worked
9 Dummies for Hours per week Husband Worked
9 Dummies for Hours per week Wife Worked
• Three Categories:
Food (food at home and food away)
Work Related Expenses (transportation and clothing)
Core Non Durables (everything else)
• Ask: “How do work status controls effect lifecycle profiles?”
Share of Expenditure:
Difference from Age 25
Demand Estimates, Transportation
0.010
0.005
0.000
-0.005
-0.010
-0.015
-0.020
-0.025
-0.030
-0.035
-0.040
-0.045
25
30
35
40
45
50
Age
55
60
65
70
75
Demand Estimates, Food Away
Share of Expenditure:
Difference from Age 25
0.005
0.000
-0.005
-0.010
-0.015
-0.020
25
30
35
40
45
50
Age
55
60
65
70
75
Share of Expenditure:
Difference from Age 25
Demand Estimates, Clothing
0.005
0.000
-0.005
-0.010
-0.015
-0.020
-0.025
-0.030
-0.035
-0.040
25
30
35
40
45
50
Age
55
60
65
70
75
Level of Lifecycle Expenditure
1.2
1.0
Log Deviation from Age 25
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
-0.8
25
30
35
Work Related
40
45
50
Core Nondurables
55
60
65
Food at Home
70
75
Level of Lifecycle Expenditure (Older Version)
1.20
1.00
Log Deviation From Age 25
0.80
0.60
0.40
0.20
0.00
25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75
-0.20
-0.40
-0.60
-0.80
Core Nondurable
Total Nondurable
What Does it Mean?
• Aguiar and Hurst (2009)
Write down a model where households maximize utility with three
consumption goods (and leisure) with the following constraints:
one good (food) is amenable to home production
one good (transport, clothes) are complements to market work
there is a time budget constraint
Assumptions:
o
o
o
conditional on work, income process is uncertain
take the lifecycle process of work as exogenous
assume that individual receives no utility for the lifecycle component
of work related expenses.
What Does it Mean?
• More on this next week (when we use the same procedure to examine the
lifecycle profile of consumption inequality and the time series profile of
consumption inequality).
• However – just by looking at the previous figures – households appear
to be much more patient than estimated by previous authors!
Some Concluding Thoughts (Again)
• Technically, preferences between “consumption” and leisure are not
substitutes.
– Leisure goes up dramatically in retirement (we will show this in a few
weeks).
– Consumption (as measured by intake) remains roughly constant (if
anything it increases slightly).
• However, “expenditures” and leisure could still be non-separable.
– Non-separability enters through “home production”
• Acknowledging this has implications for: estimating preferences,
explaining business cycle frequencies of consumption, examining time
series implications of inequality, and estimating the income process from
consumption data.