Transcript Slide 1

Micro Data For Macro Models
Fall 2011/Winter 2012
Topic 1: Consumption Inequality
Course Pre-Amble
1998 – 2000 Cohort That Are Tenured at Top Schools
(with some omissions)
Marianne Bertrand (Chicago)
Ananth Seshadri (Wisconsin)
Esther Duflo (MIT)
Amil Petrin (Minnesota)
Mike Greenstone (MIT)
Muhamet Yildiz (MIT)
Emmanuel Saez (Berkeley)
Marco Battaglini (Princeton)
Jonathan Levin (Stanford)
Xavier Gabaix (NYU)
Sendhil Mullainathan (Harvard)
Monika Piazzesi (Stanford)
Chang-Tai Hseih (Chicago)
Ricardo Reis (Columbia)
Erik Hurst (Chicago)
Enrico Moretti (Berkely)
Dirk Krueger (Penn)
Martin Schneider (Stanford)
Luigi Pistaferri (Stanford)
Annette Vissing-Jorgensen (Northwestern)
David Autor (MIT)
Mark Duggan (Wharton)
Mark Aguiar (Princeton)
Fabrizio Perri (Minnesota)
Marc Melitz (Harvard)
Alessandra Fogli (Minnesota)
Victor Chevnozhakov (MIT)
Wouter Dessein (Columbia GSB)
Ted Miguel (Berkeley)
~ 900 people got a Ph.D. from top 15
Markus Bruennermeier (Princeton)
David Lee (Princeton)
departments during this time period
~ 40- 50 (~5%) of people got tenured at top
15 departments
1998 – 2000 Cohort That Are Tenured at Top Schools
(with some omissions)
Marianne Bertrand (Chicago)
Ananth Seshadri (Wisconsin)
Esther Duflo (MIT)
Amil Petrin (Minnesota)
Mike Greenstone (MIT)
Muhamet Yildiz (MIT)
Emmanuel Saez (Berkeley)
Marco Battaglini (Princeton)
Jonathan Levin (Stanford)
Xavier Gabaix (NYU)
Sendhil Mullainathan (Harvard)
Monika Piazzesi (Stanford)
Chang-Tai Hseih (Chicago)
Ricardo Reis (Columbia)
Erik Hurst (Chicago)
Enrico Moretti (Berkely)
Dirk Krueger (Penn)
Martin Schneider (Stanford)
Luigi Pistaferri (Stanford)
Annette Vissing-Jorgensen (Northwestern)
David Autor (MIT)
Mark Duggan (Wharton)
Mark Aguiar (Princeton)
Fabrizio Perri (Minnesota)
Marc Melitz (Harvard)
Alessandra Fogli (Minnesota)
Victor Chevnozhakov (MIT)
Wouter Dessein (Columbia GSB)
Ted Miguel (Berkeley)
~ 900 people got a Ph.D. from top 15
Markus Bruennermeier (Princeton)
David Lee (Princeton)
departments during this time period
~ 40- 50 (~5%) of people got tenured at top
15 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”
https://webspace.utexas.edu/hamermes/www/JEP92.pdf
The Good News
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The creation of research is a skill just like inverting a matrix, solving
DSGE models, computing standard errors, etc.
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The more you work on it, the better you will become.
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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 only our technical prowess that distinguishes us throughout our
careers, it is our ability to innovate and/or to come up with good questions.
Those who have impact on the profession due so because of their ideas.
What Skill Are Ph.D. Students Most Deficient?
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Having the ability to identify interesting research questions
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The confusion of theoretical or empirical fire power as being an “end” as
opposed to a “means”.
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Not having the ability to explain why anyone would care about their
research.
Goal of This Class
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Get you to start thinking about writing your dissertation
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Familiarize you with many data sets that are used by macro economists
(and others) to be used as part of your dissertation.
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Expose you to literatures within macroeconomics that have strong
empirical components.
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Help you turn good research ideas into good research papers.
Some Housekeeping….
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T.A.:
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Lots of work – hopefully all of it useful
o
o
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Sebastian (with set up an email list for class participants
including auditors)
Homework
Paper extension
Virtual Paper
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Slides/Course Info
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Co-Taught with Steve Davis: Timing
Very Important
•
If you are seeking take the prelim in the Applied Macro Sequence (this
course plus the remaining courses), you must:
1) Complete a full version of your virtual paper in order to pass the
portion of the course taught by us.
2) We will not offer questions on the prelim (although the faculty who
teach the other parts of the sequence will).
3) You have to notify Steve and Erik by end of February if you are
planning to take the applied macro prelim. We will give you feedback
about our expectations for your paper.
4) You will still need to take the prelim with questions covering the
remaining faculty (Harald, John Cochrane, Lars, etc.)
My Portion of the Course
Topic 1:
Consumption Inequality
Topic 2:
Lifecycle Consumption
Topic 3:
Home Production
Topic 4:
Long Run Trends in Labor Supply
Topic 5:
Unemployment During Recessions
Topic 6:
Regional Adjustments
Topic 7:
Occupational Choice
Topic 8:
Understanding Small Businesses
Steve’s Portion of the Class
A few words from Steve…..
A Quick Detour:
Research Questions in Real Time
Personal Savings Rates Over Time
Potential Research Questions
1. What Drives The Time Series Patterns in Aggregate Savings Rates?
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Let’s brainstorm some explanations…..
2. How Much of the Time Series Patterns of Aggregate Savings Can Be
Explained by Various Factors (or at least one of the factors)?
3.
Can We Use The Pre-2007 data to Predict Savings Behavior Going
Forward?
Potential Empirical Strategies?
• Think Broadly – Lets not be constrained by actual data (yet)
Now Lets Think About Existing Data
• What data exists?
o What data easily exists? What are the limitations of these data?
o What data conceivably exists?
An Important Next Step
• Why would someone care about the paper findings?
• Note:
Too often, researchers (old and young alike) forget to answer this
next question.
Topic 1:
The Evolution of Consumption Inequality
Micro Expenditure Data: Household Surveys
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Consumer Expenditure Survey (U.S. data)
Starts in 1980
Broad consumption measures
Some income and demographic data
Repeated cross-sections
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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
Micro Expenditure Data: Household Surveys
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British Household Panel (British Data)
o
Panel data including income and expenditure
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Family Expenditure Survey (British Data)
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Bank of Italy Survey of Household Income and Wealth (Italian Data)
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Panel data including income and expenditure
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There are others….many Scandinavian countries, Japan, Canada, etc.
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Even some developing economies have detailed household surveys that
track some measures of consumption (e.g., Mexico, Taiwan, Thailand)
Micro Expenditure Data: Scanner Data
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Nielsen Homescan Data
o
o
o
o
o
o
o
o
o
Large cross-section of households
Very detailed level transaction data (at the level of UPC code)
Some demographics
Some panel component
Matches quantities purchased with prices paid
Covers most of the large MSAs
Measurement error?
Selection?
Coverage of goods?
Micro Income Data: Household Surveys
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Current Population Survey (CPS)
o
o
o
Usual data set used within U.S. to track labor supply and earnings.
Has panel component.
Can be found at www.ipums.org/cps/
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PSID
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Survey of Income and Program Participation (SIPP)
o
o
o
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Can be found at http://psidonline.isr.umich.edu/
Four year rotating panel
Large sample sizes
Over samples poor
Census/American Community Survey
o
Can be found at www.ipums.org
Income and Consumption Inequality
• Large literature documenting the increase in income inequality within the
U.S. during the last 30 years (Katz and Autor, 1999; Autor, Katz, Kearney,
2008)
• Consumption is a better measure of well being than income (utility is U(C)
not U(Y)).
• Does income inequality imply consumption inequality?
Depends on whether income inequality is “permanent”
Depends on insurance mechanisms available to households
Depends on other margins of substitution (home production, female
labor supply, etc.).
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Kevin Murphy’s Web Page
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Kevin Murphy’s Web Page
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Autor, Katz, Kearney (2008)
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Why Do We Care About Consumption Inequality?
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Why is it important?
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Learn about well being over time (economic growth, standard of
livings, inequality, etc.).
o
Learn about insurance mechanisms available to households (public
insurance, private insurance, etc.)?
o
Learn about the nature of income processes (more on this in the
next set of lecture notes).
A Classic: Attanasio and Davis (1996)
Krueger and Perri (2006)
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What they do:
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Use data from the Consumer Expenditure Survey (CEX) to track
the evolution of consumption inequality.
o
CEX is includes a nationally representative sample of households.
-
o
Designed to compute consumption weights for CPI
Short panel dimension (4 quarters)
Mostly used as repeated cross sections.
Includes detailed spending measures on expenditures by
categories.
Use repeated cross sections to track consumption inequality.
Krueger and Perri (2006): What They Find
Krueger and Perri (2006): What They Find
Krueger and Perri (2006): What They Find
Krueger and Perri (2005): What They Conclude
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Conclusions
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Income inequality is much greater than consumption inequality
o
If some of the increase in income inequality is idiosyncratic,
households can self insure (or public sector can provide insurance)
making consumption inequality respond less than income inequality.
o
Write down a model where insurance is endogenously provided.
Increasing idiosyncratic shocks to income can increase demand for
insurance (leading to more insurance). Consistent with their model,
credit card access increased during this period.
o
Bottom line:
Use the consumption data to learn about the nature
of income processes and insurance mechanisms.
A Caveat – Some Data Issues
A Data Problem: Average Real Consumption in CEX
9.8500
9.8000
9.7500
9.7000
9.6500
9.6000
9.5500
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A Data Problem: Average Real Consumption in CEX
9.8500
9.8000
9.7500
9.7000
9.6500
9.6000
9.5500
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Percent Change in Consumption in CEX (from 1981)
0
-0.02
-0.04
-0.06
-0.08
-0.1
-0.12
-0.14
-0.16
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Trends in Real NIPA Aggregate Consumption
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Can Measurement Error Alter Inequality Findings?
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Yes
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Depends on whether measurement error differs across the consumption
(income) distribution.
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Suppose richer households have been underreporting their income to a
greater extent in recent periods (relative to the past).
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The rich could be increasing their expenditure more (relative to other parts
of the distribution). However, the systematic measurement error could
also be increasing.
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How to test for group specific differences in measurement error?
Aguair and Bils (2011)
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Try to account for differential measurement error over different “incomedemographic” groups to get a sense of changing consumption inequality.
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Some particulars:
Define xijt = average expenditure on good j, by group i, at time t
j goods = food at home, clothing, utilities, entertainment, etc.
i groups = cells based on income (5) and demographics (18)
Define Xit = average total expenditure for group i at time t.
Formally:
X it 

J
j 1
x ijt
The Essence of the Exercise
(From a Discussion by Jonathan Parker; NBER EFG 2011)
Log budget
share of good:
ln wi = ln (xi /X )
Observed Inferred
Estimated
2006
2006
Engel curve
for luxury
Observed
1980
Inferred
adjustment
to ln X90
Estimated
Engel curve for
normal good
ln X10
ln X10
Log total real
expenditure:
90 X = xLux+xNormal+xNec
ln X90 ln X90 ln X
Aguair and Bils (2011)
•
Assume measurement error in expenditure……
*  t   t  v ijt
ijt
x ijt  x e
•
•
t
i
t
j
j
i
represents a good specific error (common across all groups)
represents a group specific error (common across all goods)
Aguair and Bils (2011): Some Intuition
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Difference-in-Difference Estimates (2 good case, 2 group case)
•
•
Goods = e (entertainment) and f(food)
Groups = high (rich) and low (poor)
x high , e
*

x high , e
*
low , e
x low , e
x
x h ig h , f
x h ig h , f
x lo w , f
e

high

low
(difference out good specific error)
*

*
x lo w , f
e

high

low
(difference out good specific error)
Aguair and Bils (2011): Some Intuition
•
Take differences across goods to eliminate group specific error
 x high , f
 x high , e 
ln 
  ln 
 x low , e 
 x low , f
*
*


 x high

x
,e
high , f

ln

ln

 *
 *

 x low , e 

 x low , f



•
Obtain an unbiased estimate of relative consumption inequality.
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Need to map into units of total expenditure. Want to recover:
 ln X h ig h ,t   ln X lo w ,t
*
*
(1)
Aguair and Bils (2011): Some Intuition
 ijt   ln x ijt ;
*
D efine:
*
 it   ln X it 
*
*
 h ig h , e ,t   e h ig h , t  .....
*
*
 lo w , e ,t   e lo w ,t  .....
*
*
 h ig h , f , t   f  h ig h , t  .....
*
*
 lo w , f ,t   f  lo w , t  .....
*
*
(  h ig h , e , t   lo w , e ,t )  (  h ig h , f , t   lo w , f , t )  (  e   f )(  h ig h , t   lo w , t )
*
*
*
*
*
*
Aguair and Bils (2011): Some Intuition
U sing (1), w e know that w e can express:
(  high , e , t   low , e ,t )  (  high , f , t   low , f ,t )  (  e   f )(  high , t   low , t )
*
*
*
*
*
*
as: (  h ig h , e , t   lo w , e , t )  (  h ig h , f , t   lo w , f , t )  (  e   f )(  h ig h , t   lo w , t )
*
*
Aguair and Bils (2011)
•
Suppose for true expenditures, x* :
ln x ijt  
*
•
*
jt
  j ln X ijt   j Z i   ijt
Can estimate the following using actual data in some period 0 where
systematic measurement error is less of an issue:
ln x ij 0  
•
*
j0
  j ln X i 0   j Z i  u ij 0
If there is no measurement error in the data, can uncover:
ˆ j   j
•
Assumes income elasticities are constant over time (and can be locally
estimated). Assume measurement error is zero in period 0.
Aguair and Bils (2011): Some Intuition
U sing (1), w e know that w e can express:
(  high , e , t   low , e ,t )  (  high , f , t   low , f ,t )  (  e   f )(  high , t   low , t )
*
*
*
*
*
*
as: (  h ig h , e , t   lo w , e , t )  (  h ig h , f , t   lo w , f , t )  (  e   f )(  h ig h , t   lo w , t )
*
*
Substituting in the estimated β’s, we get:
( high , e ,t   low , e ,t )  ( high , f ,t   low , f ,t )  (  e   f )(  high ,t   low ,t )
*
*
Aguiar and Bils (2011) Findings
50
Aguiar and Bils (2011) Findings
51
Aguiar and Bils (2011) Findings
Relative Spending Differences Between High and Low Income Groups
52
Aguiar and Bils (2011) Findings
53
Aguiar and Bils (2011) Findings
Different Saving Rates From the CEX
54
Conclusions: Topic 1
•
Measurement error is important in Consumer Expenditure Survey!
•
Even though there is measurement error, can still measure consumption
inequality.
•
Without controlling for measurement error, looks like small increases in
consumption inequality.
•
Much of that is due to the rich reporting less and less of their expenditures.
•
Controlling for the systematic recent underreporting of the rich increases
the estimated consumption inequality in the U.S. to levels that match the
changing income inequality.