Micro Data For Macro Models Fall 2014 Lecture 1 (Prologue and Consumption Inequality)

Micro Data For Macro Models

Fall 2014

Lecture 1

(Prologue and

Consumption Inequality)

Course Pre-Amble

1998 – 2000 Cohort That Are Tenured at Top Schools (with some omissions because my memory is bad)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) Dirk Krueger (Penn)

Enrico Moretti (Berkely) Martin Schneider (Stanford)

Luigi Pistaferri (Stanford) Annette Vissing-Jorgensen (Berkeley)

David Autor (MIT) Mark Duggan (Wharton)

Mark Aguiar (Princeton) Fabrizio Perri (Minnesota)

Marc Melitz (Harvard) Ed Vytlacil (NYU)

Victor Chevnozhakov (MIT) Wouter Dessein (Columbia GSB)

Ted Miguel (Berkeley) ~ 900 people got a Ph.D. from top 15

Markus Bruennermeier (Princeton) departments during this time period

David Lee (Princeton) ~ 40- 50 (~5%) of people got tenured at top

15 departments

1998 – 2000 Cohort That Are Tenured at Top Schools (with some omissions because my memory is bad)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) Dirk Krueger (Penn)

Enrico Moretti (Berkely) Martin Schneider (Stanford)

Luigi Pistaferri (Stanford) Annette Vissing-Jorgensen (Berkeley Haas)

David Autor (MIT) Mark Duggan (Stanford)

Mark Aguiar (Princeton) Fabrizio Perri (Minnesota)

Marc Melitz (Harvard) Ed Vytlacil (NYU)

Victor Chevnozhakov (MIT) Wouter Dessein (Columbia)

Ted Miguel (Berkeley) ~ 900 people got a Ph.D. from top 15

Markus Bruennermeier (Princeton) departments during this time period

David Lee (Princeton) ~ 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

https://webspace.utexas.edu/hamermes/www/JEP92.pdf

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 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 do so because of their ideas.

What Skill Are Ph.D. Students 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.

Goal of This Class

• Get you to start thinking about writing your dissertation

• Familiarize you with many data sets that are used by macro economists (and others) to be used as part of your dissertation.

• Expose you to literatures within macroeconomics that have strong empirical components.

• Help you turn good research ideas into good research papers.

• Teach you how to communicate your ideas to others.

Some Housekeeping….

• T.A.: Nick (with set up an email list for class participants including auditors)

• Lots of work – hopefully all of it useful

o Reading

o Homeworks

o Virtual Paper

• Slides/Course Info (on my faculty web page)

• Co-Taught with Steve Davis: Timing (Steve’s slides/homework on Chalk)

My Portion of the Course: Household Datasets

Topic 0: Prolouge

Topic 1: Consumption Inequality

Topic 2: Lifecycle Consumption

Topic 3: Home Production

Topic 4: Occupational Choice

Topic 5: Regional Economics (may make this Topic 4, depending on timing)

Topic 6: Understanding Small Businesses (if time allows)

Very Important

• Prelim

1) A completed research paper

2) Need two faculty readers. The primary reader has to be a professor who teaches in the empirical macro sequence (Steve, Erik, Rafael, or Joe).

2) Could be the completion of the virtual paper (subject to Erik and Steve’s approval).

3) You have to notify Erik by late April if you are planning to take the prelim, what the paper topic is, and who your readers are. See the syllabus for additional details. (Also need a B in all three courses)

Anatomy of Writing an Research Paper

1. Identifying a research topic a broad level

o What broad area of research are you trying to speak to?

o What existing research is done on that literature?

o Is it interesting to contribute an answer to this broad research question?

2. Identifying a research topic in the narrow

o What is the specific question you are trying to address?

o What existing research is done on this specific question?

o Is the answer to this specific question interesting?

o Do you have a way to answer this specific question?

Anatomy of Writing an Research Paper

3. Executing the specific research question

o How do you tease out your mechanism from many other mechanisms?

o What type of identification are you using to identify your mechanism (for empirical papers).

o What drives that identification (for empirical papers).

o Good questions without a good research design is just philosophy and/or killing trees.

4. Conveying the output of the research

o Your ideas need to be conveyed to others in order to have impact.

o Main way in which ideas are conveyed: written research paper.

o Seminars are another way to convey the research idea.

o Need to be clear in both writing and verbal presentations.

Example of the Research Process

1. Question in the Broad

o Why has U.S. Labor Force Participation Rate (and Employment to Population Rate) fallen so sharply in recent years and remained at such a low level.

a. Why is this question interesting?

b. Is the fact true?

Advice: It is always good to start your research process with interestingbackground facts.

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Unemployment Rate: 1970M1 – 2014M7

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Employment to Population Rate: Men

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Employment to Population Rate: Women

Summary of Facts

• Non-employment rate for prime age workers started increasing in U.S. around 2000. (Moffitt, BPEA 2012).

o The fact is particularly pronounced for lower skilled men and women.

o Some increase for high skilled men as well.

Summary of Facts

• Non-employment rate for prime age workers started increasing in U.S. around 2000. (Moffitt, BPEA 2012).

o The fact is particularly pronounced for lower skilled men and women.

o Some increase for high skilled men as well.

• Research question in the broad: Why has the employment to population ration been falling?

• Can we move from a “research question in the broad” to a “research question in the narrow”?


2. Question in the Narrow

o How much can the recent decline in manufacturing employment explain the rise in non-employment of U.S. workers?

o Why didn’t this effect show up sooner in aggregate statistics?

o What is the mechanism by which a sectoral decline can lead to increases in non-employment?

Hint: Can some preliminary data lend potential plausibility to this hypothesis?

~1 Million Jobs LostDuring 1980s and 1990s


~4 Million Jobs LostBetween 2000-2007

(Housing Boom Years)


~4 Million Jobs LostBetween 2000-2007

(Housing Boom Years)

~1 Million Jobs Lost

After 2007

U.S. Employment Trends for Non-College Men (age 21-55)

Manufacturing + Construction

Manufacturing

Construction


3. Going from research question to actual implementation…..

o Can one construct a research design to test the specific research question?

o What type of variation can help to test the hypothesis?

o What assumptions are necessary to make this variation useful to test the hypothesis?

- “Wish” criteria (what do you “wish” drives the variation).

- “Plausibility” criteria (is your “wish” plausible).

Open Lecture Slides 1a

1. The Lecture Slides “charles_noto_hurst_phd_lecture1.ppt” will go through the research design of my paper:

“Manufacturing Decline, Housing Booms, and Nonemployment”

Goals of the Class Revisited

1. Homeworks

o The homeworks are designed to getting you thinking about the components of the research process.

2. Virtual Paper

3. Lectures

o Steve and I will try to bring in our perspective on the research process through our discussions.

“Where Do Ideas Come From?”

• Question that Ph.d. students ask most.

• Where do ideas come from?

o Reading literature (seeing holes in existing literature, being unsatisfied with the consensus views on a topic, etc.)

o Trying to understand world around us (“What drives employment rates?”, “How would one measure policy uncertainty?”, etc.)

o Exploring data sources (my work on time trends, etc.)

o Talking with other graduate students!

• Pick projects you are interested in. If you are not interested in the answer to your question, no one else will be either!

Topic 1:

Consumption Inequality

Part A:Background on Household Surveys

(Nick will Expand on this in TA Sessions)

Micro Expenditure Data: Household Surveys

• 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

Micro Expenditure Data: Household Surveys

• British Household Panel (British Data)

o Panel data including income and expenditure

• Family Expenditure Survey (British Data)

• Bank of Italy Survey of Household Income and Wealth (Italian Data)

o Panel data including income and expenditure

• There are others….many Scandinavian countries, Japan, Canada, etc.

• Even some developing economies have detailed household surveys that track some measures of consumption (e.g., Mexico, Taiwan, Thailand)

Micro Expenditure Data: Scanner Data

• Nielsen Homescan Data

o Large cross-section of households

o Very detailed level transaction data (at the level of UPC code)

o Some demographics

o Some panel component

o Matches quantities purchased with prices paid

o Covers most of the large MSAs

o Measurement error?

o Selection?

o Coverage of goods?

Micro Income/Employment Data: Household Surveys

• Current Population Survey (CPS)

o Usual data set used within U.S. to track labor supply and earnings.

o Has panel component.

o Can be found at www.ipums.org/cps/

• PSID Can be found at http://psidonline.isr.umich.edu/

• Survey of Income and Program Participation (SIPP)

o Four year rotating panel

o Large sample sizes

o Over samples poor

• Census/American Community Survey

o Can be found at www.ipums.org

http://www.ipums.org/cps/

http://psidonline.isr.umich.edu/

http://www.ipums.org/

Part B:Trends in Consumption Inequality (Part 1)

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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)

Why Do We Care About Consumption Inequality?

• Why is it important?

o 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)

A Classic: Attanasio and Davis (1996)

A Short Discussion: The innovation of the Attanasio and Davis technique.The creation of synthetic cohorts from cross sectional data.

Krueger and Perri (2006)

• What they do:

o 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.

- 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.

o Use repeated cross sections to track consumption inequality.

Krueger and Perri (2006): What They Find

Krueger and Perri (2005): What They Conclude

• Conclusions

o 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.

Part C:A Caveat – Some Data Issues

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A Data Problem: Average Real Consumption in CEX

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A Data Problem: Average Real Consumption in CEX

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Percent Change in Consumption in CEX (from 1981)

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Trends in Real NIPA Aggregate Consumption

Part D:Revisiting Trends in Consumption Inequality Accounting for Measurement Error in Data

Can Measurement Error Alter Inequality Findings?

• Yes

• Depends on whether measurement error differs across the consumption (income) distribution.

• Suppose richer households have been underreporting their income to a greater extent in recent periods (relative to the past).

• The rich could be increasing their expenditure more (relative to other parts of the distribution). However, the systematic measurement error could also be increasing.

• How to test for group specific differences in measurement error?

Aguair and Bils (2011)

• Try to account for differential measurement error over different “income-demographic” groups to get a sense of changing consumption inequality.

• 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: 1

J

it ijtjX x

Log budget share of good: ln wi = ln (xi /X )

Log total real expenditure:

X = xLux+xNormal+xNecln X10 ln X90 ln X90

Estimated Engel curve for luxury

Estimated Engel curve for normal good

ln X90

Observed 1980

Observed2006

Inferred2006

The Essence of the Exercise (From a Discussion by Jonathan Parker; NBER EFG 2011)

Inferred adjustment to ln X90

ln X10


• Assume measurement error in expenditure……

• represents a good specific error (common across all groups)

• represents a group specific error (common across all goods)

*j i

t t ijtvijt ijtx x e

jtit

Aguair and Bils (2011): Some Intuition

• Difference-in-Difference Estimates (2 good case, 2 group case)

• Goods = e (entertainment) and f(food)

• Groups = high (rich) and low (poor)

(difference out good specific error)

(difference out good specific error)

*, ,

*, ,

high lowhigh e high e

low e low e

x xe

x x

*, ,

*, ,

high lowhigh f high f

low f low f

x xe

x x


• Take differences across goods to eliminate group specific error

• Obtain an unbiased estimate of relative consumption inequality.

• Need to map into units of total expenditure. Want to recover:

* *, , , ,

* *, , , ,

ln ln ln ln (1)high e high f high e high f

low e low f low e low f

x x x x

x x x x

* *, ,ln lnhigh t low tX X


* * * *

* *, , ,

* *, , ,

* *, , ,

* *, , ,

* * * *, , , , , , , ,

Define: ln ; ln

.....

.....

.....

.....

( ) ( ) (

ijt ijt it it

high e t e high t

low e t e low t

high f t f high t

low f t f low t

high e t low e t high f t low f t e f

x X

* *, ,)( )high t low t


* * * * * *, , , , , , , , , ,

Using (1), we know that we can express:

( ) ( ) ( )( ) high e t low e t high f t low f t e f high t low t

* *, , , , , , , , , ,as: ( ) ( ) ( )( ) high e t low e t high f t low f t e f high t low t


• Suppose for true expenditures, x* :

• Can estimate the following using actual data in some period 0 where systematic measurement error is less of an issue:

• If there is no measurement error in the data, can uncover:

• Assumes income elasticities are constant over time (and can be locally estimated). Assume measurement error is zero in period 0.

* * *ln lnijt jt j ijt j i ijtx X Z

0 0 0 0ln lnij j j i j i ijx X Z u

ˆj j


Substituting in the estimated β’s, we get:

* * * * * *, , , , , , , , , ,

Using (1), we know that we can express:

( ) ( ) ( )( ) high e t low e t high f t low f t e f high t low t

* *, , , , , , , , , ,as: ( ) ( ) ( )( ) high e t low e t high f t low f t e f high t low t

* *, , , , , , , , , ,( ) ( ) ( )( ) high e t low e t high f t low f t e f high t low t

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Aguiar and Bils (2011) Findings

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71


Relative Spending Differences Between High and Low Income Groups

72


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Different Saving Rates From the CEX

Attanasio, Hurst, and Pistaferri (2012)

• Also show that measurement error likely results in the underestimation of changes in consumption inequality within the U.S.

• Like Aguiar and Bils, find that consumption inequality and income inequality have moved essentially one-for-one over the past thirty years.

• Use other empirical approaches and data sets.

• You can find a copy of the paper on my web page (under working papers).


• Use CE Diary Data (a separate survey) as opposed to CE Interview Data (which wasused by Krueger/Perri and Aguiar/Bils.

• Diary data found to have less measurement error (better matches NIPA trends).


• Imputed PSID Consumption matches income inequality nearly identically.• Food PSID Consumption also matches income inequality trends (need to scale by

food income elasticity which is about 0.5).

Conclusions: Part 1 (A – D)

• 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.

Micro Data For Macro Models Fall 2014 Lecture 1 (Prologue and Consumption Inequality)

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