Micro Data For Macro Models

1. Micro Data For Macro Models Fall 2009

2. 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 Bad News/Good News

3. 1998 – 2000 Cohort At Top Schools (with likely omissions)

4. 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

5. 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.

6. 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

7. 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.

8. The Main Goal of This Class Get you to think about “questions” as opposed to “models”. Caveat: Models are good! You all have strong skills in this dimension. My comparative advantage is in questions. My style should be a complement – not a substitute – to your existing skill set.

9. 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).

10. Some Housekeeping…. Homework Three components Expectation for auditors T.A. Papers Slides Timing

11. 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.......

12. Lecture 1:Consumption

13. 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.

14. 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

15. 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).

16. 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)

17. 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?”

18. 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)

19. 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

20. Expenditure Data British Household Panel Family Expenditure Survey Bank of Italy Survey of Household Income and Wealth There are others….

21. Fact 1: Lifecycle Expenditures Plot: Adjusted for cohort and family size fixed effects

22. 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) Entertainment Services Alcohol and Tobacco Utilities Non-Durable Transportation Charitable Giving Clothing and Personal Care Net Gambling Receipts Domestic Services 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>>

23. Empirical Strategy: Lifecycle Profile of Expenditure • Estimate: (1) where 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

24. Fact 2: Hump Shaped Profile – By Education From Attanasio and Weber (2009)

25. Fact 3: Retirement Consumption Dynamics From Bernheim, Skinner and Weinberg (AER 2001)

26. The Puzzle? (Friedman, Modigliani, Hall, etc.) {Nt, Vt} are permanent and transitory mean zero shocks to income with underlying variances equal to σ2N and σ2V

27. Preferences

28. Euler Equation

29. What Are Potential Taste Shifters Over Life Cycle Family Size o Makes some difference o Hump shaped pattern still persists o See Facts 1 and 3 (above) – these were estimated taking out detailed family size controls. 2. Other Taste Shifters (that change over the lifecycle – for a given individual)?

30. Questions: What Else Drives the Hump Shaped Expenditure Profile? Why Does Expenditures (on food) Fall Sharply At Retirement?

31. 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)

32. Non-Separable Consumption and Leisure

33. Testing Non-Separable Consumption and LeisureIsolate Spending Changes Around RetirementAguiar and Hurst “Consumption vs. Expenditure” (JPE 2005)

34. 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

35. 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).

36. 8 digit food codes: Cheese Example 18 of the 100 8-digit codes for cheese. 14101010 CHEESE, BLUE OR ROQUEFORT 14102010 CHEESE, BRICK 14102110 CHEESE, BRICK, W/ SALAMI 14103020 CHEESE, BRIE 14104010 CHEESE, NATURAL, CHEDDAR OR AMERICAN TYPE 14104020 CHEESE, CHEDDAR OR AMERICAN TYPE, DRY, GRATED 14104200 CHEESE, COLBY 14104250 CHEESE, COLBY JACK 14105010 CHEESE, GOUDA OR EDAM 14105200 CHEESE, GRUYERE 14106010 CHEESE, LIMBURGER 14106200 CHEESE, MONTEREY 14106500 CHEESE, MONTEREY, LOWFAT 14107010 CHEESE, MOZZARELLA, NFS (INCLUDE PIZZA CHEESE) 14107020 CHEESE, MOZZARELLA, WHOLE MILK 14107030 CHEESE, MOZZARELLA, PART SKIM (INCL ""LOWFAT"") 14107040 CHEESE, MOZZARELLA, LOW SODIUM 14107060 CHEESE, MOZZARELLA, NONFAT OR FAT FREE

37. 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%

38. 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.

39. 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)Estimated Elasticity (α1) Retirement Effect (β1) Calories -4% (2%) -2% (4%) Protein * -1% (1%) -3% (2%) Vitamin A * 44% (5%) 36% (9%) Vitamin C * 34% (5%) 33% (9%) Vitamin E * 18% (3%) 11% (4%) Calcium * 10% (2%) 13% (4%) Cholesterol * - 26% (3%) -9% (5%) Saturated Fat * - 9% (2%) -7% (3%) * 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

40. 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)Estimated Semi-ElasticityRetirement Effect Eat Fruit 0.25 (0.03) <<59%> 0.14 (0.04) Eat Yogurt 0.14 (0.02) <<8%>> 0.01 (0.03) Eat Shellfish 0.05 (0.01) <<6%>> -0.02 (0.02) Drink Wine 0.15 (0.02) <<8%>> -0.03 (0.03) Eat Oat/Rye/Multigrain Bread 0.10 (0.02) <<9%>> 0.06 (0.04) Eat Hotdog/Sausage -0.16 (0.03) <<51%>> -0.06 (0.05) Eat Ground beef -0.10 (0.03) <<22%>> -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

41. 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.

42. Creating a Food Intake Aggregate • Where • c1, ….. cJare 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.

43. 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.

44. 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

46. 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”

47. Time, Consumption, and Expenditures Over the LifecycleGhez and Becker (1975)“The Allocation of Time and Goods Over the Life Cycle” (book)Aguiar and Hurst (2008)“Deconstructing Life Cycle Expenditure”

48. A Beckerian Model of Consumption • Consumption commodities are outputs of production functions using time (h) and expenditures on market goods (x) as inputs: Define: where σ > 1 implies x and h are substitutes • Example Commodity 1: TV Entertainment (σ < 1 – complements) Time Input: Time needed to watch the show Market Input: T.V., Cable Subscription • Example Commodity 2: A Meal (σ > 1 – substitutes) Time Input: Shop for food, prepare food, eat, clean up Market Input: Food, Appliances, Dishes, etc.

49. Two Margins of Substitution • Inter-temporal elasticity of substitution: u(c1, ….. , cN) • Intra-temporal elasticity of substitution: fn(hn,xn)

50. Model subject to: (assume C.E.S.) Let μ, λ, θ, andκbe 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.