Micro Data For Macro Models

1. Micro Data For Macro Models Topic 3: More Home Production

2. 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?

3. Part A: Estimating Parameters of Home Production Function: Using Micro Data

4. 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.

5. Aguiar and Hurst (AER 2008) Lifecycle Prices and Production

6. 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 quantitiesconstant) • 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.

7. 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.

8. 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

9. 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.

10. 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.

11. 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

12. 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.

13. 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

14. 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)

15. 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?

16. Definition: Price IndexHousehold 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)

17. Price Index

18. 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.

19. 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

20. Price and Income (Table 1) p-value of difference < 0.01 p-value of difference < 0.01

21. Price and Household Size (Table 1)

22. Price and Household Composition (Table 1)

27. 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

28. First Order Condition From Cost Minimization Need to estimate shopping function: p(s,N) Use Homescan data to estimate above equation

31. Note: PSID data

32. 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

33. First-order conditions:

34. Home Production Function • Functional Form: • MRT condition: • σ= 1/(1-ρ) = elasticity of substitution between time and goods • in home production

35. 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

38. 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.

40. 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

41. Long Run Trends in Time Use

42. 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.

43. 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)

44. 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

45. 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

46. 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.

47. 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 Meal preparation, cooking, and serving food Act11 Meal cleanup, doing dishes Act12 Cleaning house (dusting, vacuuming, cleaning bathrooms, etc.) Act14 Laundry, Ironing, Clothes Care (sewing, mending, etc.) Some codes for 1993 (time spent in): Act10 Meal preparation, cooking, and serving food Act11 Meal cleanup, doing dishes Act12 Cleaning house (dusting, vacuuming, cleaning bathrooms, etc.) Act14 Laundry, Ironing, Clothes Care (sewing, mending, etc.)

48. 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”

49. 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 • Note: 30/40 year olds have increased 1965 to 2003 • Note: Population is becoming more educated between 1965 and 2003

50. Allocation of women with children by day of week Are Time Use Samples Representative? • 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