Karen Dynan BEA Advisory Committee Meeting November 16, 2012

1. Discussion of “Consumer Income and Expenditures: Integrating Micro and Macro Data” by Clinton P. McCully Karen Dynan BEA Advisory Committee Meeting November 16, 2012

2. Paper summary • Integrates NIPA data with micro data from the Current Population Survey and Consumer Expenditure Survey data to produce estimates of real Disposable Personal Income and Personal Consumption Expenditures by income quintile, household type, and primary source of income • In doing so, it deals with conceptual differences, differences in the populations captured, and coverage issues • Very impressive effort; high marks for carefulness and attention to detail • Paper will serve as a great reference for researchers trying to create “NIPA-like” measures in their micro data sets.

3. What’s next? • McCully highlights some remaining methodology issues to work out • Perhaps expand to include other types of data • E.g. one might expand the system to include balance sheet data from the Survey of Consumer Finances and the Flow of Funds Accounts • But, the big question is whether it would be useful to put the framework into production (publish these sorts of estimates on a regular basis) • Doing this would require use of scarce resources so important to think about the benefits

4. Possible uses of distributional estimates • Assessing the level of and trends in inequality • Monitoring the evolution of economic conditions • Conducting research about fundamental economic relationships

5. Potential for assessing inequality • Clearly an important question • Consumption inequality receiving more and more attention (e.g. Attanasio, Hurst, and Pistaferri, 2012; Fisher, Johnson, and Smeeding, 2012) • Small issue: for these studies you want consumption by consumption quintile rather than by income quintile • Bigger issues: • What value do McCully-like estimates add above and beyond research based solely on the micro data? • Serious concerns about underreporting by highest income households

6. Underreporting problem evidenced by low coverage ratios for certain types of income

7. Assessing inequality (II) • Underreporting at top in CPS will lead income inequality to be understated • Tax data might be a better micro source than CPS, but you’d still have to worry about underreporting for certain types of income (e.g. proprietors’ income) • Underreporting at top is also a big problem in the Consumer Expenditure Survey (see Sabelhaus, Johnson, Ash, Swanson, Garner, Greenlees, Henderson, 2011) • If the degree of underreporting changes over time, the problem becomes even harder to deal with

8. Potential for monitoring economic conditions • Key lesson that policy analysts should have learned from financial crisis: aggregate data do not tell us enough, we need to look at what’s going on in the tails • See, for example, Dynan (2012) • Distributional data might help us figure out why recovery has been so slow and what policies might help • But, limitation of current McCully system is that the estimates will very not be timely • Need to wait until micro data are available • Might extrapolate using lagged share information from micro data but what if the shares are moving?

9. Potential for economic research • Example: much still be learned about marginal propensities to consume, average propensities to consume • Studies using aggregate data thwarted by limited variation • Some good studies using micro data (e.g. Johnson, Parker, Souleles, 2006), but noise and limited panel dimension generally a problem • McCully data could be used like a synthetic panel • Maki and Palumbo (2001) mixed data from the Survey of Consumer Finances and Flow of Funds Accounts to study wealth effects => see next slide • Though, synthetic panels bring their own set of problems (group composition subject to change over time!)

10. Example: Maki and Palumbo used SCF/FOF to study underpinnings of wealth effects TOP INCOME QUINTILE SAW: MIDDLE INCOME QUINTILE SAW: Big increase in W/Y Flat W/Y Big decline in S/Y Flat S/Y Source. Maki and Palumbo, 2012.

11. Summing up • Paper is a really nice start on a framework for parsing national accounts data into estimates for different groupings of households • Estimates have value for assessing income inequality, monitoring economic conditions, and research but there are limitations as well • Things to think about: • Use tax data to overcome income underreporting at top? • Extrapolate estimates to make them more timely? • Add additional groupings to make the data more useful for research? • Education groups less subject to change over time • Regional data would allow use of regional economic shocks for better identification

12. References • Attanasio, Orazio, Erik Hurst, and Luigi Pistaferri. 2012. “The Evolution of Income, Consumption, and Leisure Inequality in the US, 1980-2010. National Bureau of Economic Research Working Paper #17982. • Dynan, Karen E. 2012. “Is a Household Debt Overhang Holding Back Consumption?” Brookings Papers on Economic Activity (Spring): Available at: http://www.brookings.edu/~/media/projects/bpea/spring%202012/2012a_dynan • Fisher, Jonathan, David S. Johnson, and Timothy M. Smeeding. 2012. “Inequality of Income and Consumption: Measuring the Trends in Inequality from 1985-2010 for the Same Individuals.” Available at: http://www.iariw.org/papers/2012/FisherPaper.pdf. • Johnson, David S., Jonathan A. Parker, and Nicholas Souleles. 2006. “Household Expenditure and the Income Tax Rebates of 2001.” American Economic Review 96 (December): 1589-1610. • Maki, Dean M., and Michael G. Palumbo. 2001. “Disentangling the Wealth Effect: A Cohort Analysis of Household Saving in the 1990s,” Finance and Economics Discussion Series 2001-21. Board of Governors of the Federal Reserve System. • Sabelhaus, John Edward, David Johnson, Stephen Ash, Thesia Garner, John Shearer Greenlees, Steve Henderson, and David Swanson. 2012. "Is the Consumer Expenditure Survey Representative by Income?" Finance and Economics Discussion Series 2012-36. Board of Governors of the Federal Reserve System