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Www.bea.gov The Importance of, and Challenges to, Incorporating Distributional Information Into Macroeconomic Data J. Steven Landefeld, Director NABE Statistics.

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Presentation on theme: "Www.bea.gov The Importance of, and Challenges to, Incorporating Distributional Information Into Macroeconomic Data J. Steven Landefeld, Director NABE Statistics."— Presentation transcript:

1 www.bea.gov The Importance of, and Challenges to, Incorporating Distributional Information Into Macroeconomic Data J. Steven Landefeld, Director NABE Statistics Committee Meeting November 8, 2012

2 www.bea.gov Why It’s Important ▪ Existing national accounts and other macro-economic data fail to present a consistent picture of the differential impact of economic growth and change across households, regions, and industries.  Such data are important for understanding the economy and the political economy.  Such data are, in turn, important in informing economic policy, business planning, and social policy.  The causes of, and response to, the “Great Recession” and the recent election are just two examples of the importance of such data. 2

3 www.bea.gov Why It’s Important ▪ In a time of scarce statistical resources and increasing demand for data, we need to more effectively integrate household survey and tax and other administrative data into our national accounts.  BEA/Census/SOI Data - Reconciliation of aggregates and distribution of income  BEA/BLS/SOI Data – Reconciliation of aggregates and distribution of consumer spending  Future work  FRB/BEA Data – Distribution of household assets and liabilities 3

4 www.bea.gov Distributional Information Needed for Effective Economic Policy Analysis ▪ Potential impact of changes in the distribution of income on the economy:  According to research by Dynan, Skinner, and Zeldes, MPS of the top 1% is about 50%, rest is about 10% (including capital gains).  According to CEA, increase in top 1% share might have lowered PCE by 5%.  Expansion of credit and changes in underwriting standards and the recipients of that credit may have played a role in the housing bubble.  Housing losses and restrictions on credit may have disproportionally affected lower income groups’ spending during the contraction and expansion. ▪ In an attempt to get at what “most” households are experiencing, several investment banks developed measures of discretionary spending. ▪ ACCRA has developed a rough regional price to better assess the health of the economy across MSAs. 4

5 www.bea.gov 5 Are You Better Off? CPS-ASEC Household Income Disposable household Income Source: BEA, Census Bureau

6 www.bea.gov 6 Measuring Distribution of Income CPS-ASEC Household Income Disposable household Income 2006 Source: McCully, Clint. “Integration of Micro and Macro Data on Consumer Income and Expenditures.“ October 2012.

7 www.bea.gov 7 Measuring Distribution of Income CPS-ASEC Household Income Disposable household Income 2009 Source: McCully (2012)

8 www.bea.gov 8 Adjustments to Household Income CPS-ASEC Household Income Disposable household Income Source: Fixler, Dennis and David S. Johnson. “Accounting for the Distribution of Income in the U.S. National Accounts.“ September 2012.

9 www.bea.gov 9 Consumption Coverage Ratios Ratio of CES values to adjusted NIPA household consumption expenditures (HCE) for selected estimates Owner-occupied rent Household fuels Purchases of vehicles Tenant-occupied rent Food at home Clothing & footwear Restaurants/hotels Alcohol/home Self-reported Regular housing-related expenditures well reported Large irregular expenditures well-reported “Sin” expenditures not well-reported Small frequently purchased items not well reported Source: McCully (2012)

10 www.bea.gov Distribution of Spending 10 Household Consumption Expenditures by Quintiles, 2010 Source: McCully (2012)

11 www.bea.gov Relevant BEA Projects  Research on the reconciliation and incorporation of income distribution data as supplemental annual data on personal income  Research on the reconciliation and incorporation of consumer spending distribution data as supplemental annual data on personal outlays and spending  Quarterly data on the distribution of GDP by industry  Prototype annual real personal income by state  Longer-term:  Integrated distributional information on the distribution of production, spending, and wealth 11


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