Non-Durable Consumption and Housing Net Worth in the Great - PowerPoint PPT Presentation

Non-Durable Consumption and Housing Net Worth in the Great Recession: Evidence from Easily Accessible Data Greg Kaplan University of Chicago Kurt Mitman IIES Gianluca Violante New York University New Perspectives on Consumption Kaplan-Mitman-Violante, ”Consumption and Housing” p. 1 /23

Background Real House Prices Real ND Consumption 0.1 Boom Boom 0.3 0.08 0.06 0.2 0.04 Logs (1997:Q1=0) Logs (1997:Q1=0) 0.02 0.1 0 0 −0.02 −0.04 −0.1 −0.06 Bust −0.08 −0.2 Bust −0.1 1974 1979 1984 1989 1994 1999 2004 2009 2014 1974 1979 1984 1989 1994 1999 2004 2009 2014 Year Year • Boom and bust in house prices • Boom and bust in non-durable consumption Kaplan-Mitman-Violante, ”Consumption and Housing” p. 2 /23

Background • U.S. Great Recession 07 -09 ◮ Sharp drop in house prices p h ( ∼ 30 pct) ◮ Durable C expenditures tanked, as in every recessions ◮ Unusually large drop in non-durable consumption (ND-C) Kaplan-Mitman-Violante, ”Consumption and Housing” p. 3 /23

Background • U.S. Great Recession 07 -09 ◮ Sharp drop in house prices p h ( ∼ 30 pct) ◮ Durable C expenditures tanked, as in every recessions ◮ Unusually large drop in non-durable consumption (ND-C) • Causal link between p h and ND-C? How large is this elasticity? Kaplan-Mitman-Violante, ”Consumption and Housing” p. 3 /23

Background • U.S. Great Recession 07 -09 ◮ Sharp drop in house prices p h ( ∼ 30 pct) ◮ Durable C expenditures tanked, as in every recessions ◮ Unusually large drop in non-durable consumption (ND-C) • Causal link between p h and ND-C? How large is this elasticity? • Answer relevant for: ◮ Consumption insurance ◮ Sources of aggregate fluctuations ◮ Policies that mitigate welfare costs of business cycles Kaplan-Mitman-Violante, ”Consumption and Housing” p. 3 /23

Widely cited answer • Mian, Rao and Sufi (QJE, 2012) ◮ The relationship is causal (IV approach) ◮ The elasticity of non -durable consumption to changes in the housing share of net worth (HNW shock) is 0.36 Kaplan-Mitman-Violante, ”Consumption and Housing” p. 4 /23

Widely cited answer • Mian, Rao and Sufi (QJE, 2012) ◮ The relationship is causal (IV approach) ◮ The elasticity of non -durable consumption to changes in the housing share of net worth (HNW shock) is 0.36 • Methodology: ◮ Geographical (county-level) variation ◮ Instrument: Saiz (2010) local housing supply elasticities ◮ p h data: CoreLogic & ND-C data: MasterCard Kaplan-Mitman-Violante, ”Consumption and Housing” p. 4 /23

Widely cited answer • Mian, Rao and Sufi (QJE, 2012) ◮ The relationship is causal (IV approach) ◮ The elasticity of non -durable consumption to changes in the housing share of net worth (HNW shock) is 0.36 • Methodology: ◮ Geographical (county-level) variation ◮ Instrument: Saiz (2010) local housing supply elasticities ◮ p h data: CoreLogic & ND-C data: MasterCard • Limitation: proprietary data, not replicable Kaplan-Mitman-Violante, ”Consumption and Housing” p. 4 /23

Our contributions 1. Replicate the MRS analysis with (more) easily accessible data and confirm MRS estimates • p h data: Zillow & ND -C data: Kilts-Nielsen Retail Scanner Kaplan-Mitman-Violante, ”Consumption and Housing” p. 5 /23

Our contributions 1. Replicate the MRS analysis with (more) easily accessible data and confirm MRS estimates • p h data: Zillow & ND -C data: Kilts-Nielsen Retail Scanner 2. Separation of price and quantity effect in expenditures • 1/5 of drop in expenditures due to lower prices Kaplan-Mitman-Violante, ”Consumption and Housing” p. 5 /23

Our contributions 1. Replicate the MRS analysis with (more) easily accessible data and confirm MRS estimates • p h data: Zillow & ND -C data: Kilts-Nielsen Retail Scanner 2. Separation of price and quantity effect in expenditures • 1/5 of drop in expenditures due to lower prices 3. Use CEX Diary to infer elasticity for Total ND-C • Elasticity of Total ND-C to ∆ p h is 20 pct lower than that of Kilts-Nielsen bundle Kaplan-Mitman-Violante, ”Consumption and Housing” p. 5 /23

D ATA Kaplan -Mitman-Violante, ”Consumption and Housing” p. 6 /23

Expenditure data • Kilts -Nielsen Retail Scanner Data (KNRS) ◮ Weekly panel dataset of sales for over 30,000 stores affiliated with about 90 participating retail chains across 55 MSA geographically dispersed across the US ◮ Information on both quantity sold and price charged per unit at UPC (barcode) level ◮ Construct an annual store-level panel of sales Kaplan-Mitman-Violante, ”Consumption and Housing” p. 7 /23

Expenditure data • Kilts -Nielsen Retail Scanner Data (KNRS) ◮ Weekly panel dataset of sales for over 30,000 stores affiliated with about 90 participating retail chains across 55 MSA geographically dispersed across the US ◮ Information on both quantity sold and price charged per unit at UPC (barcode) level ◮ Construct an annual store-level panel of sales • Store-switching a problem? ◮ For continuing stores, not an issue ◮ Shoppers switching from exiting to surviving stores: attenuation bias Kaplan-Mitman-Violante, ”Consumption and Housing” p. 7 /23

Coverage Category All Stores (2006) Baseline Sample (2006 -09) Dry grocery 37% 37% Frozen foods 8% 8% Dairy 8% 8% Deli 2% 2% Packaged meat 3% 3% Fresh produce 3% 2% Non-food grocery 13% 13% Alcohol 5% 5% Health and beauty aids 14% 14% General merchandise 8% 9% Number of stores 31,093 14,756 • Bundle composed mostly of groceries, cosmetic, and drugs Kaplan-Mitman-Violante, ”Consumption and Housing” p. 8 /23

Correlation with NIPA PCE in ND goods and services • Exclude gasoline and energy from NIPA PCE 1.2 1.15 1.1 KNRS 1.05 1 0.95 0.95 1 1.05 1.1 1.15 1.2 NIPA • State -level correlation in 06-09 nominal sales growth: 0.54 Kaplan-Mitman-Violante, ”Consumption and Housing” p. 9 /23

Net Worth data NW i t = H i t + F i t − M i t − D i t • Financial assets F : as in MRS, we allocate financial assets in FoF proportionally to the interests and dividend income from county -level IRS-Statistics of Income • Mortgage debt M and other debt D : as in MRS, we use Equifax data underlying the FRB-NY Consumer Credit Panel • Housing H : compute number of houses by county from ACS, and multiply by Zillow Home Value Index for All Homes • Aggregate all at CBSA level (level of Saiz instrument) Kaplan-Mitman-Violante, ”Consumption and Housing” p. 10 /23

Map of US CBSA Metropolitan and Micropolitan Statistical Areas of the United States and Puerto Rico February 2013 0 200 Miles Metro area Micro area County or equivalent State Number of areas United States 381 metro areas 536 micro areas Puerto Rico 7 metro areas 5 micro areas Note: Metropolitan and micropolitan statistical areas delineated by the Office of Management and Budget as of February 2013. 0 50 Miles 0 100 Miles 0 100 Miles Source: U.S. Census Bureau • 929 Core Based Statistical Areas (Metro SA + Micro SA) Kaplan -Mitman-Violante, ”Consumption and Housing” p. 11 /23

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M ETHODOLOGY Kaplan -Mitman-Violante, ”Consumption and Housing” p. 13 /23

Regression specification • Housing net -worth shock: � � ∆ HNW i 06 − 09 = ∆ log p i H i 06 /NW i 06 − 09 × 06 • MRS statistical model: ◮ First stage: 06 − 09 = α 0 + α 1 SaizElast i + η i ∆ HNW i 06 − 09 ◮ Second stage: � ∆ log C s,i 06 − 09 + ǫ s,i ∆ HNW i 06 − 09 = β 0 + β 1 06 − 09 • Weight obs. by store sales in 06 & cluster S.E. at CBSA level Kaplan-Mitman-Violante, ”Consumption and Housing” p. 14 /23