Non-Durable Consumption and Housing Net Worth in the Great Recession: Evidence from Easily Accessible Data

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1 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

2 Background 0.3 Real House Prices Boom Real ND Consumption Boom Logs (1997:Q1=0) Logs (1997:Q1=0) Bust Bust Year Year Boom and bust in house prices Boom and bust in non-durable consumption Kaplan-Mitman-Violante, Consumption and Housing p. 2 /23

3 Background U.S. Great Recession 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

4 Background U.S. Great Recession 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

5 Background U.S. Great Recession 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

6 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

7 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

8 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

9 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

10 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

11 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

12 DATA Kaplan-Mitman-Violante, Consumption and Housing p. 6 /23

13 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

14 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

15 Coverage Category All Stores (2006) Baseline Sample ( ) 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

16 Correlation with NIPA PCE in ND goods and services Exclude gasoline and energy from NIPA PCE KNRS NIPA State-level correlation in nominal sales growth: 0.54 Kaplan-Mitman-Violante, Consumption and Housing p. 9 /23

17 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

18 Map of US CBSA Metropolitan and Micropolitan Statistical Areas of the United States and Puerto Rico February 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 Miles Miles Source: U.S. Census Bureau 0 50 Miles 929 Core Based Statistical Areas (Metro SA + Micro SA) Kaplan-Mitman-Violante, Consumption and Housing p. 11 /23

19 CoreLogic vs Zillow House Price Indexes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oreLogic: repeat-sale index Zillow: hedonic price index, includes new constructions Kaplan-Mitman-Violante, Consumption and Housing p. 12 /23

20 METHODOLOGY Kaplan-Mitman-Violante, Consumption and Housing p. 13 /23

21 Regression specification Housing net-worth shock: HNW06 09 i = logp i ( H06/NW i 06 i ) MRS statistical model: First stage: HNW06 09 i = α 0 +α 1 SaizElast i +η06 09 i Second stage: logc s,i = β 0 +β 1 HNW i ǫ s,i Weight obs. by store sales in 06 & cluster S.E. at CBSA level Kaplan-Mitman-Violante, Consumption and Housing p. 14 /23

22 A visual of the first stage Log change in Housing Net Worth, Saiz Housing Supply Elasticity Nonlinear relationship We use a quartic in the Saiz-elasticity in the first stage Kaplan-Mitman-Violante, Consumption and Housing p. 15 /23

23 RESULTS Kaplan-Mitman-Violante, Consumption and Housing p. 16 /23

24 Elasticity of ND expenditures to HNW shock Dep. var: logc s,i CBSA OLS IV IV (linear) HNW i 0.239** 0.361** 0.405** (0.029) (0.077) (0.089) N 14,756 12,701 12,701 Clusters R Remarkably similar to MRS estimate of in spite of different data on ND-C and house prices Kaplan-Mitman-Violante, Consumption and Housing p. 17 /23

25 A structural interpretation of this elasticityβ 1 Follow Berger-Guerrieri-Lorenzoni-Vavra (2015) Life-cycle model with: β(1+r) = 1, Cobb-Douglas u(c,h), deterministic income path, no borr. constraints, no trans. costs Elasticity of C to a permanent unexpected change in p h : C it /C it p h t/p h t = T τ=t( H it 1 1+r) τ tyiτ +H it +A it Kaplan-Mitman-Violante, Consumption and Housing p. 18 /23

26 A structural interpretation of this elasticityβ 1 Follow Berger-Guerrieri-Lorenzoni-Vavra (2015) Life-cycle model with: β(1+r) = 1, Cobb-Douglas u(c,h), deterministic income path, no borr. constraints, no trans. costs Elasticity of C to a permanent unexpected change in p h : C it /C it p h t/p h t = T τ=t( H it 1 1+r) τ tyiτ +H it +A it logc it = T τ=t( 1 H it +A it τ tyiτ 1+r) +H it +A it }{{} β logp h t ( H it H i,t +A it ) Similar to income pass-through coeff. of Blundell et al. (2008) Kaplan-Mitman-Violante, Consumption and Housing p. 18 /23

27 Consumption vs Expenditures Dep. var: logc s,i real CBSA, OLS IV HNW i 0.196** 0.298** (0.026) (0.085) N 14,756 12,701 Clusters R Nominal expenditures deflated through Paasche index 20 pct of drop in nominal exp. due to lower prices (0.30 vs 0.36) Kaplan-Mitman-Violante, Consumption and Housing p. 19 /23

28 From Kilts-Nielsen bundle to Total ND-C Wish to translate estimated elasticity in terms of Total ND-C Use CE Diary Survey (Attanasio-Battistin-Ichimura, 05) where items in KN bundle are better measured logc ND it = D t +β 0X it +β 1 logc KN it +ε it where X: equivalence scale, family type, age, edu, race, region Kaplan-Mitman-Violante, Consumption and Housing p. 20 /23

29 From Kilts-Nielsen bundle to Total ND-C Wish to translate estimated elasticity in terms of Total ND-C Use CE Diary Survey (Attanasio-Battistin-Ichimura, 05) where items in KN bundle are better measured logc ND it = D t +β 0X it +β 1 logc KN it +ε it where X: equivalence scale, family type, age, edu, race, region NIPA ND goods (excl. energy): KN goods + clothing and footware, tobacco, books, newspaper and magazines NIPA ND goods & services: ND goods + food away from home, clothing services, entertainment, communication, and transportation services Kaplan-Mitman-Violante, Consumption and Housing p. 20 /23

30 From Kilts-Nielsen bundle to Total ND-C Wish to translate estimated elasticity in terms of Total ND-C Use CE Diary Survey (Attanasio-Battistin-Ichimura, 05) where items in KN bundle are better measured logc ND it = D t +β 0X it +β 1 logc KN it +ε it where X: equivalence scale, family type, age, edu, race, region NIPA ND goods (excl. energy): KN goods + clothing and footware, tobacco, books, newspaper and magazines NIPA ND goods & services: ND goods + food away from home, clothing services, entertainment, communication, and transportation services Result: elasticity of total ND to KN bundle is Kaplan-Mitman-Violante, Consumption and Housing p. 20 /23

31 Elasticity of ND expenditures to housing wealth Dep. var: logc s,i CBSA OLS IV OLS IV logh i 0.124** 0.183** (0.019) (0.038) log ( H i M i) 0.072** 0.121** (0.011) (0.025) N 14,756 12,701 13,724 11,745 Clusters R More intuitive elasticities Elast. wrt to housing equity smaller b/c changes are larger Kaplan-Mitman-Violante, Consumption and Housing p. 21 /23

32 Taking stock Replication of MRS based on alternative and more accessible data largely confirmed their empirical findings Useful moment to match to validate structural models Kaplan-Mitman-Violante, Consumption and Housing p. 22 /23

33 Taking stock Replication of MRS based on alternative and more accessible data largely confirmed their empirical findings Useful moment to match to validate structural models Our preferred way to state the key quantitative finding: 1. The elasticity of total real ND exp. to housing equity is Given an aggregate drop in housing equity of 50 pct, the implied drop in aggregate ND-C is 4% (half of the total) 3. Corresponding annual MPC for ND-C out of housing equity is: C t = 0.08 Heq t C t H eq t MPC H eq = 0.08 ( Ct H eq t ) }{{} 0.37 = 0.03 Kaplan-Mitman-Violante, Consumption and Housing p. 22 /23

34 THANK YOU! Kaplan-Mitman-Violante, Consumption and Housing p. 23 /23

Non-durable Consumption and Housing Net Worth in the Great Recession: Evidence from Easily Accessible Data

Non-durable Consumption and Housing Net Worth in the Great Recession: Evidence from Easily Accessible Data Greg Kaplan Kurt Mitman Giovanni L. Violante June 29, 2017 Abstract In an influential paper, Mian,