新冠疫情企业重新开业和消费者支出.docx

《新冠疫情企业重新开业和消费者支出.docx》由会员分享，可在线阅读，更多相关《新冠疫情企业重新开业和消费者支出.docx（60页珍藏版）》请在淘文阁 - 分享文档赚钱的网站上搜索。

1、Covid-19 Businesses Reopenings and Consumer Spending *Raissa Dantast and Jacob A. Robbins*April 14, 2021Latest version here.AbstractThis paper studies if Covid-19 retail and restaurant shutdowns and reopenings were responsible for the dramatic V shaped pattern of consumer spending in the United Stat

2、es. We find reopening policies substantially increased spending for categories directly impacted by the laws: a 68.4 p.p. increase in non-essential in-store spending and a 16.7 p.p. increase in full-service indoor dining. For sectors not directly impacted essential retail, limited-service restaurant

3、s, and online we find a limited impact of reopenings. We estimate that retail reopenings are responsible for 34% of the total trough-to-peak recovery in spending, while restaurant reopenings are responsible for 15% of the recovery.Keywords: Covid-19, consumer spending, real-time dataJEL Classificati

4、on: E21“Many thanks to Daksh Joshi for excellent research assistance. We also thank Earnest Research for providing spending data. University of Illinois at Chicago, Department of Economics, e-mail: ?University of Illinois at Chicago, Department of Economics, e-mail: Table 2: Comparison of Census and

5、 Earnest dataCoverage (%)Corr m.o.m.m.o.m. adjCorr y.o.y.y.o.y. adjRetail trade ex autos0.620.910.930.380.80Food and drinking places0.460.850.950.730.98Vehicles and parts0.0410.670.630.640.65Home furnishing and furniture0.750.710.940.460.98Electronics0.760.620.92-0.330.94Building materials0.490.700.

6、750.780.79Food and beverage0.740.940.940.910.91Health and personal care0.300.640.700.750.75Gas0.450.940.940.980.98Clothing and accessories0.680.760.950.970.98Sporting goods0.750.930.950.940.92General merchandise1.070.970.970.500.48Miscellaneous store retailers0.320.660.780.300.83Nonstore Retailer0.4

7、00.870.910.730.78Notes: Data from Earnest Research and Census Retail Sales. Coverage is defined as the ratio of aggregate Earnest spending to aggregate Census spending for the category. Correlations are between growth rates in Earnest and Census Retail Sales data.MARTS m.o.m. growth. For most indust

8、ries, the correlation in month over month spending growth is fairly high.Figure 2 (a) compares m.o.m. percentage changes in retail spending excluding autos from Earnest to data from the MARTS, and shows a very tight correlation of spending growth, with q = .91. Although the series track each other c

9、losely, errors have increased somewhat since the pandemic began. There is a very strong seasonal component to retail sales, and a potential concern of the results of Figure 2 (a) is that they are driven by a common seasonal component, with little predictive power beyond seasonal effects. The y.o.y.

10、correlation removes seasonable differences andis shown in Figure 2 (b). The y.o.y. correlation is .38, although note this is for a relatively sample size of 24 y.o.y. observations.Figure 2: Comparison of Earnest and Census retail sales(a) m.o.m. growth(b) y.o.y. growth Ret Earnest Adj Earnest UnadjR

11、et Earnest AdjEarnest UnadjNotes: Data from Earnest Research and Census Monthly Retail Sales. Panel (a) compares m.o.m. growth rates of Earnest and Census retail sales. Panel (b) compares y.o.y. growth rates.While the overall y.o.y. correlation in retail spending is relatively low, many individual c

12、ategories from Earnest do much better than the total. This motivates a procedure of reweighting the Earnest data to better match overall spending trends and adjust for Earnests non-representativeness. We will reweight spending growth on two dimensions: (i) share of spending by industry (ii) online s

13、pending share. We discuss both in turn.The first dimension we reweight is on industry spending share. Figure 3 (a) compares the share of retail spending for major NAICS categories for Earnest versus MARTS data. Earnest is overweight on grocery stores and general merchandise, and underweight on healt

14、h and personal care stores, gas station, and non-store retailers.Earnest is overweight general merchandise and groceries because it has excellent coverage of the large chain stores such as Walmart and Target, as well as Whole Foods and Kroger. Although Earnest does have Amazon, the limited number of

15、 firms it covers means that it is underweight on nonstore retailers.Figure 3: Comparison of Earnest and Census(a) Share of spending(b) Online share of spendingHome furnishing and furniture ElectronicsBuilding materialsFood and beverageHealth and personal careGasClothing and accessoriesSporting goods

16、General merchandiseMiscellaneous store retailersNonstore RetailerHome furnishing and furniture ElectronicsBuilding materialsFood and beverageHealth and personal careGasClothing and accessoriesSporting goodsGeneral merchandiseMiscellaneous store retailersNonstore RetailerShare of retail spending: Ear

17、nest - CensusHome furnishing and furniture ElectronicsBuilding materialsFood and beverageHealth and personal careGasClothing and accessoriesSporting goodsGeneral merchandise Miscellaneous store retailers Nonstore Retailer-.050.05.1.15Online spending share: Earnest - CensusNotes: Data from Earnest Re

18、search, Census Monthly Retail Sales, Census Quarterly Online Survey, Census Annual Retail Survey, Census Supplemental Annual Retail Survey. Panel (a) compares the share of retail spending for major NAICS categories for Earnest versus Census data. Panel (b) compares the share of spending that is onli

19、ne for Earnest compared with the Census quarterly E- commerce report. The bar widths represent the difference between the share of retail spending in Earnest minus the share of retail spending in the Census.The second dimension we reweight is on the share of online spending. Earnest industries have

20、a greater percentage of sales that are online compared with aggregate data. Figure 3 (b) compares the share of spending that is online for Earnest compared with the Census quarterly E-commerce report. About 25% of Earnest spending is online, compared with 11.3% for the Census data. Although the leve

21、ls are off, spending growth is tightly correlated between the two sources. Figure A.l compares the correlation of online spending growth for Earnest versus Census data, and finds a correlation of .99.We thus reweight data as follows. For each industry, we initialize total retail sales using the MART

22、S for January 2018. We then initialize spending for January 2018 between online and in-store spending for each using the shares for QI 2018 from the Census E-commerce report. After January 2018, spending for online and in-store are updated using growth rates from Earnest, and total spending formed b

23、y adding in-store and online.Figure 2 and Table 2, columns 3 and 5, show the reweighted results. The overall y.o.y. correlation of spending is .8, substantially higher than the unweighted data. Many individual categories also have higher y.o.y. correlations. The higher correlations show that when th

24、e Earnest data is put on an apples to apples basis with retail sales, it does a good job of matching aggregate spending trends. This will mean less measurement error and bias when estimating the effects of reopening on aggregate spending.2.2 Pandemic and aggregate spendingWe use Earnest data to meas

25、ure the effect of the Covid-19 crisis on overall spending. Pre-crisis, retail sales were growing at a brisk clip of 4 p.p. y.o.y. In mid-March (week 11) there was a stocking up week”, with panicked consumers buying supplies of groceries and other essentials, leading to spending up 21 p.p. y.o.y. Aft

26、er the stock-up week came the dramatic V shape of spending. In the last week of March spending dropped to -22 p.p. y.o.y., before recovering slowly over the next three months. By July, spending returned roughly to its pre-pandemic trend.We further decompose spending between in-store and online. Figu

27、re 4 shows non-essential in-store spending fell by nearly 100% in the early weeks of the pandemic, then gradually increased to -15% y.o.y. Some non-essential spending shifted from in-store to online, which by mid-April increased 135 p.p. y.o.y. Essential in-store spending fell moderately after the i

28、nitial surge from the stock-up week. Essential online saw a large and sustained increase, more than doubling by mid-April. Restaurant spending saw a more dramatic spending decline and slower recov- ery, with a nadir of -48 p.p. y.o.y. Throughout the spring and summer spending recovered until the end

29、 of September, when spending was actually positive y.o.y. However, increased Covid cases and colder weather drove spending back down to -16 % y.o.y. by years end.Figure 4: Pandemic spending by channel and essential vs non-essential01jan202001apr202001jul202001oct202001jan202(b)01jan202001apr202001ju

30、l202001oct202001jan20；(c)(d)Notes: Data from Earnest Research.3 Business shutdown and reopening policiesState governments pursued three main policies that affected consumer spending: stay at home orders, retail shutdowns, and restaurant shutdowns. Stay at home orders direct individuals to remain at

31、home except for essential activities. Retail and restaurant shutdowns close non-essential businesses and in-house dining, respec-tively.We collect data on shutdown and reopening dates for retail and restaurants from official state government websites, executive orders, and press reports. Forty-five

32、states had state-wide SAHs, 46 states had retail shutdownsl()The exceptions are Arkansas, Nebraska, North Dakota, South Dakota, Wyoming. and 50 states had restaurant shutdowns. The exception being South Dakota. We are counting DC as a state. Figure 5 shows considerable variation in reopening dates f

33、or retail and restaurant. The earliest states reopened in mid-April, most opened in May, and a few opened in June.Figure 5: Percentage of states under state-level orders08jan202007may202004sep202002jan2C08mar202006jul202003nov2020SAH - Restaurants - RetailNotes: Data on stay at home and business clo

34、sure laws were compiled from state government websites and executive orders. The figure presents the percentage of US states for a given date under stay at home, retail shutdown, and restaurant shutdown orders.3.1 SAH vs shutdown ordersWhile most existing literature focuses on SAH orders, this paper

35、 studies retail and restaurant shutdowns. We do so because a preliminary analysis of the raw data shows that spending responds dramatically to the end of a business shutdown even when a SAH order is in place, while it does not respond when SAH orders end while business shutdowns are in place. In thi

36、s sense, it seems that business shutdowns are the binding constraint on spending.To show this, we focus on the 8 states in which the retail stores reopened while the SAH order was still in effect. In all those states, a graphical analysis shows that non-essential in-store spending sharply increases

37、after retail shops reopen. The states are HI, ME, NC, NH, NM, PA, SC, WA. Figure A.3 displays spending for these states, and shows that state government de- facto allowed people to visit non-essential retail shops even under a SAH order. In the weeks between the retail reopening and the end of the S

38、AH, these 8 states averaged 7.6 p.p. y.o.y. spending growth, compared to an average growth of .6 p.p. in the two weeks before reopening. In contrast, there were five states in which the SAH ended before the retail shutdown. For these states,CA, DC, KY, MA, NY the end of the SAH had no effect: averag

39、e spending growth between the SAH end and the retail reopening was .7 p.p.For restaurants there is a similar story. Twenty two states ended SAH orders before opening restaurants for indoor dining. For 19 of these 22 states, restaurant spending did not increase with the end of SAH orders, but only in

40、creased after indoor dining was reinstated.For the remaining three states (DC, PA, VT), spending does not respond to either SAH or restaurant orders being lifted.3.2 Curbside pickup vs capacity restrictionsWhen lifting retail shutdown orders, states chose between two alternatives: limiting stores to

41、 curbside pickup only, or permitting in-store shopping but imposing capacity restrictions. Most states started with a more restrictive in-store capacity limit such as 20%-33% and gradually relaxed the restriction to 50%-75% occupancy. For example, Louisiana opened for curbside pickup on April 30, an

42、d then allowed 25% capacity on May 15th. The raw data suggests that spending does not noticeably respond to opening stores for curbside pickup, but does respond to allowing in-store shopping. For this reason, we focus empirical analysis on reopening laws that allowindoor shopping. In the Louisiana e

43、xample, we define May 15th as the treatment date.Note that we are not defining our treatment variable based upon any observed spending growth 一 it is still based on legally when states reopen. However, in our preliminary analysis of what hypotheses to test, we focus on the particular reopening polic

44、ies that seem to have substantial effects.3.3 Outdoor vs indoor diningWhen lifting restaurants shutdown orders, states chose between two alternatives: reopening for outdoor dining only or allowing some restricted indoor capacity. The pattern was for more restrictive capacity rules at first, followed

45、 by a gradual reduction of restrictions.Eighteen states first opened for outdoor dining only, only later allowing for indoor dining.When lifting restaurants shutdown orders, states chose between two alternatives: reopening for outdoor dining only or allowing some restricted indoor capacity. The patt

46、ern was for more restrictive capacity rules at first, followed by a gradual reduction of restrictions.Eighteen states first opened for outdoor dining only, only later allowing for indoor dining. For instance, Maryland opened outdoor dining on May 29 but only allowed indoor dining on June 12 at 50% c

47、apacity, then 75% capacity on September 21st. Graphical analysis of the raw data suggests that restaurant spending does not noticeably respond to allowing only outdoor dining, but does react to allowing indoor dining - for this reason we focus our empirical analysis on the causal effects of the capa

48、city reopening. For instance, in the Maryland example, we define June 12 as the treatment date.When summer is underway, a few states show responses when outdoor dining reopens while in-store is closed. However, this is infrequent. Only four states (i.e., MA, PA, NJ, and MS) see noticeable increases in spending when outdoor dining reopens, but indoor remains closed.3.4 Within state variationMost variation in retail and restaurant reopenings come from across state variation, however there