Demand or Supply? Price Adjustment during the Covid-19...

Demand or Supply? Price Adjustmentduring the Covid-19 Pandemic∗∗

Almut Balleer† Sebastian Link§ Manuel Menkhoff¶ Peter Zorn∗

December 4, 2020

Abstract

We study price-setting behavior in German firm-level survey data to infer the rela-

tive importance of supply and demand during the Covid-19 pandemic. Supply and

demand forces coexist, but demand shortages dominate in the short run. A reported

negative impact of Covid-19 on current business is associated with a rise in the prob-

ability to decrease prices up to eleven percentage points. Demand shortages remain

the main driver of price-setting behavior after accounting for health risks. These re-

sults imply a role for aggregate demand stabilization policy to buffer the economic

consequences of Covid-19 while containing the pandemic.

JEL Classification: E31, E32, E60, D22Keywords: producer price setting, supply, demand, Covid-19, fiscal policy

∗∗A preprint version of this research appeared in Covid Economics: Vetted and Real-Time Papers, 31, 59–102.First Draft: June 18, 2020. We thank three anonymous referees and audiences at the Franco-German FiscalPolicy Seminar, the European Central Bank, and the Norwegian School of Economics for helpful comments,Martin Popp for sharing sectoral data on physical proximity to others at work, Jean-Victor Alipour for shar-ing sectoral data on work from home capacity, and the LMU-ifo Economics & Business Data Center (EBDC)for assistance with the data. Declarations of interest: none.

†RWTH Aachen University, IIES Stockholm, CESifo, and CEPR. [email protected].§ifo Institute, LMU Munich, IZA, and CESifo. [email protected].¶ifo Institute and LMU Munich, [email protected].∗LMU Munich and CESifo. [email protected]. Ludwigstr. 28/RG, 80539 Munich, Germany. Corre-

sponding Author.

mailto:[email protected]:[email protected]:[email protected]:[email protected]

1 Introduction

As the Covid-19 pandemic is disrupting economies across the globe, policymakers are insearch for suitable stabilization policy measures. The scope and design of effective pol-icy hinges on the channels through which the pandemic affects economic activity. Onthe one hand, policymakers must consider measures that shield productive capacity go-ing forward to weather disruptions in the supply of goods and services. On the otherhand, policymakers must consider demand stimulus measures to address potential de-mand shortages. Demand deficiencies can arise from actual and expected income riskand higher economic uncertainty (Bayer et al., 2019); materialize as a consequence of sec-toral disruptions (Guerrieri et al., 2020), or input-output propagation (Baqaee and Farhi,2020). In addition, Covid-19 itself may reduce demand if consumption of certain goods isassociated with health risks (Eichenbaum et al., 2020b). The relative importance of supplyand demand forces during the Covid-19 crisis therefore is a key input to effective policy.

This paper is the first, to our best knowledge, to study firm-level producer price-setting data during the Covid-19 recession to inform this debate.1 Prices reflect shifts indemand and supply and thus are ideally suited to infer their relative importance. Givendemand, a reduction in the supply of goods and services generates inflation. Holdingproduction constant, demand shortages lead to disinflation.

The main result is that supply and demand forces coexist, but demand shortages dom-inate in the short run. In a regression of planned price changes on the reported impact ofCovid-19 on current business, we estimate that a strongly negative impact is associatedwith a substantial rise of up to eleven percentage points in the probability to decreaseprices, net of a rich set of controls.

Firm-level panel data is a critical input to obtain these results. The cross-sectional di-mension provides two key advantages over aggregate inflation data. First, it allows tocontrol for the decline in economic activity preceding the Covid-19 outbreak. Second,it allows to dissect the heterogeneity of supply and demand forces. At the same time,the longitudinal dimension allows to control for unobserved heterogeneity. Our empiri-cal analysis exploits the unique German ifo Business Climate Survey (ifo-BCS). The ifo-BCS is a monthly, mostly qualitative, firm-level survey among a representative sampleof roughly 6,000 German firms in all relevant sectors of the economy. Key for this paperare its regular questions about price setting and additional information on firm-specificeconomic activity that allow to control for other determinants of price-setting behavior.

1In more recent work, Meyer et al. (2020) study planned price changes and Alekseev et al. (2020) inves-tigate actual price changes. Both articles present similar findings to this paper.

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Specifically, we observe realized and three-months ahead planned price changes alongthe extensive margin. Price-setting behavior in the ifo-BCS closely co-moves with officialproducer price inflation and tracks quantitative changes in industry-specific producerprice indices very well.2

A recent supplement contains questions related to the Covid-19 pandemic. Amongother things, firms in the survey assess the impact of the Covid-19 pandemic on theircurrent business situation. About 80% of all firms in the survey report adverse effects,while approximately 10% report positive effects. The effects of supply and demand alsovastly differ with impact. Supply reductions, which we proxy for using a supplementalsurvey question about the lack of intermediate inputs/final goods, are prevalent acrossall firms. Order backlogs, our proxy variable for demand, are low in almost all negativelyaffected firms and high in those positively affected.

We show that firms differentially affected by Covid-19 display very similar dynam-ics in planned price changes up to March 2020, when the pandemic reached the Germaneconomy (Buchheim et al., 2020b). Relative to firms with no or only weak impact ofCovid-19, we estimate a substantial rise of up to eleven percentage points in the probabil-ity of planned price decreases associated with a strong negative impact, and a concurrentdecline in the probability of planned price increases. Conversely, positively affected firmsdisplay an approximately ten percentage point higher chance of planned price increasesand are less likely to plan price decreases. Since the vast majority of firms report negativeeffects due to Covid-19, the sectoral frequency of planned price decreases is predicted toincrease up to about five percentage points. The frequency of planned price increases ispredicted to decline, if anything. These findings suggest a dominant role for demand alsoat the aggregate level.3

The coexistence of supply and demand extends to price-setting behavior. If we condi-tion estimates on our proxy variables for supply and demand, supply-chain disruptionsand high order backlogs reduce disinflationary pressures while low order backlogs arereinforcing. The average effect, however, shows demand shortages to dominate oversupply reductions. Lastly, in lieu of the reported impact of Covid-19, we use indepen-dent information on the subjective reasons for the adverse effects of Covid-19 given inthe survey to confirm the co-existence of supply and demand in price setting behavior.

There are two main concerns with the analysis of price data during the Covid-19 pan-demic. First, price indices do not account for short-run shifts in expenditure patterns and

2The micro data underlying the German producer price index are not available for 2020.3German producer prices decreased by 0.8% in March 2020, 1.9% in April, and 2.2% in May year-on-

year.

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potentially bias measured inflation. In the case of the German economy, Cavallo (2020)finds a marginal upward bias in consumer price inflation of 0.09 percentage points afteraccounting for changes in consumption baskets.4 Second, certain goods and services aretemporarily not available or transferable. This reduction in product variety matters forwelfare and is primarily associated with supply reductions (Jaravel and O’Connell, forth-coming). Since no market prices for these items are observable, the analysis of prices forgoods and services that remain in trade potentially undermines the importance of sup-ply shortages. For these reasons, we focus on unique data on individual planned pricechanges which both reflect shifts in expenditure patterns and are available for goods andservices that are temporarily not in trade due to Covid-19 containment measures. Weobtain similar results when we use realized price changes.

Another concern is that price decreases reflect quality deterioration in the supply ofcertain goods and services due to added health risks (see e.g., Eichenbaum et al., 2020a).This is important for policy, since it is critical to differentiate between health risks andeconomic forces behind price decreases. We follow Mongey et al. (2020) in accounting forfirm exposure to health risks based on a contact intensity index based on O*NET surveydata and a work from home capacity index by Alipour et al. (2020).5 The main findingsof an higher probability to plan price decreases and a lower probability to plan priceincreases remain to hold after controlling for health risks.

Our reduced-form evidence complements a body of quantitative work that highlightsthe importance of weak demand during the Covid-19 recession (Eichenbaum et al., 2020a;Guerrieri et al., 2020; Caballero and Simsek, 2020; Baqaee and Farhi, 2020). Brinca et al.(2020) estimate sectoral labor supply and demand shocks for the US economy and findthat supply dominate demand forces. Balduzzi et al. (2020) show that credit constraintsand deaths due to Covid-19 are positively correlated with firms revising their plannedprice changes up. Cabral and Xu (2020) study pricing of several goods such as face masks.Dietrich et al. (2020) document an increase in consumer price inflation expectations inMarch 2020. Contributions documenting changes in expenditures patterns during theCovid-19 crisis include Cavallo (2020); Baker et al. (2020); Carvalho et al. (2020); Chettyet al. (2020). These authors show strong behavioral responses in US consumer expendi-ture patterns, mirroring to some extent the heterogeneity in price-setting decision in thepresent article. Our focus on producers instead of final consumers and prices to disen-tangle the relative importance of demand and supply complements this body of work.

4Similarly, Alvarez and Lein (2020) document a downward bias of 0.4 percentage points for Switzer-land.

5Kaplan et al. (2020) employ a similar distinction between regular and social sectors in the economy.

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Finally, we relate to contributions that study the firm-level impact of the Covid-19 crisis.Bartik et al. (2020), Hassan et al. (2020), and Buchheim et al. (2020a) are examples.

The remainder of this paper is as follows. Section 2 explains the data and provides abattery of descriptive statistics. Section 3 estimates econometrically the relation betweenthe impact of Covid-19 and planned price changes, on average and conditioning on proxyvariables for demand and supply as well as health risks. Section 4 discusses the implica-tions of our results.

2 Data and Descriptive Evidence

The main data source is the ifo Business Climate Survey (ifo-BCS), a monthly surveyamong a representative sample of German firms.6 We limit the analysis to the man-ufacturing (IBS-IND, 2020), services (IBS-SERV, 2020), and retail/wholesale (IBS-TRA,2020) industries.7 The survey is mostly qualitative, including questions about firms’ busi-ness situation and expectations, factors related to the supply and demand of goods andservices, as well as planned and realized price changes. Since March 2020, the surveyquestionnaire includes supplemental questions related to the Covid-19 pandemic.8 Thecalculation of descriptive statistics in this section uses a sample of 6,081 firms (2,175 inmanufacturing, 2,101 in services, and 1,805 in retail/wholesale), surveyed in April andMay 2020. Section 3 relies on a longer sample, running from 2018:M01 to 2020:M05, toprecisely estimate time-invariant determinants of price-setting behavior.

2.1 Covid-19 Impact in the ifo Business Climate Survey

Since April 2020, the ifo-BCS asks firms to assess the impact of Covid-19 on their busi-ness situation on a seven-point scale ranging from −3 “negative” to +3 “positive”. Ta-ble 1 shows summary statistics for each Covid-19 impact category, which we refer to as“strongly negative” (-3), “negative” (−2), “weakly negative” (−1), and “no impact” (0),with analogous labels for the positive categories.9

First, the bottom panel documents substantial heterogeneity in Covid-19 impact. InApril and May 2020, 33% of all firms report a strongly negative impact, 24% are negatively

6The ifo-BCS provides the basis for the ifo Buisness Climate Index, the most recognized leading indica-tor of the German business cycle. See Sauer and Wohlrabe (2020) for a detailed documentation. Sauer andWohlrabe (2019) show that survey questionnaires are predominantly filled out by senior management suchas firm owners, members of the executive board, or department heads.

7We leave out firms in construction and insurance. Data harmonization across sectors follows Link (2020).8Online Appendix A.1 presents translations of all survey questions used in this paper.9Tables A.1 to A.3 in the Online Appendix present summary statistics by sector.

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affected, while 11% experience no impact. On the other hand, a smaller share of in total10% tells of at least weakly positive effects. In the empirical analysis below, we groupobservations in the positive or strongly positive categories (+2 or +3) and those in theweak or no impact categories (−1, 0, or +1). The latter group will serve as our basecategory and the grouping ensures a sufficient number of observations in each cell oncewe control for additional variables.

Second, Panel A shows that the share of firms that report positive business conditionsincreases monotonically with Covid-19 impact, and vice versa (bar one exception). A sim-ilar pattern emerges for business expectations. Note that business situation and businessexpectations are not sufficient to explain Covid-19 impact. Some firms with no or positiveimpact report contemporaneously negative business conditions and expectations, whileothers with no or negative impact report positive business conditions and expectations.Hence, Covid-19 impact captures independent information specific to the pandemic.

Third, manufacturing capacity utilization in April 2020 on average strongly decreasedyear-on-year in negatively affected firms, and increased in positively affected firm. Forinstance, manufacturing firms hit hardest operate at about 54% of potential.

Fourth, the April survey questionnaire asked firms about the expected percent changein revenues due to the Covid-19 crisis. As Panel A shows, this figure decreases withCovid-19 impact and strongly negatively affected firms on average expect a 37% fall.

Fifth, Panel B relates Covid-19 impact to industry-specific proxy variables for risk ofCovid-19 infection, at work or from the point of view of potential customers. Specifically,we use O*NET survey data to measure contact intensity to co-workers and customers atwork at the five-digit industry level.10 Possible answer categories for the relevant ques-tion range from 0 (“I don’t work near other people (beyond 100 ft.)”) to 100 (“Very close(near touching)”). In addition to the O*NET measure, we use the share of workers in eachtwo-digit industry able to work-from-home (WfH) provided by Alipour et al. (2020). Ifinfection risk at the work is a major disruption to the supply of goods and services, weexpect a stronger negative Covid-19 impact in industries that are less able to substituteto work from home.11 Lastly, we classify two-digit industries as “high contact goods andservices” following a heuristic classification by Eichenbaum et al. (2020b).12 This measureprimarily captures risk of infection from a customer’s perspective by gauging the degree

10 We thank Martin Popp for constructing this data by weighting the O*NET survey information onphysical proximity with occupational employment per sector using the Integrated Employment Biogra-phies (IEB) of the Institute for Employment Research (IAB) in Germany. More details about the O*NETdata is available here: https://www.onetonline.org/find/descriptor/result/4.C.2.a.3.

11Mongey et al. (2020) combine the O*NET survey data and a measure for work from home to analyzethe effects of social distancing on workers.

12All other industries are low-contact or medium-contact industries following Eichenbaum et al. (2020b).

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https://www.onetonline.org/find/descriptor/result/4.C.2.a.3

Table 1 – Summary Statistics by Covid-19 Impact

Covid-19 Impact

-3 -2 -1 0 1 2 3 Total

Panel A:Positive Business Conditions 0.008 0.039 0.208 0.524 0.593 0.793 0.865 0.188

0.091 0.194 0.406 0.5 0.492 0.406 0.343 0.391Negative Business Conditions 0.907 0.545 0.138 0.038 0.029 0.029 0.059 0.467

0.29 0.498 0.345 0.19 0.168 0.167 0.235 0.499Positive Business Expectations 0.12 0.115 0.097 0.09 0.167 0.288 0.336 0.122

0.326 0.319 0.296 0.286 0.373 0.453 0.474 0.327Negative Business Expectations 0.743 0.673 0.541 0.3 0.252 0.217 0.229 0.581

0.437 0.469 0.498 0.458 0.434 0.413 0.421 0.493Capacity Utilization (Mfg., in %) 54.432 70.4 79.662 85 87.901 81.333 84.5 71.439

20.279 18.1 15.404 13.676 12.244 21.674 19.527 20.864Cap. Util. (Mfg., y-o-y, in pp) -27.414 -13.911 -4.776 -0.222 4.538 0.132 11.176 -11.954

21.945 16.839 14.354 13.885 10.335 18.177 11.254 20.222Expected Revenue Change (in %) -37.119 -22.922 -12.658 -6.232 -0.248 3.956 13.365 -20.973

21.744 13.522 9.88 9.046 9.56 15.255 40.19 21.359

Panel B:Contact Intensity (0-100) 58.599 56.288 54.988 54.809 57.12 57.962 58.911 56.74

5.112 4.291 4.244 4.786 5.583 5.209 5.435 4.993Work-from-Home (in %) 54.108 57.02 60.933 60.988 58.664 56.651 53.702 57.376

12.273 13.095 14.203 14.224 13.525 12.211 11.597 13.492High Contact Intensity (in %) 41.557 27.223 21.857 20.9 44.891 58.917 64.574 32.629

49.289 44.519 41.336 40.676 49.784 49.277 47.936 46.888

Panel C:Planned Price Increase 0.089 0.076 0.095 0.107 0.182 0.212 0.312 0.102

0.284 0.266 0.294 0.309 0.387 0.409 0.464 0.303Planned Price Decrease 0.219 0.143 0.091 0.044 0.042 0.044 0.05 0.136

0.414 0.35 0.288 0.205 0.201 0.206 0.219 0.342Planned Price Change 0.308 0.219 0.187 0.151 0.224 0.256 0.362 0.238

0.462 0.414 0.39 0.358 0.418 0.437 0.482 0.426Price Increase 0.041 0.051 0.057 0.079 0.168 0.189 0.321 0.068

0.199 0.219 0.232 0.27 0.374 0.392 0.468 0.252Price Decrease 0.154 0.099 0.057 0.041 0.045 0.041 0.062 0.096

0.361 0.298 0.232 0.199 0.208 0.199 0.242 0.295Price Change 0.195 0.149 0.114 0.121 0.213 0.23 0.383 0.164

0.396 0.356 0.318 0.326 0.41 0.422 0.487 0.371

Observations 3623 2596 2400 1254 553 317 223 10966Percent 33.04 23.67 21.89 11.44 5.04 2.89 2.03 100

Notes: This table depicts means and standard deviations (smaller numbers below) by Covid-19 impact. Row variablesin Panels A and C are indicators for positive/negative business conditions/expectations or planned/realized price in-creases/decreases/changes, capacity utilization, year-on-year change in capacity utilization, and expected percentage changesin revenue due to Covid-19 from the ifo-BCS. Covid-19 impact measures the impact of Covid-19 on the current business situ-ation on a seven-point scale from -3 (“negative”) to +3 (“positive”) in the ifo-BCS. Appendix A.1 provides translations of allcorresponding survey questions. Row variables in Panel B are industry-specific proxy variables for risk of Covid-19 infection.“Contact Intensity (0-100)” measures contact intensity to co-workers and customers at work based on O*NET data at the five-digit industry level (see main text and Footnote 10). “Work-from-Home (in %)” indicates the share of workers in each two-digitindustry able to work from home provided by Alipour et al. (2020). “High Contact Intensity (in %”) refers to the share of firmsclassified as “high contact goods and services” at the two-digit industry level following a heuristic classification by Eichen-baum et al. (2020b). Sample: 2020:M04–2020:M05, and 2020:M04 for capacity utilization in manufacturing and expected revenuechanges.

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Figure 1 – COVID-19 Impact by Industry

Textiles

Paper Products

Printing

Chemicals

Computer, Electronic & Optical Prod.Electrical Eq.

Machinery

Wholesale

PublishingMovie & TV Production

ProgrammingInformation Serv.

Financial Serv.Oth. Fin.

Real Estate

Legal Serv.

Consulting

Architects

Sc. Research

Advertising

Oth. Prof., Sc., & Techn. Serv.

Rental & Leasing

Travel Agencies

Oth. Admin.Education

Food Products

Beverages

WoodPlasticGlass

Basic MetalsMetal Products

Cars

Furniture

Oth. Manuf.

Waste

Car Sellers

Retail

Land Transp.Warehousing

HotelsRestaurants

Empl. Act.

Facility Man.

-3-2

.5-2

-1.5

-1-.5

Cov

id-1

9 Im

pact

202

0:M

04

50 55 60 65 70Contact Intensity Index

Coefficient: -.053; p-value: .002; R^2: .21

Notes: This figure displays the relationship between Covid-19 Impact and the contact intensity index based on O*NET surveydata described in Footnote 10, averaged at the two-digit industry level. Covid-19 Impact measures the impact of Covid-19 on thecurrent business situation on a seven-point scale from -3 (“negative”) to +3 (“positive”) in the ifo-BCS in 2020:M04. Blue (green)dots represent industries above (below) the median of the work from home capacity index by Alipour et al. (2020). The red linedisplays the linear fit, weighted by the number of firms per industry. Industries with less than 20 observations not shown.

of social interactions in final sales. Panel B shows that the relationship between Covid-19impact and these proxy variables is, if anything, weak and health risks are only slightlyhigher among firms reporting a strongly negative impact.

Figure 1 plots mean Covid-19 impact against the mean O*NET contact intensity mea-sure by two-digit sector. All industries on average report to be negatively affected bythe Covid-19 pandemic. The hardest hit businesses are in services, for example travelarrangement and reservation services, hotels, and restaurants, which also tend to be rel-atively contact-intensive. The retail industry is slightly more adversely affected than thewholesale industry. About 17% of all retailers report positive impact, reflecting mostlygrocery stores. Most adversely affected sectors in manufacturing include beverage man-ufacturing, furniture, and cars. Overall, the relation between Covid-19 impact and contactintensity is negative at the industry level, although substantial variation remains holdingcontact intensity fixed. High contact industries shown as green dots are also less able tosubstitute to work from home.

Figure 2 further correlates Covid-19 impact with proxy variables for adverse supplyand demand shifts. First, the dashed orange line shows that Covid-19 impact stronglycorrelates with orders. More than 85% of all strongly negatively affected firms report a

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Figure 2 – Covid-19 Impact and Adverse Supply and Demand Shifts

020

4060

80Sh

are

of F

irms

in %

(Lin

es)

010

2030

40Sh

are

of F

irms

in %

(Bar

s)

-3 -2 -1 0 1 2 3Covid-19 Impact

Lack of Intermediates/Goods Low Order Backlog

Notes: This figure displays the relationship between proxy variables for adverse supply and demand shifts against the Covid-19Impact that measures the impact of Covid-19 on the current business situation on a seven-point scale from -3 (“negative”) to+3 (“positive”) reported to the ifo-BCS in 2020:M04 and 2020:M05. The green bars represent the share of firms for each valueof Covid-19 Impact. The solid blue line displays the share of firms that report supply shortages (asked in online panel of man-ufacturing and retail/wholesale industries in 2020:M04) over Covid-19 Impact categories. The dashed orange line displays theshare of firms that report demand deficiencies over Covid-19 Impact categories (asked in 2020:M04 and 2020:M05). Appendix A.1provides translations of all corresponding survey questions.

low order backlog. Table A.4 in the Online Appendix shows that this pattern is mostpronounced among services firms (93%), but also very widespread in the manufacturing(84%) and retail/wholesale industries (85%). Accordingly, more than half of all stronglypositively affected firms report a high order backlog. Second, Covid-19 impact also corre-lates with supply shortages (solid blue line). In April 2020 more than 40% of manufactur-ing firms in each negative category lacked intermediate products, and about 50% of retailand wholesale firms in each negative category reported supply shortages of final goods.These shares decrease as Covid-19 impact becomes weaker but increase again in positivelyaffected firms, consistent with the notion of excess demand for the goods and services ofthese firms.

In sum, Covid-19 impact appears to capture shifts in economic activity related to bothsupply and demand forces and, to a lesser extent, health risks due to the Covid-19 crisis.Although their relative importance is hard to gauge from Figure 2, fewer orders appearmore prevalent in (strongly) negatively affected firms than supply-chain disruptions, sug-gesting a dominating role for demand deficiencies.13

13Tables A.4 and A.5 in the Online Appendix provide additional descriptives corroborating that Covid-19

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Table 2 – Summary Statistics: Subjective Reasons for Adverse Effects

Covid-19 Impact

-3 -2 -1 0 1 2 3 Total

Severity of Adverse Effects due to:Finances/Liquidity 0.173 -0.249 -0.535 -0.5 -0.546 -0.626 -0.633 -0.231

1.389 1.324 1.086 0.903 1.015 0.979 1.066 1.267Domestic Demand 1.238 1.101 0.678 0.183 0.157 0.111 -0.205 0.854

1.06 1.123 1.084 0.927 1.123 1.154 1.142 1.155Foreign Demand 0.434 0.498 0.302 0.104 0.107 -0.036 -0.367 0.339

1.721 1.573 1.334 1.112 1.297 1.229 1.077 1.512Labor Supply -0.909 -0.621 -0.347 -0.192 -0.257 -0.296 -0.014 -0.571

1.271 1.104 1.008 0.897 0.968 0.951 1.138 1.148Goods Supply -0.816 -0.443 -0.162 0.088 0.282 0.623 0.871 -0.357

1.329 1.136 1.053 0.898 1.112 1.136 1.301 1.24Regulations -0.12 -0.286 0.064 0.317 0.257 0.223 0.348 -0.034

1.428 1.252 1.181 1.054 1.175 1.05 1.263 1.286

Notes: This table depicts means and standard deviations (smaller numbers below) of subjective reasons data for the adverseeffects due to the Covid-19 pandemic by Covid-19 impact. Subjective reasons capture the adversity of a given demand or supplyforce on a five-digit scale ranging from “no effect” (+1) to “large adverse effect” (+5) and were elicited in the ifo-BCS in June 2020.We impute these survey responses to April and May 2020 and, for each reason, subtract the firm-level mean of the remainingreasons. Covid-19 Impact measures the impact of Covid-19 on the current business situation on a seven-point scale from -3(“negative”) to +3 (“positive”) in the ifo-BCS. Appendix A.1 provides translations of all corresponding survey questions. Samplefor Covid-19 Impact: 2020:M04–2020:M05.

This preliminary conclusion also finds support in independent evidence from theJune 2020 online portion of the survey that asked firms about the subjective reasons forthe adverse effects of Covid-19. Specifically, the question asks to assess the adverse effectsof financing conditions, labor input, supply-chain disruptions, government containmentregulations, and demand (domestic and foreign, separately) due to the Covid-19 crisis.Possible answers categories, on a scale from one to five, range from “no effect“ (+1) to“large adverse effect” (+5). To capture their relative importance at the firm level, wecompare each subjective reason relative to the firm mean of all those remaining. Thus,positive values reflect relatively more important reasons for a given Covid-19 impact cate-gory. The results, presented in Table 2, show that negatively affected firms predominantlysuffer from demand deficiencies, the relevance of which increases with negative Covid-19impact.14 On the other hand, goods supply shortages are the most important adverse ef-

impact correlates with supply and demand forces. Covid-19 impact is also associated with a higher frequencyof business closures. Moreover, in March 2020 firms were asked about the determinants by which Covid-19affected their business. On average, 34% of firms stated negative effects due to intermediate input supply-chain disruptions and individual survey responses strongly predict negative Covid-19 impact, reported forthe first time in the subsequent month. Similarly, disruptions in the delivery or sales of final goods are alsomore frequent in negative categories. In turn, we do not find a clear relationship between Covid-19 impactand a firms’ reliance on imported intermediates or production constraints due to a lack of material, bothelicited in April 2020.

14Table A.6 in the Online Appendix presents these results by sector. While patterns are similar across sec-

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fect among positive Covid-19 impact categories, again consistent with the notion of excessdemand for the goods and services of these firms. The last column suggests that demandforces are relatively more important than supply-side forces overall.

2.2 Covid-19 Impact and Price-Setting Behavior

The ifo-BCS contains a question on whether firms plan to increase, decrease, or leaveunchanged their prices over the following three months, as well as a similar questionon price realizations in the preceding month.15 The difference between the frequency ofplanned price increases and the frequency of planned price decreases very closely co-moves with actual producer price inflation, with correlation coefficients as high as 0.75.16

Reflecting an earlier-starting decline in economic activity, the frequency of plannedand realized price increases falls throughout 2019 while the frequency of planned andrealized price decreases climbed.17 These trends strongly accelerated after March 2020,when the German government implemented strict measures to prevent the spread ofCovid-19. The timing of abrupt movements in price change frequencies is in line withBuchheim et al. (2020b), who show that most firms in the ifo-BCS were unexpectedly hitby the Covid-19 crisis after filling in the March 2020 survey questionnaires.18

Panel C of Table 1 documents the relationship between Covid-19 impact and price-setting behavior. About 16.4% of firms changed prices in March and April, where 9.6% offirms decreased and 6.8% of firms increased prices. Firms strongly affected by Covid-19,both negatively and positively, change their prices more frequently than mildly affectedfirms. The frequency of price increases rises with Covid-19 impact, while the frequency ofprice decreases falls. Looking ahead, about 23.8% of firms plan to change prices in the

tors overall, demand shortages—especially in foreign demand—are slightly more important among man-ufacturing firms, good supply shortages are relatively more important in retail/wholesale—although stillless important than demand deficiencies—, and adverse effects due to governmental regulations are sub-stantially less important in manufacturing compared to the other industries.

15The ifo-BCS questions on planned and realized price changes are used in several articles. Bachmannet al. (2019) study the relation between uncertainty and price setting, Balleer et al. (2017) investigate thelink between financial constraints and price setting, Link (2019) examines the effect of the 2015 minimumwage introduction on firms’ price setting, Balleer and Zorn (2019) study the response of producer pricesto monetary policy shocks, and Enders et al. (2019) study the effect of monetary policy announcements onfirms’ planned price changes.

16See Figure A.1 in the Online Appendix for a time series plot.17See Figure A.2 in the Online Appendix for a time series plot.18In early March, only a few German counties were strongly affected by Covid-19. Subsequently, infec-

tion rates increased exponentially resulting in nation-wide school closures on March 13 and a nation-widecurfew on March 22. Buchheim et al. (2020b) document that firms’ business outlook decreased strongest af-ter March 13. Since roughly three out of four survey respondents filled in their survey questionnaire before,April 2020 is the first month in which the majority of survey respondents report reactions to Covid-19.

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Figure 3 – Planned Price Adjustment by Industry

(a) Planned Price Decreases

Textiles

Paper Products

PrintingChemicals

Computer, Electronic & Optical Prod.

Electrical Eq.

MachineryWholesale

Publishing

Movie & TV Production

Programming

Information Serv.Financial Serv.

Oth. Fin.

Real Estate

Legal Serv.

ConsultingArchitects

Sc. ResearchAdvertising


Rental & LeasingTravel Agencies

Oth. Admin.

Education

Food ProductsBeveragesWood

Plastic

GlassBasic Metals

Metal Products

Cars

Furniture

Oth. Manuf.

Waste

Car Sellers

RetailLand Transp.Warehousing

Hotels

RestaurantsEmpl. Act.

Facility Man.

-.10

.1.2

.3.4

Year

ly C

hang

e in

Pla

nned

Pric

e D

ecre

ases

202

0:M

04


Coefficient: .007; p-value: .006; R^2: .16

(b) Planned Price Increases

Textiles

Paper Products

PrintingChemicalsComputer, Electronic & Optical Prod.Electrical Eq.

Machinery

Wholesale

PublishingMovie & TV Production

Programming

Information Serv.

Financial Serv.

Oth. Fin.

Real Estate

Legal Serv.ConsultingArchitects

Sc. Research

Advertising


Rental & Leasing

Travel AgenciesOth. Admin.

Education

Food Products

Beverages

Wood

PlasticGlassBasic Metals

Metal ProductsCars

Furniture

Oth. Manuf.Waste

Car SellersRetail

Land Transp.Warehousing

Hotels

Restaurants

Empl. Act.

Facility Man.

-.3-.2

-.10

.1.2

Year

ly C

hang

e in

Pla

nned

Pric

e In

crea

ses

2020

:M04


Coefficient: -.003; p-value: .163; R^2: .05

Notes: This figure displays the relationship between the mean contact intensity index based on O*NET survey data describedin Footnote 10 and the yearly change in the frequency of planned price decreases (left) and planned price increases (right) in2020:M04 at the two-digit industry level. Blue (green) dots represent industries above (below) the median of the work fromhome capacity index by Alipour et al. (2020). The red line displays the linear fit, weighted by the number of firms per industry.Industries with less than 20 observations not shown.

following three months, where 10.2% of firms plan to increase and 13.6% of firms plan todecrease prices. Hence, firms overall tend to decrease prices more often at the onset ofthe Covid-19 crisis and also plan to decrease prices more frequently going forward. Theseprice-setting patterns are prevalent in all sectors, in particular in retail/wholesale.19

Figure 3 plots the yearly change in the frequency of planned price adjustments inApril 2020 against contact intensity across industries. The frequency of planned price de-creases, shown in Panel (a), rises in the bulk of industries. This increase is stronger incontact-intensive businesses such as hotels, restaurants, and retail, suggesting a relation-ship between health risks and planned price decreases. Holding contact intensity fixed,the remaining variation in the frequency of planned price decreases remains substantialand economically large, however. Indeed, the R2 for the linear fit shown is 0.16. Thus,health risks appear to only explain parts of the observed price adjustment patterns duringthe Covid-19 pandemic, while other factors unrelated to health risks seem to be quantita-tively more important.

Similarly, the frequency of planned price increases falls in all but three industries.Panel (b) of Figure 3 shows that only paper products (including the toilet paper industry),financial services, and education see an increase the frequency of planned price increases.Contact-intensive businesses tend to have a more pronounced although insignificant de-crease, and the variation holding contact intensity fixed remains substantial.

19See Tables A.1 to A.3 in the Online Appendix.

11

To summarize, there is large heterogeneity in firms’ price-setting behavior that corre-lates with Covid-19 impact. Positively affected firms tend to increase their prices, whilenegatively affected firms tend to decrease their prices. In most part, these relationshipsappear to be due to other factors than health risks. These patterns are consistent withthe notion that demand deficiencies dominate the adverse impact of Covid-19. The factthat there are many more firms for which Covid-19 impact is negative also suggests thatdownward price movements prevail at the aggregate level.

3 Econometric Analysis

3.1 Average Effects of Covid-19 Impact on Planned Price Adjustments

This section formally explores differences in planned price changes across Covid-19 impactcategories while controlling for other determinants of price-setting behavior. The focuson planned price changes helps to overcome potential issues due to certain goods andservices being temporarily unavailable or not transferable.20

First, we estimate the following regression, separately for each month-year t between2018:M01 and 2020:M05:21

Yi,t = δ−31 (Covidi,04/20 = −3) + δ−21 (Covidi,04/20 = −2) + δ{2;3}1 (Covidi,04/20 = 2∨ 3)+αs + X′i,t−3β + ui,t (1)

Here, Yi,t refers to an indicator for planned price increases or decreases over the followingthree months for firm i. In addition to dummy variables for the grouped Covid-19 impactcategories as of 2020:M04, we include two-digit WZ08 industry fixed effects (αs), andseparate indicators for positive and negative responses to the questions about businesssituation, business expectations, and orders, each lagged by three months and collectedin Xi,t−3, to control for past economic activity of firms.

Figure 4 shows the time series of the frequency of planned price increases and de-creases for each Covid-19 impact category, net of controls.22 In every month, the differencebetween each line relative to the group of firms with weak or no Covid-19 impact corre-sponds to the estimated coefficient δi, with i = −3,−2, {2; 3}, from Equation (1). The

20We obtain similar results when we use realized price changes. See Table A.9 in the Online Appendix.21On June 6, 2020 the German government unexpectedly announced a temporary reduction of the value

added tax rate effective July through December 2020. We deliberately exclude survey data from 2020:M06and thereafter to isolate the impact of Covid-19 on price-setting behavior.

22See Yagan (2015) for a similar approach in a different context.

12

Figure 4 – Planned Price Adjustment before and after the Covid-19 Pandemic

(a) Planned Price Decreases0

.05

.1.1

5.2

Estim

ated

Pro

babi

lity

2018m1 2018m7 2019m1 2019m7 2020m3Date

-3 Impact -2 Impact-1/0/1 Impact 2/3 Impact


0.1

.2.3

.4Es

timat

ed P

roba

bilit

y

2018m1 2018m7 2019m1 2019m7 2020m3Date

-3 Impact -2 Impact-1/0/1 Impact 2/3 Impact

Notes: This figure shows the time series of the frequency of planned price decreases (left) and price increases (right) for eachgrouped Covid-19 impact category as of 2020:M04, net of controls. In every month, the difference between each line relative tofirms with weak or no impact corresponds to the estimated coefficient δi , i = −3,−2, {2, 3} from Equation (1). The frequency-weighted average of all lines in a given month equals the month’s sample average. Sample: 2018:M01–2020:M05.

frequency-weighted average of all lines in a given month equals the month’s sample av-erage.

Planned price changes display similar pre-trends across Covid-19 impact categories.The left panel of Figure 4 shows that the frequency of planned price decreases displaysessentially identical dynamics across impact categories prior to 2020:M03, indicated bythe vertical red line, when measures to prevent the spread of Covid-19 were installed(see Footnote 18). Likewise, the right panel shows that the frequency of planned priceincreases displays similar dynamics across impact categories prior to 2020:M03. This sug-gests that these similar trends would have continued in the absence of Covid-19.

However, we observe that price-setting behavior of firms in different Covid-19 impactcategories is highly heterogeneous after 2020:M03. The frequency of planned price de-creases skyrockets for strongly negatively affected firms, rapidly rises for firms with neg-ative impact, and remains at similar levels for positively affected firms. There is no com-parable spike in the frequency of planned price increases that would suggest upwardprice pressure during the Covid-19 pandemic. The frequency of planned price increasesremains at similar levels for firms with positive impact and falls for those with (strongly)negative impact. Overall, this suggests a strongly disinflationary effect of Covid-19 impact.

Next, we exploit the panel dimension of the ifo-BCS and the timing of events to ac-count for level differences, seasonality, and business cycle movements observable in Fig-ure 4, i.e., slight upward and downward trends in planned price decreases and increases,

13

Figure 5 – Effects of Covid-19 Impact on Planned Price Adjustment-.1

0.1

.2Es

timat

ed C

oeffi

cien

ts

Strongly Neg. Negative Positive

(a) Total

-.10

.1.2

Strongly Neg. Negative Positive

(b) By Sector

Planned price decreases Planned price increasesPrice decreases manuf. Price increases manuf.Price decreases retail/wholes. Price increases retail/wholes.Price decreases services Price increases services

Notes: This figure reports estimates from linear regressions of indicators for planned price decreases/increases on indicatorsfor Covid-19 impact categories in the pooled sample (left) and separately by sector (right), based on Equation (1). Covid-19impact measures the impact of Covid-19 on the current business situation on a seven-point scale from −3 (“negative”) to +3(“positive”) in the ifo-BCS, which we group and label “Strongly Negative” (−3), “Negative” (−2), and “Positive” (+2 and +3),and the base category “Weak/No Impact” (−1, 0, or +1). Control variables include separate indicators for positive and negativeresponses to the questions about business situation, business expectations and orders, all lagged by three months. Appendix A.1provides translations of all corresponding survey questions. Industry fixed effects are at the two-digit WZ08 level. Time fixedeffects are at the month-year level. 95%-confidence bounds are based on standard errors clustered at the firm level. Sample:2018:M01–2020:M05. Table A.7 presents the corresponding numerical estimates.

respectively, consistent with the cooling of the German economy during this period. Weestimate the following regression on the sample 2018:M01 to 2020:M05:

Yi,t =δ−31 (Covidi,t = −3) + δ−21 (Covidi,t = −2) + δ{2;3}1 (Covidi,t = 2∨ 3)+αs + X′i,t−3β + γt + ui,t (2)

We set the grouped Covid-19 impact categories to zero for all observations prior to 2020:M04.Relative to Equation (1), we augment month-year fixed effects γt.

Figure 5 presents estimation results for planned price decreases (blue diamonds) andplanned price increases (red squares). Panel (a) contains estimates based on a samplepooling firms in all industries, while Panel (b) presents regression results when estimatingEquation (2) separately for firms in manufacturing, retail/wholesale, and services.23

On the one hand, firms reporting a negative impact of Covid-19 tend to lower prices.In the pooled sample of Panel (a), the probability of planned price decreases spikes by

23Panel (a) of Figure 5 refer to Columns 2 and 5 of Table A.7 in the Online Appendix. Columns 1 and 4of that table contain, for completeness, estimation results when only the Covid-19 impact category indicatorsare included in the regression. Columns 3 and 6 use firm fixed effects instead of industry fixed effects. Panel(b) of Figure 5 refers to Columns 1 and 5 in Table 3.

14

eleven percentage points for firms strongly negatively affected, relative to the base cat-egory of weak or no Covid-19 impact, net of controls. For negatively affected firms, theprobability of planned price decreases rises about five percentage points. By contrast,positively affected firms experience a decline in the chance of planned price decreases bythree percentage points. All estimates are statistically significant and economically verylarge compared to the unconditional two-digit sectoral frequency of planned price de-creases of 3.7 percent and within-sector standard deviation equal 3.4 percentage pointsin the period 2018:M01–2019:M12. Column 3 in Table A.7 in the Online Appendix showsthat these estimates are largely unaffected by the inclusion of firm fixed effects.

On the other hand, negative Covid-19 impact is associated with less frequent price in-creases. (Strongly) Negatively affected firms display an approximately two percentagepoints lower chance of planned price increases. By contrast, firms that report a positiveimpact on their business situation show a ten percentage points rise in the probability ofplanned price increases, relative to the unconditional two-digit sectoral frequency of 21.2percent and within-sector standard deviation equal 9.2 percentage points in 2018:M01–2019:M12.

Panel (b) of Figure 5 shows that (strongly) negative Covid-19 impact is associated withhigher probability of planned price decreases across all sectors. The effects are slightlyweaker in manufacturing, possibly reflecting the presence of long-term contracts betweenbuyers and suppliers. Positive Covid-19 impact does not display any significant differencesin planned price adjustments in services, presumably because of only very few observa-tions as Section 2 discusses. Positively affected firms plan fewer price decreases in themanufacturing sector while they plan to increase their prices in the retail/wholesale sec-tor.

Overall, prices become more flexible across all Covid-19 impact categories. Columns 8and 9 of Table A.7 in the Online Appendix show that in firms with strongly negative im-pact, the probability of planned price changes increases by about ten percentage points,reflecting the increased likelihood of planned price decreases. The same is true for firmsnegatively affected by Covid-19, which increase the chance of planned price changes byabout four percentage points. The probability of planned price changes for firms withpositive impact rises by about seven percentage points, reflecting an increase in the prob-ability of planned price increases. Again, these estimates are economically sizable com-pared to the unconditional two-digit sectoral frequency of planned price change of 24.9percent and within-sector standard deviation equal 8.9 percentage points in the period2018:M01–2019:M12.

These results remain robust in three alternative specifications presented in Table A.8

15

in the Online Appendix addressing potential concerns. First, we restrict the sample tocomplete price spells and add dummy variables to control for Taylor pricing, i.e., pricechanges that occur in fixed time intervals (e.g., every six months, see Lein, 2010 and Bach-mann et al., 2019). Second, respondents may also consider realized price changes in an-swering the survey question on Covid-19 impact. Consequently, Covid-19 impact wouldbe lower if prices decreased for a given change in output. This concern is partly alle-viated by using planned price changes instead of realized price changes. Moreover, weestimate Equation (2) on a subsample of firms which did not change prices in the currentmonth. Third, the sharp decline in oil prices during the Covid-19 crisis might also leadto significantly lower producer prices, potentially affecting the results in Table A.7. Weaddress this concern by including time-by-industry fixed effects at the two-digit WZ08level which flexibly control for each industry’s reliance on oil. In all of these alternativespecifications, the main results remain robust.

3.2 Supply and Demand Effects in Planned Price Adjustments

We further explore the role of supply and demand in price-adjustment patterns usingadditional survey responses from the ifo-BCS as proxy variables. In negative Covid-19impact categories, we expect prices to increase (or decrease less strongly) if supply-sideforces are strong. Conversely, we expect prices to decrease in particular if demand-sideeffects are important. In positive categories, we expect prices to increase with higherchances in the presence of excess demand.

Let SDshi f ti,t denote an indicator for a proxy variable of a given supply- or demand-side force. We extend Equation (2) as follows:

Yi,t =η−3,01 (Covidi,t = −3) + η−3,11 (Covidi,t = −3∧ SDshi f ti,t = 1)+η−2,01 (Covidi,t = −2) + η−2,11 (Covidi,t = −2∧ SDshi f ti,t = 1)+η{2;3},01 (Covidi,t = 2∨ 3) + η{2,3},11 ((Covidi,t = 2∨ 3) ∧ SDshi f ti,t = 1)+αs + X′i,t−3β + γt + ui,t (3)

All coefficients are again estimated relative to the base group of not or weakly affectedfirms and thus can be directly compared to the results in Figure 4. We estimate Equa-tion (3) using the proxy variables for supply and demand summarized in Table A.4, oneat a time. In particular, to proxy for demand shifts, we use information about firms’ orderbacklog. This variable is observed in every month and all survey questionnaires for eachsector. To proxy for negative supply shifts, we use information about the lack of inter-

16

mediate or final goods elicited in 2020:M04 for the manufacturing and retail/wholesaleindustries. We carry forward these responses to 2020:M05. Since the specific questionswe use slightly differ across survey questionnaires for each sector, and to allow (coarsely)for heterogeneity across industries, we provide separate results for the manufacturing,retail/wholesale, and services industries in the following

Table 3 reports estimates. Since interactions are identified based on variation withinCovid-19 impact categories and industries, some of the results lack sufficient statisticalpower as cell sizes become small. We concentrate on those cases where we obtain sig-nificant results.

Planned price decreases are less likely if firms face supply reductions. Columns 2and 6 of Table 3 show that strongly negatively affected retail/wholesale firms with nosupply-chain disruptions are 18 percentage points more likely to plan price decreases andabout nine percentage points less likely to plan price increases compared to firms in thebase group. If firms in this Covid-19 impact category report a concurrent lack of final goodssupply, the likelihood of planned price decreases falls by around eight percentage pointsand that of planned price increases rises by seven percentage points. The likelihood ofprice decreases still rises in these firms, but by less than in absence of supply-chain dis-ruptions.

Disinflationary pressures are reinforced by negative demand shifts and attenuated bypositive demand shifts. Again, this pattern is most pronounced in retail/wholesale. Theestimated effects of (strongly) negative Covid-19 impact in this sector is entirely driven byfirms that report a concurrent low order backlog, see Columns 3 and 7 of Table 3. Thesefirms are at least nine percentage points more likely than weakly or not affected firmsto plan price decreases and as much as eleven percentage points less likely to plan priceincreases. Conversely, the probability to plan price decreases falls in negatively affectedfirms across all sectors in the presence of positive demand shifts, as Column 4 shows.

The subjective reasons data on the adverse effects of Covid-19, introduced at the endof Section 2.1, corroborates the co-existence of supply and demand forces. Since thisdata is only available in 2020:M06, we impute the survey responses to 2020:M04 and2020:M05 and estimate Equation (2), separately for each of the six subjective reasons,replacing Covid-19 impact. Again, to capture their relative importance at the firm level,we compare the survey responses for each subjective reason relative to the firm mean ofall those remaining.24 Table A.10 in the Online Appendix confirms that adverse demandeffects are associated with a higher probability of price decrease and lower probability ofprice increases. Conversely, adverse supply effects due to reductions in labor input and

24Figure A.3 shows similar pre-trends across low and high relative importance for each subjective reason.

17

Table 3 – Supply and Demand Effects in Planned Price Adjustment

Planned Price Decrease Planned Price Increase

(1) (2) (3) (4) (5) (6) (7) (8)

Panel A: Manufacturing

Strongly Neg. 0.086∗∗∗ 0.081∗∗∗ 0.054∗ 0.090∗∗∗ -0.0081 -0.0019 0.0049 -0.0097Negative 0.050∗∗∗ 0.024 0.062∗∗∗ 0.060∗∗∗ -0.010 -0.021 -0.0053 -0.0098Positive -0.060∗∗∗ -0.075∗∗ -0.053∗∗ -0.063∗∗ 0.069∗ 0.048 0.050 0.034Strongly Neg. × Supply− -0.022 -0.0096Negative × Supply− 0.016 0.035∗Positive × Supply− 0.014 0.0036Strongly Neg. × Demand− 0.042 -0.016Negative × Demand− -0.0080 -0.0066Positive × Demand− -0.039 0.23Strongly Neg. × Demand+ -0.018 0.073Negative × Demand+ -0.082∗∗ 0.0028Positive × Demand+ 0.013 0.068Observations 44579 31199 44006 44006 44579 31199 44006 44006

Panel B: Retail/Wholesale

Strongly Neg. 0.14∗∗∗ 0.18∗∗∗ -0.021 0.15∗∗∗ -0.062∗∗∗ -0.086∗∗∗ 0.032 -0.064∗∗∗

Negative 0.048∗∗∗ 0.065∗∗ -0.0080 0.051∗∗∗ -0.027 -0.040 -0.0049 -0.029Positive -0.021 -0.017 -0.037∗∗ -0.010 0.11∗∗∗ 0.052 0.12∗∗∗ 0.028Strongly Neg. × Supply− -0.079∗∗ 0.066∗∗∗Negative × Supply− -0.033 0.025Positive × Supply− -0.012 0.10Strongly Neg. × Demand− 0.20∗∗∗ -0.11∗∗∗Negative × Demand− 0.091∗∗∗ -0.034Positive × Demand− 0.14∗ -0.025Strongly Neg. × Demand+ -0.22∗∗∗ 0.14Negative × Demand+ -0.059 0.062Positive × Demand+ -0.024 0.20∗∗∗

Observations 38563 34851 38389 38389 38563 34851 38389 38389

Panel C: Services

Strongly Neg. 0.11∗∗∗ 0.096∗∗ 0.11∗∗∗ 0.0056 -0.0043 0.0074Negative 0.063∗∗∗ 0.044∗∗ 0.068∗∗∗ -0.025∗ -0.034∗ -0.023∗

Positive -0.00086 -0.022 0.020 0.039 0.0092 0.11∗

Strongly Neg. × Demand− 0.018 0.013Negative × Demand− 0.033 0.016Positive × Demand− 0.14 0.21∗Strongly Neg. × Demand+ -0.058 0.018Negative × Demand+ -0.11∗∗∗ -0.030Positive × Demand+ -0.035 -0.12Observations 46153 45753 45753 46153 45753 45753

Controls yes yes yes yes yes yes yes yesTime FE yes yes yes yes yes yes yes yesIndustry FE yes yes yes yes yes yes yes yes

Notes: This table reports estimates from linear regressions of indicators for planned price decreases/increases on indicators forCovid-19 impact categories and interaction terms with proxy variables for supply and demand, based on Equation (3), separatelyfor each sector. Columns 1 and 5 repeat the baseline estimates based on Equation (2). Covid-19 impact measures the impact ofCovid-19 on the current business situation on a seven-point scale from −3 (“negative”) to +3 (“positive”) in the ifo-BCS, whichwe group and label “Strongly Negative” (−3), “Negative” (−2), and “Positive” (+2 and +3), and the base category “Weak/NoImpact” (−1, 0, or +1). Supply− is an indicator for the lack of intermediate/final goods. Demand− and Demand+ are indicatorsfor low and high order backlog, respectively. Control variables include separate indicators for positive and negative responsesto the questions about business situation, business expectations and orders, all lagged by three months. Appendix A.1 providestranslations of all corresponding survey questions. Industry fixed effects are at the two-digit WZ08 level. Time fixed effects areat the month-year level. Standard errors are clustered at the firm level. Sample: 2018:M01–2020:M05. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗p < 0.01.

18

supply-chain disruptions are associated with fewer planned price decreases and moreprice increases.

3.3 Health Risks and Planned Price Adjustments

Health risks associated with physical contacts are a key characteristic of the Covid-19pandemic. Such risks potentially account for the estimated changes in price-setting be-havior. For policy, it is critical to differentiate between health risks and economic forces.If policymakers seek to contain the pandemic at minimal economic cost, stimulus policyto stabilize demand shortages related to health risks would backfire, for instance.25

This section demonstrates that health risks explain part of observed price-setting be-havior during the Covid-19 pandemic. The main findings of an higher probability to planprice decreases and a lower probability to plan price increases remain to hold after con-trolling for health risks, however. To this end, we split the sample into firms subject tostrong and weak health risk exposure. We use the O*NET contact intensity index and thework from home (WFH) capacity index by Alipour et al. (2020) and define “high healthrisk exposure” firms by above-median contact intensity and below-median WFH capac-ity.26 The remaining firms are grouped as “low health risk exposure”.

First, we re-estimate Equation (1) replacing Covid-19 impact with this indicator for highhealth risk exposure. Figure 6 shows the observed trends in the probability of plannedprice decreases and planned price increases of high health risk exposure sectors parallelthose of low health risk exposure sectors. In panel (b), the probability of planned priceincreases is in general higher for high health risk exposure sectors. During the Covid-19pandemic, the probability of planned price decreases rises and the probability of plannedprice increases drops in both groups, but relatively stronger so in high health risk expo-sure sectors.

Next, we quantify these changes in the level and the differential effect of high riskexposure during the Covid-19 pandemic in a difference-in-differences regression:

Yi,t = δ11 (HighHealthRiski = 1) + δ21 (CrisisPeriod = 1) +

δ31 (HighHealthRiski = 1)× 1 (CrisisPeriod = 1) + X′i,t−3β + ui,t. (4)

Again, Yi,t denotes an indicator for a planned price decrease or a planned price increase of

25Fetzer (2020), for example, shows that a subsidy for food and non-alcoholic drinks in restaurants inthe United Kingdom had a causal effect on higher Covid-19 infection rates.

26Results are similar if we only use above-median O*NET contact intensity to define high health riskexposure.

19

Figure 6 – Effects of Health Risk Exposure on Planned Price Adjustment

(a) Planned Price Decreases0

.05

.1.1

5.2

Estim

ated

Pro

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2018m1 2018m7 2019m1 2019m7 2020m3Date

High health risk exposure Low health risk exposure


.1.1

5.2

.25

.3.3

5Es

timat

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roba

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y

2018m1 2018m7 2019m1 2019m7 2020m3Date

High health risk exposure Low health risk exposure

Notes: The figure shows the time series of the frequency of planned price decreases (left) and planned price increases (right) byhealth risk exposure, net of controls. “High health risk exposure” refers to the group of firms in industries with above-medianO*NET contact intensity and below-median capacity to work from home, measured by Alipour et al. (2020). The remainingfirms are classified as “Low health risk exposure”. See notes to Figure 4 for details on the construction of these figures. Sample:2018:M01–2020:M05.

firm i at time t, controlling for past economic activity (Xi,t−3). The coefficient δ1 capturesany level differences in the probability to change prices between high and low healthrisk exposure before the Covid-19 pandemic. The coefficient δ2 measures a level effecton the probability to change prices of low health risk exposure firms during the Covid-19 pandemic after 2020:M03, and δ3 describes the differential effect on high health riskexposure firms.

Table 4 presents results. Column (1) shows that high health risk exposure firms areabout five percentage points more likely to decrease their price during the Covid-19 pan-demic. Beyond health risk exposure, the probability of a planned price decrease remainssignificantly elevated by about five percentage points in low health risk exposure firms,consistent with the notion of economic forces that impact planned price-setting behavior.Column (4) shows that the probability of planned price increases drops during the Covid-19 pandemic and is not statistically different between low and high health risk exposurefirms.

As alternatives to the health risk exposure measure constructed from the O*NET con-tact intensity index and the WFH capacity index, we also consider the heuristic classifica-tion of “high contact goods and services” by Eichenbaum et al. (2020b) and the subjectivereasons data from the June 2020 wave of the ifo-BCS on government containment reg-ulations adversely affecting firms. For the latter, we define firms as having high health

20

Table 4 – Health Risks Effects of the Covid-19 Pandemic on Planned Price Adjustment

Planned Price Decrease Planned Price Increase

(1) (2) (3) (4) (5) (6)

Crisis 0.052∗∗∗ 0.063∗∗∗ 0.054∗∗∗ -0.11∗∗∗ -0.11∗∗∗ -0.11∗∗∗

High Health Risk -0.0012 0.043∗

Contact Intensive (Eichenbaum et al. 2020b) 0.0087 0.096∗∗∗

Severity of Gov. Regulation 0.0099∗∗ 0.018High Health Risk × Crisis 0.051∗∗∗ -0.0074Contact Intensive (Eichenbaum et al. 2020b) × Crisis 0.015 0.012Severity of Gov. Regulation × Crisis 0.035∗∗ -0.0012Observations 120529 120529 87047 120529 120529 87047Industry FE No No Yes No No YesControls Yes Yes Yes Yes Yes Yes

Notes: This table reports estimates from difference-in-differences regressions based on Equation (4). Firms are classified into ahigh and low health risks group based on three different measures: Columns 1 and 4 group firms to “high health risk exposure” ifthey operate in industries with above-median contact intensity as described in Footnote 10 and below-median industry-specificcapacity to work from home by Alipour et al. (2020). In Columns 2 and 5, firms are sorted according to the heuristic classificationby Eichenbaum et al. (2020b) to “high contact goods and services”. Columns 3 and 6 use Covid-19 related regulations by thegovernment as a proxy for health risk exposure based on subjective reasons data in the June 2020 wave of the ifo-BCS. Controlvariables include separate indicators for positive and negative responses to the questions about business situation, businessexpectations and orders, all lagged by three months. Appendix A.1 provides translations of all corresponding survey questions.Standard errors in parentheses are clustered at the firm level. Sample: 2018:M01–2020:M05. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01.

risk exposure if their response to this survey question (here in levels, not relative to theremaining subjective reasons) lies above the median value of three (on a scale from +1(“no adverse effects”) to +5 (“large adverse effects”)). Columns (2)–(3) and (5)–(6) reportestimates using these alternative measures and show that the probability of a plannedprice decrease always increases during the Covid-19 pandemic, and vice versa for theprobability of planned price increases. The effect on high health risk exposure firms is notalways statistically different from those of low health risk exposure firms.

4 Implications

Our main estimates presented in Figure 5 imply that (strongly) negative Covid-19 impactis associated with a five (eleven) percentage points increase in the probability of plannedprice decreases, relative to weak or no Covid-19 impact. This effect remains significantafter accounting for the potential role of health risks in observed price-setting behavior.Thus, our results highlight the importance of weak demand, driven by economic forces,during the Covid-19 pandemic. Otherwise, a reduction in supply would have reversedthe observed price-setting behavior, with higher probability of price increases associatedwith negative Covid-19 impact. The fact that survey respondents report demand shortagesas the primary reason for the adverse effects due to Covid-19 provides alternative, moredirect evidence in support of this result.

21

This finding is in line with other articles that emphasize differential effects of eco-nomic shocks on demand during the Covid-19 crisis. First, higher economic uncertaintymight adversely affect the demand for durables relative to non-durables (see Bayer et al.,2019, in a non-Covid-19 related context). Second, demand deficiencies are expected tobe large in firms particularly exposed through their position in the input-output network(Baqaee and Farhi, 2020; Barrot et al., 2020). Third, demand deficiencies are larger forgoods that are mostly complements to other goods and services (Guerrieri et al., 2020).Finally, Covid-19 itself may reduce the demand for certain goods for which consumptionis associated with health risks (Eichenbaum et al., 2020a). Our results provide reduced-form evidence for differential demand shortages across firms consistent with each of thesechannels.

At the same time, the results of this paper do not rule out negative supply forces,for instance, drops in labor input or supply-chain disruptions. Rather, the estimates werepresent show that demand is on average more important than supply. Indeed, Section 3.2showed that price-adjustment patterns move in the expected direction in the presence ofsupply shortages. Brinca et al. (2020) also provide evidence for the co-existence of supplyand demand forces in United States hours worked. Unlike these authors, we find that onaverage demand deficiencies dominate over supply forces.

Weak demand is also important at the aggregate level. To reach this conclusion, we in-tegrate the estimated effects on planned price adjustments at the sector level, separatelyfor price increases and decreases, and take into account the observed heterogeneity inplanned price changes.27 We pool all observations in 2020:M04 and 2020:M05 and use fre-quency weights to aggregate estimates for each Covid-19 impact category. The frequencyof price decreases is predicted to increase by 2.8, 5.4, and 5.3 percentage points in man-ufacturing, retail/wholesale and services, respectively. These figures are economicallylarge. In manufacturing, for example, this change corresponds to a one standard devia-tion increase in the frequency of planned price decreases. The frequency of planned priceincreases is predicted to increase by no more than 0.5 percentage points in manufacturingand slightly declines in retail/wholesale and services.28

27Since our estimated effects are identified off of the response of each Covid-19 impact category relative tothe base category of no or weak impact, the level effect is not identifiable. Thus, to aggregate our estimateswe implicitly assume that the base impact category is unaffected. Following Nakamura and Steinsson(2018), this approach is common in the macroeconomic literature using cross-sectional variation for identi-fication. Indeed, Figure 4 showed that planned price decreases in this category remain almost unchangedpost-March 2020.

28In Balleer et al. (2020), we use these aggregated estimates to predict a decline in inflation by 1.5 per-centage points relative to the counterfactual of no Covid-19 pandemic. This forecast does not incorporatethe temporary reduction in the German value-added tax rate effective July 2020, which very likely willreduce inflation even further.

22

Our findings suggest a role for policy to stabilize aggregate demand while contain-ing the Covid-19 pandemic. Monetary policy, constrained by the effective lower bound,seems an unlikely candidate. Even if there was policy room, the higher frequency of pricechanges implies greater aggregate price flexibility such that monetary stimulus becomesless effective. Moreover, the impact of Covid-19 is strongly negative in the vast majorityof firms and displays differential effects not only across, but also within sectors and withrespect to health risks. Woodford (2020) argues monetary policy may not “generate a pat-tern of expenditure with the ideal sectoral composition”. Hence, fiscal policy appears amore promising candidate to stabilize the aggregate demand shortage. It can also providetargeted social insurance policy for sectors with high health risk exposure to this end.

23

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A Online Appendix

Figure A.1 – Planned Price Changes and Producer Price Inflation

(a) Manufacturing

-.04

-.02

0.0

2.0

4PP

I Q-o

n-Q

Infla

tion

-.10

.1.2

.3Pl

anne

d Pr

ice

Cha

nges

2007m1 2009m1 2011m1 2013m1 2015m1 2017m1 2019m1Date

(b) Wholesale

-.1-.0

50

.05

Who

lesa

le Q

-on-

Q In

flatio

n

-.20

.2.4

Plan

ned

Pric

e C

hang

es

2007m1 2009m1 2011m1 2013m1 2015m1 2017m1 2019m1Date

(c) Retail

-.01

-.005

0.0

05.0

1.0

15R

etai

l Q-o

n-Q

Infla

tion

-.10

.1.2

.3.4

Plan

ned

Pric

e C

hang

es

2007m1 2009m1 2011m1 2013m1 2015m1 2017m1 2019m1Date

Notes: The figure plots times series of the monthly realized change in producer price indices from the German Federal StatisticalOffice (Destatis) relative to three months before (dashed orange line; right axis) against the mean of three-months ahead plannedprice changes from the ifo-BCS, weighted by representative weights included in the ifo-BCS (solid black line; left axis) for thesamples of (a) manufacturing, (b) wholesale, (c) retail (incl. car sellers) industries. Destatis does not provide a monthly producerprice index for services, hence not displayed here. All series are seasonally adjusted.

Figure A.2 – Frequency of Realized and Planned Price Decreases and Increases over Time

.05

.1.1

5.2

Frac

tion

of F

irms

2012m1 2014m1 2016m1 2018m1 2020m1Date

Realized price increases Realized price decreases

.05

.1.1

5.2

.25

Frac

tion

of F

irms

2012m1 2014m1 2016m1 2018m1 2020m1Date

Planned price increases Planned price decreases

Notes: The figures depict the frequency of realized and planned price decreases and increases as reported to the ifo-BCS. Thetime series cover the sample used in this study, i.e., manufacturing, retail/wholesale, and services industries, and are seasonallyadjusted.

A.1

Figure A.3 – Effects of Demand and Supply Adverse Effects on Planned Price Adjustment

i) Domestic Demand Deficiencies

0.0

5.1

.15

Estim

ated

Pro

babi

lity

2018m1 2018m7 2019m1 2019m7 2020m3Date

.1.1

5.2

.25

.3.3

5Es

timat

ed P

roba

bilit

y

2018m1 2018m7 2019m1 2019m7 2020m3Date

ii) Foreign Demand Deficiencies

0.0

5.1

.15

Estim

ated

Pro

babi

lity

2018m1 2018m7 2019m1 2019m7 2020m3Date

.1.1

5.2

.25

.3.3

5Es

timat

ed P

roba

bilit

y

2018m1 2018m7 2019m1 2019m7 2020m3Date

iii) Finances/Liquidity

.02

.04

.06

.08

.1.1

2Es

timat

ed P

roba

bilit

y

2018m1 2018m7 2019m1 2019m7 2020m3Date

.1.1

5.2

.25

.3.3

5Es

timat

ed P

roba

bilit

y

2018m1 2018m7 2019m1 2019m7 2020m3Date

A.2

iv) Labor Supply0

.05

.1.1

5Es

timat

ed P

roba

bilit

y

2018m1 2018m7 2019m1 2019m7 2020m3Date

.1.1

5.2

.25

.3.3

5Es

timat

ed P

roba

bilit

y

2018m1 2018m7 2019m1 2019m7 2020m3Date

v) Goods Supply

0.0

5.1

.15

Estim

ated

Pro

babi

lity

2018m1 2018m7 2019m1 2019m7 2020m3Date

0.1

.2.3

.4Es

timat

ed P

roba

bilit

y

2018m1 2018m7 2019m1 2019m7 2020m3Date

vi) Regulations

0.0

5.1

.15

Estim

ated

Pro

babi

lity

2018m1 2018m7 2019m1 2019m7 2020m3Date

0.1

.2.3

.4Es

timat

ed P

roba

bilit

y

2018m1 2018m7 2019m1 2019m7 2020m3Date

Notes: This figure shows the time series of the frequency of planned price decreases (left) and price increases (right) for relativehigh and low exposure to demand/supply effects. The June 2020 ifo-BCS survey questionnaire asks firms for the adverse effectsof financing conditions, labor input, supply-chain disruptions, government containment regulations, and demand (domesticand foreign, separately) due to the Covid-19 crisis. Possible answers categories, on scale from one to five, include “no effect”(+1) to “large adverse effect” (+5).

A.3

Table A.1 – Summary Statistics by Covid-19 Impact: Manufacturing

Covid-19 Impact

-3 -2 -1 0 1 2 3 Total

Positive Business Conditions 0.012 0.035 0.189 0.51 0.577 0.78 0.854 0.1610.109 0.183 0.391 0.501 0.496 0.416 0.358 0.368

Negative Business Conditions 0.877 0.556 0.166 0.048 0.026 0.073 0.049 0.4710.329 0.497 0.372 0.214 0.159 0.262 0.218 0.499

Positive Business Expectations 0.147 0.123 0.088 0.096 0.121 0.284 0.31 0.1250.354 0.329 0.284 0.295 0.327 0.454 0.468 0.33

Negative Business Expectations 0.678 0.655 0.584 0.356 0.318 0.247 0.19 0.5860.468 0.476 0.493 0.479 0.467 0.434 0.397 0.493

Expected Revenue Change (in %) -30.892 -21.628 -13.565 -7.284 -0.412 0.818 29.095 -18.49316.748 12.586 9.711 8.582 9.371 16.779 71.065 18.19

Planned Price Increase 0.039 0.039 0.067 0.074 0.133 0.159 0.22 0.0580.194 0.193 0.25 0.263 0.341 0.367 0.419 0.233

Planned Price Decrease 0.197 0.148 0.101 0.06 0.025 0.024 0.049 0.1340.398 0.356 0.302 0.238 0.158 0.155 0.218 0.34

Planned Price Change 0.236 0.187 0.168 0.134 0.158 0.183 0.268 0.1910.425 0.39 0.374 0.341 0.366 0.389 0.449 0.393

Price Increase 0.027 0.037 0.045 0.066 0.108 0.117 0.2 0.0450.161 0.188 0.208 0.249 0.312 0.324 0.407 0.208

Price Decrease 0.102 0.095 0.056 0.038 0.025 0 0.033 0.0760.303 0.293 0.229 0.192 0.157 0 0.183 0.266

Price Change 0.129 0.132 0.101 0.104 0.133 0.117 0.233 0.1220.335 0.338 0.301 0.306 0.341 0.324 0.43 0.327


Notes: This table depicts means and standard deviations (smaller numbers below) by Covid-19 impact for the subset of manu-facturing firms. Row variables are indicators for positive/negative business conditions/expectations or planned/realized priceincreases/decreases/changes, and expected percentage changes in revenue due to Covid-19 from the ifo-BCS. Covid-19 impactmeasures the impact of Covid-19 on the current business situation on a seven-point scale from -3 (“negative”) to +3 (“positive”)in the ifo-BCS. Appendix A.1 provides translations of all corresponding survey questions. Sample: 2020:M04–2020:M05, and2020:M04 for expected revenue changes.

A.4

Table A.2 – Summary Statistics by Covid-19 Impact: Retail/Wholesale

Covid-19 Impact

-3 -2 -1 0 1 2 3 Total





Expected Revenue Change (in %) -32.271 -24.107 -13.115 -7.945 -.496 4.36 11.09 -19.60119.298 14.262 9.809 9.154 10.95 15.064 20.604 20.377

Planned Price Increase 0.127 0.163 0.175 0.175 0.249 0.268 0.357 0.1750.333 0.37 0.381 0.381 0.433 0.444 0.481 0.38

Planned Price Decrease 0.281 0.151 0.12 0.061 0.065 0.055 0.056 0.1660.45 0.358 0.325 0.239 0.248 0.228 0.231 0.372

Planned Price Change 0.408 0.314 0.295 0.236 0.314 0.322 0.413 0.3410.492 0.464 0.457 0.425 0.465 0.469 0.494 0.474

Price Increase 0.063 0.087 0.113 0.17 0.221 0.236 0.389 0.1230.243 0.282 0.316 0.376 0.416 0.426 0.489 0.328

Price Decrease 0.184 0.121 0.1 0.102 0.064 0.055 0.069 0.1270.388 0.327 0.3 0.303 0.246 0.229 0.255 0.333

Price Change 0.247 0.208 0.212 0.272 0.285 0.291 0.458 0.2490.432 0.406 0.409 0.446 0.452 0.456 0.5 0.433


Notes: This table depicts means and standard deviations (smaller numbers below) by Covid-19 impact for the subset of re-tail/wholesale firms. Row variables are indicators for positive/negative business conditions/expectations or planned/realizedprice increases/decreases/changes, and expected percentage changes in revenue due to Covid-19 from the ifo-BCS. Covid-19impact measures the impact of Covid-19 on the current business situation on a seven-point scale from -3 (“negative”) to +3 (“pos-itive”) in the ifo-BCS. Appendix A.1 provides translations of all corresponding survey questions. Sample: 2020:M04–2020:M05,and 2020:M04 for expected revenue changes.

A.5

Table A.3 – Summary Statistics by Covid-19 Impact: Services

Covid-19 Impact

-3 -2 -1 0 1 2 3 Total





Expected Revenue Change (in %) -46.096 -23.712 -11.571 -4.462 0.386 10.688 2.722 -24.60824.02 14.004 9.988 9.13 6.895 8.623 39.016 24.403

Planned Price Increase 0.1 0.046 0.076 0.1 0.12

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