A central motivating factor for studying price markups is their effect on consumer welfare. However, reported estimates of (firm-level) price markups in the literature often focus on industry or cross-country comparisons. These treat different industries equally rather than based on how relevant they are for consumers. We propose markup measures in which firm-level price markups are weighted according to consumption expenditures in the respective industries. Using a concordance table between consumption categories (otherwise used for the calculation of consumer price indices) and a firm’s industry classification, we report results for Germany for the years 2002 through 2016. We find that consumption-weighted price markups are higher and have increased faster than the conventionally reported revenue-weighted markups. We further show that consumption-weighted markups are highest for low-income households, highlighting the potential role of price markups as a contributing factor to changes in inequality in society.
Funding source: Deutsche Forschungsgemeinschaft
Award Identifier / Grant number: CRC TR 224
We thank an associate editor and two referees for helpful guidance in revising the manuscript. We also thank Bettina Peters, Dominic Ponattu, Markus Trunschke, and seminar participants at Stellenbosch University and the Amsterdam Inequality Conference for helpful comments and suggestions. The authors declare that they have no relevant or material financial interests that relate to the research described in this paper.
Research funding: Bernhard Ganglmair thanks the Deutsche Forschungs-gemeinschaft through CRC TR 224 (Project B02) for additional funding (http://dx.doi.org/10.13039/501100001659). Some of the results in this paper were first reported in Ganglmair et al. (2021), a study commissioned by the German Federal Ministry of Justice and Consumer Protection (BMJV).
Article Note: This article is part of the special issue “Market Power and Concentration and Developments: Evidence and Implications for Germany and Europe” published in the Journal of Economics and Statistics. Access to further articles of this special issue can be obtained at www.degruyter.com/journals/jbnst. The online version of this article offers supplementary material (https://doi.org/10.1515/jbnst-2020-0059).
A Data Appendix
Our main data source is Bureau van Dijk’s Orbis database. We use the June 2018 version of the database available at ZEW Mannheim. We supplement the data with producer-price indices (PPI) and consumer-price indices (CPI) obtained from Eurostat and Destatis (Federal Statistical Office of Germany).
A.1 Dataset Construction
A.1.1 Variables for Markup Estimation (Source: Orbis)
We retrieve the following financial data from Orbis (in Euros): sales (Orbis variable name TURN), material costs (MATE), labor costs (STAF), number of employees (EMPL), and fixed assets (FIAS), closing date of the account (CLOSDATE), and consolidation code of the account (CONS). In addition, we retrieve each firm’s industry classifications in the form of the main NACE section (NACE2_MAIN_SECTION) and four-digit NACE Rev. 2 core industry code (NACECCOD2). We download these data for all firms that are registered in Germany (including German-based subsidiaries of foreign companies) and provide financial data from unconsolidated accounts.
A.1.2 Deflators (Sources: Eurostat and Destatis)
We deflate all financial variables (sales, material costs, labor costs, and fixed assets) using two main sources as deflators: Eurostat and Destatis (Federal Statistical Office of Germany). The deflator information is calendar-year based. For observations in our data with an end-of-fiscal-year between July 1 of a year t and June 30 of a year , we deflate using the deflator for year t; and otherwise.
For sales, material costs, and total assets we proceed as follows:
NACE section A: We use the two-digit NACE code level PPI series from Destatis (series 61211-0001 and 61231-0001).
NACE sections B, C, D, E: We use the producer-price index (PPI) for the two-digit NACE divisions if available (Eurostat series sts_inpp_a); otherwise, we use the PPI associated with the main (one-digit) NACE section.
NACE section F: We use the section-level PPI (Eurostat series sts_copi_a).
NACE section G: We construct a PPI using data on revenue and deflated revenue on a two-digit NACE code level using
where j is a two-digit industry NACE division and t is the respective year (Eurostat series sts_trtu_a).
For all other services sectors: We use the consumer-price index (CPI) as a deflator, obtained from Destatis (series 61111-0003). We use our COICOP-NACE concordance table to match the CPI to the data and deflate using the four-digit COICOP classes. In cases where a four-digit COICOP class CPI does not exist, we use the two-digit level instead.
or labor costs, we use a labor costs index provided by Destatis (series 62421-0001) at the NACE section level. Because a labor costs index is missing for NACE sections A and B, we deflate the labor costs of these sections using their respective producer-price indices.
A.1.3 Sample Selection
Our sample includes financial data for German firms for the years 2000–2016. We apply the following steps for the construction of our estimation sample:
We exclude firm-year observations with missing values for any of the variables needed for the estimation of production functions and markups (sales, number of employees, material costs, labor costs, and fixed assets). We also exclude firm-year observations for which the number of employees was estimated by Bureau van Dijk.
We exclude firms that report NACE sections O (“public administration and defense; compulsory social security”), T (“activities of households as employers; undifferentiated goods- and services-producing activities of households for own use”), or U (“activities of extraterritorial organizations and bodies”) as their main activity.
We exclude all observations for firms that record at least one year with fewer than 20 employees or sales below 500,000 Euros.
We drop observations with a computed annual labor-costs-per-employee ratio below 5000 Euros. We further follow De Loecker et al. (2020) and eliminate observations with labor-costs-to-sales, material-costs-to-sales, and fixed-assets-to-sales (capital-to-sales) ratios in the top and bottom 2% (with the percentiles computed separately for each NACE section-year combination). We also eliminate observations with sales in the top 2%.
Last, we drop firms with only a single observation because our production-function estimation procedure (Ackerberg et al. 2015) requires observations from at least two consecutive periods.
A.2 Observations by NACE Section and Year
In Table 8, we summarize the number of observations by NACE section and year. We can see that the early years in our sample (in particular, 2000–2005) contain a considerably smaller number of observations/firms (because of less stringent financial reporting regulations). Moreover compared to other sections, many services-related sections have a lower observation count compared to manufacturing (C) or trade (G). These observations mirror Kalemli-Ozcan et al. (2015) who have observed that the coverage of financial data in Orbis for firms in services-related industries is lower than for firms in manufacturing.
This table reports the count of observations of the data used for price markup estimation for the sample period of 2000–2016, broken down by NACE Rev. 2 main section (single-digit level) and year. Monetary values (in million Euros) are deflated.
Additional Figures and Tables
|A||Agriculture, forestry and fishing||0.09||.||0.48||.||1.16||.||1.17||.|
|B||Mining and quarrying||11.87||.||29.64||.||7.68||.||5.96||.|
|D||Electricity, gas, steam and air conditioning supply||160.51||272.21||136.37||236.77||137.75||227.67||110.44||186.38|
|E||Water supply; sewerage, waste management and remediation activities||1.67||2.84||5.93||10.29||4.85||8.01||4.64||7.83|
|G||Wholesale and retail trade; repair of motor vehicles and motorcycles||214.50||363.78||253.93||440.89||284.01||469.40||280.41||473.23|
|H||Transportation and storage||59.17||100.35||43.56||75.64||31.57||52.18||28.34||47.83|
|I||Accommodation and food service activities||1.31||2.23||3.48||6.04||3.57||5.90||4.04||6.81|
|J||Information and communication||23.86||40.47||27.55||47.84||26.08||43.10||35.44||59.82|
|K||Financial and insurance activities||8.37||14.19||5.41||9.39||3.87||6.40||5.73||9.68|
|L||Real estate activities||3.56||6.03||2.08||3.60||1.87||3.09||2.14||3.62|
|M||Professional, scientific and technical activities||75.05||127.28||48.41||84.06||36.69||60.63||32.73||55.24|
|N||Administrative and support service activities||11.83||20.07||10.07||17.48||12.35||20.41||11.56||19.52|
|Q||Human health and social work activities||12.96||21.98||17.61||30.57||36.67||60.60||48.37||81.63|
|R||Arts, entertainment and recreation||0.04||0.08||0.36||0.62||1.17||1.93||1.26||2.12|
|S||Other service activities||1.64||2.78||3.71||6.45||4.90||8.11||5.66||9.55|
This table reports the weights for each NACE section over the entire economy (when applying equiproportional weights) for the years 2002, 2005, 2010, and 2015. In column Full, we report weights for all NACE sections; in column Covered, we report rescaled weights for those sections covered by the COICOP-NACE weights. The values for 2015 are those reported in the last two columns of Table 5. All columns sum up to 1000 (subject to rounding errors).
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