Further Details on Sample Construction

Our sample does not contain data on physical inputs (except for the number of employees) nor on physical out­

put. Instead, we observe the necessary variables in terms of their value in euros (as of a given year). To make obser­

vations comparable across time, we apply a deflator.⁴³ For revenue, material costs, and capital stock we use the producer price indices (PPI) provided by Eurostat.⁴⁴ For labor costs, we use a labor costs deflator provided by Eurostat.

• For Nace section Manufacturing (C), we use the two­

digit Nace divisions PPIs when available for all years;

otherwise, we use the PPI associated with the main Nace section.⁴⁵

• For Nace section Wholesale and retail trade (G), we con­

struct a PPI using data on revenue and deflated revenue on a two­digit Nace code level from Eurostat. We con­

struct the PPI using the formula (15) revenuet

––––––––––––––– * 100.

deflated revenuet

• For the remaining Nace sections Transportation and storage (H), Accommodation and food service activities (I), Information and communication (J), Real estate activities (L), Professional, scientific and technical activ­

ities (M), and Administrative and support service activ­

43 From Orbis, we obtain data on the closing date (i. e., the date the account was closed and the variables were collected). The relevant period for a firm is therefore from closing date in t to the one in t+1.

While the closing date of many firms is December 31st, some close at January 1st or other dates throughout the year. This misalignment of calendar year and fiscal year poses an issue. Because we only have annual deflators, we have to arbitrarily choose how we deflate our data. This means, for a misaligned fiscal year, we have to assign it to a calendar year to be able to use our deflators. We choose to use July 1st as a cutoff date. For example, all variables collected from July 1st, 2005 to June 30th, 2006 are deflated with the 2005 PPI.

44 The relevant time series can be found at https://ec.europa.eu/eu­

rostat/en/data/database.

45 This is the case for Nace division C33.

TABLE 17: Number of Firms and Observations by Industry (Nace Section)

Nace Section Firms Obs.

C Manufacturing 5,435 33,942

G Trade (Wholesale and retail trade) 3,671 22,521

H Logistics (Transportation and storage) 659 4,126

I Accomondation & food (Accommodation and food service activities) 187 1,013

J IT (Information and communication) 624 3,592

L Real estate (Real estate activities) 120 563

M Professional (Professional, scientific, and technical activities) 814 4,681

N Administrative (Administrative and support service activities) 453 2,478

Total 11,963 72.916

The table contains, for each of the 8 selected Nace sections in our estimation sample, the total number of firms and the total number of observations. Throughout this report, we use the abbreviated section names as introduced in this table.

Source: Numbers based on data obtained from Bureau van Dijk’s Orbis database and authors’ own calculations.

TABLE 18: Productivity-Markup Elasticities Over Time

Dependent variable:

ln (Productivity_it)

Total (1)

Manufacturing (2)

Trade (3)

Services (4) ln (Markupit) – 1.3840*** (0.0847) – 2.3630*** (0.142) – 4.1690*** (0.235) – 0.0338 (0.0627)

x 2008 – 0.0867 (0.0769) – 0.0446 (0.135) 0.0776 (0.234) – 0.0168 (0.0533)

x 2009 – 0.0146 (0.0827) 0.2580* (0.139) 0.2190 (0.238) – 0.0201 (0.0580)

x 2010 – 0.0189 (0.0871) 0.0743 (0.148) – 0.0207 (0.255) – 0.0172 (0.0594)

x 2011 – 0.0621 (0.0872) 0.0328 (0.148) – 0.1970 (0.269) 0.0059 (0.0598)

x 2012 – 0.1350 (0.0871) 0.0652 (0.148) – 0.0087 (0.261) – 0.0539 (0.0612)

x 2013 – 0.0893 (0.0875) 0.0923 (0.147) – 0.0386 (0.265) – 0.0439 (0.0619)

x 2014 – 0.0345 (0.0896) 0.1650 (0.150) – 0.0294 (0.268) – 0.0065 (0.0634)

x 2015 0.0215 (0.0894) 0.2650* (0.153) – 0.1760 (0.270) 0.0141 (0.0638)

x 2016 0.0488 (0.0948) 0.3670** (0.168) – 0.1170 (0.296) 0.0264 (0.0679)

The table reports results from pooled OLS regressions. Dependent variable is the natural logarithm of total factor productivity (obtained from the production-function estimation in log scale). Independent variable of interest is the natural logarithm of firm-level price markups and year-interaction terms (base year: 2007). Reported coefficients are interpreted as elasticities. For more estimation details, see the notes for Table 8. Robust standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

Souce: Authors’ own calculations.

TABLE 20.: Direct and Indirect Effect of Competition on Labor Productivity (System)

Total (a)

Manufacturing (b)

Trade (c)

Services (d) System 1 – Equation 1: Dep. Var.: ln(InnoExp_i,t-1) (Observations: 2,026)

ln(Markup_i,t-2) – 1.480*** (0.496) – 2.771*** (0.787) – 1.305 (2.271) – 1.500** (0.693)

R² 0.344 0.341 0.103 0.377

System 1 – Equation 2: Dep. Var.: ln (Labor Productivity_it) (Observations: 2,026)

ln(Markup_i,t-1) – 0.544*** (0.071) – 2.038*** (0.110) – 3.401 (0.397) 0.533 *** (0.097)

ln(InnoExp_i,t-1) 0.047*** (0.007) 0.064*** (0.006) 0.082 (0.084) 0.065*** (0.015)

R² 0.316 0.250 0.168 0.349

System 2 – Equation 1: Dep. Var.: ln(R & DExp_i,t-1) (Observations: 1,932)

ln(Markup_i,t-2) – 1.551*** (0.481) – 4.074*** (0.787) 0.957 (1.999) – 1.617*** (0.565)

R² 0.402 0.381 0.070 0.483

System 2 – Equation 2: Dep. Var.: ln (Labor Productivity_it) (Observations: 1,932)

ln(Markup_i,t-1) – 0.568*** (0.075) – 1.871*** (0.112) – 3.740 (0.316) 0.614*** (0.131)

ln(R & DExp_i,t-1) 0.054*** (0.008) 0.063*** (0.006) – 0.040 (0.151) 0.123*** (0.036)

R² 0.302 0.252 0.514 0.151

Year FE 2-digit Industry FE

Yes Yes

Yes Yes

Yes Yes

Yes Yes

The number of observations refers to the sample for the total economy. The table reports results from a two-equation system estimation for the matched sample of ZEW’s MIP and Bureau van Dijk’s Orbis database. In System 1, the dependent variable in Equation 1 is the natural logarithm of innovation expenditure in year t – 1; in System 2, the dependent variable in Equation 1 is the natural logarithm of R & D expenditure in year t – 1. In both systems, the dependent variable in Equation 2 is the natural logarithm of labor productivity in year t.

The indirect effect of competition is captured by the natural logarithm of firm-level price markups in year t – 2 in Equation 1. The direct effect of competition is measured via the natural logarithm of firm-level price markups in year t – 1 in Equation 2. Both equations control for year FE and 2-digit industry effects, and Equation 1 additionally includes firm size (as ln(Assets)) as control. Standard errors are clustered at firm level. * p < 0.1, ** p < 0.05, *** p < 0.01.

Source: Authors’ own calculations.

TABLE 19: Productivity-Markup Elasticities (Lagged) Over Time

Dependent variable:

ln(Productivity_it)

Total (1)

Manufacturing (2)

Trade (3)

Services (4) ln(Markupi,t-1) – 1.4660*** (0.0961) – 2.2540*** (0.169) – 4.4270*** (0.286) – 0.1340* (0.0725)

x 2009 – 0.0337 (0.0852) 0.0246 (0.159) 0.4380* (0.262) 0.0670 (0.0602)

x 2010 0.0269 (0.0937) 0.2280 (0.163) 0.4880* (0.292) 0.0563 (0.0654)

x 2011 – 0.0076 (0.0949) – 0.0647 (0.170) 0.1700 (0.303) 0.0885 (0.0654)

x 2012 – 0.0450 (0.0953) – 0.0363 (0.172) 0.0811 (0.307) 0.0551 (0.0664)

x 2013 – 0.0503 (0.0966) 0.0087 (0.170) 0.2120 (0.307) 0.0064 (0.0686)

x 2014 – 0.0492 (0.0975) 0.0453 (0.173) 0.1510 (0.312) 0.0399 (0.0702)

x 2015 0.0353 (0.0995) 0.1460 (0.176) 0.1400 (0.315) 0.0786 (0.0711)

x 2016 0.1220 (0.105) 0.2080 (0.189) 0.0995 (0.346) 0.1070 (0.0752)

The table reports results from pooled OLS regressions. Dependent variable is the natural logarithm of total factor productivity (obtained from the production-function estimation in log scale). Independent variable of interest is the lagged natural logarithm of firm-level price markups and year interaction terms (base year: 2008). Reported coefficients are interpreted as elasticities. For more estimation details, see the notes for Table 8. Robust standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

Source: Authors’ own calculations.

Assumption 1. A firm’s information set at period t, that is l_it, includes current and past productivity shocks {

ω

ω

[

]

Assumption 2. Productivity shocks evolve according to the