percent<sup>18</sup> smaller and 91 percent<sup>19</sup> less productive than the rest of the firms, even within narrowly defined sectors (at NACE 4-digit level). Results in column 3 captures the differences between these two groups of firms in terms of their willingness to invest in intangible assets— their “knowledge capacity”—a key determinant of firms’ expansion in the long term. Results suggest that the sample of top decile firms are almost 33 percent less likely to invest in intangible fixed assets than the remaining firms, even within NACE 4-digit sectors.<sup>20</sup> All these correlations are statistically significant at the 1 percent confidence level.

**Table 3. Characteristics of top decile firms: Conditional correlations between markups and firm characteristics (OLS and Probit results)**

||**(E**<br>|**stimation method) Dependent vari**<br>|**able**<br>|
|---|---|---|---|
||**(1)**<br>**(OLS)**<br>**Employment**<br>**(ln)**|**(2)**<br>**(OLS)**<br>**TFPR discounted for markup**<br>**(ln)**|**(3)**<br>**(Probit)**<br>**Investment in**<br>**intangibles**<br>**(yes/no)**|
|Top _decile|−0.632***|−2.422***|−0.386***|
|NACE 4-digit sector fixed<br>effects|Yes|Yes|Yes|
|County fixed effects|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|
|_R_<sup>2</sup>(pseudo_R_<sup>2</sup>)<sup>a</sup>|0.238|0.728|0.078|
|Observations|484,604|403,561|484,594|

_Source:_ World Bank staff calculations based on SBS data set. _Note:_ All firm-year observations (for the 2008–17 period) are pooled together, and NACE 4-digit sector and year fixed effects are included. OLS = ordinary least squares; TFPR = revenue-based total factor productivity. a. It is worth highlighting that _R_<sup>2</sup> value does not influence the consistency of the estimators of each population parameter, which is the objective of the current analysis. See, for instance, Cameron and Trivedi (2010 **)** for a theoretical discussion on this matter. Moreover, since the analysis draws on a large sample, the standard errors of the coefficient estimators are not affected because of the law of large numbers. The key issue would be the lack of correlation of omitted variables and the explanatory variables. However, given that the main (theoretical) determinants of the markup performance are included, the omitted variables can be understood to be random. *** Statistically significant at 1 percent.

**In addition, the group of 10 top decile firms in the markup distribution operate mainly in the services sector and are overrepresented in less-knowledge-intensive services sectors, with some exceptions.** Table 4 displays the distribution of top decile firms across NACE 2-digit sectors in 2017. To control for the fact that some sectors might be overrepresented (given the SBS survey sampling), the table shows only those sectors for which the probability of a firm being in the top decile is larger than the sector’s frequency ratio. Results show that firms in the 90th percentile of markup distribution are overrepresented in wholesale trade and retail trade sectors; the probability of a top decile firm being in these two sectors—classified as less-knowledge-intensive services, according to the Eurostat definition

> 18 This value results from the following expression: 100*[exp(beta)−1], where beta equals estimated top 90 percent dummy coefficients in an OLS regression where size is the dependent variable, controlling for 4-digit and county fixed effects (table 3, column 1).

> 19 This value results from the following expression: 100*[exp(beta)−1], where beta equals estimated top 90 percent dummy coefficients in an OLS regression where _TFPR_ is the dependent variable, controlling for 4-digit and county fixed effects (table 3, column 2). 20 The odds-ratio is computed as the relative difference between the marginal effects for each category (that is, 42 percent for the top decile firms and 56 percent for firms below the 90th percentile). Specifically, it is computed as follows: (56 percent − 42 percent)/(42 percent).

16