**3. DATA SET**

**The assessment of corporate market power in Romania draws on firm-level data from the Structural Business Survey (SBS) for the period 2008–17.** The SBS data set—provided by the National Institute of Statistics of Romania—encompasses complete financial information at the headquarters level for the 2008–17 period for the surveyed firms. This survey is exhaustive for firms with at least 20 employees and provides a representative sample for firms with fewer than 20 employees. As such, the data set has two strata: one completely enumerated covering a census of all enterprises with at least 20 employees; and the second, a random stratum, covering some smaller firms. On the basis of this sample design, an unbalanced panel is constructed by considering all listed firms (of both strata) for all years. In this way, the panel allows entry and exit and does not impose any firm size threshold.

**Firm-level information from SBS is classified at NACE 4-digit level, which is considered a proxy for the product market where a firm operates.** SBS-sampled activities include all sectors except agriculture and banking. Therefore, the analysis focuses on mining, services, and manufacturing activities. In addition, the SBS data assign one NACE 4-digit sector classification for each firm, where the firm’s main product is classified. As such, all firms within a NACE 4-digit sector classification are considered to be in one product market. In the current analysis, the _product market_ definition deliberately does not coincide with the classical definition of “relevant market,”<sup>10</sup> which normally considers the degree of product substitution, geographic location of both producers and consumers, transportation costs, and so on.<sup>11</sup>

**The SBS data contain all the variables needed to estimate the production function that underlies markup computation.** The variables needed to estimate TFPR and markups following the production approach—as described in the previous section and expressed in equation (1)—are firms’ annual total revenue from operations,<sup>12</sup> total expenditure in intermediate materials, capital stock (sum of tangible and intangible assets), and total number of hours worked per year.

**After deflating nominal accounting data and controlling for outliers, the final data set contains 405,641 firm-year markup observations for 167,756 firms over the 2008–17 period.** All accounting information from the SBS was deflated, using the country-level GDP deflator (from World Development Indicators and Eurostat), to express values in Romanian lei for 2000. Once nominal values were deflated, additional steps were taken to control for outlier observations; in particular, observations on revenue, employment, capital (tangible assets), and costs of raw materials with a value of zero or lower than zero were excluded from the estimation. This procedure generated a final (unbalanced panel) data set with 405,641 firm-

> 10 For market power assessment purposes, a _relevant market_ is defined by the set of products and geographical areas that exercise competitive constraints on each other (see Motta 2004 for further discussion). 11 In principle, this assumption—that firms under one 4-digit sector classification belong to the same market— could imply some bias in the case of multiproduct firms that sell goods that are not close substitutes. However, this bias can be mitigated by using NACE 4-digit sectoral disaggregation. In addition, in the absence of firm-level information for multiple products, it is assumed that the firm-level markup is a weighted average markup across different products within the firm.

> 12 The data set does not contain information on physical quantity produced by each firm. Deflated sales are used as a measure of physical quantity to estimate output elasticities and then to recover markups. De Loecker and Warzynski (2012) show that using this approach to recover markups potentially affects the level of estimated markup but not the temporal changes in the markup.

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