<!-- Start of picture text --> (c) Probability of an SOE Receiving a Subsidy<br>Relative to POEs<br>***<br>8.00<br>7.00 *** ***<br>6.00 ***<br>5.00<br>4.00<br>3.00<br>2.00<br>1.00<br>0.00<br>Direct Indirect Central Local<br>Direct vs indirect National or<br>subnational<br>Probability of receiving a government subsidy<br>Odds ratio<br><!-- End of picture text --> <!-- Start of picture text --> Probability of receiving a government subsidy (controlling<br>for productivity level)<br><!-- End of picture text --> <!-- Start of picture text --> (d) Log Value of Subsidy Received by SOEs<br>Relative to POEs<br><!-- End of picture text --> <!-- Start of picture text --> 2.50 *** ***<br>***<br>2.00 ***<br>1.50<br>1.00<br>0.50<br>0.00<br>-0.50<br>Direct Indirect Central Local<br>Direct vs indirect National or<br>subnational<br>Log subsidy amount (among recipients)<br>Logarithmic form<br><!-- End of picture text --> <!-- Start of picture text --> Log subsidy amount (among recipients & controlling for<br>productivity level)<br><!-- End of picture text --> Source: World Bank staff analysis using Romania MoF firm-level data, 2011-2019.

Note: The sample includes all firms. Employment is the log of the number of employees. Wage level is the log of average wage bill, where average wage bill is (real) labor cost divided by number of employees. Assets per worker is (real) total assets divided by the number of employees. Labor productivity is (real) revenue per worker. Employment growth is calculated as the difference between the number of employees in year t and t-1 divided by the average number of employees in year t and t-1. Wage growth is the difference between wage level in year t and t-1 divided by the average wage level in year t and t-1. Total asset growth (used as a proxy for investment) is calculated as the difference between (real) total assets in year t and t-1 divided by the average of total assets in year t and t-1. Labor productivity growth is calculated as the difference between (real) revenue per worker in year t and t-1 divided by the average revenue per worker in year t and t-1. Estimates in graph (c) are odds ratios indicating for the probability of receiving a subsidy based on a logit model with a dummy variable taking the value one if the firm reported a positive subsidy amount. The estimates in (d) are in logarithmic terms with the dependent variable being the log of the subsidy amount the firm reported as received. SOE variables are dummy variables taking the value one if national or subnational governments have ownership stake of 10-100% in the firm. The reference group is private-owned enterprises (POEs), defined as firms where national or subnational governments own 0-9.9%. All models include 2-digit NACE industry and year-fixed effects. Standard errors clustered at the industry level are in parentheses. ***, **, and * indicate significance at 1 percent, 5 percent, and 10 percent. Where indicated, the model includes age groups: Young (3-5), Mature (6-10), and Old (>10), with Start-ups (0-2) being the reference category, and size group: Small (1049), Medium (50-99), Large (100-499), and Very large (500+). with Micro (1-9) being the reference category. For outcome variables in level form, the age and size dummies are defined using contemporaneous age and size variables, but they are defined using lagged values for outcome variables in growth (or change) form.

# (c) Do Performance and Subsidy Allocation between SOEs and POEs Vary across Sector of Operation?

**Results suggest that some performance differences between POEs and SOEs depend on the types of sectors in which they operate** . Understanding the relative performance of SOEs across sectors which feature different technological and market features may, therefore, be important. Thus, the analysis is peerfomed for three broad sectors—competitive, partially contestable, and natural monopoly,<sup>8</sup> sectors with dissimilar intrinsic market characteristics and failures (see Section 3.3 for an explanation of the sector taxonomy). The results suggest that SOEs employed more people, paid bigger wages, had larger assets per worker ratio, but had lower labor productivity levels across the three types of sectors (Figure 8(a)).

> 8 Although the features of natural monopoly industries suggest that it is more efficient for just one firm to operate in the market, it is still possible to compare the performance of SOEs to POEs in natural monopoly industries within 2- digit NACE sectors because they are multiple firms, both SOEs and POEs, in different geographic markets of the natural monopoly sectors.

17