Figure 6: Probability of an SOE Receiving a Subsidy, and Value of Subsidies Received, Relative to POEs by Ownership Type, 2011-2019 <!-- Start of picture text --> (a) Probability of an SOE Receiving a Subsidy (b) Log Value of Subsidy Received by SOEs<br>Relative to POEs Relative to POEs<br>7.00 *** 2.50 ***<br>***<br>6.00 *** 2.00 *** ***<br>5.00 ****** 1.50<br>4.00<br>1.00<br>3.00 *<br>0.50<br>2.00<br>1.00 0.00<br>0.00 -0.50<br>10-100% 10-24.9% 25-49.9% 50% or 10-100% 10-24.9% 25-49.9% 50% or<br>more more<br>Ownership % Ownership %<br>Probability of receiving a government subsidy Log subsidy amount (among recipients)<br>Probability of receiving a government subsidy (controlling Log subsidy amount (among recipients & controlling for<br>for productivity level) productivity level)<br>Source: World Bank staff analysis using Romania MoF firm-level data, 2011-2019.<br>Note: The sample includes all firms. Estimates in graph (a) are odds ratios indicating for the probability of receiving a subsidy based on a logit<br>model with a dummy variable taking the value one if the firm reported a positive subsidy amount. The estimates in (b) are in logarithmic terms<br>with the dependent variable being the log of the subsidy amount the firm reported as received. SOE variables are dummy variables taking the<br>value one if national or subnational governments have ownership stake of 10-100% in the firm. The reference group is private-owned enterprises<br>(POEs), defined as firms where national or subnational governments own 0-9.9%. All models include 2-digit NACE industry and year-fixed<br>effects. Standard errors clustered at the industry level are in parentheses. ***, **, and * indicate significance at 1 percent, 5 percent, and 10<br>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<br>category, and size group: Small (10-49), Medium (50-99), Large (100-499), and Very large (500+). with Micro (1-9) being the reference<br>category.<br>Odds ratio<br>Logarithmic form<br><!-- End of picture text -->

# (b) Does the Control Level of SOEs Matter for Performance and Subsidy Allocation?

**There are significant differences in performance between direct and indirect SOEs relative to POEs** . Given that directly owned and indirectly owned SOEs face different incentives (and constraints) that may induce differences in their performances relative to POEs, the paper replicates the analyses, separating SOEs into directly owned and indirectly owned ones. In general, except for the wage level and growth results, the findings suggest there are indeed performance differences between direct and indirect SOEs compared to POEs (Figure 7 (a)). For instance, while directly owned SOEs saw higher employment and total assets growths relative to POEs, indirectly owned SOEs witnessed a decline in their employment and total assets growths ( <mark>Figure 7</mark> (b)). Similarly, the finding that Romanian SOEs were less productive but experienced higher (labor) productivity growth over the period than their POEs counterparts did not hold for indirectly owned SOEs. Specifically, the results showed that indirectly owned SOEs experienced higher productivity levels. At the same time, the rate with which they expanded their productivity was not significantly different from POEs.

**There are also differential performances between centrally owned and locally owned Romanian SOEs relative to POEs** . When assessing whether there are performance differences between central SOEs and local SOEs compared to POEs, results show that, overall, the performance differences between SOEs and POEs are driven by local SOEs. While locally owned SOEs were less productive than POEs, they experienced higher growth in terms of employment, total assets, and labor productivity than their privately

15