the outcomes of interest are: i) labor productivity (operating revenues per workers), ii) profit margins, iii) liquidity based on the current ratio (short-term) and solvency ratio (long-term), and iv) return on investments measured by the returns on assets (ROA) and return on equity (ROE).

The explanatory variable -BOS- denotes the measurement of the state participation in the firm i and is the main parameter of our analysis. The state participation is tested in two ways to measure both the extensive and intensive margins.

First, it takes the form of a dichotomic variable that takes the value of 1 when the firm has state participation -either direct or indirect- of at least 10 percent, and 0 otherwise. It serves to measure the state participation as the extensive margin. Second, we include a measurement of the intensive margin by breaking down the state participation in the form of a categorical variable to differentiate between minority-owned companies [10-25%), blocking minority [25%, 50%), majority participation [50%, 100), and fully owned firms by the state [100%]. When interpreting the results, the reference (excluded) category refers to privately owned enterprises (POEs) defined as firms with no state participation or in which the state has a non-controlling participation of less than 9.9%.

As explanatory variables, the vector Xi denotes firm-level controls including age, sector, size (measured by total fixed assets and number of workers), and listing status.<sup>16</sup> Additionally, ∂c, ∂s, ∂t, refer to the fixed effects by country j to remove any country-specific unobserved characteristics such as governance arrangements, sectoral and time-bound shocks. Additionally, we control for potential country, sector, and time-varying shocks. Finally, we include country-sector-year ∂cst fixed effects, which allow to demean the results, so the coefficients can be interpreted as the distance to the country-sectoral mean and therefore be comparable across countries.<sup>17</sup> Furthermore, we explore the heterogeneous response between BOSs and POEs to economic shocks, particularly to the short-term shock of the COVID-19 pandemic. For this purpose, we evaluate both levels and growth of labor force and fixed assets as a proxy of capital investments using the specification below following Brolhato, Cirera, & Martins-Neto (2023) and Ferro & Patiño-Peña (2023):

# (2) Yit= α+ β1BOSi + β1BOSi ∗2020 + β2Xit + ∂c + ∂s + ∂t +  εijt

Thus, we evaluate whether state ownership can play a role as a stabilizer during periods of crisis. As before, the BOSs variable takes value 1 if the state holds 10% or more state participation, and we also delve deeper into whether there are differences in the response to the shock by the level of state participation.

> 16 Both size and age variables are included as lagged values in t-1.

> 17 Since the BOS variable from the WB BOS database is time-invariant in the sample period, we cannot control for firm-level fixed effects in the assessment. Hence, these results do not claim causality between state ownership and firm performance. Nonetheless, we perform different robustness checks to test different specifications that can help minimize the effect of unobserved firm-level characteristics. The results hold when using shorter timeframes 5-years and 3-years, and when using the subset of firms fully owned by the state. These exercises are conducted to test the robustness of the results in the event some changes in ownership have occurred in the sample period. Estimations are conducted using OLS.

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