In our second specification, we follow Boubakri et al. (2005): B B o o o o 1P P 2P P + 3 P P B (2) ii,tt ii tt ii,tt ii,tt = αα + θθ + ββ BBooP BBooPPooBB + ββ BBooP ooPP ii,tt ii,tt−1 jj,tt ii,tt ββHere we create a post-privatization dummy (YYooBBPPoo ffPPo BBooP BBooPPooBB + γγB CCooBBooPPooCCooP P + δδCCo), which takes the value of 1 for each BBooPPCC CCooBBooPPooCCoo + εε year after the privatization in year _t_ and 0 in all other years, to capture longer term effects together ii,tt with a timing indicator (i.e., the number BBooP ooPPof years from the privatization, P P ) to show whether the relationship changes over time. Finally, we have a privatization event dummy, P P , which takes value of 1 for the year of ii,tt YYooBBPPoo ffPPoprivatization and 0 otherwise. BBooP BBooPPooBB ii,tt BBooP BBooPPooBB We include bank size ( _log of total assets)_ ), capitalization ( _equity over assets_ ) and _loans over assets_ as lagged bank-level control variables,<sup>20</sup> while _log GDP per capita_ , _GDP growth_ , _inflation_ , financial development (through _domestic credit to private sector_ ) and _banking crisis_ dummy are included to control for country-level heterogeneity.<sup>21</sup> In Table 3, we focus on bank profitability and efficiency. The first two regressions in Table 3 focus on short-term relations (Equation 1) as they use recent privatized shares between t and t-3 in regression 1 and a cumulative measure summing up the share sales between t-1 and t-3 in regression 2. These measures are not significantly associated with bank ROA suggesting a few years’ time and possibly small share sales are not enough to significantly make a difference for SOCBs. In regressions 3, on the other hand, we focus on larger sales (using the 30 percent share sale as a threshold) having longer term associations via a dummy variable capturing the period after the privatization. This specification also includes a variable for the years from the privatization allowing for decaying (or growing) relationships between privatization event and various banking outcomes over time. Finally, this specification also includes a privatization dummy taking value 1 for the event year and an IPO dummy capturing association between method of privatization and immediate banking outcomes (Equation 2). This specification with a longerterm perspective also could not capture a significant difference in ROA for banks going through large privatization events. Nevertheless, the IPO dummy is positive and significant signaling that banks going through large privatization through IPOs have higher ROA in the event year. When we analyze ROE, in regressions 4-6, we find a positive relationship between privatization and bank ROE both in the short-term and long-term, although they are only marginally significant at 10 percent. For example, the positive and significant coefficient of P P indicates that on average ROE of banks which are privatized by at least a 30 percent share sale is 4.8 percentage ii,tt points higher for the period after the privatization event. This is an economically large increase BBooP ooPP given the mean value of ROE (13.8 percent). In regression 5, the coefficient on cumulative sales is 0.038, implying that if 100 percent of the shares had been sold in the last three years the ROE would be 3.8 percentage points higher. In our sample the standard deviation of cumulative sales is 0.193, so one standard deviation increase in cumulative sales would be associated with a 0.7 percentage point (=0.193*0.038) increase in ROE. Finally, regressions 7-9 in Table 3 suggest that there are no statistically significant short-term differences in cost-to-income ratios a few years

> 20 We control for lagged ROA in the specifications except where the dependent variable was ROA, ROE or NIM. 21 All bank-level variables come from the Bankscope (1995-2014) and Bank Focus Orbis (2015-2017) databases and all macro variables come from the Global Financial Development Database.

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