THE HUMAN CAPITAL OF FIRMS AND THE FORMAL TRAINING OF WORKERS The case of firms in the Middle East and North Africa

The benefits of formal training are numerous, and yet in many regions few firms utilize them. This study builds on the literature by exploring how two forms of human capital—the quality of management practices and the proportion of university educated employees—influence the adoption of formal training. Using both cross-sectional and panel firm-level data for 29 economies in Eastern Europe and Central Asia and six economies in the Middle East and North Africa, the study finds that firm management practices are positively correlated with the implementation of formal training in Eastern Europe and Central Asia but not in the Middle East and North Africa. The proportion of university educated workers is positively correlated with formal training in both regions, but the finding is more robust for the Middle East and North Africa. These findings imply significant heterogeneity across regions in the determinants of formal training, suggesting that policies should be context specific. on practices for medium and large enterprises, our sample. Our findings show that both management practices and the share of workers overall, but there are strong differences by region. The share of university is a strong predictor of both the incidence and intensity of training in the MENA region. is also true for the ECA region, but the result is not as robust. In Our findings indicate that, accounting for a broad range of country and firm-specific controls, formal training is positively related to worker education for the ECA and MENA regions, although the finding for the latter is more robust. Good management practices are related to formal training for the ECA region but not the MENA region. The relationship between worker education and training has been documented before, although we look specifically at university education while other studies look at secondary education or the occupation level to determine skill levels. Furthermore, we validate the findings using panel estimations. Our study also builds on and confirms several findings in the literature on formal training including the positive correlation with firm size and quality certification (Almeida and Aterido, 2015; Liaqat and Nugent, 2015). Our study provides several policy implications. To the extent that there is a causal mechanism running from university education to training, increasing university education of workers in MENA can incentivize firms to invest in more training, potentially updating skills that are more robust to automation and digitization and developing soft skills. However, given the documented high unemployment among university graduates in the region, this may not be enough and may need to be coupled with reforms that strengthen the private sector and improve the business environment. Furthermore, if the prevalence of training is to adapt and update skills of educated workers towards work in the private sector, then the policy implication is not only to increase university education, but also to ensure it serves the private sector as well. Finally, firms with better management practices tend to provide more training in ECA but not in MENA. This warrants further investigation and may be because managerial practices are quite poor in the region. These findings are important for the MENA context given the low provision of training in the formal private sector.


I. Introduction
Human capital investments at work account for more than one-half of the human capital accumulated over the life cycle (Heckman et al., 1998). The contribution of work experience to human capital accumulation, acquired either through learning on the job or formal training, might be as important as the contribution of education itself (Jedwab et al., 2021). Firm-specific investment in human capital through employee training can generate rewards for firms and for workers because it can increase firm performance by providing workers with necessary skills, increasing innovation and generally raise the level of competitiveness (Almeida and Aterido, 2015). It can also update skills of workers in the fast changing world of digitization and automation through retraining, especially in the context of an aging workforce in advanced economies (Brunello and Wruuck, 2020). The wage returns to training can be high for workers (Konings andVanormelingen, 2015, Almeida andFaria, 2014). In developing economy contexts, it can help compensate for low quality schooling.
However, investments in workers by firms have been inadequate. The incidence of formal training across firms has been low, especially in the Middle East and North Africa ( disincentives to provide general skills training to their workers due to externalities, and also may face challenges in acquiring finance to fund costly training. European governments have responded by funneling subsidies to encourage training in firms (Brunello and Wruuck, 2020).
In this study, we add to the literature by exploring whether the existing base of human capital in the firm is an important determinant of whether firms invest in training. We capture human capital in two forms -one is the management practices of the firm and second is the share of workers with university degrees -by harnessing firm-level surveys that contain a unique module on management practices and a survey question on the share of workers that are university educated. We also account for manager experience. The data on management practices was only collected for medium and large enterprises, which defines our sample. Our findings show that both management practices and the share of university educated workers matter overall, but there are strong differences by region. The share of university educated workers is a strong predictor of both the incidence and intensity of training in the developing MENA region. This is also true for the ECA region, but the result is not as robust. In contrast, management practices are a strong predictor of training in the ECA region but not in the MENA region. This study builds on several studies that have explored the determinants of training (Frazer, 2006;Rosholm et al, 2007;Pierre and Scarpetta, 2013;Almeida and Aterido, 2015;Nugent, (2015,2016)).
Training represents investments in future productivity that come at a cost (Wolter and Ryan, 2011). Becker (1962) noted that firms receive little benefit in providing formal training if it is general as opposed to firm specific as workers may leave and general skills are transferable. However, extensions of the original model noted that firms may invest in general skills to attain informational advantages and monopsony power (Katz and Ziderman, 1990;Acemoglu and Pischke, 1998). Firms with better managerial quality may be better able to understand these advantages. Improving the managerial quality of a firm may lead to greater incidence of training. The measurement of managerial quality in accordance with Bloom et al. (2013) captures several dimensions including problem resolution, monitoring of performance indicators, production targets (ease of attainment, length of focus, and knowledge), basis of bonuses, promotion of non-managers and dismissal. 2 Adoption of best management practices may lead to greater incidence of training in order to facilitate understanding of best practices among employees and also upgrade skills to increase performance. Managerial quality may also entail the understanding of the importance of human capital in the firm and thus facilitate greater investments in workers. Furthermore, managers may also understand the importance of training to gauge the ability of individuals and thus gain additional information (Acemoglu and Pischke,1998).
Training can allow workers to signal their ability and attract workers of high ability (Autor, 2001;Cappelli, 2004). On the other hand, greater managerial quality may imply a lesser need for formal training. Managers may be better at hiring skilled workers that require little training, may implement automated systems with clear instructions for workers, or may decide informal direct communication with employees may substitute for the need of costly formal training. Automated systems developed by managers may facilitate adaptation of the firm to employee turnover with the understanding that costly training may provide little benefit to the firm when the trained worker leaves, which could be likely if the training largely constitutes general skills as opposed to firm-specific skills. Thus, the nature of the relationship between management practices and the prevalence of formal training is an empirical question.
The share of highly educated workers in a firm may has several implications for training. High education levels may signal high ability, and thus firms may be determined to retain these workers by investing in them, especially in developing economies where the supply of highly educated individuals is low.
Thus, the correlation between highly educated workers and the provision of formal training may be positive. Furthermore, highly educated workers may require repeated training throughout their career as they develop different skill sets with experience. Also the cost of training may decline with higher educated workers who may have developed learning skills, thereby incentivizing firms to invest in training (Bassanini et al., 2007). However, the hiring of highly educated workers may obviate the need for formal training, especially if they are of high ability and can quickly learn on the job. Furthermore, highly educated workers may entail a larger flight risk as they have more bargaining power and are therefore more likely to move to other firms. Accordingly, firms with a larger share of educated workers may be less likely to invest in formal training. Finally, the share of highly educated workers in a firm may proxy for the level of general human capital in the economy. Assuming that in general human capital is low in an economy, firms may implement training programs to invest in high ability individuals that may not have the required education in order to compensate for the lack of education, or the presence of low-quality education. Thus, formal training may be more prevalent across firms that in economies with low human capital. Whether the portion of university education workers is positively or negatively related to formal training is an open empirical question.
The challenge of low provision of formal training provided by firms is especially a concern for the MENA region. Liaqat and Nugent (2015) note that high youth unemployment, lengthy school-to-work transitions, and a sizeable gap between the skills firms want and young graduates possess are characteristics of the region. Thus, workers in the region are well poised to benefit from formal training.
However, as mentioned earlier, the incidence of training is particularly low. Also noted is that firmsupplied training is found to be more effective than government-supplied training, therefore the lack of formal training by firms cannot be easily substituted. This study provides additional focus on the MENA region given the well-documented low prevalence of formal training in the private sector.
Our empirical strategy is to exploit firm-level variation in formal training, management practices and worker education, while accounting for several confounding factors. We employ two samples. The first sample is a pooled cross-section of 8,470 firms across 35 economies. This includes two waves for economies in the MENA and ECA regions (2013 and 2019/2020) where we estimate the effect of management practices and worker education on the presence of formal training (extensive) and proportion of workers trained in manufacturing firms (intensive). Several firm-level factors are accounted for, including country fixed effects. A second sample entails a subsample of panel firms interviewed across both waves for both regions. In these estimations, firm-level fixed effects are used to account for time-invariant firm-level omitted variables. An important concern is endogeneity.
Although employee training could potentially lead to better management practices, this is unlikely as the training captured is only directed to employees, and furthermore even though training may affect some of the management practices, it is unlikely to affect the aggregate score. This is distinct from the emerging literature that has documented the effects of external management training that specifically targets improving management practices (McKenzie and Woodruff, 2017;Higuchi et al, 2019). More of concern is that training might attract high ability workers who tend to be more educated. This is most likely if the presence of training programs is the key attraction for highly educated workers. However, the presence of training programs could be correlated to several other features of firms that highly educated workers find attractive. We account for these firm characteristics to the extent the data allows us.
Our findings indicate that, accounting for a broad range of country and firm-specific controls, formal training is positively related to worker education for the ECA and MENA regions, although the finding for the latter is more robust. Good management practices are related to formal training for the ECA region but not the MENA region. The relationship between worker education and training has been documented before, although we look specifically at university education while other studies look at secondary education or the occupation level to determine skill levels. Furthermore, we validate the findings using panel estimations. Our study also builds on and confirms several findings in the literature on formal training including the positive correlation with firm size and quality certification (Almeida and Aterido, 2015;Liaqat and Nugent, 2015). Our study provides several policy implications. To the extent that there is a causal mechanism running from university education to training, increasing university education of workers in MENA can incentivize firms to invest in more training, potentially updating skills that are more robust to automation and digitization and developing soft skills. However, given the documented high unemployment among university graduates in the region, this may not be enough and may need to be coupled with reforms that strengthen the private sector and improve the business environment. Furthermore, if the prevalence of training is to adapt and update skills of educated workers towards work in the private sector, then the policy implication is not only to increase university education, but also to ensure it serves the private sector as well. Finally, firms with better management practices tend to provide more training in ECA but not in MENA. This warrants further investigation and may be because managerial practices are quite poor in the region. These findings are important for the MENA context given the low provision of training in the formal private sector.
In summary, our study makes several contributions to the literature. First it explores the role of management practices and education of workers on formal training both at the intensive and extensive margin. Second it utilizes a panel data set to account for several firm-level characteristics to validate the findings. And finally, it updates several studies in the literature by employing recent firm-level data that includes economies across Europe, Central Asia, and the MENA region. The rest of the paper is structured as follows. Section II describes the data. Section III provides the empirical specification and identification strategy, while section IV provides the results, section V provides robustness checks, and section VI concludes.

II. Data
The main source of firm-level data is the World Bank's Enterprise Surveys (ES  Paunov, 2016;Besley and Mueller, 2018;Chauvet and Ehrhar, 2018;Hjort and Poulsen, 2019;Falciola et al., 2020). A considerable advantage of these data sets is that they are composed of a set of economies surveyed around a similar time frame, employing a consistent methodology. Previous studies have typically included older enterprise surveys that did not follow the consistent global methodology of the ES (Almeida and Aterido, 2015;Liaqat and Nugent, 2015). Furthermore, those early surveys did not contain information on management practices or the share of the workforce with a university degree.
The key outcome variable is the presence of formal training. This is derived from the survey question: Over There are some small firms that are in the sample for two reasons. One is that some of these firms were identified as medium or large firms in the sample frame, and thus were administered the management practices module. Second is that some of these small firms grew to become medium firms between the 2013 and 2019/2020 waves, and thus they are retained to maintain the panel component. We retain these firms in the sample given that they are very likely to have attributes similar to medium and large firms as identified in the sample frames.
variable used is the share of workers in the firm that received formal training. This question is only asked for manufacturing firms. Figure 1 presents the incidence rates of formal training across medium and large firms in the sample at the country level. Figure  The key explanatory variable is the quality of management practices, consistent with the methodology implemented by Bloom et al., (2013). This consists of eight components: (i) Problem resolution, (ii) Number of performance indicators measured, (iii) Level of ease or difficulty to achieve production or Several control variables are also employed that were obtained from the Enterprise Surveys. These include firm size, age, outward orientation, quality certification, access to finance, informality, and perceptions of education quality and labor regulations. Summary statistics for all the variables are provided for the whole sample ( does not seem to be noticeable differences. Across both regions, training firms are not more likely to be run by women than non-training mangers. However, firms that provide training are more likely to have a female owner in the MENA region, with no noticeable differences in the ECA region.

III. Empirical strategy
The following equation is estimated for the cross-section sample.
Where is either (i) whether or not a firm offers formal training or (ii) the share of workers receiving formal training only in manufacturing firms. The variable is the share of workers with a university degree. is the average management practices score. 6 To control for as many confounding factors as possible, several firm-level variables are accounted for. These include firm size ( ), firm age ( ), manager experience in the same sector ( ), and whether the sector of activity is in the manufacturing sector ( ). Other control variables ( ) include whether the firm purchased fixed assets, exporter status, foreign ownership, whether the top manager is a woman, the proportion of temporary workers, presence of checking or savings account, ISO quality certification, website ownership, whether the firm competes against informal firms, and perceptions of whether the firm finds labor regulations to be major or severe constraint, or the inadequately educated workforce to be a major or severe constraint. Country fixed effects ( ) are included to account for time invariant country-specific omitted variables as well as year fixed effects ( ). is the standard error term with the usual desirable properties. Survey weights are used, and the standard errors are clustered at the location-sector-size strata level.
We utilize the same specification for the Panel sample as presented in equation (2) The rationale for several of the control variables is based on the literature. Firm characteristics are included, such as size, age, access to finance, and outward orientation -both in terms of exporter status and foreign ownership (Almeida and Aterido, 2015). Other covariates that have been accounted for in previous estimations include quality certification and website ownership. Perceptions of labor regulations have been used to proxy for labor regulation stringency while senior management time spent in dealing with requirements of government regulations and average number of visits or required meetings with tax officials have been used to proxy for enforcement (Liaqat and Nugent, 2015). The perceptions of whether the inadequately educated workforce is a major or severe constraint to operations has been used to account for perceptions of education quality of the workforce at large (Liaqat and Nugent, 2015). We also include the following additional control variables: whether the top manager of the firm is a woman, the share of temporary workers, whether the firm purchased fixed assets, and finally whether the firm competes with informal firms.
There are a number of challenges with the empirical estimations. An important concern is simultaneity bias. One possibility is that training could lead to better management practices. This would imply that our estimates are biased upwards for the effect of management practices on training. This may be unlikely as the training captured is only directed to employees, and furthermore even though training may affect some of the management practices, it is unlikely to affect the aggregate score. It is also possible that training could attract high ability workers who tend to be more educated. This is only likely if the presence of training programs is the key attraction for highly educated workers. However, the presence of training programs could be correlated to several other features of firms that highly educated workers find attractive, and we account for these firm characteristics to the extent the data allows us. For the panel samples, we account for firm-level fixed effects that capture time invariant firm-level omitted variables.  In columns 4, 5 and 6 of table 2 we replicate the same estimations as in columns 1, 2 and 3 using the share of workers that received formal training as the outcome variable. Note that this information is only available for manufacturing firms. 8 The findings are largely consistent. For the both the ECA and 7 We also explored the results by splitting into the 2013 and 2019 waves. For the overall sample including MENA and ECA, management practices and share of university educated workers are positively related to the incidence of formal training, regardless of whether it is the 2013 wave or the 2019 wave. For the share of workers trained in manufacturing firms, the results largely stand for the 2019 wave but are statistically insignificant for the 2013 wave. For MENA the findings for university education are largely driven by the 2019 sample. While the findings for ECA is mostly driven for both the 2013 and 2019 samples for management practices, the coefficient for university educated workers is statistically insignificant for the outcome variable of share of workers trained in manufacturing firms for the 2013 wave. Results are available from the authors upon request. 8 We also ran the estimations for the incidence of training for the manufacturing firms alone. The sign and statistical significance of the coefficient for management practices is the same for both MENA and ECA manufacturing firms and the whole sample (manufacturing plus services). However, for the ECA sample of manufacturing firms, there is no statistically significant relationship between proportion of university educated workers and the incidence of formal training. For the MENA subsample, the sign and statistical significance of the coefficient of the proportion of university educated workers are retained. These results are available upon request.

IV. Results
MENA samples, the proportion of university educated workers is positively related to the share of workers receiving formal training, with a coefficient statistically significant at the 1 percent level for MENA and 5 percent level for ECA. However, the coefficient for management practices is negative but not statistically significant for the MENA region. For the ECA sample, we see the opposite. The coefficient for management practices is positive and statistically significant at the 1 percent level. In terms of magnitude, a 1 percent increase in the share of university educated workers increases the share of workers that received formal training by 0.78 percent in MENA and 0.11 percent in ECA. A 1 percent increase in the management score increases the share of workers that received formal training in ECA by 0.67 percent. One interesting result for the ECA sample is that the coefficient for the proportion of temporary workers is negative and statistically significant at the 1 percent level. This implies that the larger the proportion of temporary workers, the lower the proportion of workers that receive formal training.
In table 3 we explore whether different types of management practices matter. We run the estimations replacing the overall score with the 8 subcomponents. For the overall sample (column 1) as well as the ECA sample (column 3), four subcomponents of management practices have statistically significant coefficients -(i) Number of production or service provision performance indicators monitored, (ii) Personnel's knowledge of production or service provision targets, (iii) Basis for promoting nonmangers, and (iv) When underperforming managers were dismissed or reassigned. However, for the MENA subsample, only the coefficient for the number of production or service provision performance indicators monitored is statistically significant (column 2). The results are starker when the outcome variable is the share of workers that received formal training (manufacturing firms only). None of the management practices subcomponents has statistically significant coefficients for the MENA sample.
For the overall as well as ECA samples, the number of production or service provision performance indicators monitored score and Personnel's knowledge of production or service provision targets score have positive coefficients that are statistically significant at the 1 percent level. These results confirm the fact that management practices, regardless of types, are far less a contributing factor to the use of formal training by firms in MENA than the rest of the sample.
In table 4, we turn to the MENA panel estimations. 9 In column 1 we present the findings with whether or not the firm provides formal training. In column 2 we present the findings with the share of workers with formal training (manufacturing firms only). 10 The coefficient for the proportion of workers with university education is positive and statistically significant at the 1 percent level for both outcome variables. The magnitudes are larger in the panel sample than the cross-sectional sample. The coefficient for the overall management practices score is positive but not statistically significant for both outcome variables. In columns 3 and 4 of table 4, we explore whether the subcomponents of management practices matter. For the incidence of formal training (column 3) only one subcomponent has a positive and statistically significant coefficient -Action when problem in the production/service provision arose.
However, the management practices subcomponent on when underperforming managers were dismissed or reassigned is negatively related to the prevalence of training, statistically significant at the 5 percent level. One plausible explanation is that the firms that are slow in dismissing managers are more likely to stick with personnel, and thus more likely to train. The results change somewhat when exploring the results for the share of workers that received formal training (manufacturing firms only, In table 5, we turn to the ECA panel estimation results. In column 1 we present the findings on whether or not the firm provides formal training. In column 2 we present the findings with the share of workers with formal training (manufacturing firms only). The coefficient for the overall management score is positive and statistically significant at the 10 percent level for the incidence of formal training and 1 percent for the intensity of formal training. The magnitudes are larger for the panel sample than the cross-sectional sample. The coefficient for the share of university educated workers is positive but statistically insignificant for the incidence of formal training. However, the coefficient for the share of university educated workers is negative and statistically significant at the 5 percent level. These findings run counter to the pooled cross-sectional results for the ECA sample. In columns 3 and 4 of table 5, we explore whether the subcomponents of management practices matter. For the incidence of formal training, two of the management score coefficients are positive and statistically significant -(i) the action when problem in the production/service provision arose score, and the (ii) Personnel's knowledge of production or service provision targets score. However, the basis for promoting nonmanagers score is negative and statistically significant at the 10 percent level. For the incidence of training, 3 scores have a positive and statistically significant coefficient -(i) Personnel's knowledge of production or service provision targets score, (ii) Focus of production targets score, and (iii) When underperforming managers were dismissed or reassigned score. However, the basis for promoting nonmanagers score is negative and statistically significant at the 10 percent level. In terms of comparisons across panel and cross-section ECA samples, only the management practices subcomponent on personnel's knowledge of production or service provision has a statistically significant coefficient across both samples. Overall, the findings show some heterogeneity across the components of the management score in the ECA sample, but the main finding that firms with better management practices seem to have higher incidence and intensity of formal training remains. Incidence of numeracy and math skills (0.04 percent), problem solving or critical thinking (0.03 percent) and foreign language skills (0.08 percent) are extremely low. We present the findings for all types of training for completeness, but the results for types of training with low incidence should be interpreted with caution. We find no statistically significant relationship between management practices and any of the training types. However, the proportion of university education has a positive and highly statistically significant coefficient (1 percent level) for training that entails job-specific technical skills.
In contrast, for the ECA sample, the overall management score has positive and statistically significant coefficients for managerial and leadership skills training and job-specific technical skills training. The share of university educated workers is positively related to foreign language skills training and interpersonal and communication skills training. Since this information is only available for the latest round of the survey, we are unable to employ panel estimation techniques. The one insight that can be drawn from these findings is that firms in the developing MENA region do train workers when they are highly educated, but the training is largely towards job-specific skills. This may be one way in which they exert monopsony power over their workers. On the other hand, it may also be that highly educated workers typically acquire skills meant for the public sector, and thus require training to adjust to work in private sector firms. For the ECA region we see heterogeneity in the effects of management practices and the share of educated workers across types of training (table 7).

V. Robustness checks: Skills, gender composition and regulations and enforcement
In this section we consider as robustness checks a number of additional variables that could be correlated with the prevalence of formal training or have been found to by the literature to be of importance. These include the skill level of production workers in manufacturing firms, the gender composition of workers, and labor regulations and enforcement. In table 8 we include a variable that captures the skill level of the worker mostly based on the occupation. This is to account for the possibility that the portion of university educated workers may simply be capturing skilled workers, and also to check if our results stand after accounting for a more commonly used measure in the literature. In the survey, highly skilled workers were defined as those who were professionals and tasks In table 9 we replicate the same estimations presented in table 8 by substituting skilled workers with the proportion of women workers in the firm. The coefficient for the proportion of university educated workers remains statistically significant at the 1 percent level for the MENA cross-section sample after accounting for the proportion of women workers (table 9 columns 1 and 2). This is true regardless of whether the outcome variable is the incidence or the intensity of formal training. The coefficient for the proportion women workers is positive but statistically insignificant for the MENA cross-section sample.
However, the results change somewhat for the MENA panel sample (table 9,  In table 10, we explore the relationship between labor regulations and perceptions of the workforce in the economy using cell averages consistent with Liaqat and Nugent (2015).

VI. Conclusions
This study explored the relationship between human capital in the firm in two forms -management practices and the share of university educated workers -and the incidence and intensity of formal training. It provided insights for the MENA region where training provisions by firms have been known to be low. The study also harnessed firm-level panel data from the developing MENA region to validate the findings. The findings show that the share of university educated workers is a robust predictor of formal training in MENA, while management practices is a robust predictor of formal training in ECA.
The study provides some important policy implications. First, increasing university education in the workforce in the MENA region may incentivize firms to invest more in their workers. This has positive implications of improving the workforce by retraining and updating skills. However, the high unemployment among the university graduates documented in the region suggests that this is not sufficient and would need to be coupled with regulatory reforms that strengthen the private sector and improve the business environment. Furthermore, if the prevalence of training is to adapt and update skills of educated workers towards work in the private sector, then the policy implication is not only to increase university education, but also to ensure it serves the private sector as well. Second, good management in firms in the MENA region may not be enough and government interventions to improve the university education of the workforce may be needed. Third, unlike the MENA region, in ECA, better management practices are likely to lead to greater incidence and intensity of formal training.
The study has a number of limitations. Despite the robustness checks, and the use of panel estimations, we cannot completely rule out the possibility of simultaneity bias between our key variablesmanagement practices and share of university educated workers -and our outcome variables of incidence and intensity of formal training. Regardless, the study leverages new data and points to interesting directions for future research. For one, it would be interesting to theoretically explore why certain types of management practices are more likely to lead to training, and what types of training are more likely. Second, it may be worth investigating if policies that led to increases in university education had corresponding effects on firm behavior as the pool of workers available to firms became more educated and possibly more valuable.    Adjusted R2 0.249 0.212 0.311 0.289 note: *** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered at the country level. Controls are not shown but are the same as in the base estimations in table 2, These include top manager experience in sector (years), log of age of firm, proportion of temporary workers (out of all workers), log of size of the firm, senior management time spent in dealing with requirements of government regulations (%), average number of visits or required meetings with tax officials, whether firms purchased fixed assets, exporter status, foreign ownership, female top manager, checking or savings account, ISO certification, website ownership, inadequately educated workforce as a major or severe constraint, labor regulations as a major or severe constraint, informal competition, and manufacturing sector. Estimates also include a constant. Note that some control variables do not vary over time. Adjusted R2 0.197 0.240 0.277 0.345 note: *** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered at the country level. Controls are not shown but are the same as in the base estimations in table 2, These include top manager experience in sector (years), log of age of firm, proportion of temporary workers (out of all workers), log of size of the firm, senior management time spent in dealing with requirements of government regulations (%), average number of visits or required meetings with tax officials, whether firms purchased fixed assets, exporter status, foreign ownership, female top manager, checking or savings account, ISO certification, website ownership, inadequately educated workforce as a major or severe constraint, labor regulations as a major or severe constraint, informal competition, and manufacturing sector. Estimates also include a constant. Note that some control variables do not vary over time.   236 note: *** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered at the strata level (firm size, sector, and within country location) for cross-section, clustered at the country level for panel. Controls are not shown but are the same as in the base estimations in table 2, These include top manager experience in sector (years), log of age of firm, proportion of temporary workers (out of all workers), log of size of the firm, senior management time spent in dealing with requirements of government regulations (%), average number of visits or required meetings with tax officials, whether firms purchased fixed assets, exporter status, foreign ownership, female top manager, checking or savings account, ISO certification, website ownership, inadequately educated workforce as a major or severe constraint, labor regulations as a major or severe constraint, informal competition, and manufacturing sector. Estimates also include a constant. Note that some control variables do not vary over time. : *** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered at the strata level (firm size, sector, and within country location) for cross-section, clustered at the country level for panel. Controls are not shown but are the same as in the base estimations in table 2, These include top manager experience in sector (years), log of age of firm, proportion of temporary workers (out of all workers), log of size of the firm, senior management time spent in dealing with requirements of government regulations (%), average number of visits or required meetings with tax officials, whether firms purchased fixed assets, exporter status, foreign ownership, female top manager, checking or savings account, ISO certification, website ownership, inadequately educated workforce as a major or severe constraint, labor regulations as a major or severe constraint, informal competition, and manufacturing sector. Estimates also include a constant. Note that some control variables do not vary over time. 282 note: *** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered at the strata level (firm size, sector, and within country location) for cross-section, clustered at the country level for panel. Controls are not shown but are the same as in the base estimations in table 2, These include top manager experience in sector (years), log of age of firm, proportion of temporary workers (out of all workers), log of size of the firm, senior management time spent in dealing with requirements of government regulations (%), average number of visits or required meetings with tax officials, whether firms purchased fixed assets, exporter status, foreign ownership, female top manager, checking or savings account, ISO certification, website ownership, inadequately educated workforce as a major or severe constraint, labor regulations as a major or severe constraint, informal competition, and manufacturing sector. Estimates also include a constant. Note that some control variables do not vary over time.

MG1 Problem resolution (r1)
Score Action when problem in the production/service provision arose Most structured: We fixed it and took action to make sure that it did not happen again, and had a continuous improvement process to anticipate problems like these in advance 1 Second most structured: We fixed it and took action to make sure it did not happen again 0.667 Second least structured: We fixed it but did not take further action 0.333 Least structured: No action was taken 0 MG2 Number of performance indicators monitored (r3) Score Number of production or service provision performance indicators monitored 10 or more indicators 1 3-9 indicators 0.667 1-2 indicators 0.333 No indicators 0 MG6 Length of focus of production targets Score Focus of production targets Combination of short-term and long-term targets 1 long-term only 0.667 short-term only 0.333 No targets or targets not achieved 0 MG3 Level of ease or difficulty to achieve production or service provision targets (r6) Score Level of ease or difficulty to achieve targets No targets or targets not achieved 0 Achieved without much effort 0.2 Only achieved with extraordinary effort 0.4 Achieved with some effort 0.6 Achieved with normal amount of effort 0.8 Achieved with more than normal effort 1 MG4 Knowledge of production or service provision targets (r7) Score Personnel's knowledge of production or service provision targets All managers and most workers 1 Most managers and most workers 0.667 Most managers and some workers 0.333 Only senior managers 0 No targets 0

MG5 Basis of bonuses (r9)
Score What managers' performance bonuses were usually based on Their own performance as measured by targets 1 Their team or shift performance as measured by targets 0.75 Their establishment's performance as measured by targets 0.5 Their company's performance as measured by targets 0.25 No performance bonuses 0 MG7 Promotion of non-mangers Score Basis for promoting non-mangers Based solely on performance and ability 1 Based partly on performance and ability, and partly on other factors (for example, tenure or family connections) 0.667 Based mainly on factors other than performance and ability (for example, tenure or family connections) 0.333 Non-managers are normally not promoted 0

MG8 Dismissal
Score When underperforming managers were dismissed or reassigned Within 6 months of underperformance 1 After 6 months 0.5 Rarely or never 0 Note: "Don't know" responses are equated to 0, assigning the worst level of management practices