rafmacalaba/datause-encoder-data huggingface dataset:
On closer inspection the average revenue is misleading since most Indian software companies are small in terms of revenues and number of employees, indicating the “easy phase” of exports (Figure 1).<sup>13</sup> Most new firms end up clustering around low-end activities (see BusinessWeek online 2003).<sup>14</sup> The top 20 Indian software exporters still account for about 60% of total exports, leaving more than 800 firms with the remaining 40% of the software market (Data Quest, various issues). The top 10 firms based in Bangalore contribute more than 50% of Bangalore’s exports (Okada 2004: 286). In theory, size per se is not important since most software projects tend to be small and thus well suited for entrepreneurial initiatives. In practice, however, large firms are able to carry out multiple projects simultaneously and thus not only spread risks across projects and markets but also carry out large, complex projects by quickly mobilizing large numbers of professionals. Large enterprises, local or foreign, can exercise monopsonist clout relative to smaller firms, though they too could be subject to periodic shortages of specific skill sets. Even the <u>Economist, favorably disposed toward India, reports a shortage of over 1 million “suitably qualified</u> people” by 2010 (Economist 2005: 58). As we will see below extensive growth itself is reproduced in a systemic way.

The fragmentation of the industry suggests that irrespective of size most Indian companies pursue whatever projects they can secure and which maximize the absolute difference between costs and revenues. This generalist and undifferentiated nature of Indian firms suggests competition based on price (Arora et al. 2001). Thus most improvements in productivity are typically passed on to the foreign (read US) clients (Arora and Athreye 2002: 255). The implication of this is that innovative capability is likely to be confined to a few large firms and a handful of medium-sized highly entrepreneurial firms, resulting from founder’s or team’s particular technical strengths, professional networks, and first comer advantages. It also implies that the incentives to form creative UILs are not high at this stage of extensive growth.

13In comparison software revenues per employee in 1995 were for Israel $100,000, Ireland $142,000, India $9,000, and the US $126,000 (Arora and Athreye 2002: 259).

> 14The correlation coefficient between number of software employees and revenues per employee at the firm level for 2002 for Bangalore’s 102 firms is not significant at 0.297.

12
31 Research Centre for Marketization<sup>and Property Reform. Gdansk. Poland.</sup> Fan. Qimiao. and Mark Schaffer.<sup>1993. "Government Financial Transfers and Enterprise Adjustment in</sup> Russia with Comparisons<sup>to Central and Eastern Europe." mimeo.. The World Bank.</sup> Frvdman. R-, and A.<sup>Rapaczynski. 1994. "Privatization In Eastern Europe: Is the State Withering</sup> Away?" Central University<sup>Press.</sup> Frvdman. Roman. and Stanislaw<sup>Wellisz- 1991. "Tha Ownership-Control Structure and the Behavior of</sup> Polish Enterprises During<sup>the 1990 Refonrs: Macroconomiic Measus and Microeconomic</sup> Response." In _Reforming_<sup>_Central and Easrern European Economies: Initial Resius and._</sup> _Challenges-_ edited bv Vittorio<sup>Corbo. Fabrizio Coricelli and Jan Bossak. Washington: World</sup> Bank Symposium.

Gomulka. Stanislaw. 1993.<sup>"The Financial Situation of Polish Enterprises 1992-93 and its Impact on</sup> Monecaiy and Fiscal Policies."<sup>Studies and Analyses 6. CASE Research Foundation. Warsaw..</sup> Hume, Ian. and Brian Pinto.<sup>1993. "Prejudice and Fact in Poland's Industrial Transformation.' Finance</sup> & Development. vol.<sup>_30._no. 2. 18-20.</sup> Kharas. Holmi. 1991. 'Restructuring<sup>Socialist Industry. Poland's Experience in 1990." World Ba.-nk</sup> Discussion Paper 142.

7ipton. Davidi and Jeffre,<sup>Sachs. 1990a. "Creating a Market Economy in Eastern Europe: The Case of</sup> Poland." Bwokings Papers<sup>on Economic Activity, 1:1990. 75-133-</sup>

-. 1990b. Privatizaion in Eastern<sup>Europe: The Case of Poland.' Brookings Papers on Economic</sup> Activity. 2:1990. 293-341.

-McKinnon. Ronald. I. 1993. 'The<sup>Order of Economic Liberalization: Financial Control in the Transition</sup> to a Market Economy." Baltimore:<sup>Johns Hopkins Universitv Press.</sup>

Pinto. Brian. 1992 "Incomes<sup>Policy and Wage Setting Behavior: Evidence from Polish SOEs during the</sup> Econoniic Transformation<sup>Program." In</sup><sup>_Wage Policy During the Transirion to a Market econony:_</sup> _Poland 1990-91._ edited<sup>by Fabrizio Coricelli and Ana Revenga. World Bank Discussion Paper</sup> 158.

Pinto. Brian, Marek- Belka.<sup>and Stefan Krajewvski. 1991. "Microeconomic Response to the Economic</sup> Transformation Program:<sup>Evidence from the Largest Polish SOEs."</sup><sup>_Mimeo._Washington: The</sup> World Bank.

1992. "Microeconomics of<sup>Transformation in Poland: A Survey of State Enterprise</sup> **s."** Policv Research Working<sup>Paper WPS 982. Washington: The World Bank</sup> (September).
In practice this means that one regresses ∆<sup>_y_</sup> _i_ on ∆ _X i_ , ∆<sup>_P_</sup> _i_<sup>and their interactions with</sup> ∆<sup>_P_</sup> _i_ and collects all terms involving ∆<sup>_P_</sup> _i_ to calculate the total program effect. Since the estimated TPE is linear in the θ<sup>ˆ</sup> parameters its standard error can be obtained from the covariance matrix of the OLS-coefficients.

It is instructive to consider the special case of equation (5) where _Di_ = ∆ _Pi_ is a binary variable taking the value 1 for the treatment group and 0 for the control group, i.e. the case of a difference-in-difference analysis. Equation (5) now reduces to 2 since in this case _Di_ = _Di_ . Compared to a standard diff-in-diff regression this equation contains the interaction term _Di_ ∆ _X i_ .

The total program effect will in this case be estimated as This shows that when the sample is representative sample means can be used to construct the total program effect. The interaction term in (7) avoids the bias resulting from correlations between treatment effects and either program participation or controls.

Many diff-in-diff studies do not include the interaction terms (e.g., Khandker _et al_ ., 2009 or Almeida and Galasso, 2010). Studies that do often report estimates of impact for different values of the controls _X_ which makes it difficult to assess the aggregate impact of a program.

7
highway and housing reconstruction than between public buildings and housing reconstruction), (2) the situation of the construction sector and the available worker skills.

# **Box 4 – Enhancing reconstruction capacity in Mumbai**

The ARIO model also allows us to assess the benefits of sets of policies that aim to accelerate reconstruction. We find that, for the July 2005 event, the indirect effect of the disaster on the local economy can vary by a factor of 4 (see box figure), depending on the amplitude and quickness of response of the construction sector. Of course, the ability to reconstruct impacts mostly the reconstruction duration and through it the rule of thumb proposed in Section 2.3.3.

<!-- Start of picture text --> 0.8<br>{120% ; 3 months }<br>No overproduction<br>0.7<br>{ 120% ; 6 months }<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>{ 150% ; 6 months }<br>0<br>{ 150% ; 3 months }<br>-0.1<br>0 2000 4000 6000 8000<br>Direct losses (million USD)<br>Ratio indirect losses / direct losses<br><!-- End of picture text --> _Figure. Indirect losses to direct losses ratio, as a function of the amount of direct losses, for four sets of adaptation parameters (the first parameter describes the amount of additional production that is possible, and the second parameter the time it takes to increase production to this level)_ Public resources can be used to increase supply‐side capacity in the reconstruction sector – for instance by supporting the migration of workers in the affected areas with temporary housing and the rapid provision of basic services. It can also focus on restoring transport infrastructure to facilitate the import of reconstruction materials, equipment, and skilled workers, and more generally of rationed goods and services.

Public resources can also be used to help private actors reconstruct and rebuild, especially where insurance penetration is low. The goal is to accelerate reconstruction (but using private actors to determine priorities) and mitigate the needed reduction in consumption (see previous section). More generally, they can support the population through classical safety nets, in particular to help it cope with 24
cases, but in theory it could cause some truncation problems. We have estimated total abatement costs for each treatment point by adding operations and maintenance expenditure to annualized services from abatement capital.<sup>5</sup> Data on treatment volume, influent concentration and effluent concentration are taken directly from the recorded measurements.

The current state of compliance in China is highlighted by the range of variation in abatement activity recorded in the data base. As Table 3.2b indicates, end-of-pipe abatement ranges from 0 to 100%, with median abatment of standard water pollutants (BOD, COD, TSS) in the 70-80%/o range and first-quartile abatement around 50%. The willingness of many plants to report noncompliant discharges to NEPA indicates that the levy system is working as intended. Excess discharges are subjected to a fee under the levy system, but they are not illegal.

China's abatement statistics compare very favorably with those of wealthier Southeast Asian economies such as Indonesia and Philippines, whose median abatement activity is closer to _50%_ (Hettige, et. al., 1995). Table 3.2a shows that water pollution would be much worse without existing control. Influent (pre-abatement wastestream) concentrations for most plants greatly exceed Chinese discharge standards (Table 3.3). Median influent concentrations for TSS, COD and BOD (567, 850 and 264 mg/l, respectively), are far above discharge standards for the lowestquality water bodies in both Guangdong and Beijing. As Table 3.3b shows, median effluent concentrations are much closer to existing standards for medium-low quality water bodies, and 1 st-quartile concentrations are generally in the medium-high quality range. However, a large number of plants remain out of compliance.

Tables 3.2 and 3.3 show that Chinese factory managers in polluting sectors must contend with great diversity in both process emissions and location-sensitive concentration standards. Their abatement decisions, and the overall level of abatement costs, will be significantly affected by the degree of pollution control necessary to bring their emissions into compliance with prevailing standards.

# 4. THE DETERMINANTS OF ABATEMENT COSTS

Traditionally, abatement cost estimates have been based on plants' reported direct costs of installing and operating pollution control equipment. Coupled with information about the benefits of reducing pollution, such cost estimates can provide a basis for setting sensible regulatory standards. Until recently, the scarcity of appropriate plant-level data has prevented detailed empirical studies of average and marginal abatement costs by pollutant.<sup>6</sup> Policy analyses have 5 We have used an interest rate of 10% for this exercise.

**6** For other recent work **oii** this issue. see Hartman. Wheeler and Singh (1995) and Mundle, et. al., (1994).

3
performance. Institutions from Eastern Europe and Central Asia and those from South Asia out-performed those from other regions in terms of financial self-sufficiency. An institution’s age and size are significantly positively linked to financial performance across all three indicators.<sup>14</sup> Finally, neither the indicator for being constituted formally as a for-profit bank nor the average loan size variable are strongly linked to the financial performance indicators. The latter result shows that, even after controlling for region and other covariates, institutions that make smaller loans are not less profitable on average.<sup>15</sup> Given the maximum and minimum values for our variables in Table 1, there is some concern that outliers partly drive the results. We therefore use a robust estimation technique that weights observations based on the absolute value of their residuals, in which observations with large residuals are assigned smaller weights (Appendix B Table 1).<sup>16</sup> Those results are similar to the base results, although there are a few minor differences. First, the coefficient on the portfolio yield is no longer significant in the OSS specifications, which could be because outliers were driving that result in Table 7. This also could be an indication of the importance of the adjustments to the raw data made by the MIX. That is, the unadjusted OSS data might have included outliers that drove a spurious positive correlation with yields. In any event, the yield coefficient in the FSS and ROA regressions remains positive and significant. The coefficients and significance levels for the costs variables are also similar to the base specifications, as are the

> 14 In addition to controlling for age in the base regressions, we also ran models on subsets of MFIs of similar vintage (5-20 years old).  Because the performance indicators for young MFIs are widely dispersed, our results are at least as strong when we restrict the sample in this way. See Appendix A for reasons why data from young MFIs might be most in need of adjustment.

> 15 The basic pattern of results also holds when we control for regional variation in different ways.  For example, in unreported specifications we allowed for correlation between observations from the same country using clustered standard errors. In another set of unreported specifications, we allowed for random effects at the country level. Given the small size of our dataset, we were not able to incorporate country fixed effects in our models.  See Deaton (1997, pp. 73-78) for a detailed discussion of clustering.

> 16 We use the “rreg” command in STATA.

17
# Table D.6: **Supply Side** – Ex-Post Access to Credit

This table reports evidence on the financial access of beneficiary households after the program. Each column reports results from a separate regression. The dependent variable in column (1) is a dummy indicating whether a household applied for a new loan after the program. The dependent variable in column (2) is a dummy equal to one if a new loan application was successful. The dependent variable in column (3) is the interest rate for new loans and the dependent variable in column (4) is the log approved loan amount for successful applications. Additional controls include bank*district, interviewer, and month-of-survey fixed effects. Standard errors in parentheses are clustered at the bank*district level. + p _<_ 0.10 ** p _<_ 0.05 *** p _<_ 0.01.

_Panel A: Summary Statistics on New Loan Applications_

||Applied for|New loan|Interest|Log amount|
|---|---|---|---|---|
||new loan|approved|rate|approved|
||(1)|(2)|(3)|(4)|
|Treatment [N=1,181]|376|346|7.82|82,617|
|%|[31.8]|[92.0]|||
|Control [N=1,716]|297|264|8.04|93,897|
|%|[17.3]|[88.8]|||

_Panel B: Ex-post Access to Institutional Credit_

||Applied for<br>new loan|New loan<br>approved|Interest<br>rate|Log amount<br>approved|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|100% Relief|0.09|0.267|0.547|-1.135|
||[0.24]|[0.50]|[2.89]|[0.95]|
|100% Relief|||||
|*Balance|-0.003|0.058|0.212|0.018|
||[0.01]|[0.05]|[0.30]|[0.12]|
|*Hectares from cutoff|-0.119|-0.113|-0.655|0.294|
||[0.14]|[0.17]|[1.83]|[0.29]|
|*Pre-program wealth|0.037*|0.033|-0.175|-0.106|
||[0.02]|[0.03]|[0.17]|[0.10]|
|*Pre-program total debt|-0.008|-0.023|-0.101|0.075|
||[0.02]|[0.05]|[0.28]|[0.09]|
|Balance|0.029**|-0.044|-0.092|-0.019|
||[0.01]|[0.03]|[0.20]|[0.08]|
|Hectares from cutoff|0.094|0.118|-0.292|-0.610*|
||[0.10]|[0.13]|[1.45]|[0.31]|
|Pre-program wealth|0.013|0.005|0.118|0.288***|
||[0.02]|[0.02]|[0.15]|[0.05]|
|Pre-program total debt|0.015|0.003|0.111|0.313***|
||[0.02]|[0.04]|[0.27]|[0.11]|
|Additional controls|No|No|No|No|
|Fixed effects|Yes|Yes|Yes|Yes|
|Observations|2,830|663|492|554|
|R-squared|0.102|0.13|0.179|0.301|

61
dependent variable estimated by the OLS and the fixed effects estimation. The coefficient of foreign direct investment lends support to our hypothesis above with respect to the negative and significant impact of economic development on youth unemployment. The positive and significant coefficient of labor market regulations in 2 out of the 3 system GMM specifications is consistent with the findings of Bernal-Verdugo et al. (2012), which show that the existence of strict hiring and firing regulations has a statistically significant positive effect on youth unemployment. The coefficient of political opposition is not significant and does not provide strong evidence to our hypothesis above. The negative coefficient for inflation suggests a negative effect of higher price levels on the youth unemployment. However, the coefficient is not statistically significant in various specifications. The surprising result in table 1 comes from the negative and significant effect of the M0/M1 variable capturing the size of the informal sector in the economy. This result suggests that countries with a high ratio of liquid monetary base tend to have a lower youth unemployment indicator. While this result is counterintuitive at first glance, one could however explain this finding by the fact that the development of the informal sector in the economy pushes over the long run a growing segment of young and unemployed population to explore alternative work opportunities in the unstructured and unregulated market. As the number of new entrants to the unofficial economy increases, the officially reported number of job seekers in the formal economy will systematically decrease.

Finally, notice that across various specifications, the Hansen J-test of overidentification restrictions (with P-value 0.1) and the Arellano-Bond test for autocorrelation (first and second order serial correlation with P-value <0.1 and >0.1, respectively) both confirm the joint validity of our instrument and the consistency of our estimation.

Page | 14
employers do not hire mothers of young children because of the pervasive doubt about the latter’s “attachment to the labor market”? The Pew Research surveys find that 84 percent of Indians agree with the statement: “when jobs are scarce, men should have more right to a job than women” (Pew Research Center 2010). Such attitudes are found to be negatively correlated with women’s labor force participation across many countries and are a reflection of social norms that reinforce the traditional role of women and also mediate the way institutions treat women and perpetuate gender inequality in the labor market (Das et al. 2015; World Bank 2013). Norms are often selfperpetuating, in that employers and investors look for what they expect to find. Unless they make a concerted effort to question the conventional wisdom, norms will remain largely stubborn to change.

The state can signal a more enabling vision for women and mothers in different ways. Publicly funded information campaigns that value women as workers and project child care as a shared responsibility in the home, are likely to remove some of the guilt that women often experience when they leave children behind to go out to work. Such campaigns have been effectively used in India and elsewhere to achieve health outcomes such as family planning, immunization, or for literacy and to enroll participants in social programs such as conditional cash transfers schemes. Both the state and the private sector in India are beginning to gingerly experiment with options to attract larger numbers of female workers into the labor market. We need a better understanding of what these initiatives are, how well they work and how they can scale up.

Finally, there is the issue of data and the analysis of the complexity of Indian women’s decision to stay out of the labor market. We need data that better capture norms, perceptions and aspirations, in addition to conditions of work, transport, housing, and infrastructure. Better analysis is also required about the mediating factors for women’s employment such as the extent to which infrastructure and other services (such as toilets in the work place, hours of water supply in the home, connectivity through transport and information technology) inhibit or encourage women’s participation in the labor market, since women and men are differentially affected by the availability of services.

In conclusion, this paper contributes to the understanding of urban India women’s decisions to participate in the labor market by assessing the influence of young children in the household. It finds that while the responsibility of child bearing has gone down due to the secular decline in 17
# **Effects of Corporate Governance on the Performance of State-Owned Enterprises**

**Kyoungsun Heo**<sup>**1**</sup> Korea Institute of Public Finance

> 1 Dr. Kyoungsun Heo is a Research Fellow at the Research Center for SOEs, Korea Institute of Public Finance. The author is thankful to Dr. Hanjoon Park, Immanuel Steinhilper and Matthew Collin for their valuable peer review comments. The author may be contacted at ksheo72@gmail.com. JEL classification: G34, L25, L33, L98. Keywords: State-owned enterprises, corporate governance, performance.
26 FERNANDES-KLENOW-MELESHCHUK-PIEROLA-RODR ́ıGUEZ-CLARE with _Zi ≡_ ( _Zi_ 1 _, ..., ZiJ_ ) distributed according to where _dij ≡ Dij_ + _μα_ + ( _σ −_ 1) _μφ,i_ and ̄ _σφ,i ≡_ ( _σ −_ 1) _σφ,i_ .

Using firm-level data from the EDD and China across different origins and destinations, we can estimate the parameters in (32) as well as mean log fixed trade costs (up to a constant) and their dispersion using maximum likelihood methods. The Online Appendix D shows how to derive the density function _gXi_ 1 _,..,XiJ_ ( _x_ 1 _i, ..., xiJ_ ) for the case when we observe sales to _J_ destinations. We simplify the analysis by considering only data for fifteen destinations (USA, Germany, Japan, France and the 11 largest destinations by exports value for each origin), which we label _j_ = 1 _, ...,_ 15 for the year 2007 for each of 39 origins. We compute _gXi_ 1 _,...,XiJ_ ( _x_ 1 _i, ..., xiJ_ ) for each observation in our dataset (which is a realization of _{Xi_ 1 _, ..., XiJ }_ that we observe). Since all random variables are independent across firms, we can compute the log-likelihood function as a sum of log-densities, where _N_<sup>�</sup> _i_ is the number of firms from _i_ that sell to either of the fifteen destinations we consider, and where _ki_ is an index for a particular observation in our dataset (for origin _i_ it takes values in 1 _, ..., Ni_ ) and Θ _i_ is an origin-specific vector of parameters that we want to estimate, where _μ_ ̄ _f,ij_ = ln _σ_ + _μf,ij_ and _ρ_ = _σα,iσαf,σf,ii_<sup>.As the likelihood is potentially not concave in</sup><sup>_θi_and</sup> because there are 34 parameters per origin to estimate, we rely on the estimation methodology proposed by Chernozhukov and Hong (2003).<sup>27</sup> We use the Metropolis-Hastings MCMC algorithm to construct a chain of estimates Θ<sup>(</sup> _i_<sup>_n_)</sup> for each origin country. Chernozhukov and Hong

> 27Roughly speaking, this methodology uses the likelihood function as a probability distribution of the set of parameters to be estimated, and then via simulation finds the expectation of this distribution. This expectation is used as the estimator for the parameters. Note that this is not maximum likelihood estimation, since we are not selecting the point where the density is maximized. A detailed description of the estimation procedure can be found in the Online Appendix E.