rafmacalaba/datause-encoder-data huggingface dataset:
The problem with the traditional approach of estimating wage regressions to test theories of wage determination is that, without independent measures of worker productivity, it is difficult to determine whether wage differentials associated with workers’ characteristics reflect productivity differential or some other factors. For example, typical wage regressions report positive coefficients on age (conditional on a variety of controls). These positive coefficients neither imply that older workers are more productive than younger ones, nor that wages rise faster than productivity. Similarly, without direct measures of the relative productivity of workers, factors associated to wage differentials cannot be established.

Verner (1999) analyzes the determinants of wages and productivity in Zimbabwe, using a matched employee-employer manufacturing sector data, the Regional Program on Enterprise Development (RPED) survey data from 1993. The number of firms and employees interviewed are 201 and 1609, respectively. In each firm, about 10 randomly selected workers from different occupational categories are interviewed to obtain the worker level data. Here workers are the unit of observation and firms are included in the individual vector of variables. The main conclusions from this study were that formal education, training, and experience impact wages and productivity positively, and that females were being paid less than male employees, despite that females are not measurably less productive. This analysis is innovative; nonetheless, this model uses firm-level productivity as a measure of the individual productivity, while the worker characteristics variables and the endogenous wage variable are at the individual level – regardless of the differences in education and training, all workers in the firm are assigned the same productivity measure. The problem is that the effect of worker characteristics on productivity will not be properly identified when there is no variation in the workers’ productivity within a firm but differences in workers’ characteristics exist.<sup>1</sup> Hellerstein, Neumark, and Troske (1999) use a unique plant-level dataset in the United States, the Worker Establishment Characteristics Database (WECD), which matches long-form respondents to the 1990 Decennial Census of Population to data on

> 1 Verner (2000) uses the same methodology to analyze wages and productivity gaps in Ghana.

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<!-- Start of picture text --> Net Job Creation ( 2000‐2011)<br>8,000<br>6,000<br>4,000<br>2,000<br>0<br>‐2,000<br>‐4,000<br>‐6,000<br><!-- End of picture text --> Source: LMMIS, CSA

**Figure 14: Sources of total Job Creation by City (2000-2011)**

<!-- Start of picture text --> 6,000<br>5,000<br>4,000<br>3,000<br>2,000<br>1,000<br>0<br>Job creation (by Incumbents) Job creation (by entrants)<br><!-- End of picture text --> Source: LMMIS, CSA In fact, on average, only 3 percent of total new job creation across cities comes from new entrants. There is little variation across cities – Bahir Dar and Sebeta top the range at 9 and 8 percent respectively. The overwhelming source of job creation is incumbent firms – see Figure 14. This creates a big conundrum for policy makers in Ethiopia – entrants to the market not only do not graduate, they also leave scarcely a mark on the 15
Who Controls East Asian Corpor1⁄2ations

# I. Introduction

Much of the literature on the role and function of the modemn firm is based on the assumption of the prevalence of widely dispersed ownership. The dispersion of ownership has resulted in the notion that "the awner of industrial wealth is left a mere symbol of ownership" (Berle and Means, 1932, p.68), as the control over companies is being transferred to professional managers. The literature on corporate govermance often starts from this principal-agent relarioiiship and its associated problems. This notion, and the following corporate governance literature, originally derives from the Berle and Means study which concluded that almost half of large American corporations did not have a single owner who controlled more than 20% of the stock. It has also been propagated by Baurnol (1959), Jensen and Mecking (1976) and Grossman and Hart (1980), among others.

A more recent line of the empinical literatu re fmds results whhich are at odds with this traditional assumption. Deemsetz (1983), Shleifer and Vishny (1986), Morck, Shleifer, and Vishny (1988) have shown that som.e concentration of ownership and control exists even among the largest American corporations. La Porta et al. (1998) find even larger concentration of control in a cross-section of developed anid developing countries. They also point to a number of country-specific studies that document the existence of large ownership stakes. La Porta, Lopez-de-Silanes, and Shleifer (1998) is the first study that investigates the important issue of ultimate control, i.e., they trace the chain of ownership to find who has the most voting rights. Their findings suggest that ownership is largely concentrated in the hands of families ard the state even in some of the most developed countries. The concentration of ownership is enhanced tloough the use of pyramid structures. deviations from one-share-one-vote rales, cross-holdings, and the appointment of managers and directors who are related to the controlling family.

East Asian corporations have already long been considered to be an exception to the notion of widely-held ownership (Faukuyam a, 1996; Rajan and Zingales, 1998), although more recent research (La Porta, Lopez-de-Silanes5 and Shleifer, 1998) shows that most developing and some developed countries have similar degree of ownership concentration. The degree to which ownership is concentrated in East Asian countries 2
the uncertainty and expected error up. Figure 2 shows the estimated errors across the World Bank’s geographical regions and four income groups. Evidently, the errors tend to be higher for regions and income groups than for the world as a whole. The only subgroups with lower errors are Europe & Central Asia and High-Income Countries.

# Alternative missingness assumptions

Our analysis so far may provide too optimistic errors if the data are systematically missing in the sense that the correlation between the probability of missingness and the value of the indicator is not zero. This is the case with SDG 1.1.1---the share living below the international poverty line---where less data is associated with higher poverty. Countries with at most five poverty estimates since 1980 have an average poverty rate of 32%, countries with 6-10 poverty estimates have an average poverty rate of 22%, and countries with more than 10 estimates have an average poverty rate of 4%. On the other hand, the errors we have presented so far might be too pessimistic if imputations are used to get proxy values for the countries with missing data.

In this section, we try to address these two issues. First, we assume that all missing values are imputed using regional averages. This is a common way of dealing with missing values in applied work. Second, we order countries by their share of missingness in an indicator in the years without full coverage and delete observations using this order rather than at random. The purpose is to only retain the data for the countries that are most likely to have data in any other year. The results from these two exercises are shown in Figure 3.

Using the empirical missingness from WDI does not systematically make the errors greater. This suggests that the probability of missingness in WDI is not systematically correlated with the indicator values and that our main results are not too optimistic. Yet, if for specific indicators the probability that data is missing is correlated to the values of the indicator, as with SDG 1.1.1, then we would be underestimating the error. Imputing with regional averages reduces the error by about 20%. If true imputations are better than using regional averages, then the expected error will be even lower.

_Figure 3: Relationship between data coverage and accuracy with alternative assumptions_ <!-- Start of picture text --> 1<br>0.75<br>0.5<br>0.25<br>0<br>0 0.25 0.5 0.75 1<br>Share of group without data<br>Baseline<br>With empirical missingness<br>With imputations<br>Error (standard deviations from mean)<br><!-- End of picture text --> _Figure 4: Comparing error with population coverage and country coverage_ <!-- Start of picture text --> 1<br>0.75<br>0.5<br>0.25<br>0<br>0 0.25 0.5 0.75 1<br>Coverage threshold (share without data)<br>Satisfies population but not country coverage<br>Satisfies country but not population coverage<br>Error (standard deviations from mean)<br><!-- End of picture text --> 5
We winsorize the top 5% of the distribution for consumption, livestock revenue, and value of outstanding loans, and find that effect sizes decrease slightly in magnitude, but retain the same sign and significance, and continue to be large. Results are presented in Table A11 of the Online Appendix. Per capita monthly consumption impacts decrease from USD 24 PPP (30% increase, p-value < 0.0005) to USD 20 PPP (26% increase, p-value < 0.0005), and monthly livestock revenue decreases from USD 63 PPP (p-value < 0.0005) to USD 56 PPP (p-value < 0.0005). The gap between treatment and control indebtedness decreases from USD 733 PPP to USD 276 PPP (p-value 0.002), with average control household indebtedness decreasing from USD 1,382 PPP to USD 874 PPP.

Table A12 of the <u>Online Appendix presents the impacts on all primary outcomes when</u> removing stratification dummies, and Table A13 presents the same measures when including baseline imbalance covariates. The changes in the point estimates for primary outcomes are small, and standard errors increase slightly when removing stratification dummies. Since the p-values associated with the main specification already indicated highly significant results, the reduction in precision does not change any of the results. Adding baseline covariates does not change the main results either.

The results from robustness checks suggest that findings from the primary specification are highly stable, supporting the validity of the results.

# 6. COST-BENEFIT ANALYSIS

While the program shows large economic and social impacts, the per capita cost of the program is significant, both when compared to other interventions aimed at improving the lives of the poor, and when directly comparing against other TUP programs around the world. Therefore, a critical question is whether the benefits exceed the costs.

Table 9 presents the program costs, economic benefits, and estimated returns from the program using a similar framework and assumptions as in Banerjee et al. (2015). To estimate the cost-benefit ratio we use a 5% social discount rate, in line with World Bank/IMF guidance (IMF, 2010). Reported total program costs are shown in line 4: AFN 114,541 (USD 6,198 PPP or USD 1,688 nominal) per recipient. Since poverty reduction is the primary goal of the program, we use consumption gains as the primary welfare measure.<sup>20,21</sup> We assume that the (unmeasured) consumption impacts one year after the program start are equal to those observed in year two (lines 6 and 7). Line 8 shows the present value of all future consumption benefits assuming they continue for 10 years after the asset transfer. This information allows

> 20 Banerjee et al. (2015) also include assets in their welfare measure. We do not follow this approach because consumption already includes revenue and rents from productive assets; and because a consumption measure that excludes flow utility from non-productive assets is more conservative.

> 21 We use the 95% winsorized consumption values to provide a conservative estimate.

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The second aspect concerns the failure of the debt ratio, i.e. government debt divided by annual GDP, to experience a significant decline despite the achievement of a primary surplus since 1994 and the fact that the rate of interest has been lower than the growth rate of GDP throughout the 1990s. Data compiled by the Bank of Greece show that the general government debt ratio fell from 110.3 to 104.9 percent of GDP between 1993 and 2002, a reduction of 5.2 percentage points (Table 3.12).<sup>25</sup> However, the cumulative primary surplus over this period amounted to 36 percent of GDP, while the net effect of income growth and interest cost contributed to a decline of the debt ratio by an additional 10 percentage points.

|**Table 3.11: Intere**<br><br>|**st Cost (% of**<br>|**GDP)**<br>|
|---|---|---|
|1981<br>4.8|1993|13.1|
|1982<br>4.6|1994|14.0|
|1983<br>5.9|1995|12.7|
|1984<br>6.9|1996|11.8|
|1985<br>8.0|1997|9.8|
|1986<br>8.2|1998|9.1|
|1987<br>9.6|1999|8.7|
|1988<br>8.7|2000|8.0|
|1989<br>8.4|2001|7.4|
|1990<br>12.0|2002|6.5|
|1991<br>9.3|2003|6.1|
|1992<br>11.6|||

Source: Bank of Greece and Ministry of Finance The failure of the debt ratio to show a similar decline is explained by the so-called stockflow adjustment, which relates to all expenditures and receipts or assumption of liabilities that do not affect the primary balance but cause an increase or decrease in the level of debt (Table 3.10). Over recent years, especially in the early 1990s and again in 2000 and 2001, the Greek authorities were forced to recognize liabilities that had previously gone unreported, to issue bonds in recognition of the non-servicing of government-guaranteed loans, and to record various other operations, such as issuing privatization certificates and using revenue securitization techniques (Bank of Greece 2003:233-236). To the extent that some of these liabilities existed but were not properly recorded and disclosed, they would imply that the level of public debt had been higher than reported. To the extent that they represented the realization of contingent liabilities, they would reflect the cost of undertaking such operations. Official projections suggest that the impact of stock-flow adjustment would be much smaller in the future and that the decline in the debt ratio would accelerate in view of the continuing primary surplus and the fall in the level of interest rates. The latest projections of the Ministry of Finance indicate a fall in the debt ratio to 90 percent by 2006 (Ministry of Finance 2003).

> 25 Reports written in the late 1980s and early 1990s show a debt to GDP ratio that was already higher than 110 percent of GDP. However, this ratio was lowered with subsequent upward revisions of GDP. The Bank of Greece analysis discussed in the text refers to the same set of GDP estimates for the 1990s.

39
production during the period from July 2003 to June 2004. The surveys also recorded climate and weather related perceptions of the farmers. Data on farm size and land value per hectare were also collected. Land values vary substantially by country as shown in Table 1. The Andean countries (Colombia, Ecuador, and especially Venezuela) have distinctly lower land values. This reflects smaller farms, poorer growing conditions, and more labor intensive farming technologies.

The climate data come from two sources: temperature observations from the US Defense Department Satellite and rainfall observations from World Meteorological Organization. In earlier comparisons across Brazil, it was found that the temperature measures from the satellite were superior to the interpolated weather station measures (Mendelsohn et al 2006a). Most rural areas do not have a weather station nearby and so require interpolation. The satellites make direct observations over the entire land area using microwave imagers. These measures are very effective at capturing temperature but cannot directly capture precipitation. Satellites can measure soil wetness but this index is inferior to the interpolated station measures of precipitation because it is influenced by irrigation, large water bodies, and dense forests (Mendelsohn et al. 2006a).

Soil data were obtained from the FAO digital soil map of the world CD ROM. The data was extrapolated to the district level using Geographical Information System. The data set reports 26 major soil groups, soil texture, and land slope at the district level.

Some variables used in earlier Ricardian studies of the US such as the precise latitude of the farm, water availability, and some soil definitions are not available in this study. However, this study includes detailed data about individual farms, farming activities, and farmers that was not available in the US studies.

9
to zero. Thus, given the effectiveness of the interventions (for this outcome), there is no space for complementarities. Interestingly, for physical abuse in the last year, where neither intervention has an effect as large as the control mean, the interaction term is very close to zero (although none of the coefficients are statistically significant). There is also no evidence of complementarities in Tables 4 and 6.

# **7 Attrition**

Tables 7 and 8 presents analysis of sample attrition using baseline data for the females and males, respectively, to confirm that attrition does not vary by treatment status or baseline characteristics in a way that may be driving our results. The outcome in all panels is an indicator equal to 1 if the female or male left the sample (attrited).

In both tables, Panel A shows estimates from a regression of attrited on treatment indicators, including the _Goal_ interaction with each clustered treatment arm. For both females and males, there is no evidence that there is differential attrition by treatment assignment. In Panel B, we further test for differential attrition across treatment arms by demographic characteristics. These estimations also show little evidence of differential attrition. We provide a more detailed breakdown of the outcome means at baseline according to attrition status in appendix figures A2 and A3, which broadly show no evidence of differential attrition, aside from evidence that goal setting participants who experienced physical abuse at baseline are less likely to attrit. If anything, this would imply positive bias in our estimate of the impact of goal setting on physical abuse, which would bias us away from finding the negative impact of goal setting on physical abuse that we see in Table 2.

# **8**

We now present evidence on the cost-effectiveness of our interventions. The per-female cost of the _Boys_ treatment is $41 and the per-female cost of the _Goal_ treatment is $38. To ease comparison across treatments and studies, we also normalize the cost of each treatment in terms of a 0.25 standard deviation reduction in IPV. In the case of the _Goal_ 32
# **Brazil: Base money**

# Ratio to GDP

<!-- Start of picture text --> 0.10<br>0.08<br>0.06<br>0.04<br>0.02<br>0.00 ,_,_\/,_ .<br>1990 1991 1992 1993 1994 1995<br>- BRHVG-DP l<br><!-- End of picture text --> Source: IMF, International Financial Statistics Chart 1 shows important fluctuations in the demand for real base money, indicating a very low average: roughly 4 percent of GDP.

The following example demonstrates the evolution of total debt (domestic and foreign) as a **ratio** to GDP. As evidence suggests, a sharp drop was experienced in 1990, proceeded by a substantial recovery until 1995, with the exception of 1993 when the ratio sustained a slight stumble.

19
possibility that profits and sales had fallen by 10 or 20 percent. That is, lack of a statistically significant effect does not mean that these studies show that training has no effect, only that they cannot detect what the effect is.

Many of the subsequent studies have attempted to improve the precision of these estimates by training a larger sample of firms, screening the sample to reduce heterogeneity, and pooling together multiple rounds of follow-up surveys. The result is that confidence intervals are narrower for some of these more recent studies. Most of these recent studies have positive point estimates, but many also have confidence intervals that contain zero. Most studies therefore cannot reject the hypothesis that training did not improve profits or sales, but they almost all allow for the increase in profits of 5 percent that I noted in the previous section as realistic based on both course length and return on investment. Using a random-effects meta-analysis model,<sup>10</sup> we can combine the results of all these studies to get an overall estimate of the effect on profits of 10.1 percent (95% C.I.: +4.1, +16.1), and on sales of 4.7 percent (95% C.I.: +0.2, +9.2). That is, looking at the totality of evidence from all of these studies, one concludes that business training has improved business outcomes, just not be enough to be detectable in most individual studies. The red line shows these average effects, which lie inside the confidence intervals for almost every study. Note also that the meta-analysis puts the largest weights mostly on studies that have happened more recently, while some of the older studies that had small samples and very wide confidence intervals get very little weight.

> 10 The meta-analysis is carried out using Stata’s meta forestplot command. This takes as inputs the point estimate and standard error from each business training study.  The study-specific effect is modeled as , where it is assumed that the observed study-specific effect sizes are sampled from a population of effect sizes with mean θ and 2 2 jj jj jj _u_ and ε are independent with � and ). Maximum-likelihood is then used to estimate θ as a θθ = θθ+ uu + εε weighted average of the different effect sizes, where the weights are estimated as jj jj jj 2 2�. The result is that εε ~NN�0, σσ uu ~NN(0, ττ higher weight is given to studies that have smaller standard errors. jj 1/�σσ� + ττ̂

12