rafmacalaba/datause-encoder-data huggingface dataset:
division between urban and rural zones is the main determinant of inequality across all opportunities (electricity, water, sewerage and primary completion), except school enrollment.

Figure 5. Relationship between access to opportunities <!-- Start of picture text --> Argentina: census(1980) Bolivia: census(1976) Brazil: census(1980)<br>Electricity Electricity Electricity<br>.510 supplyWater .510 supplyWater .510 supplyWater<br>1 .4 1<br>.5 Sanitation .2 Sanitation .5 Sanitation<br>0 0 0<br>.95.85.9.8 enrollmentSchool .51 enrollmentSchool .510 enrollmentSchool<br>.51 completionPrimary .510 completionPrimary .6.4.20 completionPrimary<br>.4 .6 .8 1 0 .5 1 0 .5 1 .8 .85 .9 .95 0 .5 1 0 .5 1 0 .2 .4 .5 1 0 .5 1 0 .5 1 0 .5 1 0 .5 1<br>Chile: census(1982) Colombia: census(1985) Costa Rica: census(1984)<br>Electricity Electricity Electricity<br>.510 supplyWater .510 supplyWater .8.6.41 supplyWater<br>1 1 1<br>.5 Sanitation .5 Sanitation .5 Sanitation<br>0 0 0<br>.9.81 enrollmentSchool .51 enrollmentSchool .9.8.7.6 enrollmentSchool<br>.8.6.41 completionPrimary .8.6.4.2 completionPrimary .51 completionPrimary<br>0 .5 1 0 .5 1 0 .5 1 .8 .9 1 0 .5 1 0 .5 1 0 .5 1 .5 1 .4 .6 .8 1 .4 .6 .8 1 0 .5 1 .6 .7 .8 .9<br>Dominican Republic: census(1970) Ecuador: census(1990) Mexico: census(1970)<br>Electricity Electricity Electricity<br>supplyWater .510 supplyWater .8.6.4.2 supplyWater<br>Sanitation .8.6.4.2 Sanitation .6.4.20 Sanitation<br>.51 enrollmentSchool .9.8.7.6 enrollmentSchool .51 enrollmentSchool<br>.50 completionPrimary .51 completionPrimary .6.4.20 completionPrimary<br>.5 1 0 .5 1 0 .5 1 .2 .4 .6 .8 .6 .7 .8 .9 .2 .4 .6 .8 .2 .4 .6 .8 0 .2 .4 .6 .5 1<br>Panama: census(1980) Peru: census(1993) Uruguay: census(1985)<br>Electricity Electricity Electricity<br>.8.6.41 supplyWater .510 supplyWater .8.61 supplyWater<br>.510 Sanitation .510 Sanitation .9.8.71 Sanitation<br>.8.61 enrollmentSchool .51 enrollmentSchool .95.85.9 enrollmentSchool<br>.8.6.4.2 completionPrimary .510 completionPrimary .6.8.7 completionPrimary<br>.4 .6 .8 1 0 .5 1 .6 .8 1 0 .5 1 0 .5 1 0 .5 1 .5 1 .6 .8 1 .6 .8 1 .7 .8 .9 1 .85 .9 .95<br><!-- End of picture text -->

**Source:** Own elaboration based on IPUMS data.

16
interact the treatment effect estimate with indicators for geographic regions of intermediate size (smaller than the total experiment area, but larger than the unit of observation) and to report an _F_ -test of the null hypothesis that the experimental effect does not vary across these sub-regions. In our case, this would be blocks, which divide districts and contain villages. In Ahmednagar district, there are 11 blocks that contain at least one treatment and control village, allowing 10 degrees of freedom. Three blocks with only treatment or control villages are therefore dropped, resulting in a 10 percent reduction in the sample; nevertheless the “after” effect estimate is a similar 0.35 ( _t_ = 2 _._ 13) in this subsample.<sup>23</sup> The _F_ -statistic of 1.76 has a _p_ value of 0.091, weak evidence of heterogeneous treatment effects across blocks. As Allcott and Mullainathan suggest, if the treatment effect is detectably heterogeneous across blocks, than perhaps it is also plausible that it would be heterogeneous across districts.

A second approach is to explicitly model heterogeneity in the treatment effect. We matched the data on the 60 villages in Ahmednagar to information about those villages from the 2001 Indian census. This allowed us to check for interactions (strictly speaking, triple differences, because the main specification is a difference-in-differences): do any village level census variables predict larger or smaller treatment effects? We found no interactions with population density (triple difference _t_ = _−_ 0 _._ 54), fraction of the population that belongs to a Scheduled Tribe ( _t_ = _−_ 0 _._ 54), or fraction of the population that belongs to a Scheduled Caste ( _t_ = 0 _._ 62). However, with a relatively small number of villages, this study probably did not have much power to detect such an interaction, if any exists;<sup>24</sup> nor was such an interaction built into the study design by _a priori_ blocking. Thus, this approach offers little evidence about treatment effect heterogeneity.

> 23This suggests another statistical significance test for the paper’s main result of an effect on height. In the spirit of Ibragimov and M ̈uller (2010), estimating 11 treatment effects, one for each block, produces 11 coefficient estimates. Of these 11, 9 are positive. The probability of at least 9 positive numbers in 11 independent draws of numbers equally likely to be negative or positive can be computed exactly from a binomial distribution; it is 0.033, an alternative _p_ -value for a median positive effect on height.

> 24 Spears (2012a) finds an interaction between population density and the effect of a sanitation program using variation at the district level across 500 districts.

26
elderly, the HCA would not assign an economic value to old individuals that have either retired from the workforce or do not make significant contributions to household work. This obviously is a serious shortcoming of the HCA approach.

The estimated cost of mortality in India based on HCA is presented in Table A1.16. Average annual income is approximated by GDP per capita, corresponding to around 57 thousand Rs. per year. The estimates are from equation (1).

# **Table A1:** **<u>Cost of Mortality (per Death) using HCA</u>**

||Average Number of<br>Years Lost|Thousand Rs|
|---|---|---|
|**_Adults:_**|||
|Mortalityfrom Urban Air Pollution|8|430|
|Mortalityfrom Indoor Air Pollution|7|390|
|Mortalityfrom Lead Exposure|8|430|
|**_Children:_**|||
|Mortalityfrom Urban Air Pollution|65|1,148|
|Mortalityfrom Indoor Air Pollution|65|1,148|
|Mortality from Diarrheal Illness, Typhoid , children<br>under 5|65|1,148|
|Mortality from Diarrheal Illness, Typhoid , children<br>under 19|55|1,863|

# **B. Value of Statistical Life.**

While the HCA involves valuation of the death of an individual, VSL is based on preferences for reducing mortality risk by a small amount. Everyone in society is constantly facing a certain risk of dying. Examples of such risks are occupational fatality risks, risks of traffic accident fatality, and environmental mortality risks. It has been observed that individuals adjust their behavior and decisions in relation to such risks. For instance, individuals demand a higher wage (a wage premium) for a job that involves a higher than average occupational risk of fatal accident, individuals may purchase safety equipment to reduce the risk of death, and/or individuals and families may be willing to pay a premium or higher rent for properties (land and buildings) in a cleaner and less polluted neighborhood or city.

Through the observation of individuals’ choices and willingness to pay for reducing mortality risk, it is possible to measure or estimate the value to society of reducing mortality risk, or, equivalently, measure the social cost of a particular mortality risk. For instance, it may be observed that a certain health hazard has a mortality risk of 1/10,000. This means that one individual dies every year (on average) for every 10,000 individuals. If each individual on average is willing to pay 10 Rupees per year for eliminating this mortality risk, then every 10,000 individuals are collectively willing to pay 100 thousand Rupees per year. This amount is the VSL. Mathematically it can be expressed as follows: VSL = WTPAve * 1/ R (2) where WTPAve is the average willingness-to-pay (Rupees per year) per individual for a mortality risk reduction of magnitude R. In the illustration above, R=1/10,000 (or R=0.0001) and 30
by the coefficients of the interaction terms in column 10. This effect is relatively large and important given the sample average that 17% of children work on average in the full sample of households.

# 6.3 Discussion of Robustness

Various exercises for robustness checks generally support the cross-sectional results presented above. First, to address worries about household and community characteristics omitted from the base regression, we checked additional specifications controlling for these variables. The results are robust to controlling for land and house ownership as proxies for asset holdings, indicator for having a bank in the village, village home ownership rate (except the household in question), infrastructure such as road, market access, access to clean water, and the presence of slums in a village. Taking into account all fixed effects at the district level, by including district dummies, gives qualitative similar results even though the standard errors tend to be larger due to smaller sources of variation, unsurprisingly.<sup>13</sup> Second, in an attempt to improve precision, additional analysis was performed in a restricted sample of the major migrant-sending provinces. The small capture of migrant households in the IFLS, 5% or less in the full sample, is a sample size concern and is likely to lead to imprecise estimates. Restricting the analysis to major migration provinces can bring the migration rate up to roughly 8% in 2007, but at the cost of a much lower number of observations. In the end, most analysis in the restricted sample turned out not to improve precision. The results for the restricted sample are available upon request, but only those for the full sample are reported. In addition to robust standard errors, bootstrapping standard errors gave similar results. Standard errors clustered at the village level tend to be slightly larger, but also gave qualitative similar results.

Third, the same analysis for 2000 suggests broadly similar results, except for those about child labor supply. For 2000, only 1993 network could be used as an instrument

> 13 The only additional control that seems to affect some of the results is the fraction of households in the village with electricity.  This is a concern only to the extent that one argues electricity coverage affects household labor supply and children’s school and work behavior, and is correlated with historical migration.  Even so, it is not clear if this is a good control variable since the relationship between access to electricity and hours worked, for example, can be reversed causality.

19
# 3. EMPIRICAL FINDINGS

Initially, the econometric analysis was conducted on the total gross portfolio investnent flows series for each individual recipient country. Cross-correlation coefficients showed high positive correlations between the countries within a region (as shown by the shaded numbers in Table 3). This implies that when portfolio inflows to Mexico increase, those to Argentina and Brazil would be observed to increase as well. This finding suggests the existence of a regional focus or some kind of "contagion effect" of foreign portfolio investors in their investment decisions. Interestingly, portfolio flows to South Korea are highly correlated with those to Argentina, Brazil and Mexico.

Table **3.** Cross Correlation Coefficients-Total Portfolio Flows

||ARGENTIN|A BRAZIL|CHIL|E MEXIC|O INDIA|INDONESI|A KOREA T|HAILAND|
|---|---|---|---|---|---|---|---|---|
|ARGENTIN|A<br>1.000||||||||
|BRAZIL|;>.<br>'9<br>iY|1.000|||||||
|CHILE|(0.109)|(0.105)|1.000||||||
|MEXICO ;|<>O[<br>|2o2.jfi;|0.017|<br>1.000|||||
|**INDIA**|(0.022)|(0.018)|0.562|<br>0.051|1.000||||
|INDONESIA|<br>0.343|0-335|0.525|<br>0.549|0.497|1.000|||
|KOREA|:o:&;C0930>||0.074|>g,3989.|.9 0.048|0.489|1.000||
|THAILAND|<br>0.198|**0.196**|0.606|<br>0.189|0.762|<br>0.420|0.287|1.000|

The Q-tests for serial coirelation gTable 4) show that there is no serial correlation in any of the individual country series for total gross portfolio flows. This result is supported by the plots of the autocorrelation coefficients.

Table 4. Q-Test Results for Serial Correlation **-** Total Portfolio Flows

|_Country_|_Box-Pierce_<br>a<br>_Stadstc._|_Probabiity_|_Liung-Box C_<br>_Statisdc_|_0 Probability_|
|---|---|---|---|---|
|ARGENTINA|<br>6.74|0.99|15.94|0.53|
|BRAZIL|6.59|0.99|15.90|0.53|
|CHILE|4.03|1.00|10.70|0.87|
|MEXICO|7.71|0.97|15.32|0.57|
|KOREA|4.07|1.00|10.03|0.90|
|INDIA|6.47|0.99|18.26|0.37|
|INDONESIA|7.38|0.98|14.34|0.64|
|THAILAND|4.03|1.00|17.23|0.44|

19
We then examined whether the smaller contributions of entry salaries and salary growth to the aggregate gap reflects a flat compensation system with a strong preference for equity that seeks to close gaps where they exist. For instance, at the WBG, the compensation methodology seeks to promote equity within each grade by accelerating increases for those staff positioned below the midpoint and moderating for staff positioned above the midpoint, so that over time, equal performance is compensated in an equitable way. This is supported by the 2015 introduction of a four-zone salary band.

In fact, we found that salary responds strongly to performance ratings at the WBG. Among the cohort of GF entrants between 2000 and 2005, staff in the lowest performance decile gained 26 percent in real salaries over a 10-year period while those in the highest performance decile gained 83 percent. We also did not find evidence for the systematic application of informal management practices that could limit performance-related pay increases, such as not awarding staff the highest performance rating in two successive years. The hiring and compensation system at the WBG is thus reasonably successful at restricting subgroup differences among staff hired in the same grade, while maintaining salary incentives for high performance. However, it is not as successful in retaining high performing staff as exits from the institution are not correlated with historical performance.

Our econometric approach to diversity and inclusion issues in a large firm contrasts with a complementary human resource approach, where individual cases are examined on the basis of cross-sectional data to ensure compliance with equal pay policies. Our attempt here is to apply the tools available in the labor literature to the internal labor market of a single firm to generate policy insights for compensation policies in such environments. The combination of a data-based simulation approach thus bridges the large literature on subgroup differences in the labor market and growing interest in the organization of large firms.

In addition, the empirical approach we follow also complements, but is conceptually different from, a literature that decomposes wage differences across subgroups into those arising from employee characteristics and those arising from the returns to these characteristics. Given the longitudinal data available to us, our decomposition allows us to identify the sources of wage gaps and provides a starting point for further institutional efforts. For instance, relative to the cross-sectional data used by Filmer et al. (2005), these data allow us to incorporate employee turnover and pipeline effects (historical hiring will affect gaps today), which could mask or artificially amplify salary gaps in cross-sections. One key finding is that pay gaps arise at different points in staff careers depending on their entry grade. The organizational structure that leads to different gaps in different grades merits further debate and it is in understanding 6
Table 8: Effects of innovation on employment, accounting for Organizational Innovation

||OLS|IV|
|---|---|---|
|Process innovation only|-0.062***|0.021|
||(0.02)|(0.03)|
|Organizational only|-0.068***|0.013|
||(0.02)|(0.03)|
|Sales growth d.t. new products|0.621***|1.118***|
||(0.05)|(0.12)|
|Size 20 to 99|-0.003|-0.006|
||(0.01)|(0.02)|
|Size over 100|0.000|-0.004|
||(.)|(0.02)|
|Constant|0.074***|0.070|
||(0.01)|(0.08)|
|N|4404|4404|
|R-Squared|0.35|0.31|
|First stage F-statistic||109.43|
|Hansen-Sargen Overid testp-value||0.34|

Note: *, respectively. All regressions include country-industry and size dummies, constrained to sum to zero. Dependent variable is net labor growth (minus growth in sales of old product). Instrumental variables are indicator whether new product is _completely new to the firm_ and whether firms _invests in R&D_ .

34
_5_ Theoretic and empirical work on bubbles is vast. We will briefly summarize the more relevant work for our purposes, given that well-known surveys have already been done: Camerer (1989) performed a general survey of both the theoretical and empirical literature, while Flood and Hodrick (1990) concentrated on the empirical branch. A more recent survey of both approaches can be found in Campbell (2000).

In general, a bubble (Bt) is defined as the difference between the fundamentalsdetermined price (PPV) and the observed price (Pt). In the case of stocks, the fundamentals price can be expressed as the sum of discounted expected future cash flows-or dividends-to the holder of the asset.<sup>6</sup> (1) The bubble term, B, if it exists, is expected to grow at the real rate of interest.<sup>7</sup> The bulk of the bubble-testing literature falls into three types of tests. The first type examines the relationship between (a) the observed price and (b) the present-value price or the fundamentals used to forecast it. For example, tests of the bubble hypothesis in exchange rates examine the existence of a long-run equilibrium (cointegrating) relationship among exchange rates, money supplies, and prices (Meese, 1986; Chin and Meese, 1995; and Mark, 1995). Tests of the bubble hypothesis in stock markets examine the existence of equilibrium (cointegrating) relationships between prices and dividends (Campbell and Shiller, 1987 and Campbell, Lo, and McKinlay, 1997). A second type of bubble test compares the volatilities of observed prices and the present-value prices (Shiller, 1981; Le Roy and Porter, 1981; and West, 1988). The third type of test is more elaborate and indirect; it estimates a reduced-form price equation by two alternative methods and verifies whether the parameter values are the same.

The first method is a simple (instrumental variables) projection of prices onto the information set of the fundamentals forecasting. The second method is a simultaneous estimation of two equations: The first is the fundamental forecasting process, and the second is a reduced-form equation that assumes no bubbles and imposes cross-equation restrictions as in Hansen-Sargent (1981). If certain parameters are estimated as being the same by both methods, then the no-bubble hypothesis cannot be rejected. This approach, pioneered by West (1987) for stock market prices, has been used by Casella (1989) for price level bubbles and by Meese (1986) for exchange rate bubbles.

Testing for bubbles is plagued with problems and obstacles. At a theoretical level, reconciling rational behavior with the existence of bubbles is not trivial, and the conditions under which they can emerge are quite restrictive (Santos and Woodford, _6_ **_pP_** _=E e, r(s_<sup>_1_</sup> _+l)E, [D,]_ is one solution to the equation P, _= erE, [D, + Pt,, ]._ It is the _s=t_ particular solution attained after imposing a transversality condition.

The bubble tern is generally described as Bt+1= **Bt** (l+r) + **bt,** where b **t+** is the innovation in the bubble at time t+l (Flood and Hodrick, 1990).
**Table 1: Average annual changes in real per capita Gross Value Added and GDP by** **<u>period</u>**

||**Mining **|**Manufacturing **|**Construction**|**Taxes**|**Agriculture**|**Services**|**GDP**|
|---|---|---|---|---|---|---|---|
||||**2004-2006**|**Expansion**||||
|Less Developed|4.4|4.9|16.4|6.7|3.1|4.4|5.3|
|Transition|6.6|11.0|17.0|7.5|4.5|3.9|6.2|
|More Developed|7.0|7.3|16.7|7.7|2.6|4.8|6.5|
|National|6.0|7.7|16.7|7.3|3.4|4.3|6.0|
||||**2008-2009**|**Recession**||||
|Less Developed|-6.0|-4.5|-10.5|-5.1|1.6|-2.0|-2.8|
|Transition|-2.4|-5.2|-11.0|-5.0|2.3|-1.7|-2.5|
|More Developed|-4.6|-6.0|-13.0|-6.3|4.1|-1.9|-3.8|
|National|-4.3|-5.2|-11.5|-5.5|2.7|-1.9|-3.0|
||||**2010-2011**|**Rebound**||||
|Less Developed|6.5|12.5|24.8|14.7|9.4|5.2|9.0|
|Transition|9.6|14.2|15.7|12.8|2.9|5.4|7.5|
|More Developed|6.4|11.5|16.5|11.4|2.7|5.2|7.7|
|National|7.5|12.7|19.0|13.0|5.0|5.3|8.1|
||||**2014-2017**|**Expansion**||||
|Less Developed|3.9|7.4|3.4|4.7|2.7|4.9|4.6|
|Transition|6.5|6.0|5.0|4.2|1.6|3.9|4.0|
|More Developed|3.2|4.9|6.6|4.9|0.9|4.5|4.6|
|National|4.5|6.1|5.0|4.6|1.7|4.4|4.4|
||||**2018-2019**|**Recession**||||
|Less Developed|11.9|-1.2|-10.2|-1.3|-0.9|2.9|1.1|
|Transition|4.7|-4.7|-6.7|-0.6|2.4|3.0|1.0|
|More Developed|-0.1|-1.2|-6.5|-1.5|1.8|2.1|0.4|
|National|5.5|-2.4|-7.8|-1.2|1.1|2.7|0.8|
||||**Full Sample**|**: 2004-2019**||||
|Less Developed|4.1|4.6|4.5|4.1|3.1|3.5|3.7|
|Transition|5.3|4.7|4.2|3.9|2.5|3.1|3.4|
|More Developed|2.6|3.7|4.7|3.6|2.1|3.3|3.4|
|**National**|4.0|4.3|4.4|3.9|2.5|3.3|3.5|

Note: Mining is short for Mining and Utilities, which includes mining and quarrying, electricity, gas, steam and air conditioning supply, and water supply, sewage, and waste management.

18
of manufacturing firms. As described in section 2, the training data include detailed information on several training categories provided by SENAI. Based on the theoretical mechanisms discussed in the introduction, technical upgrading is the training category that is more likely to be related to exporting. These are courses aimed at workers seeking to update, expand or complement the professional skills they have acquired in a specific occupation or field or work. The other categories consist of longer term investments seeking to reconvert unqualified workers or retooling for new occupations workers whose skills became obsolete. Hence, we estimate (1) separately for the share of workers who completed technical upgrading and other courses. Column 1 uses the pooled data controlling only for year effects. Column 2 adds log firm size and other firm controls, as well as state and sector dummies. Column 3 adds firm fixed effects, but excludes firm controls and state dummies. Column 4 reports results from our preferred difference-in-differences specification, which includes firm, sector and state effects, as well as firm controls. For all these specifications, the results indicate that export participation is positively associated with the share of workers receiving technical upgrading within firms. By contrast, the effects of export participation on the share of workers trained in categories other than technical upgrading are insignificant when including firm effects.

Table 5 presents estimates from specifications similar to those in Table 4, but considering a sample including only switchers. Once again, we find that export participation is positively associated with the share of workers who received technical upgrading. In all specifications, the coefficient of the export status is positive and statistically significant. The effects of exporting on the share of workers trained in other categories are once again insignificant.

As discussed above, a concern that remains after restricting the estimation sample to switchers is that there may be unobserved firm-specific shocks (e.g. changes in management practices) that induce some firms to both enter export markets and improve the skills of the workforce through worker training. This would lead to a positive bias in OLS estimates, as we would be attributing the observed increase in technical upgrading to the effects of export participation when in reality it would be reflecting other underlying 11