rafmacalaba/datause-encoder-data huggingface dataset:
Table 1 Post Harvest Survey (sample sizes)

|Province|1997|1998|1999|2000|2001|2002|
|---|---|---|---|---|---|---|
|Central|654|674|648|795|663|701|
|Eastern|1,225|1,197|1,255|1,437|1,248|1,292|
|Southern|895|828|835|961|835|850|
|Lusaka|246|252|243|244|185|182|
|Copperbelt|370|349|379|464|367|372|
|Luapula|803|775|799|869|760|761|
|Northern|1,211|1,190|1,348|1,551|1,293|1,376|
|Nwestern|409|423|429|543|435|431|
|Western|706|648|725|835|699|733|
|Total|6,519|6,336|6,661|7,699|6,485|6,698|

Note: Own calculations based on the Post Harvest Surveys 1997-2002.

Table 2

|Perce|ntage|of Farm<br>1997|ers Gro<br>- 2002|wing C|otton||
|---|---|---|---|---|---|---|
|Province|1997|1998|1999|2000|2001|2002|
|Central|24.6|22.6|16.6|10.3|14.7|20.2|
|Eastern|35.2|32.7|31.7|20.4|32.1|39.0|
|Southern|9.9|10.7|11.7|4.3|8.8|12.8|
|Lusaka|5.4|3.3|4.7|0.4|5.1|8.2|
|Copperbelt|0.8|0.6|0.3|0.0|0.0|0.0|
|Luapula|0.0|0.0|0.0|0.0|0.0|0.0|
|Northern|0.0|0.1|0.0|0.0|0.0|0.0|
|NWwestern|0.3|0.0|0.0|0.0|0.0|0.2|
|Western|1.3|0.6|0.4|0.1|0.1|0.0|
|Total|11.0|10.4|9.4|5.4|9.0|11.6|

Note: Own calculations based on the Post Harvest Surveys 1997-2002.

28
magnitude of these effects varies by the type and number of measures used. In some cases, studies show effects that are comparable with the estimates found for cognitive skills (Bowles, Gintis, and Osborne, 2001; Heckman, Stixrud, and Urzua, 2006; Heineck and Anger, 2010; Mueller and Plug, 2006; Nyhus, and Pons, 2005).

Many studies have used the _Big Five personality traits_ —openness, conscientiousness, extraversion, agreeableness, and neuroticism (Goldberg, 1990) as proxies for noncognitive skills (Heineck and Anger, 2010; Mueller and Plug, 2006). Mueller and Plug (2006) used data from the Wisconsin Longitudinal Study and examined the effect of these five personality traits on earnings separately for men and women. They found that controlling for IQ, occupation, and a range of covariates, openness, agreeableness, and neuroticism had small albeit significant effects on earnings among men. In contrast, openness and conscientiousness significantly predicted earnings among women. Heineck and Anger (2010) used the Big Five personality measures to estimate the impact of noncognitive skills on labor market outcomes in Germany. In addition to the Big Five, they added measures of _locus of control_ and _reciprocity_<sup>_7_</sup> to their analytical model. Their results indicated that after controlling for cognitive abilities and several socio-demographic and job-related characteristics, personality was a significant predictor of earnings. Other research has also explored noncognitve skills outside of the Big Five. Heckman, Stixrud, and Urzua (2006) used the NLSY 1979 data, which included measures of individuals’ self-worth and individuals’ perceived degree of control over their lives, to determine the effect of these measures on labor market outcomes. They found small but significant effects (less than 1 percent) for these measures of noncognitive skills in predicting wages. Their findings suggest that the estimated effects of noncognitive skills on wages are as strong as those estimated for cognitive skills when one controls for schooling and family characteristics.

As technology changes, and its use continues to increase in labor markets throughout the world, some research has expanded the definition of cognitive skills to include those skills necessary for success in technology-rich environments. In this study, in addition to reading proficiency, we proxy cognitive skills with a variable that measures the complexity of computer use on the job. The literature on this technological transition shows that the changes introduced by the use of technology (mostly computers) has shifted the premiums for those skilled workers who are able to complement what technology can do, introducing a skill bias. These changes have likewise replaced workers whose skills can be substituted by technology in a labor-saving

> 7 _Locus of control_ refers to an individual’s perception of the relation between her/his own behavior and its consequences. _Reciprocity_ entails showing much more cooperation than predicted in response to friendly actions from others and, conversely, being “much more brutal” in response to hostile actions (Heineck and Anger, 2010).

6
the share of households with reliable access to electricity (i.e. with a score of 4 or 5). The index ranges between 0 and 1; the higher the index score, the greater the extent of reliability in the community. Apart from school access, the data show significant gaps in access to infrastructure between urban and rural communities. For instance, the share of urban communities with access to grid electricity is almost twice the share of rural communities with grid access. Interestingly, however, reliability in electricity supply appears to be a significant problem for both urban and rural households.

Further, socioeconomic attributes of individuals such as age, gender, education level, employment status, household size, type of housing, and asset ownership (TV and mobile phone) are also included in the analysis.

Next, we compute an index of national identity. This variable measures the extent to which individuals identify themselves more with their country relative to an ethnic group. It is an ordinal variable ranging from 1 to 5 where a larger value implies that an individual identifies himself (herself) more with the country relative to his (her) ethnic group. Overall, we observe a relatively high degree of national identity, with a mean index of 3.8.

# **3.2.2 The Enterprise Surveys data**

The analysis of tax-revenue gains uses data from the Enterprise Surveys, a data set on firm performance and their constraints to doing business. The data set is available for most countries in the region between 2002 and 2015. In this analysis, we focus on a section of the data set collected between 2006 and 2015, mainly because of the harmonization of the survey methodology since 2006. For each country in the data set, we focus on the latest round of the survey.

To obtain a measure of the impact of outages on firms’ revenue, we compute the weighted average of the share of firm revenue lost due to power outages. Firms in the data set vary regarding sales, size, production, industry composition, and the extent of dependence on electricity for production activities, among other things. As a result, using the arithmetic mean of the share of firm revenue lost due to outages may over- or under-estimate the impact. We resolve this potential problem by using a weighted rather than arithmetic average.

Further, there are concerns about the accuracy of firms’ self-reported estimate of the impact of outages on their revenue. For instance, firms may over-estimate the impact in their quest to draw the attention of policy makers to resolve the challenges in the power sector. Alternatively, firms might find it difficult to predict the direct and indirect impacts of outages on their production and consequently, revenue. Hence, the effect of measure13
**Table 2: Chinn-Ito Index of capital account liberalization for selected groupings**

||**1990–1994**|**1995–1999**|**2000–2004**|**2005–2009**|
|---|---|---|---|---|
|**Core Countries**|83.2|96.1|97.4|100.0|
|**Non-Core Countries**|19.5|80.5|96.6|100.0|
|**Other Euro Countries**|-37.9|-9.5|24.7|81.3|
|**Non-Euro Countries**|8.8|39.0|66.9|87.9|

Note: The Chinn-Ito Index is expressed in terms of its highest value for all countries considered in their sample. Thus, a value of 100 means complete liberalization. Core countries include Austria, Belgium, France, Germany, and the Netherlands. Non-Core countries include Greece, Ireland, Italy, Portugal, and Spain. Other Euro countries include Estonia, Malta, the Slovak Republic, and Slovenia. Non-Euro countries are comprised of Sweden and the United Kingdom. Source: Chinn and Ito (2008).

8. Financial integration resulted in a sharp increase in cross-border capital flows with claims by core countries on non-core countries‘ banking systems dominating the increase. Lending by German and French banks to non-core countries‘ banks was by far the most important factor in this increase.

**Figure 2: Core bank claims in Non-Core countries**

<!-- Start of picture text --> US$ billions<br>2500<br>Dutch banks<br>2000<br>German banks<br>French banks<br>1500<br>Belgian banks<br>1000<br>500<br>0<br>Note: Non-Core countries are Greece, Ireland, Italy, Portugal, and Spain.<br>Source: Bank for International Settlements.<br>Dec.1999 Jun.2000 Dec.2000 Jun.2001 Dec.2001 Jun.2002 Dec.2002 Jun.2003 Dec.2003 Jun.2004 Dec.2004 Jun.2005 Dec.2005 Jun.2006 Dec.2006 Jun.2007 Dec.2007 Jun.2008 Dec.2008 Jun.2009 Dec.2009 Jun.2010 Dec.2010 Jun.2011 Dec.2011<br><!-- End of picture text --> 5
determining the respondents’ willingness to adopt REDD+ contracts. The significant coefficient results are robust across the econometric specifications and have expected signs. The significant results indicate the following:

1. As the required firewood reduction increases, respondents are less likely to choose that option.

2. For non-CF households as the required grazing reduction increases, respondents are less likely to choose that option.

3. As the percentage of the payment going to the community increases, respondents are more likely to choose that option.

4. As the payment values (amount) increases, respondents are more likely to choose that option.

Result (3), that respondents prefer more of the payment to be given to the community is somewhat surprising, but reflects the feedback from the focus groups. One possible explanation is that since households are currently not receiving any cash payments under the current CF management regime, the respondents did not believe higher amounts would be forthcoming under the REDD scheme in question. Adhikari et.al (2003) found similar results in their study around the buffer zone of Chitwan National Park in Nepal. In contrast, on-going work with a similar choice experiment survey in Ethiopia finds that respondents prefer more of the payment to go to the households.

By including attribute interaction terms (specification 14) we can better analyze the structure of the preferences for REDD contracts and relationship between attributes. Results for the specification 14 are shown in column 3 in Table 3 and Table 4. We find that for non-CF households (Table 3) the interaction terms between the _firewood reduction_ variable and _payment_ 21
**<u>Table 5: Estimated yield effects of different predecessor crops for cereals and sunflower</u>**

||<br>**2016/17**|<br>**Cereals**<br>**2017/18**|<br>**2016-18**|<br>**2016/17**|<br>**Sunflower**<br>**2017/18**|**2016-18**|
|---|---|---|---|---|---|---|
|% after sunflower|-0.484***<br>(0.0257)|-0.705***<br>(0.0352)|-0.705***<br>(0.0352)||||
|% after maize|-0.221***|-0.199***|-0.197***|-0.141***|0.126**|0.130***|
||(0.0439)|(0.0435)|(0.0436)|(0.0532)|(0.0491)|(0.0491)|
|% after cereals||||-0.330***<br>(0.0473)|-0.360***<br>(0.0451)|-0.356***<br>(0.0451)|
|% after others|-0.294***|-0.509***|-0.507***|-0.469***|-0.290***|-0.288***|
||(0.0318)|(0.0376)|(0.0377)|(0.0597)|(0.0535)|(0.0535)|
|% self twice|-0.299***|-0.786***||-0.936***|-0.607***||
||(0.0319)|(0.0434)||(0.0608)|(0.0574)||
|% self twice only|||-0.762***<br>(0.0538)|||-0.445***<br>(0.0752)|
|% self thrice|||-0.831***<br>(0.0748)|||-0.902***<br>(0.105)|
|No. of VCs|6,540|6,610|6,610|5,356|5,495|5,495|
|R<sup>2</sup>|0.199|0.168|0.168|0.118|0.142|0.144|
|Mean dep. var.|3.557|3.486|3.486|2.962|3.104|3.104|
|SD dep. var.|0.412|0.454|0.454|0.501|0.463|0.463|
|Elasticity: twice|-0.0595|-0.131|-0.0917|-0.100|-0.0604|-0.0331|
|Elasticity: thrice|||-0.0382|||-0.0228|
|El after sunflower|-0.166|-0.234|-0.234||||
|F-test|||||||
|Self 2x=self 3x|||0.54|||11.11***|

_Note:_ Results are from regressions at village council level for two-year periods in columns 1 and 4 (2016-17) as well as 2 and 5 (2017-18) and for the 3-year period 2016-18 in columns 3 and 6. Dependent variable is log of the output value of cereals (wheat and barley) per ha in columns 1-3 and the log of physical yield (dt/ha) in columns 3-6. The soybean-sunflower rotation the omitted category. A constant as well as mean farm size is included throughout but omitted. Standard errors in parentheses: * p<0.10, ** p<0.05, *** p<0.010.

19
on exchange rate policy from the World Competitiveness Yearbook (WCY) survey for all years to ensure over time comparability for this source, as it was not asked in 2008. For similar reasons we have dropped a question on the composition of public spending from the Global Competitiveness Report survey (GCS). And finally, at the time of the release of the last round of the WGI, data from the 2007 Cingranelli-Richards Human Rights database (HUM) and the US State Department Trafficking in Persons Report (TPR) were not available, and so we used data from 2006. We have now updated the 2007 data to reflect the available 2007 data, and we use the same data for 2008 as well (as the latest 2008 data was not available at the time of writing).

Readers interested in any further details of these changes in individual indicators can compare Appendix A of this paper with Appendix A of Kaufmann, Kraay, and Mastruzzi (2008) describing the 1996-2007 dataset. These minor revisions to the historical data result in only trivial changes in the six aggregate WGI over the period 1996-2007 when compared with last year's data. In all cases over the past 10 years, the correlation between the original and the revised indicators is 0.99 or higher, and in no cases are changes to scores for a single country due to revisions statistically significant at the 90 percent level.

# **2.3  Aggregation Methodology**

We combine the many individual data sources into six aggregate governance indicators, corresponding to the six dimensions of governance described above. The premise underlying this statistical approach should not be too controversial – each of the individual data sources provides an imperfect signal of some deeper underlying notion of governance that is difficult to observe directly. This means that, as users of the individual sources, we face a signal-extraction problem – how do we isolate an informative signal about governance from each individual data source, and how do we optimally combine the many data sources to get the best possible signal of governance in a country based on all the available data? The statistical procedure we use to perform this aggregation, known as the unobserved components model, is described in detail in our past work (see for example Kaufmann, Kraay and Mastruzzi (2004), as well as Appendix D). The main advantage of this approach is that the aggregate indicators are more informative about unobserved governance than any individual data source.

12
Policy ReseaRch WoRking PaPeR 4694

# **Abstract**

This paper investigates the relationship of household income with child labor. The analysis uses a rich dataset obtained in the context of a conditional cash transfer program in a poor region of Nicaragua in 2005 and 2006. The program has a strong productive emphasis and seeks to diversify the work portfolio of beneficiaries while imposing conditionalities on the household. The author develops a simple model that relates child labor to household income, preferences, and production technology. It turns out that child labor does not always decrease with income; the relationship is complex and exhibits an inverted-U shape. Applying the data to the model confirms that the relationship is concave when all children (8-15 years of age) are included in the sample. Expanding the analysis by stratifying the sample by age and gender shows that the relationship holds only for older children, both genders. The author investigates the effect of the conditional cash transfer program on child labor. The results show that the program has a decreasing effect on total hours of work for the full sample of children. Disentangling labor into two types – physically demanding labor and non-physical labor – reveals that the program has opposite effects on each type; it decreases physically demanding labor while increasing participation in non-physical (more intellectually oriented) tasks for children.

This paper—Social Protection Division, Human Development Network—is part of a larger effort in the department to understand the impact of social programs on children's work. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at xdelcarpio@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
factories in control districts).

First, consider the minimum wage increase. While the minimum wage increase arose out of the post Rana Plaza protests (as described in section 2.3) it applied to the whole industry, and thus any effects that apply equally in treated and non-treated districts are netted out of the treatment effects given in equation 1. However, it is possible that international scrutiny leads to increased enforcement of existing labor laws given the role of multinationals in enforcing labor law (Boudreau, 2019). In this case, our identification strategy would correctly attribute this aspect of the minimum wage increase to international scrutiny.

Worker flows might affect garment workers in control districts if workers either voluntarily left (or were fired from or laid off) garment sector jobs in treated districts and went to work in garment factories in control districts. While we cannot use the panel component of our labor force survey data to speak to the empirical frequency of this event (as workers are not tracked if they move), a separate survey conducted by Heath (and collaborators Laura Boudreau and Khandker Wahedur Rahman) speaks to the phenomenon. In that survey, workers surveyed in 2017 by Kabeer, Haq and Sulaiman (2019) were recontacted in either November 2020 or March-April 2021. The workers all initially worked in treated districts. Of the workers who were found and still worked in the garment industry, 94% (266 of 283) were still in the same district. Among those workers who had moved, only 1 (i.e., .003 %) had moved to a control district. Thus, this survey presents one piece of evidence that flows of garment workers between treated and control districts are relatively small.

Next, we consider indirect effects through subcontracting, or spillover effects of international scrutiny. In particular, garment workers in control districts might be impacted if they had previously been in factories that subcontracted with export factories, and this subcontracting was discouraged by the international scrutiny following Rana Plaza. Or their factories might fear worker reprisal if they don’t make some of the same improvements to working conditions or wages that factories in treated districts do.

If so, the identification assumption for the triple difference might be violated. To alleviate this concern, we estimate a double-difference specification that compares garment workers to non21
So, the wage for a relatively unskilled worker has gone up something like five-fold. But wages for some of the skilled occupations – say, computer programmer or an English interpreter – may have gone up ten times or even more. Thus, a careful study of wage inequality is likely to show rising inequality. However, how wage inequality translates into household inequality is very complex. For a surplus worker from a large rural household who gets one of the newly created jobs in a shoe factory, earnings go from zero to $50 per month. Thus, if a large number of new wage jobs are created and if these typically pay a lot more than people earn in the rural or informal sector, then a country can have rising wage inequality but stable or even declining income inequality (in Vietnam the Gini coefficient for household income inequality actually declined between 1993 and 1998). In rich countries, on the other hand, where most people are wage earners, the higher wage inequality is likely to translate into higher household income inequality, which is what we have seen over the past decade.

A third point about wage inequality and household income inequality that is relevant for rich countries is that measures of wage inequality are often made pre-tax. If the country has a strongly progressive income tax, then inequality measures from household data (which are often post-tax) do not have to follow wage inequality, pre-tax. Tax policy can offset some of the trends in the labor market.

Finally, there is the important issue that households can respond to increased wage inequality by investing more in the education of their children. A higher economic return to education is not a bad thing, provided that there is fair access to education for all. In Vietnam, there has been a tremendous increase in the secondary school enrollment rate in the 1990s (from 32% to 56%). This increase partly reflects the society’s and the government’s investment in schools (supported by aid donors), but more kids going to school also reflects households’ 27
w r (5) pp pp CCjj (iibbdThird, we estimated changes in the energy cost of brick production [wweett ccCCwwbbEEee ttCCOO) = CC̅jj (tt) + (C rree(tt) ∙CCOOjj(tt)) ] by partially pp substituting coal with advanced biomass fuels, such as pellets, for ZZK brick kilns across different ii CC (tt) provinces ( _Equation 6_ ). The energy cost constitutes a major cost of brick production. The costs associated with a different combination of coal [ c ] and pellets [ ] is calculated using the fuel price information (fuel price index with the price of coal index as 1) collected from the rrcc ppeec eerr eeh eeh rapid survey.

Fourth, we estimated the social cost of brick production. The social cost includes both private costs, i.e., direct costs to producers, and indirect or external costs to society that are not reflected in the market price. These external costs associated with brick production include environmental costs, such as costs associated with air pollution, GHG emissions, and soil degradation, health costs, such as workers’ exposure to dust, and other societal impacts. In this study, the estimated social costs are limited to environmental costs [ 2 ], which is the product SS of the environmental damage cost of CO2 emissions from brick production using carbon tax as a jj,C CC (tt) proxy for the social cost of CO2 [ C 2 ] and the corresponding CO2 emissions (Equation 7). The CO2 emissions for brick production are estimated using the emission factor [ 2 ], SSCC (tt) specific energy consumption [ ] and the average weight of the brick [ ]. Likewise, jj,C OOEE (tt) we estimated health damage costs associated with PM2.5 [jj 2.5 ] using a similar approach ii,jj SSOOCC (tt) ww (tt) SS as that for CO2 emissions ( _Equation 8_ ). The social cost values for COii,jj,P 2 and PM2.5 are collected CC (tt) from existing literature.

Lastly, we estimated the changes in costs of brick production using alternative non-fired bricks such as hollow concrete blocks (HCB) and compressed stabilized earth blocks (CSEB). We 10