rafmacalaba/datause-encoder-data huggingface dataset:
|Table 10|a: Chang|e in real val<br>|ue added in 2|030, perce|nt change<br>|from bas<br>|eline, ct<br>|d||
|---|---|---|---|---|---|---|---|---|---|
|||Rest of|||Rest of|Iran,|Egypt,|||
||Poland|Eastern<br>Europe|Kazakhstan|Kyrgyz<br>Republic|Central<br>Asia|<br>Islamic<br>Rep.|<br>Arab<br>Rep.|Turkey|Rest of<br>MENA|
|Agriculture|0.6|1.8|2.9|-9.8|0.8|1.2|0.2|1.3|0.4|
|Minerals n.e.s.|-0.3|-0.8|0.2|-3.1|0.1|1.1|-0.3|-1.7|-0.4|
|Coal|-0.8|-0.2|1.0|15.3|-0.7|10.1||-7.8|-5.0|
|Oil|1.0|-1.0|-0.8|5.1|-0.9|-0.6|0.1|-6.9|0.8|
|Gas|0.0|0.2|-0.7|-3.8|2.2|4.1|0.1|2.1|1.4|
|Textiles|0.3|-0.4|-0.5|3.8|0.7|3.9|-0.2|3.3|-2.3|
|Wearingapparel|-0.1|0.3|-0.5|32.7|3.2|2.6|0.2|4.9|-2.3|
|Leathergoods|-0.3|-2.3|-0.3|156.8|3.9|4.0|0.2|3.6|-4.7|
|Processed foods|0.8|1.5|1.0|-5.0|1.8|1.7|0.3|4.5|0.6|
|Woodproducts|-0.5|-1.2|-0.9|1.4|0.7|0.1|-0.5|-0.1|-3.1|
|Paperproducts, publishing|0.2|-0.6|-0.2|-8.2|-0.7|-1.7|0.0|-1.5|-2.2|
|Petroleum,coalproducts|-0.5|0.2|2.5|12.6|-0.5|4.4|-0.5|1.5|3.8|
|Chemical,rubber, plastic|-0.3|-0.6|0.2|-9.3|1.6|14.0|-0.5|-0.9|1.6|
|Energyintensive manu.|-0.6|-2.3|0.6|-0.2|-1.5|0.4|-1.1|-1.8|-1.6|
|Metalproducts|-1.0|-1.7|-1.2|-16.2|-1.5|-0.2|-1.2|0.8|-3.2|
|Electronics|-7.5|-6.0|-7.3|-10.3|-3.6|-3.8|-8.8|-4.1|-10.5|
|Machineryand equipment|-1.7|-2.2|-2.0|2.8|1.5|-6.6|-5.3|-2.9|-7.8|
|Transport equipment|-0.5|-1.5|4.3|-3.5|13.0|1.2|0.2|10.0|-5.3|
|Manufactures,n.e.s.|-1.2|-1.4|-0.5|-9.6|-0.2|-1.8|-0.4|6.1|-1.7|
|Electricity|0.2|-0.1|0.6|6.8|1.2|3.4|-0.3|1.5|1.1|
|Construction|0.1|-0.3|-0.5|9.1|1.0|2.0|0.0|-0.9|0.8|
|Trade services|0.0|0.2|0.2|8.5|0.4|-2.5|-0.1|-5.2|0.3|
|Other transport|0.0|-0.3|0.0|7.4|-2.0|-3.0|-0.2|-1.1|0.4|
|Water transport|1.3|1.0|-0.1|3.4|-0.8|-0.5|0.6|-8.8|1.9|
|Air transport|-0.7|0.1|-0.1|11.1|-0.6|15.8|-0.5|1.6|0.2|
|Hospitalityservices|0.5|0.1|0.1|-5.1|0.4|-0.3|0.3|1.2|0.4|
|Other business services|0.4|-0.1|-0.3|-5.4|0.1|-0.3|0.0|0.3|0.1|
|Other services|0.1|0.1|0.1|0.9|0.1|-0.1|0.0|0.0|-0.2|
|**Agriculture**|0.6|1.8|2.9|-9.8|0.8|1.2|0.2|1.3|0.4|
|**Manufacturing**|-0.6|-1.0|0.2|-0.7|1.7|1.5|-0.5|2.4|-2.1|
|**Services**|0.0|0.0|0.0|0.2|-0.1|-0.8|0.0|-0.6|0.2|
|**Other**|-0.5|-0.4|-0.1|2.4|0.3|2.1|0.1|-2.4|0.8|
|**Total**|0.00|0.0|0.000|0.01|0.00|0.01|0.00|0.00|0.01|

- 48 -
older children is again likely to increase in developing countries via intra-household substitution effects, even with demand-side implementations.

To empirically study this question we bring to bear harmonized income survey microdata from 170 countries covering response years between 1960 and 2020. The data include over 1,700 unique nationally representative survey years covering over 180 million respondents. We combine these surveys with detailed data on trade agreements reported to the WTO that include non-trade characteristics such as whether an agreement includes provisions banning the use of child labor and how said provisions are enforced. Because child labor is less of a concern in developed countries, we focus our analysis on a sub-sample of 101 developing countries. We estimate a variety of difference-indifferences (DiD) and triple DiD models on this developing country sample to assess within-country changes in child labor market outcomes before and after a given trade agreement enters into force, and whether these effects differ for trade agreements with the child labor provisions compared to those without.

We find that, consistent with our model predictions, child labor—especially employment of 14 to 17 year olds—declines following entry into force of RTAs that do not include child labor protections with corresponding increases in school enrollment rates. However, in trade agreements that include provisions banning child labor, those provisions perversely _increase_ child employment for both young (under 14 years of age) and, especially, 14 to 17 year olds. Consistently with this finding, we also observe a decrease in school enrollment rates for both young and older children. Finally, we find evidence that these provisions decrease inter-household income inequality for households with children, presumably through increases in child labor income.

This is the first paper to establish systematic evidence on the impacts of child labor protections in RTAs on child labor and related outcomes. In doing so, we also make significant contributions to the literature on the impacts of free trade on child labor more broadly. Our empirical setting offers credibly exogenous variation in trade openness applied to a broad panel of developing countries and improves upon existing identification strategies in the literature that leverage cross-country differences in trade and child labor. Our study also provides new causal evidence on the impacts of child labor bans on child labor, a literature which has largely focused on domestic labor bans 2
and perhaps conservative, signal of program intensity.

# **4.1.2 Dependent variables: Pratham’s Annual Status of Education Report**

– – Since 2005, Pratham a large Indian non-governmental organization has coordinated the Annual Status of Education Report (ASER) survey. The ASER is a household survey that is implemented annually by local organizations in every rural district of India and is based on a nationally standardized set of educational achievement tests for children. This data collection effort visits about 15,000 Indian villages each year. In addition to cognitive tests, the survey includes a very small set of household and child survey questions and a few questions about the child’s village.

The dependent variables of this paper are children’s outcomes on the educational tests in the ASER surveys. There is a reading test and a math test. From these tests, we construct three dependent variables that distinguish ability levels among Indian six year olds: ability to recognize letters, ability to recognize single-digit numbers, and ability to recognize twodigit numbers. As table 1 shows, about three-fourths of the sample can recognize letters and the simple numbers, and about one-third of the sample can recognize larger numbers.

All districts with a rural population are included in ASER’s sample (ASER, 2010). Within each district, ASER randomly selected 30 villages using probability proportionate to size sampling based on village sizes in the 2001 census. Within each village, they used a field-implemented random sampling strategy, stratifying within hamlets if necessary, to randomly sample 20 households by drawing a map. Although this strategy is representative (and self-weighting) at the district level (and districts are the level at which our independent variable varies), households in smaller districts are more likely to be selected into the national sample than households in larger districts. We attempt to estimate a causal effect, not merely describe the population, so weights may be inappropriate, as Solon et al.

> will be negatively correlated with other social, political, and economic factors shaping test scores, that is _ε_ , then the omitted variable bias due to inflation of administrative records would be _away from_ the positive that we

12
**Global inequality:  From class to location, from proletarians to migrants**

Branko Milanovic<sup>1</sup> JEL Classiification: D31, I3, N3 Key words: global inequality, inequality between nnations, migration, labor in 19<sup>th</sup> century and today

> 1 Resesarch Department, World Bank.  The paper was written while I was a Visiting Fellow at Universidad Carlos III in Madrid.
about it because the conflict intensities have evolved similarly across the data sets. Any presence of recall bias, however, would bias down our estimation results.

We also compare the spatial distribution of the events between our data set and the ACLED database, which was used as our basis for selecting the most appropriate geopolitical zones for the telephone survey. By comparing figure B.2, panel a (the number of events in the ACLED data set) and panel b (household victimization in the telephone survey), we can indeed see that the geographical patterns across the geopolitical zones are similar in the two data sets.

Given that our outcomes are measured at the household level, we would ideally want to capture victimization of all household members. However, our victimization measure almost certainly excludes certain types of victimization. First, given that most of the respondents are male, events experienced by female household members might be underreported, which is supported by the fact that we have almost no reports on sexual violence. Second, we have very low reports of police or military violence; this might also be underestimated because the telephone survey was administered by the government.

The data also show that many events reported in the survey were not reported to anyone in the community, including community and religious leaders as well as authorities.<sup>12</sup> Given that the number of conflict-event reports in the survey is much higher than the number reported to authorities, we believe that despite our concerns of underreporting, our respondents show trust in the enumerators. Indeed, the households were visited multiple times by the LSMS/National Bureau of Statistics (NBS) survey teams since 2010—that is, prior to the telephone survey—which surely helped build trust. However, keeping in mind that households might be underreporting some specific types of events, our victimization measure—and therefore also our estimation results—might be downward biased.

The evolution of the consumption levels is displayed in figure 2, panel a, over the three waves of the analysis. We can see that consumption has been increasing in all three regions, but the increase is especially rapid in the South South.

Figure 2, panel b, shows the evolution of the CSI over time. The CSI is a simple tool to assess food insecurity by measuring behavior. The basic logic is to answer this question: “What do you do when you don’t have enough food and don’t have enough money to buy food?” The

> 12 Among the victimized households, 77 percent in the North East, 34 percent in the North Central, and 26 percent in the South South had not reported the most recent event to any authority.

11
entail lower food prices and hence allow new manufacturing enterprises to pay low wages, further stimulating growth in the non-farm economy. China’s success in labor-intensive manufacturing clearly rested in part at least on the availability of cheap wage goods.

There is also evidence for China that the agricultural sector is an important generator of positive externalities favoring non-farm development. Using farm-household level panel data from four provinces of post-reform rural China, Ravallion (2005) finds evidence of strong geographic externalities, stemming from spillover effects of the level and composition of local economic activity and private returns to local human and physical infrastructure endowments. This suggests an explanation for rural underdevelopment arising from under-investment in certain externality-generating activities, of which agricultural development emerges as the most important.

Developing countries keen to industrialize have tried often to accelerate the process. Indeed, even China may well have switched its sectoral attention out of agriculture too quickly. It seems that after attaining a degree of food security, and higher incomes for the peasant class, the political economy demanded higher living standards for the relatively better off middle- and upper-income groups. The associated shift in the sectoral and geographic pattern of China’s growth fuelled rising inequality and dulled the impact of subsequent growth on aggregate poverty incidence. (In this respect, Africa might actually learn more from Vietnam, which appears to have maintained its sectoral emphasis on agriculture and rural development for a longer period than China did at a comparable point in time.) This lesson appears to be highly relevant to SSA today. Christianson and Demery (2007) have argued convincingly that development strategy for Africa that is firmly grounded in agricultural and rural development can bring a larger and more sustained impact on poverty.<sup>38</sup> Just as has happened in China, there will be a time when the emphasis in Africa will naturally shift to secondary and tertiary sectors. But with the levels of poverty prevailing in SSA today, and the sub-continent’s (still) relatively abundant supply of (not too unequally distributed) land, an agriculture-based strategy must for now be at the center of any effective route out of poverty, just as it was in China during the early 1980s.

> 38 Also see Dorward et al. (2004), Mwabu and Thorbecke (2004) and World Bank (2007).

20
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# **Table S7. Bivariate Correlation between Statistical Indexes and Key Development Indices**

|Index||GDB||ODB||ODIN||SCI||SPI|
|---|---|---|---|---|---|---|---|---|---|---|
|Economic Complexity Index|0.66***|ODB, ODIN, SCI, SPI|0.63***|GDB, ODIN, SCI, SPI|0.65***|GDB, ODB, SCI,|0.58***|GDB, ODB, ODIN,|0.72***|GDB, ODB|
|Environmental Performance Index|0.61***|ODB, ODIN|0.59***|GDB, ODIN SPI|0.49***|GDB, ODB SPI|0.07**||0.5***|ODB, ODIN|
|OECD Better Life Index|0.48***|ODB, ODIN, SCI, SPI|0.39**|GDB, ODIN, SCI,|0.58***|GDB, ODB, SCI, SPI|0.15|GDB, ODB, ODIN|0.62***|GDB ODIN|
|UN Human Development Index|0.72***|ODB, ODIN SPI|0.71***|GDB, ODIN SPI|0.65***|GDB, ODB SPI|0.46***||0.69***|GDB, ODB, ODIN|
|WB Human Capital Index|0.74***|ODB, ODIN SPI|0.73***|GDB, ODIN SPI|0.73***|GDB, ODB SPI|0.56***||0.76***|GDB, ODB, ODIN|
|World Press Freedom Index|0.49***|ODB, ODIN SPI|0.54***|GDB , SPI|0.39***|GDB , SPI|0.18**||0.46***|GDB, ODB, ODIN|

**Note** : The correlations are shown for the listed indices and the averaged value of the SDG over the past two years (to reduce volatility). The years used for the indicates are: Economic Complexity Index (2021,2020), Environmental Performance Index (2022), OECD Better Life Index (2018,2017), UN HDI (2021,2020), WB HCI (2020), World Press Freedom Index (2023,2022). The indices listed in the right column under each heading do not have a statistically distinguishable correlation coefficient with the index in the heading and the specific SDG in the row. For instance, for SDG1: Extreme Poverty (the first row), the GDB index does not have a statically significantly different correlation from those of the ODB, ODIN, SPI, and SCI.  We use the <mark>R package “cocor” (Diedenhofen and Much, 2015) to test for the correlations with overlapping samples.</mark>

56
Overall, 76 countries around the world impose some form of interest rate caps on loans. Table 1 gives a complete list of countries with interest rate caps<sup>2</sup> .

**Figure 1. Number and Percentage of Countries with Interest Rate Ceilings, by Region**

<!-- Start of picture text --> SA 3 (38%)<br>MENA 6 (29%)<br>LAC 14 (48%)<br>ECA 7 (23%)<br>EAP 7 (44%)<br>SSA 24 (50%)<br>0 5 10 15 20 25 30<br><!-- End of picture text --> _Source_ : Helms and Reille 2004; Mbengue 2013; Castellanos 2012; Porteous, Collins, and Abrams 2010; iff/ZEW 2010; EIU 2012, 2013; CGAP-MIX 2011; Steiner and Agudelo 2012.

_Notes_ : The value in parenthesis represents the share of countries with interest rate caps in the region. EAP = East Asia and the Pacific; ECA = Europe and Central Asia; LAC = Latin America and the Caribbean; MENA = Middle East and North Africa; SA = South Asia; SSA = Sub-Saharan Africa.

In this exercise, we found that the main reasons for using interest caps on loans were to protect consumers from excessive interest rates, to increase access to finance, and to make loans more affordable. Most countries regulate interest rates with the broad aim of protecting consumers, as in the case of Spain. Other countries provided more specific objectives, such as protecting the weakest parties (Portugal); shielding consumers from predatory lending and excessive interest rates (Belgium, France, the Kyrgyz Republic, Poland, the Slovak Republic, and the United Kingdom); stopping the abuses arising from too much freedom (Greece); controlling over-indebtedness (Estonia); and decreasing the risk-taking behavior of credit providers (the Netherlands). Similarly, in Thailand authorities stated that the purpose of the caps was to make finance affordable for low-income borrowers.<sup>3</sup> Finally, Zambia’s authorities introduced the caps to mitigate the perceived risk of over indebtedness and the high cost of credit, as well as to enhance access to the underserved.

> 2 The stock-taking exercise was conducted during January-February 2014. Therefore, changes after that date are not reflected in this document.

> 3 http://www.microfinancethailand.com/.

6
As Figure 2 illustrates, conflict-affected countries are poorer than other developing countries, have smaller economies, and attract less private participation in infrastructure, both in absolute terms and as a share of their population. Not surprisingly, levels are lower still in those countries characterized as having weak or non-functioning governments.<sup>12</sup> Whereas a developing country that has not suffered from recent conflict will attract, on average, US$22 of PPI per capita, conflict-affected countries with functioning government will attract about US$14 per capita of PPI and countries with non-functioning governments will attract about $9 per capita—most of which is coming from the mobile telephony sub-sector.

**Figure 2: GDP and PPI per capita in Developing Countries, Conflict-affected Countries and Conflict-affected Countries with Weak or Non-Functioning Governments.**

<!-- Start of picture text --> 1852 $.022<br> Non-conflict<br>Countries<br>$.014 Conflict<br> 973 Countries<br>$.009<br>688 Weak Non<br>functioning<br>Countries<br>Average GDP per capita(US$) PPI per capita (US million per thousand<br>person)<br><!-- End of picture text --> Source: PPI Database and WDI. Authors’ calculations.

How long, then, does it take for investments in the form of private infrastructure commitments to return to conflict-affected countries? What sectors are more likely to attract private partners or investors and to close transactions? By zeroing out end-dates of conflicts for a set of 31 countries that have suffered from conflict over the last 20 years, we can establish a fixed point from which to consider investment trends. That is, “Year 0 (Zero)” is the year at which a conflict is considered to have terminated in a country so that conflicts which ended 15 years apart can be put on the same timeline. By creating this normalized timeline, we can see that investments

> 12 The conflict-affected countries are: Countries with Weak or-non Functioning Governments: Afghanistan, Algeria, Angola, Azerbaijan, Bosnia and Herzegovina, Burundi, Cambodia, Congo, Dem. Rep. Congo, Rep. El Salvador, Ethiopia, Georgia, Iraq, Lebanon, Liberia, Mozambique, Myanmar, Nicaragua, Rwanda, Sierra Leone, Somalia, Sudan Tajikistan, Yemen, and Rep. Yugoslavia. The Post Conflict-functioning countries are: Colombia, Peru, Philippines, Russian, Federation, Sri Lanka, and Turkey.

17
<!-- Start of picture text --> _. _ Absolute gains in consumption expenditure (fractile groups), 1993-2008<br>fake)<br>QR<br>(atsas<br>ar<br>Q<br>2<br>aio}<br>aN<br>=<br>(a)<br>ao —— ee ee ee EE EEE EE EEE EE EE EE EE EE EE EE ee<br>xv<br>oO<br>ic<br>Lwo<br>a<br>=<br>=)<br>con<br>fe)<br>ro)<br>oO —<br>=|<br>iS<br>iS<br>oO<br>£°<br>cw<br>oR)<br>oO 1<br>10 20 30 40 : 50 . 60 70 ; 80 90 100<br>Percentile groups of African distribution of consumption expenditure<br>Y-axis is using the inverse hyperbolic sine transformation.<br>Absolute gains in annual average consumption expenditure of the fractile group (not the fractile). Weighted by population.<br>Evaluated at ventile groups (e.g. bottom 5%).<br>Horizontal line shows absolute gain in African mean of USD10 (2011, PPP-adjusted).<br><!-- End of picture text -->

<!-- Start of picture text --> Distribution of consumption gains by percentile group: 1993-2008<br>40 39.4<br><< 30<br>=<br>140)<br>o>)<br>g<br>2 20<br>—<br>°<br>L<br>=<br>w 171.3)<br>10 7.9<br>29 3:3 ae%- al = a<br>2.0 2.3 2.6<br>03 0.6 08 10 1.1 13 16 18<br>0<br>12 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>Share of total gain in annual consumption expenditure of the fractile group (not the fractile). Weighted by population.<br>Evaluated at ventile groups (e.g. bottom 5%).<br>Although average consumption for some quantiles decreased, total consumption could still increase due to population growth.<br><!-- End of picture text -->
18

# **Figure 11.  Latin American Poverty and GDP per capita (1980-2006)**

<!-- Start of picture text --> 60.0 9000<br>8500<br>50.0 8000<br>7500<br>40.0 7000<br>6500<br>30.0 6000<br>1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006<br>4 USD a day GDP per capita, PPP (constant 2005 international $)<br>Headcount Poverty Rate<br>constant 2005 international $<br><!-- End of picture text --> _Source: World Bank (2009b)._

**Figure 12.  International Gini Coefficients (1970-1999)**

(Household per capita income) <!-- Start of picture text --> 50<br>40<br>30<br>20<br>10<br>0<br>1970s 1980s 1990s<br>Latin America Asia OECD Eastern Europe<br><!-- End of picture text --> _Source: World Bank (2004)._
|**Table 1: Firms and E**|**mployees, M**|**ining and M**<br>|**anufacturing**<br>|**, 2003 and 201**<br>|**3**<br>|
|---|---|---|---|---|---|
|||2003<br>(number)<br>(1)|2013<br>(number)<br>(2)|Change,<br>2003 to 2013<br>(number)<br>(3)|South/north<br>in (3)<br>(ratio)<br>(4)|
|**a. Firms**||||||
||National|125|294|169||
|Mining|North|22|33|11|144|
||South|103|261|158|.|
||National|23,797|101,789|77,992||
|Manufacturing|North|4,623|31,281|26,658|19|
||South|19,174|70,508|51,334|.|
|**b. Employees**||||||
||National|14,869|40,120|25,251||
|Mining|North|247|645|398|624|
||South|14,622|39,475|24,853|.|
||National|116,774|271,863|155,089||
|Manufacturing|North|10,386|44,507|34,121|35|
||South|106,388|227,356|120,968|.|

_Source_ : Authors’ estimates based on data from the 2003 National Industrial Census and the 2014 Integrated Business Establishment Survey.

22