rafmacalaba/datause-encoder-data huggingface dataset:
same amount (since � �1).<sup>12</sup> This motivates our comparison of the ���� additional effect (relative to improving information only) of conditional transfers aimed at parents to that of conditional transfers aimed at children.

# C. Study Design

In order to assess the relevance of our analytical framework, as well as to help define the design of the Randomized Controlled Trial (RCT) described below, we first undertook a qualitative analysis in the province where the RCT took place but in areas that were not included in the trial. The information gathered during focus group discussions with parents and (separately) with their daughters age 11-15 gives support to the hypotheses that (i) both parents and girls of this age have an influence on school attendance decisions and (ii) children have private information on their school attendance. In addition, data collected in the baseline household survey in the experimental sample asked parents (both in treatment and control areas) whether they thought it would be useful to see a weekly report showing whether their daughter had attended school regularly, and, if they answered that it would, a follow-up, open-ended question then asked why they thought such report would be useful. Eighty percent of parents responded “yes” to the first question, and among those, when asked (the open-ended question of) why they thought it would be useful, 98% responded that it would allow them to monitor their child’s school attendance.

Other than providing a first pass confirmation of the relevance of our analytical framework to the study area, the preliminary focus groups aimed to establish how to incentivize girls effectively and in a manner that would be acceptable to the local population. The main conclusions were that giving cash 12 And the effect of incentivizing children should be all the larger than that of incentivizing parents the larger the informational wedge. A CCT which improves parental information such as ours should therefore lead to a reduction in the additional effectiveness of incentivizing children relative to parents.

14
A number of indicators have been developed for measuring statistical capacity (PARIS21 2017c). Most of these measures are based on data collected directly from the staff of national statistical offices (NSOs) or local experts. Although this procedure may provide more in-depth analysis and uncover finer details about the organization of an NSS, it is more expensive and far more timeconsuming than measures that are publicly available and easy to access.<sup>2</sup> In addition, these laborintensive assessments are conducted at the request of the NSS. As such, they are infrequent and cannot be used to track country-specific or global trends in statistical capacity.

To overcome these deficiencies, the World Bank launched the Statistical Capacity Building Indicator (SCBI) in 2004. The SCBI is based on objective indicators that can be updated annually for all emerging market and developing countries (but not for developed countries). International and national agencies have adopted it to measure progress in statistical capacity building and related investments (World Bank 2020b).

Since the launch of the SCBI, its methodology and coverage have remained unchanged despite the marked changes in the data landscape. Open data agendas have propagated; both high- and lowcapacity NSSs are modernizing. The SCBI does not capture these advances. Its lack of coverage of high-capacity NSSs means that it is not possible to track advances across the full spectrum of countries.

This paper uses the World Bank’s Statistical Performance Index (SPI), designed to assess a country’s statistical capacity, to identify areas for improvement in NSSs and to monitor the progress of reforms in statistical capacity building. This index was developed to address the limitations in the SCBI and facilitate intertemporal and intercountry comparisons.<sup>3</sup> The SPI uses publicly available data on a set of readily observable and verifiable indicators. It provides internationally comparable, objective, country-level assessments across the globe. It views statistical capacity as the range of products and processes an NSS uses to produce and disseminate data.

> 2 Even the best evaluator can bring personal biases, nonuniform conceptions of capacity, or other subjective elements. Interviewing government officials might bias responses and complicate comparability across countries.

> 3 The World Bank SPI by Dang, Pullinger, Serajuddin, Stacy (2021) used in this paper for the analysis was an extension of one developed by Cameron et al. (2020).

6
<!-- Start of picture text --> 0.5 0.5<br>0.2 0.2<br>-0.1 -0.1<br>1998 2003 2015 1995 2003 2015<br>-0.4 -0.4<br>-0.7 -0.7<br>after 81 80-76 75-71 after 81 80-76 75-71<br>70-66 65-61 60-56 70-66 65-61 60-56<br>55-51 55-51<br><!-- End of picture text --> Source: Our own estimation based on household surveys and O*NET In summary, during the last two decades, the workplace in Argentina and Uruguay has experienced some changes in terms of the type of tasks which workers perform in their jobs. In this sense, we can detect an increase in the relative importance of non-routine cognitive tasks and a reduction in the intensity of manual tasks. In contrast with that seen in developed countries, work intensive in routine cognitive tasks has shown signs of growth. These variations correspond to various explanatory factors. On one hand, the process of technological change which incentivizes the modification of the optimum combination of tasks within an occupation. At the same time, and for the case of Uruguay, there is also still an effect of sectoral change, that is, the movement of work between branches of activity, moving from those which are more intensive in manual tasks to sectors with a greater emphasis of occupations intensive in cognitive tasks.

All changes are enhanced by the educational expansion that took place in the labor force, since as the workers increase their levels of qualification, they increase their chances of dedicating a larger proportion of their time to the performance of cognitive tasks. In any case, it is the young generations who dominate these trends, which suggests that they have a greater capacity for adaptation than the older generations.

# 4.4 Polarization of the labor market

The process of adopting new production technologies based on automation, robotics and digital communication would permit an increase in global productivity and therefore economic efficiency. However, that also implies a risk from the point of view of distribution. That being through the generation of a possible polarization of the labor market, since the transition from

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The legal enforceability of the PTA obligations is established according to the language used in the text of the agreements. In other words, it is assumed that commitments expressed with a clear, specific and imperative legal language, can more successfully be invoked by a complainant in a dispute settlement proceeding, and therefore are more likely to be legally enforceable. In contrast, unclearly formulated legal language might be related with <u>policy areas that are covered but that might not be legally enforceable.</u> <!-- Start of picture text --> Figure 18: PTA depth, selected economies (2015)<br>a. Latin America b. BRICS Countries<br>35 1 1 2 1 1 1 2 22 8 10 13 35 1 4 8 11 18<br>30 30<br>25 25<br>20 20<br>15 15<br>10 10<br>5 5<br>0 ‐<br>max depth avg. Depth # of agreements max depth avg. Depth # of agreements<br>Source: Calculations based on World Bank PTA content dataset (2016).<br><!-- End of picture text --> Compared to other agreements, Mercosur does not include disciplines such as investment, competition policy, export taxes, customs and movement of capital. Appendix Tables A3 and A4 illustrate policy areas where Mercosur “performs” better than (green), worse than (red) or as well as (yellow) other agreements in terms of coverage of provisions. Compared to other agreements signed by countries in the Latin America region, Brazil does relatively better in terms of coverage of disciplines such as TBT, SPS and anti‐dumping. However, provisions such as customs, export taxes, public procurement, TRIPs and countervailing measures (CVM), that are currently not in force in Mercosur, are legally enforceable in at least 74 percent of agreements signed by Latin American countries. In the same way, WTO‐X disciplines such as competition policy, investment, movement of capital and intellectual property rights (IPR) are legally enforceable in at least 62 percent of Latin American agreements, but they are either not included or not in force in Mercosur (see Table 2 and Appendix Table A3). Similarly, most of the agreements signed by BRICS include legally enforceable provisions on customs, export taxes, CVM, state aid, competition policy, investment, movement of capital and IPR which are not included in Mercosur. However, Mercosur covers disciplines such as regulations on TBT and SPS and GATS, which are not very common among other agreements signed by BRICS (See Appendix Table A4).

24
<!-- Start of picture text --> 200<br>184<br>= lf Rural and Urban India were separate countries, they<br>= 150 would be in the top six countries with the largest number of<br>Sr"a poor at the international extreme poverty line<br>5<br>3<br>Paes 87 a4<br>y<br><!-- End of picture text --> <!-- Start of picture text --> a<br>S 58<br>Eso 31 46<br>= 29<br>= 27 55 = 16°43 12° 12-49 41g GC Cg<br>0<br>&PLPSF2 oSeo FEESF & £ EESFCP ILOs* PSEEESeq LEeS oF EES2 2 ES= es<br>< eS “ss < wo s Pid<br><!-- End of picture text -->
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------------------------------------------------------------------------

__---__------

# **TABLE 3.13** Purchasing Power Parities, Braz. **'/USA** (Cruzeiros to the **USS)** and Mexico/USA (Pesos to tne USS). 1975

|PPP:<br>US<br>Quantity<br>Weights<br>(1)|Cruzeiro<br>Brazil<br>Quantity<br>Weights<br>(2)|s/US $ Geometric<br>Average<br> <br>(3)|PPP:<br> <br>US<br>Quantity<br>Weights<br>(4)|Pesos/US<br>Mexico<br>Quantity<br>Weights<br>(5)|S<br>Geometric<br>Average<br> <br>(6)|
|---|---|---|---|---|---|
|Grain Mill Products<br>7.58|6.94|7.25|13.13|12.34<br>|12.73<br>|
|Sugar&Sugar Products<br>2.48<br>|2.14<br>|2.30<br>|3.74<br>|3.62<br>|3.68<br>|
|Malt and Malt Beverages<br>8.10<br>|7.73|7.91|17.66<br>|17.66<br>|17.66|
|Tobacco and Tobacco Products<br>4.94<br> <br>|4.38<br>|4.65<br>|9.35<br>|7.64<br>|8.45<br>|
|Textiles<br>13.33|9.81<br>|11.44|18.69|17.45|18.06|
|Footwear<br>and Leather Products5.62<br>|4.77<br>|5.18<br>|12.85<br>|11.32<br>|12.06<br>|
|Pulp and Paper<br>10.53|8.24<br>|9.31|22.77|19.83|21.25|
|Soap and Detergents<br>8.45<br> <br>|4.39<br>|6.09<br>|11.55<br>|9.07<br>|10.24<br>|
|Paints<br>4.07<br> <br> <br>|4.39<br>|4.23<br>|15.41<br>|14.76<br>|15.08<br>|
|Agricultural<br>Fertilizers<br>12.01|11.77|11.89<br>|8.40|7.61|8.00|
|Petroleum<br>Refining<br>and Products<br>14.56<br>|14.39<br>|14.47<br>|11.02<br>|10.76<br>|10.89<br>|
|Tires and Inner Tubes<br>13.17|11.64|12.38|32.68|29.29|30.94|
|Cement<br>9.90|<br>9.90|9.90|12.18<br>|12.11|12.14|
|Bricks<br>4.03|3.62|3.82|9.79|13.60|11.54|
|Iron and Steel<br>8.95|7.27|8.07|14.09|11.25|12.59|
|Radio and TV Receivers<br>7.72<br>|7.64<br>|7.68<br>|8.92<br>|11.39<br>|10.08<br>|
|Motor Vehicles<br>6.49|6.32|6.40|13.74|13.55|13.64|
|Weighted average PPP for sample||||||
|(value added -US census concept-||||||
|weights)<br>9.10|<br>6.58|7.74|14.81|11.71|13.20|
|Exchange<br>Rates<br>8.13|8.13|8.13|12.50|12.50|12.50|

# Source and note:

Cruzeiros/US$ PPPs derived from table 3.11; Pesos/US$ PPPs derived from table 3.12. Includes adjustments for indirect taxes and subsidies for malt and malt beverages, tobaccco and tobacco products and petroleum refining and products in the Mexico/USA comparison, and for quality differences in the motor vehicles industry in both country comparisons.
2021), encompassing the period of rapid growth in the world economy and characterized by increasing integration of production and consumption in the world economy. This period includes the economic integration into the world economy of Japan, the Republic of Korea, and other East-Asian countries and the continuous integration of countries within the European Union. While it covers until 2000, I only use the data until 1990 since this is the year other input-output tables start covering more countries.

For the period 1990 to 2015 I use the Eora MRIO databse (Lenzen et al., 2012, 2013). Eora consists of multi-region input-output tables that cover 190 countries. The version of Eora used in this paper is the simplified version, Eora26, thanks to the 26-sector harmonized classification that allows for comparison across countries and sectors. Finally, for 2015-2021, I use the ADB-MRIO (ADB, 2022) covering 62 economies and 25 sectors. Besides covering fewer countries than the data used for previous years, the ADB MRIO nowcast the most recent years in the data. But these recent years can be used to better understand globalization dynamics up to 2021.

One final remark is that all input-output tables provide data for several sectors that I aggregate up to three aggregate industries (manufacturing, agriculture, and services). The services data tend to be of lower quality than goods data, but I include it for the completeness of the analysis and due to the importance of the services sector. Also, I tend to use the data for the whole period 1965-2021 for most analyses, but some of them, - like the regional analysis, require focusing the work on the period 1990 2015, which covers more countries.

# **Results 3**

This section presents the results, starting with a general look at globalization dynamics since 1965 across sectors and countries. I disentangle the dynamics of several countries for 1965-2021 to provide deep insights. And for a regional perspective, I estimate the globalization dynamics across World Bank regions for 1990-20215. Finally, I disentangle the effect of regional trade agreements in globalization using the specific example of Spain.

10
Dar, Tzannatos, Canagarajah and Thomas (1997) have fitted earnings functions to the GLSS samples for the 1987-8 and 1991-2 periods (Table 3). Their results are reproduced in Table 3.4. **Table 3.4 Coefficients of Earnings Function and Contribution of Explanatory Variables to Inequality**

||1987|^88|||1991-92||
|---|---|---|---|---|---|---|
||Coefficient|Explained|Inequality|Coefficient|Explained|Inequality|
|C|2.453*|(SD units)<br>0.000|(Percent)<br>0|3.182*|(SD units)<br>0.000|(Percent)<br>0|
|Female|0.125|0.004|2|-0.006|0.000|0|
|Age|0.031|0.070|39|0.038*|0.090|43|
|Age20|-0.0002|-0.039|-22|-0.0003|-0.053|-25|
|Schooling|0.030*|0.053|30|0.056*|0.138|66|
|Public|0.560*|0.087|49|0.243*|0.037|17|
|State|0.488*|0.006|3|0.057|-0.001|0|
|Union|0.420*|-0.005|-3|-0.003|0.000|0|
|Urban|-0.072|0.003|2|-0.039|-0.001|0|
|||0.178|100||0.210|100|
|SD ln(Wages)=|0.761|||0.774|||
|R2=<br>:|0.234|||0.272|||
|SD*R2= _|0.178|||0.210|||

_Source: Dar_ **_et al,_** _1997; * Significant at the 5_ **_percent_** _level._ For both dates age and education are important determinants of earnings differences and of degree of inequality in the wage sector along with the public-private divide. In fact most of the observed inequality is almost entirely due to these three attributes. There are, however, significant changes in the relative importance of the three factors between these two dates. In particular, the impact of education as an explanation of earnings differences seemed to have increased markedly relative to the public sector influence. This is seen both in terms of the enhanced coefficient of the education variable in the earnings function, as well as in the percentage of earnings inequality accounted for by theses two variables. The premium associated in public sector employment has dropped from around 55 per cent to 24 per cent (after controlling for the other factors). At the same time the share of inequality due to education has doubled from 30 to 66 per cent, while that due to public sector has declined from 49 to just 17 per cent. These are dramatic changes which suggest strongly that the importance of the public sector in offering premium wage differentials has been reduced drastically in the wage paying sector of Ghana. There are, however, two important caveats to keep in mind.

First, comparing the distribution of employees by education levels, as given in Table 3.3 it is seen that a much larger proportion of employees with secondary education 14
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# Table 6: Organizational quality and compositional characteristics of different types of secondary schools, Brazil 1988

|||Sch|ool Tvpe|
|---|---|---|---|
|School characteristic|Federal<br>technical|<br>SENAI|Teacher<br>General<br>training<br>secondary|
|Organization||||
|Complexity (factor score)|0.86|-1.40|0.20<br>-0.05|
|Number of classes|12.0|6.0|10.25<br>9.8|
|School size|420.0|180.0|400.0<br>377.4|
|Number of shifts|3.0|2.0|2.4<br>2.1|
|Private (%)|0|0|25.0<br>27.1|
|Day school (%)|100.0|100.0|87.5<br>58.3|
|Class size|35.0|25.0|37.5<br>38.8|
|Quality||||
|Teacher salaryaj<br>CZ2<br>(US|50,000 CZ25<br>$425) (US$|0,000 CZ<br>425) (U|112,100<br>CZ121,329<br>S$191)<br>(US$206)|
|Hours of Portuguese|1.75|3.0|2.63<br>2.58|
|Hours of mathematics|2.0|3.0|2.38<br>2.29|
|High fee private (%)|0|0|12.5<br>20.8|
|ComPosition||||
|Average SES (factor score)|<br>.11|.08|-.20<br>.04|
|Average working hours|4.98|13.13|8.82<br>14.53|
|Percent female|19.3|11.9|96.6<br>56.5|
|Sample size (schools)|4|2|8<br>48|

a/ November 1988 Cruzeiros. The average exchange rate for that month was CZ588.1 - US$1.

23. We next sought to explain the school type and sector effects on achievement, by examining the extent to which differences in school organization, quality or composition influenced the social distribution of achievement.
Policy Research Working Paper 5047

# **Abstract**

This paper documents an unusual and possibly significant phenomenon: the export of skills, embodied in goods, services or capital from poorer to richer countries. The authors first present a set of stylized facts. Then, using a measure that combines the sophistication of a country’s exports with the average income level of destination countries, they show that the performance of a number of developing countries—notably China, Mexico and South Africa—matches that of much more advanced countries—such as Japan, Spain and the United States. The authors create a new combined dataset on foreign direct investment (covering greenfield investment as well as mergers and acquisitions). The analysis shows that flows of foreign direct investment to developed countries from developing countries—like Brazil, India, Malaysia and South Africa—as a share of their GDP, are as large as flows from developed countries – like Japan, Korea and the United States. The authors suggest that it is not just the composition of exports but their destination that matters. In both cross-sectional and panel regressions, with a range of controls, a measure of uphill flows of sophisticated goods is significantly associated with better growth performance. These results suggest the need for a deeper analysis of whether the benefits of development might derive not from deifying comparative advantage but from defying it.

This paper—a product of the Trade and Integration Team, Development Research Group—is part of a larger effort in the department to understand the pattern and implications of globalization. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at amattoo@worldbank.org and asubramanian@ PIIE.com.

_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
<!-- Start of picture text --> Figure 5 ‐ Gross public fixed capital formation<br>(in % of GDP)<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>Fast growing countries Comparison group<br>Figure 6 ‐ Government Effectiveness<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1996 2007<br>1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br>Botswana Indonesia Korea, Rep. Malaysia Mauritius Singapore Thailand Chile Costa Rica Mexico Philippines Turkey Uruguay Venezuela<br><!-- End of picture text --> 48
Policy Research Working Paper 6843

# **Abstract**

This paper uses firm-level data for 87 developing countries to analyze how the likelihood of a firm having female vs. male top manager varies across sectors. The service sector is often considered to be more favorable toward women compared with men vis-à-vis the manufacturing sector. Although the exploration of the data confirms a significantly higher presence of female managers in services vs. manufacturing, the finding is entirely driven by retail firms, with little contribution from other service sectors, such as wholesale, construction, and other services. The analysis also finds that the higher presence of female managers in the retail sector vs. manufacturing is much higher among the relatively small firms and firms located in the relatively small cities. These findings could serve as useful inputs for the design of optimal policy measures aimed at promoting gender equality in a country.

This paper is a product of the Global Indicators Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at aislam@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