rafmacalaba/datause-encoder-data huggingface dataset:
**Table 10: Similarity Index with All Seven Policy Variables Raised to Median**

|Sector|BWA|LSO|NAM|SWZ|ZAF|
|---|---|---|---|---|---|
|41‐43 Hides,Skins|0.38|0.52|0.46|0.50|0.45|
|61‐63 Clothing|0.52|0.61|0.48|0.57|0.74|
|64‐67 Footwear‐ Final|1.50|1.69|1.50|1.64|1.33|
|64‐67 Footwear ‐ Intermediate|1.44|1.66|1.45|1.61|1.43|
|84‐85 Mach/Elec ‐ Intermediate|1.71|2.47|2.05|2.43|1.57|
|50‐60 Textiles|1.77|2.03|1.76|1.97|1.70|
|90‐97 Miscellaneous ‐ Intermediate|1.99|2.80|2.38|2.76|1.84|
|39‐40 Plastic/Rubber|1.88|2.67|2.24|2.63|1.90|
|84‐85 Mach/Elec ‐ Final|2.27|2.90|2.52|2.86|1.96|
|86‐89 Transport ‐ Intermediate|2.20|3.04|2.62|3.00|2.06|
|68‐71 Stone/Glass|2.26|3.12|2.69|3.08|2.10|
|86‐89 Transport ‐ Final|2.38|3.30|2.87|3.26|2.25|
|72‐83 Metals|2.34|3.26|2.83|3.22|2.25|
|90‐97 Miscellaneous ‐ Final|2.57|3.50|3.07|3.47|2.41|

Source: Authors’ calculations

**Table 11: Similarity Index with All Seven Policy Variables Raised to 60**<sup>**th**</sup> **Percentile**

|Sector|BWA|LSO|NAM|SWZ|ZAF|
|---|---|---|---|---|---|
|41‐43 Hides,Skins|0.29|0.33|0.27|0.31|0.40|
|61‐63 Clothing|0.52|0.61|0.48|0.57|0.74|
|64‐67 Footwear‐ Final|1.32|1.44|1.29|1.39|1.31|
|84‐85 Mach/Elec ‐ Intermediate|1.51|1.84|1.60|1.80|1.34|
|64‐67 Footwear ‐ Intermediate|1.26|1.39|1.22|1.34|1.39|
|90‐97 Miscellaneous ‐ Intermediate|1.79|2.17|1.92|2.13|1.61|
|39‐40 Plastic/Rubber|1.69|2.05|1.80|2.01|1.68|
|50‐60 Textiles|1.56|1.72|1.51|1.65|1.68|
|84‐85 Mach/Elec ‐ Final|2.08|2.33|2.10|2.28|1.75|
|86‐89 Transport ‐ Intermediate|2.00|2.43|2.17|2.38|1.84|
|68‐71 Stone/Glass|2.06|2.49|2.24|2.44|1.87|
|86‐89 Transport ‐ Final|2.18|2.67|2.42|2.62|2.02|
|72‐83 Metals|2.15|2.63|2.38|2.59|2.02|
|90‐97 Miscellaneous ‐ Final|2.37|2.87|2.61|2.82|2.17|

Source: Authors’ calculations

# VI. Conclusion

This paper traces performance in GVC sectors to underlying capabilities that are required to participate in a sector, which, in the context of this study, means exporting a set of GVC‐related products. The revealed 20
21 account for amounts devoted to feeding programs. The _World Development Report_ 1990 cites several studies demonstrating a clear connection between nutrition and learning capacities.

|**Table 6**<br>St|udies of the Link between Ed|ucation**and Health**|
|---|---|---|
|Cochrane_et al._(1980).|Survey of Range of<br>Studies|The studies looked at the link between<br>parental education and child mortality.<br>Data for 17 countries were studied. A<br>significant relationship was found in all<br>but two. In a further 7 studies controls<br>were made for other variables such as<br>income or indicators of income.|
|Kenkel (1991).|The Effects of Education<br>on Health in USA|Survey of health knowledge and<br>behavior. 14,177 males and 19,453<br>females surveyed in 1985 by the Health<br>Promotion/Disease<br>Prevention<br>supplement to<br>the<br>1985 Health<br>Interview Survey.|
|Gomes-Nato, Hanushek,<br>Leite<br>and<br>Frota-<br>Bezzera.<br>(1992).|Survey of 1516 students in<br>Brazil in 1985.<br>387<br>surveyed in 1987.|Study looked at effects of health on<br>school performance.|

It would be desirable that these studies should be complemented by cross-country comparisons similar to those used to assess education, fertility and growth. Dasgupta and Weale (1992) provide preliminary evidence to suggest that health improvements are linked with economic improvements but more work needs to be done in this area.

# **_Swnma*y_**

This survey of studies of the effect of education leads to a number of important conclusions. First of all there is very little doubt that education is associated with increased earning power for individuals. Nevertheless there are reasons for believing that an estimated return of 20% p.a. or more from one year of schooling is too high. The estimates are calculated in a way which does not adjust for quality, does not reflect the fact that the return comes from literacy and numeracy skills rather than from education _per se,_ and does not take account of the fact that people from a more advantageous background are more likely to be educated. Macroeconomic analyses of the links between education and growth are consistent with an external benefit linking
characteristics in the analysis, because they can directly affect both the level of women’s empowerment and the probability of electricity expansion. Although the survey was carried out in both urban and rural areas, village characteristics were collected only for the rural sample. We therefore use only the rural sample, consisting of 28,446 households and 21,896 eligible women in the 2012 survey.

Table 1 shows the distribution of sample households across six geographic regions as well as the union territories as a group. The rural electrification rate varies widely by region. The union territories and the rural vicinity of the national capital have the highest electrification rate (almost 100 percent). East India, which includes the states of Bihar, Jharkhand, Orissa, and West Bengal, has the lowest rate (less than 65 percent). For rural India as a whole, the electrification rate was 77 percent in 2012.

Table 2 presents summary statistics of various observed indicators of women’s empowerment. Almost all the variables indicate stronger empowerment for women in households with electricity supply from the grid and the differences are statistically significant. For example, compared with women in off-grid households, women in grid-connected households are more likely to have decided alone on their work, traveled out of state, had a bank account, and participated in women’s self-help groups.

# **IV. Measuring empowerment and the effects of electrification**

# _a. Measuring latent indicators of empowerment_

Empowerment is an abstract concept and manifests itself in multiple aspects of life. For example, whether a woman is empowered to make decisions may be reflected by whether she can decide on her own work, medical treatment or how many children to have. In such a case, it is difficult to measure empowerment directly using one single indicator. One way to resolve this issue is to use an indexing or scoring approach in which individual attributes are combined to form an index or score.<sup>4</sup>

> 4 The use of indexes is common in development practices. For example, the World Bank’s Doing Business index combines multiple variables into scores. In a study of reproductive behavior, Beegle, Frankenberg, and Thomas

8
welfare services (health care, food) and long-term infrastructure services is more acute for the scare public finance resource. They further extended the data until 2005. It also finds that the increased access to infrastructure services (i.e., improved quantity and quality of infrastructure) would decrease income inequality as their econometric models show a strong negative correlation between the income inequality measure (Gini coefficients) and the synthetic indices used to measure infrastructure quantity and quality. The correlation coefficients vary between -0.47 and - 0.56. The study also shows that improved infrastructure not only reduced income inequality and poverty in Sub-Saharan Africa but also all over the world, more prominently in East and South Asia.

Chatterjee and Turnovsky (2012) developed a general equilibrium growth model to analyze the impacts of investment on income inequality (and also on infrastructure and economic growth). Considering four alternative sources to finance the public investment: lump-sum tax, capital income tax, labor income tax and consumption tax, their model shows that public spending on infrastructure (public capital) would have differing impacts between the short-run and long-run. In the short-run, public investment causes income inequality to decline; however, the declining trend decreases over time, and income inequality gets worse (increase) in the long-run.

Using state-level panel data on infrastructure investment and per capita income for the 1950 -2010 period, Hooper et al. (2018) investigates the relationship between infrastructure and income inequality. The study finds that investment in physical infrastructure (highways) and human capital (higher education) reduces income inequality (Gini indices) in the United States. The relationship is found stronger at the bottom 40 percent of the income distribution. The study also finds that investments in highways are more effective at reducing inequality. US states with a higher Gini coefficient at the bottom 40 percent had lower infrastructure investment during the previous decade. The finding suggests that infrastructure investment is an important factor in reducing poverty and income inequality not only in developing countries but also in developed ones.

There does not exist that many studies to directly investigate the relationship between infrastructure investment and poverty reduction. Chotia and Rao (2017a) is one of them among the few studies. Employing an autoregressive distributed lag (ARDL) bounds testing approach on 24
may be viewed as enclaves within China. Although they are physically inside China’s border, they function outside China’s customs regulations. Companies in FTZs are eligible for tax refunds on exports, import duty exemption, and concessionary valueadded tax.

Currently, there are 15 FTZs in 13 coastal cities (see annex B for a list of the FTZs). Upon China’s entry into the WTO, the original unique advantages of FTZs faded. To maintain their competitive edge, China has been linking FTZs with nearby ports since 2004. This process has expanded the size of FTZs and strengthened their logistics and warehousing functions in international trading (China Knowledge Online 2009).

**_Export-processing zones._** Export-processing zones (EPZs) were created to develop export-oriented industries and enhance foreign exchange earnings. The first EPZ was inaugurated in Kunshan in 2000. So far, 61 EPZs  have been set up in China;  44 of them are located in the coastal region, while  the other 17 are inland. EPZs are similar to FTZs but are solely for the purpose of managing export processing. FTZs are the preferred locations for companies involved in export-trading and processing, while  EPZs  are more advantageous locations  for manufacturing companies that export  most,  if not all, their goods to locations  outside  China (ProLogis  2008).

The success of state-level SEZs spurred the speedy development of new ones by different levels of governments. By 2004, there were nearly 7,000 industrial parks in China. To curb the blind expansion of industrial parks, China stepped up its efforts to clean up unqualified industrial parks. By the end of 2006, the number of industrial parks had been reduced to 1,568, among which 222 are state-level zones. The total planned area had been reduced from 38,600 square kilometers to 9,900 square kilometers (74.4 percent less) (China Knowledge Online 2009).

# Contributions of SEZs to China’s Development

The SEZs have made crucial contributions to China’s success. Most of all, they— especially the first ones—successfully tested the market economy and new institutions and established role models for the rest of the country to follow. By 1992, the concept of openness had been extended to the entire coastal region and to all capital cities of provinces and autonomous regions in the interior, and various types of SEZs had begun to spring up throughout the country. Thus, when Deng Xiaoping made his famous southern tour that year, the mission that had started with the creation of the first five SEZs had in many respects been accomplished: the “special” economic zones by that time were no longer so special (Yeung, Lee, and Kee 2009).

**_Contribution to GDP._** Economically, SEZs have contributed   significantly to national

12
18 International FOB prices of kerosene and diesel are essentially the same, so that if the same tax policy is applied, retail kerosene and diesel prices should be comparable. But many governments subsidize non-aviation kerosene more than diesel or tax it less on the grounds that kerosene is a fuel of the poor, leading to a large difference in the prices of these two fuels. The distribution of the ratios of retail kerosene and diesel prices is shown in figure 12. When there is a large price difference in favor of kerosene—if this ratio is markedly smaller than 1—kerosene is inevitably diverted to the automotive diesel market. In most countries, gasoline is just as expensive, if not more so, than diesel, so that gasoline may also be adulterated with kerosene. However, adulteration of gasoline with kerosene is readily detected by vehicle drivers unless kerosene is added in very small quantities. In contrast, kerosene is a nearly perfect substitute for diesel and a large quantity of kerosene can be blended in diesel without detection. For this reason, international experience has demonstrated that even a small difference in price in favor of kerosene can lead to large-scale diversion, making the subsidy for kerosene regressive while fueling corruption and criminal activities.

Figure 12: Distribution of the ratio of retail prices of kerosene to diesel in January 2012 <!-- Start of picture text --> 50<br>43<br>40<br>30<br>22<br>20 15<br>13<br>10 7<br>0<br>Below 0.5 0.5 to 0.75 0.75 to less than 1 1 Above 1<br>Ratio of prices<br>Percentage of countries<br><!-- End of picture text --> _Sources:_ Figure A2.1 and author’s calculations.

Lastly, retail prices of LPG sold in cylinders typically used by households are shown in figure 13. Among the four fuels, FOB prices of LPG had the largest regional variation, in part because North American prices have been markedly lower than in the rest of the world in recent years. Another reason is that LPG consists of propane and butane, butane prices are higher—and sometimes much higher—than propane prices, and the relative amounts of propane and butane in LPG vary from market to market. As a result, the reference FOB prices for LPG in January 2012 varied from US$0.67 per kilogram (kg) in the United States to US$0.91 per kg in countries where LPG consists primarily of butane. The differences in retail prices, however, were much larger: the prices in the 52 countries in the sample varied by two orders of magnitude, ranging from a mere US$0.04 per kg in the Arab Republic of Egypt to US$4.56 per kg in Japan.
19 into account and there would have been no pay-outs in those years, leading to a cheaper product and one which is also a better fit to the farmers’ actual yield losses, apart from making them more insurable from the insurance companies’ perspective.

To further compare the relative accuracy of the rainfall index and the hybrid index (rainfall and irrigation) products, a correlation analysis of the aggregated loss data (as obtained from the district damage ratios and supplemented with loss data using feedback from farmers) was carried out. Losses and insurance pay-outs were simulated stochastically, based on historical rainfall, tank storage, and crop loss data. Correlation analyses were carried out between the levels of simulated losses and simulated pay-outs for the two different types of indices. For losses, both the incidence of losses (using a binary indicator) and the extent of losses (using a Gamma distribution) were correlated to the incidence of payouts (using a binary indicator) and the extent of pay-outs (based on the index structure).

**Table 3.  Summary of Correlations for Vavuniya**

||Correlation of incidence of<br>losses and occurrence of pay-<br>outs|Correlation of extent of losses<br>and extent of pay-outs|
|---|---|---|
|Rainfall index|57%|49%|
|Hybrid<br>index<br>(Rainfall<br>+<br>Irrigation)|76%|64%|

**Table 4: Summary of Correlations for Anuradhapura**

||Correlation of incidence of<br>losses and occurrence of pay-<br>outs|Correlation of extent of losses<br>and extent of pay-outs|
|---|---|---|
|Rainfall index|58%|52%|
|Hybrid<br>index<br>(Rainfall<br>+<br>Irrigation)|81%|69%|

The correlation analyses indicate that the hybrid index performs better than the rainfall index for farmers with access to major irrigation. The hybrid index performs particularly better at detecting the incidence of losses, which is natural since significant crop losses occur only when both rainfall is low and water available for irrigation is inadequate. The correlations are generally weaker when considering the extent of losses, which is also understandable since the extent of losses also depends on other factors, such as access to water from the tanks, soil types, planting dates, etc. The correlations for the hybrid index are
# **Appendix. Additional Figures and Tables**

## **Figure A1 – Energy Prices and Firm Leverage**

- <u>This figure displays the bank level share of lending to the energy sector in Mexico from 2013Q1 to 2016Q2.</u>

<!-- Start of picture text --> 2013q1 2014q1 2015q1 2016q1<br>Time<br>Oil Prices Leverage<br>120 56<br>100 54<br>52<br>80<br>Oil Prices<br>Leverage - Percent<br>50<br>60<br>48<br>40<br><!-- End of picture text --> 35
One question that did not count as a disability question and yet is often found in surveys is a question on why someone did not work (or attend school for children), with disability as one possible answer. It is relevant only for the subset of adults who do not work and thus cannot capture the broader population of people with disability. The review was conducted from November 2019 to November 2020.

Each of the data sets above was reviewed by at least one researcher. It was reviewed by two researchers if at least one disability question was found.

This analysis has limitations. We only reviewed surveys and censuses that had questionnaires available in English, French, Portuguese, or Spanish. We did not cover surveys with a focus on children.<sup>3</sup> In addition, questions may be in a questionnaire and yet not be implemented in the field. We also did not review how questionnaires were implemented, notably how survey staff might have been trained on disability related questions. The list of data sets under consideration is not exhaustive in that some were not covered as their questionnaires were not available or not in a language that the authors understood. It is also possible that some were missed. National data sets were prioritized in that we did not search for data sets that focused on regions within countries and has thus did not cover some recent international research and data collection efforts (e.g. Mactaggart et al 2018, Trani et al 2015). Despite these limitations, this study has noteworthy results.

# **5. Results**

Tables 1 and 2 and Appendices 1 through 4 give the lists of countries and data sets for each type of disability question: WGSS, other functional difficulty, ADL, broad activity limitation, general disability question and other types of questions. Table 1 gives the list of countries and surveys with the WGSS between 2009 and 2018. In total, 32 countries, 46 data sets and 52 data set-years have included the WGSS between 2009 and 2018. The WGSS seems to have been adopted in Asia and in Sub-Saharan Africa more than in other regions. Further, it has been adopted in a variety of surveys, including international ones such as the Demographic and Health Survey (DHS), the Household Income and Expenditure Surveys and the LSMS (Malawi HIS, Nigeria GHS, Tanzania NPS, Uganda NPS). It has also been adopted in thematic surveys such as the Survey of Water, Sanitation, and Hygiene in Tajikistan. In six countries (Bangladesh, Ethiopia, Liberia, Nigeria, Tanzania, Uganda), the WGSS was used in multiple years for the same survey. Results are presented for one decade (2008-2018), with more data sets with the WGSS in recent years: 40% of data set-years with the WGSS were for the 2009-13 and 60% in 2014-18 periods, suggesting an increasing adoption of the WGSS in recent years.

> 3 We did not cover the UNICEF Multiple Indicator Cluster Surveys.

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# **Table 10: Regulatory regimes in ECA**

|**ECA Countries**|**Pledged Kyoto commitment for**<br>**2008-12 emissions**<br>**(percentage of baseyear orperiod)**|**2008-2012 EU ETSs Cap**<br>**(annual million EUAs)**|
|---|---|---|
|**Joint Implementation - EU ETS countries**|||
|Bulgaria|-8|42.30|
|Czech Republic|-8|86.80|
|Estonia|-8|12.72|
|Hungary|-6|26.90|
|Latvia|-8|3.43|
|Lithuania|-8|8.80|
|Poland|-6|208.50|
|Romania|-8|75.90|
|Slovak Republic|-8|30.90|
|<br>Slovenia|-8|8.30|
|**Other Joint Implementation countries**|||
|Croatia|-5||
|Kazakhstan|0||
|Russian Federation|0||
|Ukraine|0||
|**CDM countries**|||
|Albania|||
|Armenia|||
|Azerbaijan|||
|Belarus|||
|Bosnia & Herzegovina|||
|FYR Macedonia|||
|Georgia|||
|Kosovo|||
|Kyrgyz Republic<br>Moldova|||
|Montenegro|||
|Serbia|||
|Tajikistan|||
|<br>Turkmenistan|||
|Uzbekistan|||
|**Annex I without Annex B commitments**|||
|Turkey|||

Source: UNFCCC and Europa (2007). Note: Kazakhstan declared its commitment to limit emissions in September 2009 (UNFCCC, FCCC/SBI/2010/6). Turkey became a party to the UNFCCC in 2004 and to the Kyoto Protocol in 2009 (Government of Turkey, 2011.) 47
<!-- Start of picture text --> st<br>SGP<br>TWN<br>N HKG KOR<br>= ARIA CHN<br>Q> usalun IRUT, n | MYS<br>S fo) BRe NOR onkGgenMiniPUD NLDRN<br>To Mi<br>6 Ouiie<br>O ZAF “rom _ GRE JOR<br>ol Gua ZWE<br>a. PHL<br>YT<br>-1.5 -1 5 0 5 1<br>Conditional test score<br>coef = 1.9804387, se = .21707105, t= 9.12<br><!-- End of picture text -->

<!-- Start of picture text --> N<br>SGP<br>MAR rape<br>~ TUN TwhSBRA<br>c HKG<br>S +P)<br>@ PRT KOR jpiNORper<br>_ —E DNK CHL<br>c=fe)Bo EGY is BRIEaexiSfortGRE ISR JPN<br>3 TUR taggin NED UT ROM<br>6 IRN No Mer "<br>= URYCEQL<br>ZWE<br>JOR<br>Sy<br>-4 -2 0 2 4<br>Conditional years of education<br>coef = 0264058, se = 07839797, t = .34<br><!-- End of picture text -->
Collection of cost data can and must be<sup>incorporated into all kinds of operational</sup> research. Indeed, some data collection is beginning<sup>as programs get underway. For the most</sup> part, these efforts have followed the process outlined<sup>in the paper by Herz and Measham:</sup>

- Define basic unit of care (eg. Health Post).

- Make judgements about what interventions work<sup>best to achieve- desired</sup> outcomes.

- Determine how many and what types of staff will<sup>be needed, and at how many</sup> posts, to carry out these interventions.

- Calculate the costs of staff; transport (maintenance,<sup>gas); in-service training and</sup> supervision; equipment and supplies; health<sup>education; and monitoring and</sup> evaluation.

This general approach was necessary in the<sup>early stages of Safe Motherhood programs</sup> because it allowed for estimation when reliable<sup>cost data were not available. With program</sup> experience since 1987, it is now possible to construct<sup>more accurate field-based estimates. A</sup> common framework for data collection, with categories<sup>that can be used across different</sup> projects, will facilitate this process and help ensure<sup>the comparability of results. Categories</sup> include:

- Inputs: These include staff time, vehicles, equipment,<sup>and other resources essential to</sup> providing services.

- Input prices: Each input will have a cost associated<sup>with it. This is the price that the</sup> project, Ministry of Health, or other source<sup>must pay to use it.</sup>

- Services: These are the activities accomplished<sup>by the project. Services are provided</sup> by using inputs, and their costs are the total spent<sup>on the inputs used. Examples</sup> of services would include women provided emergency<sup>transport or increased</sup> number of birth attended by midwives.

- Outcomes: These are the changes in health status<sup>caused by the program. Examples</sup> might include mortalities and morbidities<sup>prevented through provision of services,</sup> or such measures as discounted healthy<sup>life years (DHLYs).</sup>

The primary advantage of calculating costs from<sup>basic input prices is that it facilitates the</sup> comparison of alternative programs. Safe Motherhood<sup>programs can vary in either the type and</sup> quantity of inputs used to provide a service,<sup>or in the type of services offered. In both cases,</sup> it is possible to determine costs by adding up the<sup>number of inputs used multiplied by input</sup> prices.

11