rafmacalaba/datause-encoder-data huggingface dataset:
China in the early 1980s. Brazil’s higher inequality meant that, with no change in inequality, Brazil would have needed even higher growth than China to attain the same rate of poverty reduction. Underlying this high inequality of incomes, one finds inequality in human resources development, notably schooling attainments, which have a marked income gradient in Brazil. These inequalities limited the ability of the poor to participate in, and to benefit from, aggregate economic growth.

However, there is a very important difference between Brazil in its reform period (after the mid-1990s, say) and China (and also India, which I return to). Brazil saw a reduction in inequality over time, including inequality between regions and between urban and rural areas (Ferreira et al., 2008). As we saw earlier, this was the key factor that allowed Brazil to reduce poverty despite modest growth.

Similarly to China, the pattern of Brazil’s growth mattered to the outcomes for the poor. While it was growth in the agricultural sector that had the dominant role in reducing poverty in China, in Brazil it was in the services sector, which was consistently more pro-poor than growth in either agriculture or industry. There was a lower growth rate in the services sector after 1994, which had a (small) negative effect on the rate of poverty reduction. So the reform pattern of growth was not pro-poor.

However, this change in the pattern of growth in Brazil was more than compensated for by slightly positive overall growth after 1994. In fact, the bulk of Brazil’s poverty reduction in the period since the mid 1980s took place after 1994. Using regression decomposition methods, Ferreira et al. (2009) find that the main factors bringing down the poverty measures from 1994 onwards were the substantial reduction in inflation rates (under the Real Plan) and the expansion and reforms to the Federal government’s social assistance spending, including on _Bolsa Familia_ .<sup>30</sup> Indeed, in the absence of these transfer policies, and given the generally poor performance in terms of economic growth, Ferreira et al. (2009) estimate that the headcount index in Brazil would have been about 5 percentage points higher in 2004.

The poverty impacts of social assistance spending, inflation and other changes in the policy environment entailed distributional effects on poverty (given that they are still found after

> 30 Unlike Mexico’s CCT, the PROGRESA (later renamed _Opportunidades_ ), Brazil did not invest heavily on impacts evaluations of _Bolsa Familia_ , so it is difficult to infer impacts. Ferreira et al. (2009) rely on time series data. Soares et al. (2006) use instead inequality decomposition methods calibrated to household survey data. They find that, although the size of the average transfer was low, the excellent targeting meant that _Bolsa Familia_ alone could account for one fifth of the decline in inequality in Brazil after the programs’ introduction.

16
Policy Research Working Paper 10331

# **Abstract**

This paper studies the impact of child labor standards in regional trade agreements on a variety of child labor market outcomes, including employment, education, and household inequality. It develops a stylized general equilibrium model of child labor in an economy open to international trade and considers the impact of regional trade agreements with and without child labor bans. The paper empirically investigates the effects of these clauses in trade agreements in a broad international panel of 101 developing countries, using harmonized survey microdata. Exploiting quasi-experimental methods to obtain plausibly causal estimates, the analysis finds that regional trade agreements without child-labor bans lead to reductions in child employment and increases in school enrollment, particularly for older children aged 14–17 years. Child labor bans in regional trade agreements perversely increase employment of children aged 14–17 years and decrease school enrollment for both young and older children. These effects appear to decrease inter-household income inequality - through increased child earnings. The findings are consis tent with the theoretical predictions from the model and the literature on child labor bans.

This paper is a product of the Development Research Group, Development Economics and the Macroeconomics, Trade and Investment Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at rabman@sdsu.edu, clundberg@sdsu.edu, jem6x@virginia.edu, and mruta@imf.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
Figure B1. Topic content of the presidential speeches of Argentina using country-specific LDA, 1940-2016 <!-- Start of picture text --> 100%<br>Infrastructure<br>75%<br>Economic<br>National Policies planning<br>50%<br>Peronism 2<br>25%<br>Social development<br>Peronism 1<br>0%<br>1940 1953 1960 1965 1975 1986 1990 1994 1998 2002 2006 2010 2014<br>Topic proportion<br><!-- End of picture text --> Note: This Figure shows the topic probabilities estimated with LDA for the presidential speech given each year. The topic probabilities can be thought of as representing the proportion of each document that belongs to each topic. Only topics whose average document probability exceeds 10% in at least one decade are presented, the rest are grouped as a residual.

Table B1. Top probability terms defining the topics in Argentina _(In descending order of probability of term within the topic)_

|“Infrastructure”|“Peronism 1”|“Peronism 2”|“National<br>policies”|“Social<br>development”|“Economic<br>planning”|
|---|---|---|---|---|---|
|Public works|The people|liberation|national|social|sector|
|executive|social|Peron|policy|growth|plan|
|national|doctrine|reconstruction|social|system|bank|
|number|economic|Justicialista|nation|education|world|
|currency|men|science|development|investment|people|
|services|justice|area|The people|program|economy|
|construction|nations|educational|life|development|province|
|increase|economy|banks|production|quality|justice|
|exercise|homeland|university|right|security|workers|
|plan|Peronist|rural|effort|policies|growth|

Note: This Table shows the words that have the highest probability of belonging to each topic. Because LDA defines topics as distributions over words, the lists of terms define the subject of each topic.

31
fixed premium on top of a (variable) electricity price (premium FiT). They can sometimes be guaranteed for a fixed number of years (as in Denmark, Norway, and Netherlands) or come with no such guarantee (in which case the length of contract will be 0).<sup>21</sup> Because wholesale electricity prices are generally not observed, the final remuneration price under a premium FiT is unobtainable. In the empirical analysis, we restrict samples to standard and variable FiTs, and introduce policy dummy variables to differentiate these two types of policy design.

Finally, four countries in the sample, i.e. Poland, UK, Italy and Sweden opted for a quota obligation scheme facilitated with TGCs to achieve renewable targets<sup>22</sup> . There is a rich literature on the comparison of policy effectiveness of FiT and TGC. Although this is not the focus of this paper, we nonetheless collect data on the annual average certificate prices and estimate the correlation between the value of TGCs and the wind installation results.

It should be noted that in addition to FiTs and TGCs, many countries have enacted additional renewable energy provisions, such as investment tax credits, grants, or VAT exemptions. State and local renewable policies are also prevalent (Fischer and Preonas, 2011). Country fixed-effects will reflect these additional incentives if they are stable across years. However, if additional incentive policies are adjusted according to FiT remuneration levels, then our estimated investment elasticity with respect to the FiT rates will be biased.

Data on annual wind generation are obtained from IEA world energy statistics and balances database. All price variables are deflated by the CPI and are expressed in 2000 prices.

> 21Spain, Slovenia, and the Czech Republic allow generators to pick between the standard and premium FiTs. In the dataset, the standard FiTs are assumed to be the relevant ones, because these represent the more guaranteed incentive. No definitive reference was found as to which policy wind generators are likely to pick, but a 2008 review of Spanish FiTs (Gonz ́alez [2008]) implies that fixed FiTs were the preferred incentive scheme.

> 22Italy has implemented both FiT and TGC since 2008. But no wind installation was observed under FiT. In addition to TGC, UK started implementing a premium FiT in 2010.

16
Policy Research Working Paper 9587

# **Abstract**

Training using virtual reality has been applied in many fields of education, but primarily in the fields of health and safety, engineering and technical education, and general education. Numerous studies assessing the use of immersive training in education have yielded promising results in educational outcomes, but there is not yet in the literature a systematic analysis of the effects of virtual reality training on student learning. This paper presents a meta-analysis of the results of available studies that assess virtual reality training’s impact on student learning and skills development, and which rely on robust evaluation methods. The study’s primary purpose is to identify the extent to which immersive training can successfully develop students’ skills across different fields of education and the size of the effects encountered. The analysis presented here relies on 31 primary studies and more than 90 experiments. The results indicate that, on average, virtual reality training is more effective than traditional training in developing technical, practical, and socio-emotional skills. The results are particularly promising in fields related to health and safety, engineering, and technical education. The results also indicate that students who are exposed to virtual reality training are more efficient in using inputs and time and/or avoiding performance errors than students receiving traditional training.

This paper is a product of the Education Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at dangelurdinola@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
Figure 1. Growth and Poverty Reduction Effects of Accelerated versus Gradual Reform <!-- Start of picture text --> GDP growth (change from base) Poverty (headcount)<br>3<br>,0.25<br>2.5<br>2<br>,0.15<br>Reform 1 (accelerated)<br>Reform 2 (gradual) 1.5<br>,0.05<br>1<br>Reform 1 (accelerated)<br>2010 2011 2012 2013 2014 2015 0.5<br>-,0.05 Reform 2 (gradual)<br>0<br>-,0.15 2010 2011 2012 2013 2014 2015<br>)<br>percentage point change (from base<br>percentage point change (from baseline)<br><!-- End of picture text --> Source: DCGE model results. Note: GDP is at market prices, including indirect taxes

# **Timing and Design of the Reform Matter**

Rapid phasing out leads to an initial drop in growth and a sharper spike in poverty, while gradual reductions smoothen the growth and poverty effects. Slow phasing out is therefore preferable from a growth and poverty-reduction perspective; growth shocks, especially in the agricultural sector, are less pronounced, and total household income losses are about 20 percent lower. However, slow reform comes at a higher fiscal cost because subsidies effectively need to be financed for two more years compared with the accelerated one-year phase-out, amounting to additional costs of 391 billion YER (Table 5). Thus, the faster the phasing out of subsidies, the more fiscal space exists for the government to compensate households and to invest.

While the direction of growth and poverty impact is the same, the size of the impact by sector differs depending on their production structure. The agricultural sector would be the hardest hit under both the accelerated and the gradual reforms, mainly due to its strong dependence on diesel for irrigation (Table 7). Even before subsidy reduction, fuel is the most expensive item in crop production, as nearly one-third of crop production expenditure is used for fuel purchase. The output especially of waterintensive crops like qat, fruits, and vegetables drops and hurts household incomes from agriculture and agricultural exports.<sup>13</sup> Given its high profitability, Qat is less affected despite its intensive use of diesel

> 13 More than 75 percent of irrigated land uses groundwater for irrigation, contributing to rapidly falling water tables. Most of the land under irrigation is planted with qat, followed by cereals and vegetables. The subsidization of irrigation pumps and the subsidies of diesel have led to a serious overuse of groundwater, resulting in rapidly falling water tables. In addition, cheap fuel has also encouraged traders to extract water and transport it to distant

13
# References

- Acosta, Pablo, Takiko Igarashi, Rosechin Olfindo, and Jan Rutkowski. 2017. “Developing -

- Socioemotional Skills for the Philippines' Labor Market.” _Directions in Development Human Development_ . Washington, DC: World Bank.

- Bartel, Ann. 1995. Training, wage growth, and job performance: Evidence from a company database. Journal of Labor Economics, 13, 401–425.

- Becker, Gary. 1962. Investment in human capital: A theoretical analysis. Journal of Political Economy, 70, 9–49.

- Di Gropello, Emanuela, Hong Tan, and Prateek Tandon. 2010. “Skills for the Labor Market in the Philippines.” _Directions in Development-Human Development._ Washington, DC: World Bank.

- Euler, Dieter. 2013. _Germany’s dual vocational training system: a model for other countries_ ? A study commissioned by the Bertelsmann Stiftung.

- Federal Ministry for Economic Affairs and Energy. “Vocational training in Germany” http://www.make-it-in-germany.com/en/for-qualified-professionals/traininglearning/training/vocational-training-in-germany-how-does-it-work.

- Lee, Kye Woo, Dae Hong Kim, and Ha Kyeong Lee. 2016. “Is the Meister Vocational High School More Cost-effective?” _International Journal of Educational Development_ 51: 84–95.

- Mapa, Dennis S., Josefina V. Almeda, and Manuel Lenord F.Abis, 2016. _A Cost Benefit Study on Dual Training System Philippines._ Philippine Chamber of Commerce and Industry – Human Resources Development Foundation, Inc.

- Orbeta, Aniceto, Jr. and Emmanuel Egsuerra. 2016. “The National System of Technical Vocational Education and Training in the Philippines: Review and Reform Ideas.” Discussion Paper Series 2016–2017. Philippine Institute for Development Studies.

- Orbeta, Aniceto Jr. and Michael Ralph M. Abrigo. 2012. “An Assessment of TESDA Scholarship Programs” Discussion Paper Series No. 2012-25. Philippine Institute for Development Studies.

- <mark>Rutkowski, Jan J., Yuko Okamura, Rosechin Tomes Olfindo, Jorge Avalos. 2016.</mark> _<mark>Philippines - Labor Market Review: Employment and Poverty</mark>_ <mark>. Washington, DC: World Bank.</mark>

Technical Education and Skills Development Authority (TESDA). http://tesda.gov.ph/Home

- Tullao, Tereso S. Jr, Christopher James Cabuay, Emmanuel Garcia, Dustin Timothy Ang, and Rosanina Sayoc. 2014. _Costing and Financing Higher Education in the Philippines_ . Manuscript. De La Salle University Angelo King Institute for Economic and Business Studies.

15
32 Despite the scope for improving agricultural growth and the poverty focus of existing agricultural programs, about 87 percent of the core poor in 1986 and about 89 percent of the hard core poor in 1992 are engaged in some form of agriculture. The bulk of depth and severity in poverty 97.4 percent and 97.9 percent respectively in 1986 are in agriculture. The same trend is shown in 1992.

**Table 4.21: Poverty Incidence in Nigeria by Occupation of Head of Household 1985-92 (Poverty Line=N395.41)**

|||**_RU_**|**_RAL_**||||
|---|---|---|---|---|---|---|
|||**1985/86**|||**1992/93**||
|**Main Occupation of**|||||||
|**Household Head**|**P0**|**P,**|**P2**|**Po**|**P,**|**P2**|
|Profess./Technic.|0.293|0.107|0.050|0.329|0.120|0.059|
|Admin./Managers|0.417|0.131|0.069|0.611|0.158|0.083|
|Clerical Related|0.327|0.084|0.040|0.234|0.106|0.051|
|Sales Workers|0.319|0.080|0.033|0.266|0.110|0.059|
|Service Industry|0.373|0.088|0.037|0.228|0.090|0.046|
|Agric./Forestry|0.543|0.210|0.108|0.391|0.176|0.106|
|<br>Product./Transp.|0.447|0.119|0.052|0.213|0.133|0.068|
|Manufact./Process.|0.341|0.103|0.043|0.337|0.072|0.023|
|Others|0.378|0.094|0.034|0.257|0.093|0.045|
|Student/Apprentice|0.220|0.096|0.045|0.318|0.120|0.068|
|_All Rural_|_0.495_|_0.169_|_0.085_|_0.364_|_0.167_|_0.098_|
|||_UR_|_BAN_||||
|||**1985/86**|||**1992/93**||
|**Main Occupation of**|||||||
|**Household Head**|**P0**|**P1**|P2|**P0**|**P1**|P2|
|Profess./Technic.|0.309|0.077|0.028|0.273|0.139|0.082|
|Admin./Managers|0.177|0.037|0.007|0.269|0.102|0.037|
|<br>Clerical Related|0.219|0.058|0.020|0.331|0.114|0.060|
|Sales Workers|0.235|0.071|0.027|0.263|0.103|0.058|
|Service Industry|0.259|0.079|0.030|0.298|0.109|0.056|
|Agric./Forestry|0.473|0.175|0.079|0.357|0.151|0.080|
|Product./Transp.|0.265|0.112|0.051|0.396|0.121|0.057|
|Manufact./Process.|0.351|0.096|0.037|0.233|0.111|0.064|
|Others|0.305|0.087|0.032|0.292|0.100|0.054|
|Student/Apprentice|0.414|0.087|0.029|0.379|0.157|0.086|
|_All Urban_|_0.317_|_0.124_|_0.051_|_0.304_|_0.140_|_0.076_|
|Source: NCS, 1985/86 a|nd 1992/93.||||||

Most rural areas depend on the labor market for their sustenance. Focusing on the link between urban and rural patterns of welfare as mediated through the labor market, an ILO study based on data from 1964 to 1985 demonstrated that except for intermittent increases, real wages in Nigeria declined sharply between 1973 and 1985 (Jamal and Weeks, 1988). The periodic jumps were observed between 1973 and 1975 and again between 1980 and 1983, the first jump corresponding to the oil price shocks of the early 1970s. Not withstanding these exceptions, real wages in 1985 were a little more than half of the 1973 level. Some data available for 1980 to
mandates the NEA to strengthen ECs technical and financial viability and prepare them for changes in the electricity market.

A study on ECs in 2009, found that their performance varied significantly, depending on factors such as sales and customer density, customer structure, size (measured by sales and number of customers), system loss and number of customers per employee. The study concluded among other things that ECs venturing into low density areas, pressured by local politicians, would worsen their performance further.<sup>12</sup> Electric cooperatives are rated annually on a based on a set of parameters developed by NEA that includes collection efficiency, system loss, payment to power suppliers. 70 ECs received rating AAA (95-100) in 2015, while BENECO’s performance rating in 2014 was AA (90-94).

# 3.3.1 Performance

A factor that had contributed to earlier problems of supply shortages was the overall level of nontechnical losses (transmission and distribution). These have fallen steadily from around 13 percent in 2003 to 9 percent in in 2017 (see **Figure** **_11_** ) largely because of the stricter regulation of ERC on the private DISCOs and the ECs. The system loss of the transmission sector has not been reduced despite 10 years of privatization.

**Figure 10: Transmission and distribution non-technical losses as a  percent of total generation, 2003-2017**

<!-- Start of picture text --> 14.0<br>12.0<br>10.0<br>8.0<br>6.0<br>4.0<br>2.0<br>0.0<br>2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text --> _Source_ : Philippines Department of Energy.

An indirect measure of possible systemwide overall efficiency gains driven by the reform is given by the average annual spot settlement price on the wholesale electricity spot market (WESM).

> 12  Valderrama and Bautista, “Efficiency Analysis of Electric Cooperatives in the Philippines”, 2009.

34
Table A13. Regional emissions intensity index of non-CO2 emissions, Siberian Federal District, SUST-RUS sectoral classification (thousands tons of emission per trillion nominal RUB of output of 2006)

|**Siberian FD**<br>**Row Labels**|**Hydrocarbons**<br>**CnHm**|**Carbon**<br>**monoxide**<br>**CO**|**Nitrogen**<br>**oxide**<br>**NOx**|**Particulate**<br>**matter**<br>**PM**|**Sulphur**<br>**dioxide**<br>**SO2**|**Volatile**<br>**organic**<br>**components**<br>**VOC**|
|---|---|---|---|---|---|---|
|A|14,70|119,10|21,91|103,40|29,21|4,47|
|CA_col|1 327,75|109,31|33,49|151,88|50,43|0,45|
|CA_gas|71,24|244,72||83,18|1 700,75|41,63|
|CA_oil|471,34|2 343,85|92,50|467,86|1 793,23|943,72|
|CB|15,72|685,02|115,21|478,23|1 143,84|27,80|
|DA|5,31|152,47|45,67|99,74|123,01|15,13|
|DB|9,47|234,60|76,18|242,72|401,40|16,28|
|DC||43,79|50,95|382,68|1 185,63|5,74|
|DD|2,20|486,51|71,13|283,81|149,80|31,30|
|DE|6,70|261,24|120,84|428,29|1 147,77|28,10|
|DF|62,69|234,01|53,26|86,36|641,07|234,71|
|DG|42,87|414,38|112,26|298,39|554,91|174,48|
|DH|19,17|90,88|38,05|49,22|17,17|114,12|
|DI|11,10|448,26|202,95|817,94|448,94|35,90|

112