rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Figure 2. Key growth drivers in WAEMU and Figure 3. Key growth drivers in WAEMU and<br>SSA (2011-2016) SSA (2000-2010)<br><!-- End of picture text --> <!-- Start of picture text --> 3.5 3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>-0.5 WAEMU SSA WAEMU SSA<br>Structural Stabilization Residual (unexplained growth) Persistence<br>External Persistence External Stabilization<br>Residual (unexplained growth) Structural<br>Source: Authors’ estimates based on data described in Section 3. Source: Authors’ estimates based on data described in Section 3.<br>Figure 4. WAEMU and SSA: Structural drivers of Figure 5. WAEMU and SSA: Structural drivers<br>growth (2011-2016) of growth (2000-2010)<br>1.8 0.8<br>1.3 0.6<br>0.8 0.4<br>0.3 0.2<br>0.0<br>-0.2 WAEMU SSA<br>WAEMU SSA<br>-0.2<br>-0.7<br>Schooling Credit/GDP Trade/GDP<br>Gov Cons./GDP Infrastructure Schooling Credit/GDP Trade/GDP<br>Source: Authors’ estimates based on data described in Section 3. Source: Authors’ estimates based on data described in Section 3.<br>Figure 6. Key growth drivers in WAEMU Figure 7. Structural drivers of growth in<br>countries (2011-2016) WAEMU countries (2011-2016)<br>4.5 2.5<br>3.5 2.0<br>1.5<br>2.5<br>1.0<br>1.5<br>0.5<br>0.5<br>0.0<br>-0.5<br>-0.5<br>-1.5<br>Residual (unexplained growth) Persistence -1.0<br>External Stabilization Schooling Credit/GDP<br>Trade/GDP Gov Cons./GDP<br>Structural<br>Infrastructure<br>Source: Authors’ estimates based on data described in Section 3. Source: Authors’ estimates based on data described in Section 3.<br>Annualized GDP p.c. growth (in %) Annualized GDP p.c. growth (in %)<br>Annualized GDP p.c. growth (in %)<br>Annualized GDP p.c. growth (in %)<br>Annualized GDP p.c. growth (in %) Annualized GDP p.c. growth (in %)<br><!-- End of picture text --> Furthermore, the baseline sample is re-specified to include only countries that were below the median or mean of GDP per capita in 1995 (Annex Table 2). This is important, as the baseline model includes 11
# **3 More Country-Specific Information on AD Sources and Data**

For each of the AD user countries for which we have detailed data formatted as described in section 2, we describe in this section the additional and “unique” information specific to each user country.

Table 3.1 reports the sources of much of our information on national government agencies handling the dumping and injury determination of the antidumping process, their websites (if available) and the names of official government documents in which antidumping investigation notifications are published.

## **Table 3.1 : User Countries’ Antidumping Investigative Agencies, Government Reporting**

## **Publications and Websites**

|**Country**|**Government Agency Handling**<br>**Dumping Investigation**|**Government Agency Handling**<br>**Injury Investigation**|**Government Publications that**<br>**Report Antidumping Activity**|
|---|---|---|---|
|Argentina|Secretariat of Industry and Trade<br>(Secretaría de Industria y Comercio)|National Commision of Foreign<br>Trade (Comisión Nacional de<br>Comercio Exterior (CNCE))|Resolutions and Acts published by<br>the Documentation and Information<br>Center of the Argentine Economic<br>Ministry<br>http://infoleg.mecon.gov.ar/<br>|
|Australia|Dumping Unit of the Australian<br>Customs Service|Dumping Unit of the Australian<br>Customs Service|Commonwealth of Australian<br>Gazette, Australian Financial Review,<br>and Australian Customs Dumping<br>Notices (ACDNs)<br>http://www.customs.gov.au/|
|Brazil|Department of Commercial Defense<br>(Departamento de Defesa Comercial<br>(DECOM)) at the Secretariat of<br>Foreign Trade (Secretaria de Comercio<br>Exterior (SECEX))|Department of Commercial<br>Defense (Departamento de Defesa<br>Comercial (DECOM)) at the<br>Secretariat of Foreign Trade<br>(Secretaria de Comercio Exterior<br>(SECEX))|Official Diary of the Union (_Diario_<br>_Oficial da Uniao_), a circular of the<br>Secretariat of Foreign Trade<br>(SECEX)<br>http://www.desenvolvimento.gov.br/s<br>itio/secex/secex/competencia.php|
|Canada|Anti-Dumping and Countervailing<br>Directorate of the Canada Border<br>Services Agency (CBSA)<br>http://www.cbsa-asfc.gc.ca/sima/menu-<br>e.html|Canadian International Trade<br>Tribunal<br>http://www.citt-<br>tcce.gc.ca/index_e.asp|_Canada Gazette_<br>http://canadagazette.gc.ca/|
|Colombia|Subdirección de Prácticas Comerciales<br>(Assistant Director of Trade Practices)<br>in the Ministerio de Comercio,<br>Industria y Turismo (Ministry of Trade,<br>Industry, and Tourism)<br>http://www.mincomercio.gov.co|Subdirección de Prácticas<br>Comerciales (Assistant Director<br>of Trade Practices) in the<br>Ministerio de Comercio, Industria<br>y Turismo (Ministry of Trade,<br>Industry, and Tourism)<br>http://www.mincomercio.gov.co|_Diaro Oficial_<br>http://www.imprenta.gov.co/print-<br>int/HTML/frameset/inter_diariofi.htm|

13
Table 8. Estimated impact of selected randomized interventions (standard deviations per 100 US dollars)

|Intervention|SD/$100|
|---|---|
|Providing earnings information, Madagascar|118.338|
|Streaming by achievement, Kenya|34.784|
|Linking school cmte to local govt, Indonesia|34.624|
|Electing school cmte & linking to local govt, Indonesia|13.337|
|Teacher incentives (year 2), Kenya|6.291|
|Textbooks for top quintile, Kenya|3.563|
|Remedial education, India|3.069|
|Camera monitoring, India|2.278|
|Village‐based schools, Afghanistan|2.126|
|Extra contract teacher + streaming, Kenya|1.971|
|Individually‐paced computer assisted learning, India|1.551|
|Girls Scholarships, Kenya|1.384|
|Read‐a‐thon, Philippines|1.176|
|Minimum conditional cash transfers, Malawi|0.06|
|Contract teachers,Kenya|‐0.299|

Source: Abdul Latif Poverty Action Lab (2014) The total cost for each year of the Reading Booster Programme was 794,243 PGK (250,549 USD) based on data from the World Bank project which supported the program. Because this program was implemented during regular teaching hours, there is no additional cost of teachers; these costs reflect training and distribution of materials as well as monitoring and evaluation. The program benefitted 4,272 students, yielding a cost of 186 PGK (59 USD) per student (World Bank 2016c). Table 9 presents the average impact of the program in standard deviations per 100 USD, which is calculated by dividing the impacts presented in Table 7 by 0.59. Per 100 USD, the impact of this program on the two targeted reading domains ranges from 1.04 to 1.16 standard deviations. For the other reading domains where a statistically significant effect was found, effect sizes range from 0.61 to 1.07 standard deviations per 100 USD. The reader booster program is most cost effective for girls; cost effectiveness, like effect size presented in Table 7, is higher in the five domains that have statistically higher effect sizes than males.

16
||268,403|27,136|29,365|45,490|
|---|---|---|---|---|
|United Rep. of Tanzania|265,184|680,862|109,286|102,099|
|Zimbabwe|190,950|4,127|4,435|6,389|

|**Top 10**|**by asylum, b**<br>**1990**|**ased on 20**<br>**2000**|**13**<br>**2010**|**2013**|
|---|---|---|---|---|
|Kenya|14,249|206,106|402,905|534,938|
|Chad||17,692|347,939|434,479|
|Ethiopia|773,764|197,959|154,295|433,936|
|Uganda|145,718|236,622|135,801|220,555|
|Sudan*|1,031,050|414,928|178,308|159,857|
|Cameroon|49,876|43,680|104,275|114,753|
|Dem. Rep. of the Congo|416,435|332,509|166,336|113,362|
|United Rep. of Tanzania|265,184|680,862|109,286|102,099|
|Mauritania|60,000|350|26,717|92,767|
|Rwanda|23,601|28,398|55,398|73,349|

Note: Authors’ aggregation based on UNHCR statistical population online dataset, accessed in September 2014.* denotes that the data on Sudan would be inflated (e.g. 35,529 in 2013) if people in refugee‐like situations were included in the figures (see notes, Figure 1).

Naturally, the ranking of the countries hosting most refugees is a bit distorted since refugees are more likely to flee to neighboring countries. Large (and for most, landlocked) countries, like e.g. the Republic Democratic of Congo, Ethiopia, Kenya and Sudan are likely to share borders with more conflict‐prone countries and to host many refugees, while being themselves affected by social unrest and be a source of a fair amount of refugees. As can be seen from Table 3, a different pattern may be obtained while weighting such figures by either the size or the population of the hosting countries. That is particularly the case when weighting by the size for countries like DRC, or Tanzania. On the contrary, smaller countries like Burundi, Rwanda, or Djibouti ranked at the top in terms of number of refugees hosted per inhabitant.

10
36

**_get_** _us anywhere In terms of Informing what should be done in the next two or three years [1988-1990] in terms of policy, because I suspect that pollcy prescriptions for the next two or three years or even outer years are going to be pretty much the same, whatever we believe Is in fact on technological opportunities._

_Successful technology policies, whatever they are, may jack up our estimates of potential growth rates by 1/2 percent, maybe even 1 percent in other outer years, but my impression is it is not a very fruitful ground for policy discussion. Would that be a biased view? What can we do in terms of a policy perspective? The best thing we can probably do is to make sure investment takes place so that new technology gets embodied. And that means that we have to somehow cr&"k up investment, bring interest rates down. Incidentally, that Is a general question I would like to discuss in the long-term outlook, what happens to interest rates, real interest rates, which is not something that Boltho really looks at [In his paper). But that may in fact we one of the crucial policy variables that affect technological absorption and implementation._ _MR. BANETH: I think the policy implications are indirect, and we are not going to discuss very much how one would promote technological change or its embodiment except through investment._ _But very much in the background and indeed In the forefront of German reluctance, up to now, to stimulate the economy is, at least at the verbal level, the argument that "we are close to, or indeed above, our potential growth rate." And if you want to work that further, it has to imply the lack of available teehnology to embody in the capital. You have no increase in the labor supply, and basically you can have a 1.5 percent per annum productivity increase because, no matter how much investment you have, that's about all the technological change you can embody in it._ _Conversely, if there is a general feeling that in fact you do have the technology available for embodying into capital, which is not yet embodled, you can have faster change, and this removes one constraint on policy. One could then have faster growth without inflation. Indeed, if you do have these autonomous pressures on wages, if wages are marching forward at a certain rate, then you might in fact have a negative tradeoff between growth and inflation, if the technology is available._ _Structural MR. OHLIN: There are two points. I think the negative chang....... one is the rapidity of the thing. The acceleration of structural changes, the rapidity or the acceleration of structural changes in the 1970s and 1980s, for that matter you can go back almost to the_ WP/BOLTHO/SF:Ig
# XIV INTRODUCnON

Figure 1 Annual Increments to Female Life Expectancy (in years), by Initial Level, Compared wTth U.N. and Previous World Bank Pattems <!-- Start of picture text --> 0.9 _<br>o .s<br>0.7<br>0.6<br>CUPPENT<br>0.5 -U<br>0.4<br>0.3-<br>0.2 -<br>0.1<br>35 40 45 S0 55 60 65 70 75 80 85<br>INITIAL LIFE EXPECTANCY<br>Figure 2 Annual Decrements to Infant Mortality Rate (per thousand), by Initial<br>Rate, Compared with the U.N. and Previous World Bank Pattems<br>-0.2<br>-0.4-<br>-0.6<br>-0.8 -<br>-1. 025<br>-1.48<br>-2.0-<br>-2.2-<br>-2.4-<br>-2.6-<br>-2.8<br>160 150 140 130 120 110 100 90 130 70 60 50 40 30 20 10<br>INITIAL INFANT MORTALITY RATE<br><!-- End of picture text -->
# _Notable Korean policies include_ :

#  The Webhard Registration System

The “Korean government launched the “Webhard registration system” to reduce the distribution of online illegal reproductions. It also [initiated] a digital evidence collection and analysis system regarding infringed copies, dramatically reinforcing investigation capabilities of the Special Judicial Police Force for Copyright Protection. In particular, the government introduced the “Citizens’ Opening Monitoring Project” by recruiting monitoring staff who work from home by using computers, including disabled persons, while operating the “Silver Monitors” to watch infringed works year-round. Such efforts contributed not only to uprooting online and offline distribution of illegal reproductions, but also to creating jobs for socially disadvantaged people.”<sup>59</sup>

#  Overseas Copyright Offices

To ensure that Korea’s copyright is protected overseas, the government has established offices in Beijing, Bangkok, Manila, and Hanoi.<sup>60</sup>

#  Copyright Collective Management Organizations

Korea’s Ministry of Culture, Sports and Tourism has authorized other organizations to provide copyright collective management services. As of now, they are 12 organizations (related to creative work), which have been granted this permission. They include: the Korean Society of Authors, the Korea Music Copyright Association; and the Korea Database Agency, to name a few.<sup>61</sup>

- Education and Public Awareness

In order to create a healthy “copyright ecosystem” in which authors are respected and highquality works are smoothly distributed and consumed, it is still insufficient to merely make related legal improvements and develop IT technology. What is ultimately required is to change the perceptions and attitudes of people, who are members of the copyright ecosystem, toward a more desirable direction.<sup>62</sup> — Korea Copyright Commission, 2012 Korea “has concentrated its policy capabilities on education and public awareness programs designed to raise awareness of the importance of copyright, and has implemented a wide range of policy measures.”<sup>63</sup> The curriculum here is focused on two areas: i)

> 59 Ibid.

> 60 “Overseas Office,” Korea Copyright Commission, accessed April 14, 2015,

> <u>http://www.copyright.or.kr/eng/about-kcc/overseas-offices/beijing-office.do.</u>

> 61 _2012 Annual Report on Copyright in Korea_ (Seoul: Korea Copyright Commission, 2012), 116136, accessed April 14, 2015, <u>http://www.copyright.or.kr/eng/doc/activities/2012AR.pdf.</u>

> 62 _2012 Annual Report on Copyright in Korea_ , 54.

> 63 Ibid.

20
# **Table 1. Share of Foreign Currency-Denominated Loans**

||Banks Loans<br>to Households|Mortgage<br>Loans|Variable Interest<br>Rates|
|---|---|---|---|
|_New EU Countries_||||
|Bulgaria|29.82|36.00||
|Czech Republic|0.10|0.10||
|Estonia|82.35|||
|Hungary|70.21|46.30|73.60|
|Latvia|87.38|||
|Lithuania|61.64|||
|Poland|39.83|54.90||
|Romania|58.73|88.50||
|Slovak Republic|2.79|4.60|78.00|
|Slovenia|||78.00|
|_Other Countries_||||
|Armenia|23.58|||
|Croatia|69.10|88.10|95.60|
|Kazakhstan|50.80|||
|FYR Macedonia|44.30|||
|Russia|10.00|20.90||
|Turkey|0.70|6.60||
|Ukraine|71.90|||
|Belgium|||32.40|
|Denmark|||30.00|
|France|||32.00|
|Ireland|||64.40|
|Italy|||86.90|
|Netherlands|||36.00|
|Portugal|||95.40|
|United Kingdom|||34.90|

Note: The variable rate refers to an interest rate that remains unchanged for up to one 1 year. Data for Croatia and Slovenia refer to 2007.

Sources: European Central Bank; National Central Banks; and UniCredit.

Variable Interest rate data Sources: Bank of Slovenia (2008); Croatian National Bank (2008); EMF (2006); and OECD/Schich and Ahn (2007).

1
# **Heterogeneous Effects of Rural Electrification: Evidence from Bangladesh**<sup>**1**</sup>

## **Hussain Samad                              Fan Zhang**

**Key Words: Rural Electrification, Heterogeneous Effects, Reliability of Electricity, Distributional Impact, Bangladesh**

### **JEL:  D6, Q4, R2**

1 Hussain Samad is a consultant at the World Bank. Fan Zhang is a senior economist at the World Bank's Office of the Chief Economist for the South Asia Region. This paper is part of a broader analytical effort to assess the cost of power sector distortions in South Asia conducted by Office of the Chief Economist of the South Asia Region. The authors are grateful to Bangladesh Rural Electrification Board (BREB), particularly Latiful Azam, for providing access to the data. We thank Martin Rama and Zubair K. M. Sadeque for helpful comments on earlier drafts. Research assistance from Fatima Najeeb and financial support from the Partnership for South Asia Trust Fund are greatly appreciated.
<mark>Poverty. Washington, DC: World Bank. © World Bank.</mark> <mark>https://openknowledge.worldbank.org/handle/10986/13137 License: CC BY 3.0 IGO.</mark>

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26
and omitted variables are the two main weaknesses faced by cross-country research designs attempting to establish causality. However, causality is a necessary condition for inequalityreducing policies to promote economic growth. Even if researchers were able to establish the existence of a causal link between gender inequality and economic growth in a given sample, it is difficult to extrapolate to other samples if we do not know the social/cultural mechanisms through which this link operates. The issue of external validity is the third weakness of cross-country research designs. We will show that even the basic correlations are not robust or resistant to changes in the sample of countries or in time periods, and they vary erratically depending on the source of variation used.

# **_3.1 Reverse Causality_**

The first weakness of cross-country research designs is that cross-country variation in gender inequality may be generated by cross-country differences in economic growth. In other words, different countries might exhibit different levels of gender inequality _because_ they are at different stages of development. Economic theory has highlighted several reasons why development might reduce gender disparities. First, the process of development is associated with technological advances that reduce the value of men’s comparative advantage in physical strength, thereby raising the returns to women’s education and labor-market participation. The idea that technology determines gender roles is supported, for example, by Alesina et al.’s (2010) analysis of the relationship between historical agricultural techniques and contemporaneous gender disparities. These authors show that countries whose geographical characteristics allowed the use of the plough and whose operation required men’s levels of physical strength tolerated a more unequal division of labor across genders as well as social norms that discriminate between genders.

The process of development is also associated with changes in relative prices that affect returns to women’s education and labor force participation. Two recent microlevel studies lend support to this idea. Munshi and Rosenzweig (2006) examine the impact of genderbased division of labor in India on the returns to (specifically) English-language education and find that women benefit disproportionately because men already participate in lowerskilled sectors owing to caste networks and are channeled or directed into instruction in the vernacular language. Because women are not required to maintain these male networks 7
6 The data on international remittances in this paper come from the International Monetary Fund (IMF) publication, Balance of Payments Statistics Yearbook. This publication records the annual amount of international remittances received by each labor-sending country.<sup>4</sup> However, it should be emphasized that these IMF records are incomplete because they only include data on official international remittance flows, that is, remittance monies which are transmitted through official banking channels. In virtually every labor-sending country a large (and unknown) proportion of remittance monies is transmitted through private, unrecorded channels. These private transfers include remittances brought home by friends, relatives and even the migrant himself. As a result, the level of international remittances recorded by the IMF significantly underestimates the actual flow of remittances (official and unofficial) returning to laborsending countries.<sup>5</sup> All of the poverty data used in this paper comes from the results of nationallyrepresentative household budget surveys, as recorded on the World Bank website, Povcal <u>Net. One of the problems with these poverty data is that many developing countries have</u> not conducted the type of household budget surveys needed to produce reliable poverty figures. For example, of the 157 developing countries classified as low- or middleincome by the World Bank,<sup>6</sup> only 81 countries (52%) have published the results of any household budget survey. Of these 81 countries, missing data on international remittances reduced the sample used here to 76. However, not all of these 76 countries conducted household surveys in the year 2000, which is the year for which all the data on the skill composition of migrants are available (see below). As a result, all the poverty data used in this study comes from that single household survey which was done closest