rafmacalaba/datause-encoder-data huggingface dataset:
**Table** 7. **Currency composition of long-term debt, 1997**

|**Country**|**Deutsche**<br>**mark**|**French**<br>**franc**|**Yen**|**Multi**|**Pound**|**SDR**|**Swiss**<br>**franc**|**US**<br>**dollar**|**EMU**|**Other**|
|---|---|---|---|---|---|---|---|---|---|---|
|**_West and_**|||||||||||
|**_Central. Africa_**|||||||||||
|Benin|0.2|9.2|1.0|15.0|0.3|2.0|0.0|55.3|18.2|17.5|
|Burkina Faso|0.1|_3.5_|0.0|19.0|0.0|2.0|0.0|62.4|10.1|13.0|
|Cameroon|14.5|31.0|0.3|9.2|0.6|0.2|0.8|25.8|55.2|17.7|
|Centr.Afr.Rep.|0.3|4.8|0.6|18.0|0.0|7.1|2.4|58.8|11.7|8.4|
|Chad|0.0|5.9|0.0|23.0|0.0|0.2|0.0|56.8|12.9|13.9|
|Congo, Rep. of|2.7|41.4|0.2|1.7|6.3|_0.5_|0.3|26.0|54.9|21.0|
|<br>Cote d'lvoire|3.5|27.1|1.1|16.0|0.8|0.2|0.2|40.9|35.7|9.8|
|Eq. Guinea|5.0|7.1|0.0|15.0|0.0|0.0|0.0|43.3|26.8|29.3|
|Gabon|7.3|45.2|0.8|8.7|6.0|0.0|0.7|11.2|63.4|20.3|
|Guinea-Bissau|0.3|1.0|0.0|24.0|0.0|0.0|12.2|31.9|28.8|30.5|
|Mali|0.1|18.9|2.7|19.0|1.9|0.0|1.7|25.9|35.8|30.2|
|Niger|0.0|30.9|1.8|0.0|1.4|3.2|0.0|40.2|42.1|22.4|
|<br>Senegal|1.6|15.3|2.9|12.0|0.2|0.9|0.3|41.3|29.8|25.1|
|Togo|1.4|7.4|5.8|7.1|1.7|1.1|10.4|49.7|26.9|15.3|
|<br>Gambia, The|0.1|1.5|0.0|21.0|5.8|0.1|0.0|50.7|11.8|20.4|
|<br>Ghana|3.6|1.6|11.8|6.2|4.8|0.8|0.0|61.2|10.3|10.1|
|Guinea|0.6|8.3|2.6|7.4|0.9|1.2|0.3|51.6|22.8|27.1|
|Liberia|9.7|1.0|6.6|20.0|1.3|_1.5_|0.0|53.5|13.9|6.4|
|Mauritania|0.3|7.2|3.4|_1.5_|0.6|2.4|0.0|38.1|30.8|46.5|
|Nigeria|10.9|10.2|11.9|11.0|13.0|0.1|0.5|31.9|27.1|11|
|Sierra Leone|_1.5_|2.6|6.8|13.0|0.7|11.0|2.6|42.3|16.6|19.8|
|**_East and_**|||||||||||
|**_Southern Africa_**|||||||||||
|<br>Angola|0.3|3.9|0.8|0.9|1.0|0.9|0.3|86.8|7.1|5.2|
|<br>Botswana|3.3|0.6|9.6|45.0|4.2|1.9|0.0|12.5|15.6|23.3|
|Burundi|0.1|5.3|2.5|22.0|0.0|1.8|0.0|56.4|11.2|11.6|
|Congo, Dem. R.|6.0|14.9|3.2|4.1|1.0|0.4|0.2|44.5|33.9|25.6|
|<br>Eritrea|0.0|0.0|0.0|0.0|0.0|1.6|0.0|48.9|24.7|49.4|
|Ethiopia|1.8|0.1|0.1|8.8|0.2|0.2|0.1|27.5|32.6|61.2|
|<br>Kenya|2.9|4.5|17.2|10.0|3.4|0.4|2.5|43.0|17.8|15.7|
|<br>Lesotho|0.0|6.6|0.0|21.0|1.0|6.4|0.0|23.7|27.1|41.0|
|Madagascar|1.8|13.4|7.5|9.1|0.1|2.7|1.1|49.0|23.9|15.2|
|<br>Malawi|0.2|0.8|9.3|19.0|8.2|6.3|0.1|54.3|2.2|2.1|
|Mauritius|1.3|18.0|4.2|11.0|1.4|0.0|0.1|52.5|25.0|11.2|
|Mozambique|3.9|8.4|1.6|8.2|2.1|1.1|.0.|63.2|18.1|11.6|
|Namibia...|||.....|||....|||...||
|Rwanda|0.0|7.5|1.0|11.0|0.0|3.8|0.0|50.8|20.8|26.5|
|Somalia|0.0|4.2|2.6|7.2|0.1|1.3|0.0|51.8|20.6|32.8|
|South Africa|2.5|0.0|4.8|0.0|0.0|0.0|0.0|92.6|2.6|0.1|
|Sudan|0.8|3.0|2.5|1.6|3.8|_0.5_|16.9|50.8|30.8|20.1|
|Tanzania|1.9|1.9|10.1|6.7|10.0|4.9|0.0|45.7|13.2|18.7|
|Uganda|0.2|0.3|_1.5_|11.0|1.9|7.6|0.0|66.4|6.0|11.0|
|Zambia|13.1|2.6|9.4|7.0|6.9|0.3|0.1|46.7|22.8|13.9|
|Zimbabwe|10.8|4.8|3.4|27.0|5.9|0.4|0.7|26.6|26.7|20.7|
|**_Sub-Saharan_**|**4.6**|**10.6**|**4.9**|**8.9**|**3.8**|**1.1**|**1.4**|**46.9**|**25.6**|17.9|

**_Africa_**

_Note:_ EMU currencies include the Deutschemark, French franc, and Swiss franc and 50 percent of "other" currencies. Long-term debt is defined as debt with a maturity above one year.

_Source:_ World Bank 1999c.

19
carrying out such justice include prosecution and sentencing in domestic courts and special tribunals, in international tribunals, in joint international and domestic tribunals, or in foreign courts. Lustration, administrative procedures used to lock perpetrators and collaborators out of jobs and positions as well as expose them to public scrutiny and social shaming, is a weaker measure also falling into this category (Nalepa 2006; Gloppen 2005: 18). In post-conflict democratic settings these mechanisms are more likely to be carried out as part of the reconciliation process and as a response to humanitarian rights principles. In autocratic settings they are more arbitrary and usually directed toward the losing side of the conflict, such as separatist rebels, failed coup-makers, or leaders of the previous regime. In these situations, trials often take place as _show-trials_ where the outcome is known prior to the legal process (Elster 2004). There are, however, psychological as well as political reasons to believe that post-conflict retributive justice, either legal justice or show trials, may have a peace-bringing effect that goes beyond the legal and moral aspect of war-crime punishment.

The psychological rationale for retributive post-conflict justice stems from the need to understand and heal the trauma of victims and survivors of conflict. Civil wars by definition involve victims, regardless of the purpose or mission of the belligerents.<sup>6</sup> According to Bass (2005: 3) there is little doubt that victims desperately want to see some kind of punishment for those that have destroyed their life or killed their loved ones making amnesty an incomplete and unacceptable solution. Holding perpetrators responsible for gross human rights violations, genocide, ethnic cleansing, torture and other types of war-crime, can be important for those affected by these crimes, enabling them to attain closure and restore healthy relations toward one another (Gloppen 2005: 22).

The psychological effects of post-conflict justice on peace and reconciliation are, however, disputed. Some have suggested that PCJ may, in fact, create greater resentment among former participants to civil conflict, opening old wounds and inflicting new ones in already fragmented societies (e.g. Long & Brecke 2003: 68), while others (e.g. Stover 2005; Stover & Weinstein 2005) find both international and local trials to have very limited effect on victims’ attitudes toward the enemy. Others, such as Fletcher & Weinstein (2002) have suggested that trials lead to an individualization of guilt that reduces the collective incentive for forgiveness. But

> 6 Armed conflicts in the Uppsala-PRIO dataset, by definition, involve at least 25 battle related deaths and a non-state actor in opposition to representatives of the state.

4
public support for a reduction in energy subsidies across countries is unpacked by examining heterogeneity between respondents based on their prior knowledge and beliefs, such as their perception of their relative income.

Second, this large-scale, online randomized survey experiment adds to the growing body of work using this approach to understand how people reason and how their knowledge and beliefs influence their policy preferences. Seminal work along these lines using large-scale, randomized survey experiments has been conducted in recent years in the United States and Western Europe to examine a range of topics, such as inequality (Kuziemko et al. 2015), social mobility (Alesina et al. 2018), and immigration (Alesina et al. 2023). Two of the latest works using this methodology involved testing the impact of information about climate change on public support for policies in high- and middle-income countries (Dechezleprêtre et al. 2022; Dabla-Norris et al. 2023), but these studies did not examine energy subsidies. The current study extends this literature in several ways. It examines public support for energy subsidy reform, which is somewhat distinct from many other topics, as there is a notable lack of awareness about the existence of subsidies (as opposed to incorrect beliefs or misperceptions), the implications of removing energy subsidies are very tangible (i.e., higher energy prices) and there is a unique legacy of sensitivity around this topic (e.g., riots in many countries as a response to removal of subsidies). It also is designed with several layers of both experimental and nonexperimental variation, which allows for a deep understanding of what drives support for reform, in part by exploiting natural variation across countries (such as variation in the main type of energy subsidized). Finally, this is one of the largest randomized survey experiments in middle-income countries in this literature, and it incorporates best practices for the design of randomized survey experiments from cross-country research in high-income countries.

The paper is structured as follows. Section 2 provides background information about the related literature, hypotheses for this study, and the countries that are included. Section 3 describes the survey methodology in detail, including survey design, data collection, determination of sample sizes, and empirical analysis. Section 4 presents the descriptive findings 7
Where we instrument the follow-up survey report of whether the firm had received a municipal inspection in the past year with assignment to the inspector treatment group. We estimate this equation using the follow-up survey data for the control group and inspector group only. We consider both ALF registration, which is the registration form most closely tied to municipal inspection, as well as our overall measure of formalizing.

Table 8 displays the results. We see the point estimates range from 0.214 to 0.265, so that receiving an inspection results in a 21 to 27 percentage point increase in the likelihood of formalizing. The statistical significance is greatest for ALF registration, where the p-value is 0.108 for definite registration, and 0.051 for definite or probable registration. This is the impact on the group of firms who answer the follow-up survey. It therefore removes firms which had closed or which cannot be found easily, but still includes some firms which were actually already formally registered. Our estimate is thus a lower bound for the impact that inspections would have on formalization of informal firms.

The estimated cost of an inspection is R$64.34, which is based on an estimated inspection taking 56 minutes per visit plus 17 minutes of travel time (estimates provided by PBH). The inspectors visited 387 firms (the rest being closed or not found), so the total cost of inspections is estimated at R$24,900.Taking our estimated impact of 11 to 22 more firms formalizing, the cost per firm formalized is R$1132-2264. Our IV estimates suggest that since many of these inspections would have occurred anyway, approximately four additional inspections are required to get one firm to register for an ALF license, so the estimated cost of formalizing one firm is approximately R$256.

Annual tax revenue is R$620 for a MEI, and based on 4 percent revenue tax on the average revenue of R$57,000 for newly formalized firms with an ALF, is R$2280. Firms report that firms like theirs typically only report only half their revenues, which would take the SIMPLES annual tax take down to R$1140. So the cost of formalizing a firm in our experiment via inspections appears that it would be gained back within the first year of tax payments (or more than gained back if we consider only the marginal visits under the IV estimation), with subsequent years of tax payments then a net gain for the government.

However, while total government revenues appear to warrant bringing firms into the formal system via inspections, the municipality (paying for the municipal inspectors) only gets a share of the revenue, with the remainder going to the state and federal governments. Apart from the annual 23
# **Toward Labor Market Policy 2.0: The Potential for Using Online Job-Portal Big Data to Inform Labor Market Policies in India**<sup>**1**</sup>

**Shinsaku Nomura*, Saori Imaizumi, Ana Carolina Areias, Futoshi Yamauchi**

The World Bank JEL Classification: C55, C81, J23, J24, J31, J71 Key words: labor market analytics, big data, wage, gender, skills demand, behavioral, forecasting 1 This work was supported by the World Bank Strategic Research Program Trust Fund and the Jobs Umbrella Trust Fund, which is supported by the Department for International Development/UK AID, and the Governments of Norway, Germany, Austria, the Austrian Development Agency, and Swedish Development Agency SIDA. The authors are grateful to Babajob, with special thanks to Sean Blagsvedt and Vir Kashyap for their continuing collaborations and generous data sharing, and to John Gibbons for operational task management. John D. Blomquist has provided continuous guidance to the team since the early stage of this collaborative research. The paper has been prepared to contribute to the Skill India Mission Operation project and skills development sector knowledge in India, and the team is grateful to Shabnam Sinha and Muna Meky for their advice. The team benefited from technical discussions with Omar Arias, Mattias Lundberg and the World Bank India education team at various stages of this research, and overall guidance by Keiko Miwa. * Corresponding author: The World Bank, Education Global Practice. Washington, D.C.: <u>snomura@worldbank.org.</u>
# **Learning from Power Sector Reform Experiences: The Case of Vietnam**

**Alan David Lee and Franz Gerner**<sup>1</sup>

<u>Keywords: power sector reform, electric utility, power generation, energy access, state-owned</u> enterprise, regulation, electricity pricing, Vietnam.

<u>JEL Codes: D4; L94; P18; P28; P31; Q4</u> 1 Energy & Extractives Global Practice, World Bank. The authors are grateful for guidance from Vivien Foster and for invaluable contributions from colleagues and consultants, including, among others: data collected by Eric Groom and Macroconsulting S.A (Argentina); interviews conducted in Vietnam by Nguyen Trinh Hoang Anh, Adam Fforde, and Tran Dinh Long; inputs and support from Anshul Rana, Rahul Kitchlu, Ky Hong Tran, Thi Ba Chu, Hoa Chau Nguyen, Pedro Antmann, and Joeri de Wit; and review comments from Cia Sjetnan, Sebastian Eckardt, Oliver Behrend, and Madhu Raghunath. All views expressed and any shortcomings are the authors.
share of resource revenues in total revenues, corresponding to T<sup>R</sup> /T in the model, is estimated at 26.5 percent, based on the calculations of Lundgren et al. (2013, Table 1) for 2010. To calculate τ<sup>R</sup> the formula T<sup>R</sup> /T = τ<sup>R</sup> p<sup>R</sup> Y<sup>R</sup> /T = τ<sup>R</sup> (p<sup>R</sup> Y<sup>R</sup> /Y)(Y/T); given the above numbers, this formula gives 0.265 = τ<sup>R</sup> (0.331)(1/0.201), or τ<sup>R</sup> = 16.1 percent. In the same vein, to calculate τ<sup>NR</sup> the formula T<sup>NR</sup> /T = τ<sup>NR</sup> (Y<sup>T</sup> + z<sup>-1</sup> Y<sup>N</sup> )/T = τ<sup>NR</sup> [(Y<sup>T</sup> + z<sup>-1</sup> Y<sup>N</sup> )/Y](Y/T), which gives, given the data provided earlier, 1 - 0.265 = τ<sup>NR</sup> (1-0.331)(1/0.201), or τ<sup>NR</sup> = 22.1 percent.<sup>14</sup> The initial ratio of noninterest current spending in GDP, G/Y, is set equal to the share of total (interest-inclusive) expenditure in GDP for 2012, 19.6 percent, minus debt service after debt relief, 1.9 percent of GDP in 2012, and minus infrastructure investment, which is estimated by the World Bank at 2 percent of GDP in 2012 (of which 1.5 of GDP on road infrastructure). This gives G/Y = 15.7 percent. Thus, components of capital expenditure other than infrastructure investment in the budget data are treated as current expenditure.<sup>15</sup> Given this result, the share of infrastructure investment in total government spending, <sup>G</sup> , can be estimated as (I<sup>G</sup> /Y)(G/Y)<sup>-1</sup> = 0.02/0.157 or equivalently <sup>G</sup> = 12.7 percent. As noted earlier the steady-state solution of the government overall fiscal balance (22), which gives T = r<sup>W</sup> D<sup>G</sup> + G, is thus used to calibrate the initial value of lump-sum taxes, T<sup>L</sup> , as a proportion of GDP.

The parameter that captures the allocation of investment in infrastructure to nontraded goods, <sup>GN</sup> , is set at 0.62, within the range of estimates of the share of nontradables in total investment for Côte d’Ivoire, Gabon, Ghana, and Uganda reported by Bems (2008, Table 8).

> 14These calculations assume implicitly that the share of grants is also fixed as a share of GDP.

> 15This is a reasonable assumption given that the model does not capture the stock effects of other productive components of public investment, on education and health for instance.

23
<!-- Start of picture text --> year-to-year GDP growth total unemployment<br>10.0% 3,000,000<br>8.0%<br>6.0% 2,500,000<br>4.0%<br>2,000,000<br>2.0%<br>0.0%<br>1,500,000<br>-2.0%<br>-4.0%<br>1,000,000<br>-6.0%<br>-8.0%<br>500,000<br>-10.0%<br>-12.0% -<br>I 2007 II 2007 III 2007 IV 2007 I 2008 II 2008 III 2008 IV 2008 I 2009 II 2009 III 2009 IV 2009 I 2010 II 2010 III 2010 IV 2010<br>number of people<br>annual percentage growth<br><!-- End of picture text --> _Source:_ Own calculations using data from the National Institute of Statistics and Geography ( _Instituto Nacional de Estadística y Geografía_ — INEGI), the Economic Data Bank ( _Banco de Informacion Economica_ —BIE), and the Mexican Occupation and Employment Survey ( _Encuesta Nacional de Ocupación y Empleo_ —ENOE), 4th quarter 2010.

By March 2011 total employment had recovered, but unemployment and informality rates still compared unfavorably with the situation during the worse of the crisis. Even though the Mexican labor market has shown signs of an irregular recovery during the last months, as of early 2011 it remained in worse condition than before the crisis. The unemployment rate, which reached the almost record high of 6.41 percent in September 2009, declined every month (with the exception of the seasonal peak of January) to 4.81 percent in March 2010 but then rose to above 5 percent for most of 2010 (see figure 2.4). It again rose above 5 percent in April 2011 and reached 5.2 in June 2011. Thus, unemployment has hovered above 5 percent since October 2009, which is above the pre-crisis level. The 12-month average unemployment rate has flattened out but it is nearly 2 percentage points above the average in early 2008. A significant reduction in unemployment is still needed to grow the workforce to pre-crisis levels.

6
We use several metrics of women’s economic empowerment as the dependent variable in our models. Our main outcome variable is women’s labor force participation rate. We also look at this outcome disaggregated by education level as Olivetti and Petrongolo, 2017 suggest that maternity leave is more beneficial for women earning lower incomes and, as such, we use education level as a proxy for a woman’s earning potential. On the other hand, the model of Eckberg et al. 2015 suggests that women with higher skill levels may disproportionately suffer when the gap in leave allocation between both parents is large. As such, the differential relationship between leave policies and women’s skills is an empirical question we investigate in the data. We also test the relationship between family leave policies and the proportion of women in a country that are employed in wage and salaried jobs (a proxy for employment in the formal sector). We consider the relationship between a country’s family leave policies and the proportion of women in managerial positions, a proxy for the seniority of employment. Additionally, we ask whether there is an association between leave policies and the gender wage gap. Finally, we examine the correlation between leave policies and attitudes towards women’s work. Our outcome variables are summarized in Table 1 below.

_Table 1: Dependent variables and data sources_

|**Variable**|**Source**|
|---|---|
|**Labor forceparticipation rate, female(% of femalepopulation ages 15-64) **|World Bank Gender Data Portal|
|**Labor force participation rate for females with basic education (% of female**<br>**working-age population with basic education)**|World Bank Gender Data Portal|
|**Labor force participation rate for females with intermediate education (% of**<br>**female working-age population with intermediate education)**|World Bank Gender Data Portal|
|**Labor force participation rate for females with advanced education (% of**<br>**female working-age population with advanced education)**|World Bank Gender Data Portal|
|**Gender wage gap (unadjusted)**|ILO & OECD|
|**Wage and salaried workers(or employees), female(% of female employment) **|World Bank Gender Data Portal|
|**Female share of employment in senior and middle management(%)**|World Bank Gender Data Portal|
|**Attitudes with respect to whether men should have more right to a job than**<br>**women whenjobs are scarce**|World and European Values Surveys|

# _4.2 Estimation results_

Considering the correlation between leave policies and women’s labor force participation, table 2a shows that a higher gap between the leave allocated to mothers versus fathers is negatively correlated with women’s participation in the labor market. The absolute number of leave days allocated to mothers is also negatively associated with women’s participation in the labor market. If we consider the association between leave policies and labor supply disaggregated by education, the results are not intuitive. A larger leave gap is associated with a greater labor force participation rate for women with the lowest levels of
> 2 The recruitment agencies we worked with were eager to attract workers from Sorsogon Province, particularly for jobs that require less-specialized work experience, for which they reported difficulty in filling vacancies. They were hesitant to recruit in rural areas because although they had no difficulty identifying qualified workers, in the past they found that applicants would initiate but could not complete the process.

3 A barangay is the smallest administrative division in the Philippines. The municipalities we selected each have between 25 and 65 barangays, and there are a total of roughly 42,000 barangays in the country.

4 For the passport sample, we also required that individuals be between ages 20-40. Tables A9 and A10 demonstrate that our results are not affected by including the 855 respondents ages 41-45 who participated in the baseline survey.

> 5 Based on authors’ calculations from 2000 data from the Global Bilateral Migration Database (World Bank Group 2011, Özden et al., 2011). Overall, the global stock of migrants is predominantly male. However, as of 2000, the estimated stock of migrants from the Philippines was 61.1% female, while the stock was 44.7% female from Mexico, 42.4% female from Bangladesh, and 39.0% female from India.

6 This and all other conversions based on the average exchange rate from February-June 2010, 1 USD = 45.0497 PHP (OANDA, 2012).

> 7 See section A.1 for additional details on the endline survey.

8 See A.1 for additional details 9 The full text of these interventions is included in an online appendix, which can be found at <u>https://sites.google.com/site/eabeam/webappendixa_interventions.pdf. Note that pilijobs.org is no longer</u> available, since it was taken down when our project ended.

10 One potential reason for this is that more accurate information may dissuade overly optimistic individuals from searching, balancing out an increase in search from individuals who undervalue the gains from migrating. Indeed we find (and show in Table A7) that providing only financial information or passport information without other facilitation has a small negative impact on job search, consistent with individuals understating the costs and complexity of moving.

31
|Other|(7)|-0.01|(0.02)|0.05***|(0.01)|0.06<br>Yes|9200|0.08||
|---|---|---|---|---|---|---|---|---|---|
|Other government<br>health facility|(6)|-0.005|(0.05)|-0.043**|(0.02)|0.12<br>Yes|9200|0.02|ages).|
|Self-<br>treatment|(5)|-0.03*|(0.02)|0.03**|(0.01)|0.37<br>Yes|9200|0.09|ent areas.<br>ted in percent|
|Traditional<br>healer|(4)|-0.013*|(0.008)|-0.002|(0.005)|0.028<br>Yes|9200|0.03|within catchm<br> illness (repor|
|Private-for-<br>pro...t|(3)|0.03|(0.02)|-0.02|(0.014)|0.25<br>Yes|9200|0.04|vel.<br> are clustered <br>viders in case of|
|NGO|(2)|-0.004|(0.006)|0.008|(0.005)|0.03<br>Yes|9200|0.09|) percent le<br>ance terms<br>service pro|
|Project facility|(1)|0.034*|(0.019)|-0.08***|(0.013)|05<br>0.26<br>Yes|9200|0.08|...cance at the 1 [5] (10<br>n parenthesis. Disturb<br>izens’use of di¤erent|
|Dependent variable|Speci...cation|Program impact|(Treatment*2005)|2005||Mean control group 20<br>Facility ...xed e¤ects|Observations|R2|a. *** [**] (*) denote signi<br>b. Robust standard errors i<br>c. Dependent variable is cit|

53