rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 7562

# **Abstract**

In Latin America, labor markets have been the main channel through which growth has reduced poverty, with higher labor income accounting for 49 percent of the reduction in poverty in 2008–13. Understanding labor markets is critical to designing policies and programs aimed at reducing poverty. With close to 70 percent of the population under age 30 years, labor markets are bound to be central to defining Haiti’s future. Yet, labor analysis in Haiti has been constrained by the dearth of data and the focus on - measuring the impact of the 2010 earthquake. This pres ent paper contributes to filling this gap by providing an overview of Haiti’s labor markets and the determinants of labor income over a decade, focusing on growing urban areas. The paper also contributes to the research on Haiti in general, as well as labor markets in fragile countries such as Haiti, in particular through an unprecedented effort to harmonize three household surveys conducted between 2001 and 2012. Building on this exercise, the study provides new insights into the development of labor markets in a particularly turbulent decade for Haiti, one that was marked by the political crisis of 2004 and the earthquake of 2010. In spite of the earthquake, the analysis shows that Haiti’s labor markets are characterized by continuity over the period. Somewhat surprisingly, the defining features remain overall unchanged in spite of the shock, pointing to heavy forces shaping economic and labor dynamics.

This paper is a product of the Poverty and Equity Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at arodella@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
The fact that the program impact is not heterogeneous along the gender and the student socio-economic dimension alleviate equity concerns about the impact of the program. Nevertheless, the program could still be disproportionately benefiting high SES students if they are disproportionately represented in the group of high performers. We assess in our sample by looking at the composition of students who scored credit and above in GABECE. Column 1 in Table A6 shows that the share of the student who scores credit and above is similar in each of the first three quartiles of student SES (ranging 42-43%) and 53% in the top quartile. There is also a 5 percentage point gap in favor of boys (47% vs 42%). Both of these variations are not disproportionately large as anticipated. Furthermore, column 2 in the same table shows that of all the students who scored credit and above in GABECE, 25.4% are in the bottom quartile of SES and 61% are in the bottom half of SES, of which the gender composition is about balanced as shown in the bottom panel of Table A6. Surprisingly, there are more girls than boys high performers at baseline. Of all the high performers at baseline, (60.4% were girls and 39.6% were boys, and thus the program benefited more girls than boys. If the observation from our sample is true at the national level, then we can conclude that the program does not pose a significant equity issue in terms of student socio-economic background and does benefit girls more than boys. This may not be surprising because the poor kids who make it to high school are often among the high performers.

# 5. Qualitative Assessment

To arrive at a fuller picture of the program’s impact and draw relevant policy implications, we complement the rigorous estimation of the program’s impact on test scores with qualitative analysis. The qualitative analysis includes a qualitative survey, as well as focus group interviews with teachers, school administrators, and the Ministry of Education officials. The goal of the qualitative study was to understand teachers’ perceptions of the technology, whether any implementation challenges occurred, and to what extent the challenges would help explain the results obtained in the rigorous quantitative evaluation.

School principals and teachers have highly positive views about the program. For instance, about 80 percent of the mathematics teachers and 95 percent of the science teachers 25
# **Tobacco Price Elasticity and Tax Progressivity in Moldova**

Alan Fuchs and Francisco Meneses<sup>1</sup> JEL Codes: H23, H31, I18, O15

> 1 Fuchs: Poverty and Equity Global Practice, World Bank, 1818 H Street NW, I 4-405, Washington, DC 20433 (email: <u>afuchs@worldbank.org). Meneses: Duke University (email: fjmeneses@gmail.com). Irina Guban, contributed with the collection of</u> health data. Support for the preparation of this report was provided by the World Bank’s Global Tobacco Control Program, cofinanced by the Bill and Melinda Gates Foundation and Bloomberg Philanthropies. The authors are grateful to Luis Felipe Lopez-Calva, Oscar Calvo-Gonzalez, Patricio Marquez, Maria Eugenia Genoni, Olena Doroshenko, Sheila Dutta, Gabriela Inchauste and Alexandru Cojocaru for providing comments and support for this paper. The findings, interpretations, and conclusions in this research are entirely those of the authors. They do not necessarily represent the views of the World Bank Group, its Executive Directors, or the countries they represent.
Table 1 also shows that SMEs are numerous and employ the majority of workers in China. They account for 55% and 79% of China’s value added and employment in 2008, respectively. In terms of gross exports, their contribution is much smaller – only 28%. This low share of exports is consistent with the conventional view that most small firms do not export because of the potentially high fixed export costs. In terms of value added exports, they account for 42%. The much larger contribution to VAX implies that SMEs have a higher share of indirect exports, either through other SMEs or other types of firms. In terms of the aggregate gross exports and VAX, SOEs and SMEs look similar, but both the share of gross and value added exports by SMEs decreased from 2007 to 2010. We will reveal key underlying differences in terms of their distributions across industries and the channels through which they achieve a high value added to gross export ratio below.

As expected, FIEs are much more export-oriented. They are small in number, similar to SOEs, but account for close to half of Chinese gross exports. Their share in total value added exports is much smaller (only 27%), consistent with the literature that finds low domestic value added in Chinese exports, particularly in processing exports (Koopman, Wang, and Wei, 2012; Kee and Tang, 2013). To the extent that most of the processing firms are FIEs, which include firms owned by investors from Hong Kong, Macau, and Taiwan (HKMT), the results are not surprising. Processing firms import a large fraction of intermediate inputs and are responsible for the final stage of production, by taking advantage of the low labor costs in China.

# **4.1.2 The Domestic Segment of GVCs (VAX based on the Forward-linkage Approach)**

Next, we use our split IO tables to decompose VAX by firm type into direct and indirect VAX, based on both the forward- and backward-linkage approaches, as described in Section 2. We will first report results based on the forward-linkage approach.

For indirect VAX, we further measure the paths through which a firm type export indirectly. Table 2 presents these results, along with the volume of gross exports by firm type. Before turning to the details of indirect VAX, it is worth highlighting that for the 4 firm groups considered here, both SOE and SME have the VAXR exceeding 1. Specifically, Panel A shows that the VAXR of SOEs and SMEs are 1.17 and 1.02 in 2007, respectively. As a comparison, the VAXR of FIEs and LPs are 0.36 and 0.70, respectively. The finding of SOEs’ VAXR larger than unity confirms the results in Table 1 that SOEs’ contribution to Chinese exports is much larger if measured in value added terms than in gross terms. Moreover, these findings contrast sharply with the evidence for developed countries, such as the United States, where large firms’ share in gross exports is usually higher than that in value-added exports (i.e., the 21
Table A5: Upstream Variety Counts

|Industry Sector||||Ye|ar||||
|---|---|---|---|---|---|---|---|---|
||2000|2001|2002|2003|2004|2005|2006|2007|
|01:live animals (1-5)|287.7|288.2|292.1|289.9|291.2|293.5|291.5|293.4|
|02:vegetables (6-14)|333.4|335.0|340.4|339.2|340.9|344.1|342.3|342.8|
|03:animal or vegetable oil (15)|294.2|294.5|297.9|295.6|296.4|299.2|297.3|298.0|
|04:beverages & spirit (16-24)|307.3|308.4|313.3|311.7|313.2|316.2|314.3|315.3|
|05:mineral products (25-27)|253.5|256.0|258.9|261.2|262.6|265.2|266.5|265.4|
|06:chemical products (28-38)|304.5|307.4|312.4|313.5|315.5|318.6|319.8|316.9|
|07:plastics & rubber (39-40)|263.6|263.6|268.4|268.1|270.9|273.2|273.6|272.1|
|08:raw hides & skins (41-43)|308.1|309.1|312.8|310.8|312.1|314.5|314.1|312.2|
|09:wood & articles (44-46)|186.2|188.2|192.3|192.0|194.1|195.2|193.6|193.2|
|10:pulp of wood (47-49)|202.6|205.3|207.3|209.4|209.6|213.3|210.8|209.8|
|11:textiles (50-63)|445.7|447.2|452.0|449.6|452.3|454.4|453.1|451.8|
|12:footwear & headgear, etc. (64-67)|374.5|374.6|378.6|376.5|379.5|381.1|380.4|378.6|
|13:stone, plaster, cement, etc. (68-70)|282.2|284.2|288.9|289.9|292.3|294.6|295.5|293.6|
|14:precious metals (71)|310.3|313.5|319.3|320.1|323.8|326.3|326.9|324.4|
|15:base metals (72-83)|348.7|352.7|359.5|361.0|366.4|369.0|370.4|367.4|
|16:machinery, mech, elect eqmt (84-85)|447.6|450.9|456.3|457.6|461.6|463.6|464.3|462.9|
|17:vehicles & aircraft (86-89)|296.4|297.3|302.6|304.7|308.1|309.4|310.9|311.0|
|18:optical, photographic, etc. (90-92)|421.6|424.6|430.7|430.9|435.7|437.6|438.3|435.8|
|20:misc manufacturing (94-96)|326.8|328.5|333.5|333.0|336.6|338.4|338.5|336.4|

Source: China’s Customs Trade Data and National Bureau of Statistics Manufacturing Survey. Each variety is de...ned as a HS-6 digit product.

21
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Policy Research Working Paper 9249

# **Abstract**

This study reports the findings of a large-scale, multiple-arm, cluster-randomized control study carried out in rural Punjab, India, to assess the impact of a flagship sanitation program of the Government of India. The program, the Clean India Mission for Villages, was implemented between October 2014 and October 2019 and aimed to encourage the construction of toilets, eliminate the practice of open defecation, and improve the awareness and practice of good hygiene across rural India. It utilized a combination of behavioral change campaigns, centered on the community-led total sanitation approach, and financial incentives for eligible households. The study also evaluates the incremental effects of intensive hygiene awareness campaigns in selected schools and follow-up initiatives in selected communities. The study finds that the coverage of “safely managed” toilets among households without toilets increased by 6.8–10.4 percentage points across various intervention arms, compared with a control group. Open defecation was reduced by 7.3–7.8 percentage points. The program also had significant positive impacts on hygiene awareness among adults and children, although the interventions of school campaigns and intensive follow-up were of limited additional impact.

This paper is a product of the Water 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 Landres@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
36

# TABLE 9 : Productivlty Equation for OLDAREA

Dependent variable is LOLDPROD 18 observations used for estimation from 1968 to 1985

|Regressor<br>Co|efficient|T-Ratio|
|---|---|---|
|CONS|-2.8642|-6.3135|
|TR66|.0206|2.5136|
|LPRICOCO(-2)|.3905|4.4258|
|R-Squared|.7924||
|F-statistic F( 2, 15)|28.6345||
|R-Bar-Squared|.7648||
|S.E. of Regression|.1483||
|Residual Sum of Squares|.3298||
|Mean of Dependent Variable|-.4164||
|S.D. of Dependent Variable|.3057||
|DW-statistic|2.2468||

Diagnostic Score (L") Test Statistics A:Serial Correlation CHI-SQ( l)-.8410 B:Functional Form CHI-SQ( 1)=.0372 C:Normality CHI-SQ( 2)- .2994 D:Heteroscedasticity CHI-SQ( 1)= .0424
# **1. Introduction**

Recent popular and academic writing has suggested that Africa may be at a turning point in its long economic decline (Ndulu et al. 2006; Commission for Africa 2005). Beginning about the middle of the 1990s, growth accelerated in a number of countries, and the region’s average growth rate began to approach that of other developing countries for the first time since the mid-1970s. To understand whether Africa is at a turning point, however, it is useful to understand the stylized facts of the region’s post-independence long-run growth performance.

Using the most recent purchasing power parity (PPP) data for 44 sub-Saharan African countries, this paper examines the characteristics of long-run growth in Africa between 1975 and 2005. We were interested in examining the following issues: crosscountry income structure, convergence, the country-level distribution of income, and growth persistence. Also, we examined the data for evidence of the formation of country groups or “clubs.” The time period includes the first oil shock and commodities prices plunge, when many African economies collapsed and several conflicts erupted; the introduction of structural reforms, which brought significant changes in many economies; and the growth recovery observed more recently. Given the rich and varied economic changes experienced by African countries, our time-series may well capture long-term economic trends.

The next section describes our data and methods and is followed by a section examining the characteristics of Africa’s long-run growth experience. The fourth section of the paper looks at the structure of gross domestic product (GDP) per capita across Africa. We then identify four groups of countries according to their income levels and growth experiences, and we look for some common characteristics that are associated with the groups. Next, we probe more deeply into the consequences of growth volatility for economic performance and test for some correlates of volatility.

# **2. Data and methods**

We analyzed patterns of GDP per capita growth at 2000 international PPP prices and their respective standard deviations (SDs) and coefficients of variation (CVs), which are our measures of volatility. Although there are differences between GDP per capita at 2
26 acute respiratory infection (ARI) treatment variable is therefore only defined for the subgroup of children who experience cough and breathing difficulties coming from the chest. By contrast, many DHS do not include a question on where the respiratory problems originate, and the medical care seeking question is asked for _all_ children with cough _or_ breathing problems. The absence of a question on whether the breathing problems come from the chest prevents us from computing an ARI treatment variable for the DHS that is identical to that of the MICS. We are, however, able to obtain an ARI treatment variable that is consistent across all DHSs by defining ARIs (and thus the sample for which the treatment variable is computed) as having a cough which coincides with difficulties breathing.<sup>28</sup> Other indicators for which we do not achieve full consistency are 4+ antenatal care visits, children under 5 sleeping under insecticide-treated bed nets and measles immunization.<sup>29</sup> In some cases where the original survey questions differ, we try to harmonize indicators using an ex post adjustment of the population (and quintile) rates. Specifically, we flag and subsequently adjust cancer screening and inpatient utilization rates whenever the reported recall period differs from our preferred recall period: 5 years for pap smears and 2 years for mammograms according to WHO recommended screening intervals for our preferred age-groups 30-49 (pap smears) and 50-69 (mammograms) (World Health Organization 2013a, 2014), and 12 months as the most

> 28 ARI treatment rates constructed from WHS data are substantially higher than those obtained from DHS and MICS. We suspect these differences to be due to divergences in the survey methodology: Among other things, WHS data are only available for the _first_ health care seeking response if the _youngest_ child under 5 in the household suffered an ARI, whereas DHS and MICS ARI treatment data come from _all_ health care seeking responses of _all_ children under 5 with an ARI in a household. The HEFPI database therefore does not include ARI treatment data from the WHS.

> 29 For antenatal care, the MICS 2 antenatal care visit data only refer to visits to a _specific provider_ , whereas all later MICS waves and all DHSs do not impose this limitation. For all DHSs and all MICSs from 2002 onwards, a bed net is considered treated if it a) is a long-lasting treated net, b) a pre-treated net that was purchased or soaked in insecticides less than 12 months ago, or c) a non-pre-treated net which was soaked in insecticides less than 12 months ago. By contrast, data limitations in the MICS 2 wave (collected before 2002) restrict our definition of treated nets to those _ever_ treated. For antenatal care, the MICS 2 antenatal care visit data only refer to visits to a specific list of providers, whereas all later MICS waves and all DHSs do not impose this limitation. WHS measles immunization data are only available for the _younges_ t child in the household, whereas our DHS and MICS measles immunization data come from _all_ children under 5 in a household.
**Table A.3: Use of public transportation**

||**(1)**|**(2)**|**(3)**|
|---|---|---|---|
|_Distance to public transportation stops_||||
|1–2 km|−0.017|0.021|0.032*|
||(0.018)|(0.018)|(0.018)|
|2–3 km|−0.117***|−0.083***|−0.084***|
||(0.027)|(0.027)|(0.027)|
|More than 4 km|−0.266***|−0.198***|−0.169***|
||(0.025)|(0.026)|(0.027)|
|_Demographic characteristics_||||
|Male||0.100***|0.071***|
|||(0.015)|(0.015)|
|Secondary||0.189***|0.165***|
|||(0.021)|(0.021)|
|University||0.302***|0.206***|
|||(0.023)|(0.024)|
|Other||0.113***|0.080***|
|||(0.027)|(0.027)|
|Youth (15–24)||−0.072**|−0.077**|
|||(0.033)|(0.032)|
|_Net monthly income_||||
|<br>US$25–US$199||0.277***|0.251***|
|||(0.025)|(0.025)|
|US$200–US$399||0.259***|0.213***|
|||(0.031)|(0.032)|
|More than US$400||0.038|−0.017|
|||(0.034)|(0.035)|
|_Job characteristics_||||
|Employer|||−0.141***|
||||(0.031)|
|Self-employed|||−0.158***<br>|
||||(0.019)|
|Private|||−0.030*|
||||(0.018)|
|Industry|||0.218***|
||||(0.046)|
|Services|||<br>0.250***|
||||(0.042)|
|Other|||<br>0.233***|
||||(0.040)|
|Adj-R<sup>2</sup>|0.012|0.097|0.124|
|Obs.|5,747|5,645|5,490|

_Note:_ Standard errors in parentheses.* _p_ < 0.1, ** _p_ < 0.05, *** _p_ < 0.01.

27
Although access to qualified personnel continues to be an important driver, during the crisis this had lower relative salience compared to cost (Lewin et al., 2009). This is temporarily offsetting the search for global talent as a mayor driver for offshore services. However, the ORN study argues that the slowing demand for talent can be explained by increased unemployment fueled by the economic crisis, which has made this talent more available domestically.

**Figure 7. Changes in Key Drivers of Offshoring Decisions, November 2008**

<!-- Start of picture text --> 80% 74%<br>70% 61% 59%<br>55%<br>60% 51%<br>48%<br>50% 42% 39% 41% 41% 39%<br>40% 31% 32%<br>25%<br>30%<br>20%<br>20% 11%<br>7% 6% 7% 8%<br>10% 1%<br>0%<br>Labor cost Enhancing Part of Increasing Access to new Access to Growth<br>saving efficiency company speed to markets for qualified strategy<br>through global strategy market products and personnel<br>business services offshore<br>process<br>redesign<br>Less important No change More important<br><!-- End of picture text --> Source: CGGC, Duke University based on (Lewin et al., 2009).

On the supply side, the economic crisis has also forced providers to reduce prices, which has decreased both annual hiring and salaries. The recession has increased the motivation of providers to improve the efficiency and quality of their services. Some providers have found new opportunities as a result of the crisis by engaging in innovative solutions for their clients. Providers are reevaluating their value proposition to their customers and they have been forced to develop out-of-the box services (NASSCOM Newsline, 2009).

Table 3 shows the revenues of offshore service providers, both prior to and during the economic crisis. The largest decline in provider revenues was during the April-June 2009 trimester. Almost all companies show negative growth rates compared to the same period in 2008. Accenture ́s revenues dropped 16% in the period, while those of IBM declined by 13.3%. Providers from developed countries show a sharper decline than providers from India (Tata Consultancy Services, Wipro, Infosys Systems). The July-September 2009 trimester showed signs of recovery.

Many leading service providers from the developed world have more employees in emerging nations than in their own countries. This trend has accelerated during the recession. In order to mitigate the effects of the economic crisis, large providers have decided to expand their 14