rafmacalaba/datause-encoder-data huggingface dataset:
<mark>Duflo, E., (2012). Women empowerment and economic development.</mark> _<mark>Journal of Economic Literature</mark>_ <mark>,</mark> _<mark>50</mark>_ <mark>(4), pp.1051-1079.</mark>

- <mark>Duflo, E., Glennerster, R., & Kremer, M. (2007). Using randomization in development economics research: A toolkit.</mark> _<mark>Handbook of development economics</mark>_ <mark>,</mark> _<mark>4</mark>_ <mark>, 3895-3962.</mark>

- <mark>Guilmoto, C. Z. (2012). Son preference, sex selection, and kinship in Vietnam.</mark> _<mark>Population and Development Review</mark>_ <mark>,</mark> _<mark>38</mark>_ <mark>(1), 31-54.</mark>

- Hallman, K., Quisumbing, A.R., Ruel, M. and Brière, B.D.L., (2005). Women’ work and child care: findings from the urban slums of Guatemala City. _Economic development and cultural change_ , 53(4): 855-885.

- <mark>Havnes, T., & Mogstad, M. (2011). Money for nothing? Universal child care and maternal employment.</mark> _<mark>Journal of Public Economics</mark>_ <mark>,</mark> _<mark>95</mark>_ <mark>(11-12), 1455-1465.</mark>

- Heckman, J., LaLonde, Robert, J; and Smith Jeffrey A. (1999). The economics and econometrics of active labor market programs. In _Handbook of Labor Economics_ 1999; volume 3, edited by Ashenfelter, A. and D. Card. Elsevier Science.

- <mark>Imbens, G.W. and Lemieux, T., (2008). Regression discontinuity designs: A guide to practice.</mark> _<mark>Journal of econometrics</mark>_ <mark>,</mark> _<mark>142</mark>_ <mark>(2), pp.615-635.</mark>

- <mark>Lee, D.S. and Lemieuxa, T., (2010). Regression discontinuity designs in economics.</mark> _<mark>Journal of Economic Literature</mark>_ <mark>,</mark> _<mark>48</mark>_ <mark>(2), pp.281-355.</mark>

- Lanjouw, P., Marra, M., & Nguyen, C. (2017). Vietnam’s evolving poverty index map: Patterns and implications for policy. _Social Indicators Research_ , 133(1), 93-118.

- Lefebvre, P., and Merrigan, P. (2008), Child‐care policy and the labor supply of women with young children: A natural experiment from Canada. _Journal of Labor Economics_ , 26(3), 519-548.

- <mark>Li, Y. (2017). The effects of formal and informal child care on the mother's labor supply— Evidence from urban China.</mark> _<mark>China Economic Review</mark>_ <mark>,</mark> _<mark>44</mark>_ <mark>, 227-240.</mark>

- Lokshin, M.M., Glinskaya, E. and Garcia, M., (2004). The effect of early childhood development programmes on women’s labour force participation and older children’s schooling in Kenya. _Journal of African Economies_ , 13(2): 240-276.

- Long, J. S. (1997). _Regression Models for Categorical and Limited Dependent Variables_ . Thousand Oaks, California: SAGE Publications, Inc.

- <mark>Martínez, C., & Perticará, M. (2017). Child care effects on maternal employment: Evidence from Chile.</mark> _<mark>Journal of Development Economics</mark>_ <mark>,</mark> _<mark>126</mark>_ <mark>, 127-137.</mark>

- <mark>Maurer-Fazio, M., Connelly, R., Chen, L., and Tang, L. (2011). Childcare, eldercare, and labor force participation of married women in urban China, 1982–2000.</mark> _<mark>Journal of Human Resources</mark>_ <mark>, 46(2), 261-294.</mark>

25
MOZAMBIQUE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Power and ICT infrastructure networks follow population and concentrates at the nodes of the transport corridors. Greater density of power and ICT provision is thus found in the south-central and southern areas of the country (figure 3b, c).

The relevance of Mozambique in the regional context should not be overlooked. In terms of transport, the areas around Beira, Zambezi Valley, Nacala, and Limpopo—all covered by the railroad corridors— see their economic potential powered by complementarities with the economies of landlocked neighbors (Zimbabwe, Zambia, and Malawi) whose closest and natural ports are Beira, Maputo, and to a lesser extent Nacala. Over the past years, Mozambique has made a big effort to capitalize on these geographic advantages, integrating different transport modes within the country and with neighboring countries. The central and south railway lines depart from the Beira and Maputo ports, respectively, and connect with a network of primary and secondary roads that extend to Malawi, Zimbabwe, and South Africa. And the recent construction of a new terminal building in the Maputo Airport expended its passenger and cargo capacity.

The regional importance of Mozambique also extends to the power and ICT sectors. The country, already a net exporter of electricity and a member of the Southern Africa Power Pool (SAPP), still has huge untapped hydropower potential and the possibility of becoming a key player in the regional power market. In the realm of the ICT, Mozambique has developed a network of fiber optics connecting the country and its neighbors to the nearby South Atlantic 3 (SAT-3) submarine cable.

5
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continuation as some enrolled youth will be absent the day of the ENLACE application and others may have repeated a grade/grades and thus not be in the ENLACE application. In our estimations, our objective will be the comparison of levels to derive impact. Still it is noteworthy in Table 3 that enrollment levels for indigenous continuation rates are even lower both for females and males than for the overall sample of children in medium to very poor areas.<sup>11</sup> Table 3: Enrollment Rates in 7th Grade, 9th Grade and 12th Grade

||Primary schools in<br>High Marginali|6th Graders in 200<br>Medium to Very<br>zed Areas|8<br>Indigenous Pr|imarySchools|
|---|---|---|---|---|
||Male|Female|Male|Female|
|7th grade|59.13%|63.79%|45.56%|44.92%|
|9th grade|59.22%|62.97%|50.16%|49.27%|
|12thgrade|29.31%|32.43%|24.95%|24.96%|

Source: Author’s calculations using ENLACE databases.

Table 4 shows the distribution of sixth grade students who enroll in seventh grade and in which types of schools. The majority of indigenous children enroll in tele-secondary schools with about 20% enrolling in technical secondary schools. Only about 10% enroll in general secondary schools. The proportions in tele-secondary schools are somewhat lower for the overall sample of youth in marginalized areas at about 59% for boys and 46% for girls. Girls have a correspondingly higher probability of enrolling in a general school within our sample of schools located within marginalized areas.

Table 4: Secondary School Attended in 7<sup>th</sup> Grade

||6th<br>Primary Schools in<br>High Marginali|Graders in 2008<br>Medium to Very<br>zed Areas|Indigenous Pri|marySchools|
|---|---|---|---|---|
||Male|Female|Male|Female|
|General|20.78|32.75|10.3|11.11|
|Private|0.88|2.75|0.72|0.92|
|Tele-secondary|58.79|45.5|67.64|66.12|
|Technical|19.55|19|21.34|21.85|

Source: Author’s calculations using ENLACE databases.

Finally, Tables 5a and 5b show results in the mathematics ENLACE score between sixth grade and twelfth grade for the population that continue in school until twelfth grade, with separate tables for students in schools in marginalized and very marginalized areas in sixth grade and for students in indigenous primary schools in sixth grade. The tables make clear that a majority of children in both samples have poor results on the ENLACE test, with the vast majority attaining

> 11 Table 3 shows that for children in indigenous primary schools, enrollment in ninth grade is higher than enrollment in seventh grade. This likely reflects higher absentee rates for the ENLACE in seventh versus ninth grade for this population.

17
Policy Research Working Paper 7935

# **Abstract**

Global poverty is becoming increasingly concentrated in Sub-Saharan Africa and among households engaged in subsistence agriculture in environments characterized by uncertainty. Understanding how to achieve sustainable increases in household incomes in this context is key to ending extreme poverty. Uganda offers important lessons in this regard. Uganda experienced conflict, drought, and price volatility in the decade from 2003 to 2013, while at the same time experiencing the second fastest percentage point reduction in extreme poverty per year in Sub-Saharan Africa. This study analyzes a nationally representative panel of 2,356 households visited four times between 2006 to 2012, in combination with data on conflict events, weather, and prices. The study describes the type of income growth households experienced and assesses the importance of these external events in determining progress. The study finds substantial growth in agricultural incomes, particularly among poorer households. Many of the gains in agricultural income growth came about because of good weather, peace, and prices, and not technological change or profound changes in agricultural production. Therefore, although overall progress during this period was good, there were years in which average income growth was negative. This was particularly the case in the poorer and more vulnerable Northern and Eastern regions, and thus their overall income growth was also slower.

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 rhill@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
we examine the relationship between _contemporaneous_ exposure to pollution and hour worked. Note that an important omitted variable is local level of traffic. High traffic could raise air pollution and, by increasing transportation costs, also reduce labor supply. This alternative explanation would imply a contemporaneous relation between these two variables. However, the relationship is small and statistically insignificant (see column 2 Table 4).<sup>37</sup> Columns 3 through 5 in Table 4 check the robustness of our results to alternative specifications. Results are robust to using non-linear functions of weather, a narrower definition of susceptible population, and using a log-linear specification. In column 6, we also test alternative variance structures, and estimate standard errors using the procedure suggested by Conley (1999).<sup>38</sup> A key finding is that PM2 _._ 5 has different effects on hours worked in households with and without susceptible dependents. As discussed in Section 3.2, there are some observable differences between these two types of households. Households with susceptible dependents tend to have more income earners, relatively younger workers, and be slightly poorer. A relevant concern is that these differences, not the presence of susceptible individuals, are driving the heterogeneous results. This could happen, for instance, if poverty, worker’s age or household size influence how pollution affects labor supply.

We examine this alternative explanation by estimating a model using individuals in both types of households and adding interactions of PM2 _._ 5 with an indicator of having a susceptible individual and other observable characteristics (see Table B.3 in the Appendix). This specification allows us to examine other sources of heterogeneous effects of air pollution. Results, however, confirm the original findings: PM2 _._ 5 only seems to reduce hours worked among individuals in households with susceptible individuals.

Finally, we examine sample attrition as a source of selection bias. Note that, by using a panel sample and household fixed effects, we reduce concerns of residential sorting bias, i.e, bias due to systematic household differences that are correlated with exposure to pollution. However, households may drop from the panel sample in a systematic way. For example, wealthier, better educated, households may be more able to re-locate in response to changes in air pollution, and thus be more likely to drop from the panel sample. To the extent that

> 37Results are also insignificant when using average PM2 _._ 5 in weeks _t_ + 1 or _t_ + 2.

> 38 We use the STATA ado file _reg2hdfespatial_ developed by Fetzer (2014) and based on Hsiang (2010). We use a 1 year time lag and a distance cutoff of 8 kilometers.

18
- 14 -

as South Africa, Chile, Singapore, and Malaysia have a dominant or a very important contractual savings sector.

**Figure 3: Contractual savings in system financial assets (%,1996)**

<!-- Start of picture text --> tO0% - 1 _ l 1 |<br>90%<br>70%<br>60% *<br>50% EI 6||<br>40% |l | X l1 l<br>30% il l<br>20X %ll<br><!-- End of picture text -->

**Source:** 1998 OECD Institutional Investors Statistical Yearbook and WB institutional investors database.

Figure 4 shows the positive correlation between the financial assets of contractual savings institutions and market capitalization as a fraction of GDP for a cross section of OECD and non-OECD countries in 1996 (the positive relation is very stable for different years). Those countries with a more developed contractual savings sector are also countries with more developed stock markets. Furthermore, Figure 5 indicates a positive relationship between contractual savings development and the liquidity of the capital markets (measured by value traded over GDP).

Figure 6 explores the relationship between **changes** in contractual savings as a fraction of GDP and **changes** in market capitalization over GDP for the same countries between 1990 and 1996. Figure 7 presents a similar relationship between **changes** in contractual savings and **changes** in value traded as a fraction of GDP. It is clear that those countries that were able to develop their contractual savings sector also show a higher growth in their stock markets in terms of capitalization and value traded in the same period. The same conclusions are reached with estimates using panel data for 26 countries and with about 300 observations. **'**<sup>**5**</sup> savings institutions belong to the household sector. Of course, there is some double counting since assets of contractual savings institutions include cash and bank deposits. 15 See Impavido and Musalem, 2000
health problems affecting the population are mainly infectious diseases and parasites (malaria, diarrhea, respiratory infections). Outbreaks of meningitis and cholera are frequent in the country and cause many deaths among the poor and destitute.

## 2.2  Health System and Services

The health system in Chad is structured into four levels of responsibility. First, the ministry of health (MoH) is at the highest level and is in charge of stating national objectives for the health sector and formulating the adequate health policies. In second come the 14 Regional Health Delegations (RHDs) which are in charge of implementing the national strategy and managing health personnel at the regional level. Each RHD is composed of several districts for a total of 49 in the country. The districts, which constitute the third level of the health system, manage each several health facilities within their responsibility. Finally, the health facilities are at the bottom of the pyramid and are responsible for the provision of health services to the population. As of 2003, only 407 health centers out of 657 were functional (World Bank, 2004) and most could not provide all the services included in the minimum package of activities.

There are currently three doctors, two midwives and 4 nurses per 100,000 inhabitants, significantly below the WHO standard of 10 doctors, 20 midwives and 20 nurses. The vast majority of formal health sector personnel are active in the public and non-profit sectors. The formal, private for-profit, sector employs less than 1% of the health personnel (World Bank, 2004).

In addition to the formal health sector, there are many unaccounted for traditional healers and other informal practitioners known as “doctor choukous” and other resellers of medication. These informal health providers are active in both rural and urban areas.

# 3  Data and Survey

The data used in this paper come from the 2004 Quantitative Service Delivery Survey (QSDS) for which we drafted the survey instruments, organized the survey 3
# **Estimating Poverty among Refugee Populations: A Cross-Survey Imputation Exercise for Chad**

_Theresa Beltramo Hai-Anh H. Dang Ibrahima Sarr Paolo Verme_<sup>1</sup>

**JEL classifications** : C15, F22, I32, O15, O20.

**<mark>Keywords</mark>** <mark>: Refugees; Forced displacement; Targeting; Poverty; Chad.</mark>

1 * Theresa Beltramo: United Nations High Commissioner for Refugees, Rue de Montbrilliant 92, Geneva 1201, Switzerland (email: beltramo@unhcr.org); Hai-Anh Dang: DECAT- Analytics and Tools Team-The World Bank, 1818 H St. N.W. Washington, D.C. 20433 (e-mail: <u>hdang@worldbank.org); Ibrahima Sarr: United Nations High Commissioner for Refugees (e-mail: sarri@unhcr.org); Paolo Verme:</u> <mark>Fragility, Conflict, Violence - T</mark> he World Bank, 1818 H St. N.W. Washington, D.C. 20433 (email: pverme@worldbank.org <mark>). The authors are grateful to Andrew Dabalen, Giacomo De Giorgi, Kristen Himelein, Aly Sanoh, and participants to the American Economic Association’s session on “Comparing Poverty of Refugees and Their Hosts” (San Diego, January 5</mark><sup>th</sup> <mark>, 2020) for helpful comments on previous versions. This work is part of the program ``Building the Evidence on Protracted Forced Displacement: A Multi-Stakeholder Partnership''. The program is funded by UK aid from the United Kingdom's Department for International Development (DFID), it is managed by the World Bank Group (WBG) and was established in partnership with the United Nations High Commissioner for Refugees (UNHCR). The scope of the program is to expand the global knowledge on forced displacement by funding quality research and disseminating results for the use of practitioners and policy makers. We further thank DFID for additional funding through its Knowledge for Change (KCP) program. This work does not necessarily reflect the views of DFID, the WBG or UNHCR.</mark>
Figure 4.4: Years of Schooling of Domestic Migrants and Nonmigrants in Haiti, 2001 <!-- Start of picture text --> 10.0<br>9.0 Migrated<br>8.0 Not migrated<br>7.0<br>6.0<br>5.0<br>4.0<br>3.0<br>2.0<br>1.0<br>0.0<br>Artibonite Center Grand-Anse North Northeast Northwest West South Southeast Total<br>Region of Birth<br>Years<br><!-- End of picture text --> _Source:_ Own calculations based on ECVH 2001.

A variety of factors contribute to explain migration because not all migration is based on direct self-selection. To some surprise the majority of domestic migrants are women. The main reason for many women to migrate is that they follow their families (Table 4.10). However, positive assortative mating,<sup>32</sup> as described by Becker (1973), would mean that the spouse of a self-selected migrant also has better traits than his or her peers. In addition, the fact that some traits are hereditary means that youth who follow their families are likely to be positively selected. On the other hand, the insecurity and changes that follow migration may stress youth and thereby represent a risk factor. More research on migration is needed to reveal its implications for migrating youth.

Table 4.10 reveals that education is an important factor for migration. This explains to some extent why urbanization is taking place and the majority of migrants move to Port-au-Prince, and also why youth and adults living in the capital have more education on average than those in other regions.

|Table 4.10: Reason for Migr<br>Youth and Adults,2001(per|ation,<br>cent)|
|---|---|
|Followed family|36.5|
|Educational reasons|13.0|
|Personal reasons|11.7|
|Lack of work|9.4|
|Other(includingwork)|29.4|

_Source:_ Own calculations based on ECVH 2001.

> 32 Mating of similar people “—positive assortative mating—is extremely common, whether measured by intelligence, height, skin color, age, education, family background, or religion...” (Becker 1973).

22
TA3LE OF CONTENTS

# PAGE NO.

# PREFACE

|I. INTRODUCTION...............<br>..<br>*.****,*..........|01|
|---|---|
|II. THE GOVERNMENT AND THE CENTRAL BANK:<br>A UNIFIED APPROACH...............................|06|
|III. THE ANALYTICS OF BUDGET DEFICITS AND DEBT<br>ACCUMULATION<br>.....<br>...<br>to**.......12||
|IV. AN EMPIRICAL INVESTIGATION:<br>THE CASE OF KENYA<br>AND GHANA.<br>............<br>. ......<br>.....<br>.|20|
|1. Purpose of empirical investigation<br>and||
|description<br>of methodology.....................|21<br>|
|2. Kenya..................<br>000|24<br>|
|a. Background...........<br>...................|24<br>|
|b.Empirical results.........<br>. ..............|26<br>|
|3. Ghana......<br>*see*...............<br>*.e...*<br>......<br>*..|34|
|a.Background*........<br>..<br>..|34|
|b. Empirical results..<br>.........<br>sees|39|
|V. SUMMARY, CAVEATS AND CONCLUSIONS<br>........<br>...|51|
|TABLES||
|Table1. Kenya: Basic Fiscal Indicators..............|27|
|Table 2. Kenya: Fiscal Policy Concepts and||
|Macroeconomic Variables.................|30|
|Table 3. Kenya: A Consistent Fiscal Policy Framework....|32|
|Table4.Kenya: A Consistent Macroeconomic Framework....|35|
|Table 5. Ghana: Basic Fiscal Indicators<br>............|40|
|Table 6. Ghana: Fiscal Policy Concepts and<br>Macroeconomic Variables...........<br>....|43|
|Table 7. Ghana: A Consistent Fiscal Policy Framework....|46|
|Table8.Ghana: A ConsistentMacroeconomic<br>Framework....|48|