rafmacalaba/datause-encoder-data huggingface dataset:
|P000817|1989|1991||4 Closed|Gambia,The|
|---|---|---|---|---|---|
|P000817|1989|1992||5 Closed|Gambia,The|
|P000818|1993|1993||1 Closed|Gambia,The|
|P000818|1993|1994||2 Closed|Gambia,The|
|P000818|1993|1995||4 Closed|Gambia,The|
|P000818|1993|1996||7 Closed|Gambia,The|
|P000818|1993|1997||8 Closed|Gambia,The|
|P000818|1993|1998||10 Closed|Gambia,The|
|P000818|1993|1999||12 Closed|Gambia,The|
|P000819|1989|1989||2 Closed|Gambia,The|
|P000819|1989|1990||4 Closed|Gambia,The|
|P000819|1989|1991||6 Closed|Gambia,The|
|P000819|1989|1992||8 Closed|Gambia,The|
|P000819|1989|1993||10 Closed|Gambia,The|
|P000819|1989|1994||13 Closed|Gambia,The|
|P000819|1989|1995||14 Closed|Gambia,The|
|P000819|1989|1996||15 Closed|Gambia,The|
|P000819|1989|1997||15 Closed|Gambia,The|
|P000821|1990|1990||1 Closed|Gambia,The|
|P000821|1990|1991||7 Closed|Gambia,The|
|P000821|1990|1992||11 Closed|Gambia,The|
|P000821|1990|1993||13 Closed|Gambia,The|
|P000821|1990|1994||15 Closed|Gambia,The|
|P000821|1990|1995||16 Closed|Gambia,The|
|P000821|1990|1996||19 Closed|Gambia,The|
|P000821|1990|1997||21 Closed|Gambia,The|
|P000821|1990|1998||23 Closed|Gambia,The|
|P000821|1990|1999||24 Closed|Gambia,The|
|P000822|1990|1990||1 Closed|Gambia,The|
|P000822|1990|1991||3 Closed|Gambia,The|
|P000822|1990|1992||6 Closed|Gambia,The|
|P000822|1990|1993||8 Closed|Gambia,The|
|P000822|1990|1994||10 Closed|Gambia,The|
|P000822|1990|1995||13 Closed|Gambia,The|
|P000822|1990|1996||14 Closed|Gambia,The|
|P000822|1990|1997||17 Closed|Gambia,The|
|P000822|1990|1998||18 Closed|Gambia,The|
|P000825|1998|1998||1 Closed|Gambia,The|
|P000825|1998|1999||3 Closed|Gambia,The|
|P000825|1998|2000|6/27/2000|7 Closed|Gambia,The|
|P000825|1998|2001|6/19/2001|9 Closed|Gambia,The|
|P000825|1998|2002|4/25/2002|11 Closed|Gambia,The|
|P000825|1998|2003|4/15/2003|14 Closed|Gambia,The|
|P000825|1998|2004|3/2/2004|16 Closed|Gambia,The|
|P000825|1998|2005|4/28/2005|18 Closed|Gambia,The|
|P000828|1994|1994||2 Closed|Gambia,The|
|P000828|1994|1995||4 Closed|Gambia,The|
33 in the 1986 survey. One plausible explanatlon of the second observation is that because the LSMS intervlew requires considerable time from the women interviewed, those reinterviewed in 1986 had learned not only the costs of an interview in time, but also how to dismiss intervlewers. And one can conjecture, based on the first observation, that interview effects exist, which would explain in part differences between the 1985 and 1986 infant mortality rates.

The 1985 data show levels of infant mortality comparable to the IFS and EPR estimates. But the 1986 data clearly underestLmate the 1975-79 infant mortality rates, as suggested above.

The 1985 data and the data from the subsample of households newly included in 1986 are used for the mortality analysis to provide a sufficient number of observations. There is no indication, however, of how the suspected omission of deaths in 1986 will affect inferences about the relations between household characteristLcs and mortality.

Infant and child mortality is still very high in Cote d'Ivoire (table 4). During 1967-86, 5 percent of live births died in the first month of life; another 5 percent died in the postneonatal period. Among children who survived to 12 months, 6 percent died before age five.
# **3. Methods**

Our paper focuses on the literature with robust causal evidence, limiting our scope to randomized controlled trials (RCTs) and regression discontinuity designs. We included evaluations of government programs, smaller scale RCTs, and existing systematic reviews. We utilized a formal search strategy incorporating peer-reviewed journal articles and working papers from EconLit, World Bank eLibrary, NBER working papers, and PubMed databases; restricted to articles published in English from January 1, 2000, to May 31, 2024, and focused on lowerand middle-income countries (LMIC). The search strategy used the six types of SP noted above and incorporated age-specific terms, such as adolescents and youth. We focused on the following outcome domains: education, cognitive skills, employment, migration, pregnancy, marriage, and unpaid work. We complemented our search using consultations with experts. The formal search strategy, full list of excluded articles, and the detailed extraction sheet are available at https://sites.google.com/site/decrgberkozler/publications.

We do not attempt a formal meta-analysis, but instead undertake a narrative review that summarizes the findings by three themes: transfers and scholarships, active labor market policies, and life skills. We conclude with some key lessons for policy and areas for future research.

4
Policy Research Working Paper Series

||Title|Author|Date|Contact<br>for paper|
|---|---|---|---|---|
|WPS100|6 Preparing Multiyear Railway<br>Investment<br>Plans: A Market-Orient<br>Approach|Jorge M. Rebelo<br>ed|October 1992|A. Tumer<br>30933|
|WPSI 007|Global Estimates<br>and Projections<br>of Mortalty by Cause, 1970-2015|RodoHfo<br>A.Bulatao<br>Patience W. Stephens|October 1992|0. Nadora<br>31091|
|WPS100|8 Do the Poor Insure? A Synthesis of<br>the Literature<br>on Risk and<br>Consumptio**n**<br>i Developing<br>Countrie|Harold Alderman<br>Christina H. Paxson<br>s|October 1992|C. Spooner<br>32116|
|WPS100|9 Labor and Women's Nutrition:<br>A Study of Energy Expenditure,<br>Fertility, and Nutritional<br>Status in Gha|Paul A. Higgins<br>Harold Alderman<br>na|October 1992|C. Spooner<br>32116|
|WPS101|0 Competition<br>and Efficiency<br>In<br>Hungarian<br>Banking|Dimitri Vittas<br>Craig Neal|October 1992|W. Pltayatonakarn<br>37664|
|WPS101|1 How Tax Incentives<br>Affect Decision<br>to Invest in Developing<br>Countries|s Robin Boadway<br>Anwar Shah|November<br>1992|C. Jones<br>37754|
|WVPS10|12<br>The Brady Plan, the 1989 Mexican<br>Debt Reduction<br>Agreement,<br>and Ban<br>Stock Returns In the United States<br>and Japan|Haluk Unal<br>k Ashl DemirgOg-Kunt<br>Kwok-Wai<br>Leung|November<br>1992|W. Patrawimolpon<br>37664|
|WPS101|3 The Impact of Mexico's Retraining<br>Program on Employment<br>and Wages|Ana Revenga<br>Michelle Riboud<br>Hong Tan|November<br>1992|D. Young<br>30932|
|WPs101|4 Ethnicity, Education,<br>and Earnings<br>In Bolivia and Guatemala|George Psacharopoulo|s November<br>1992|L. Longo<br>39244|
|WPS101|5 Benefit Incidence<br>Analysis in<br>Developing<br>Countries|Thomas M. Sslden<br>Michael J. Wasylenko|November<br>1992|C. Jones<br>37754|
|WPS101|6 Europe and Central Asia Region,<br>Middle East and North Africa Region<br>Population Projections,<br>1992-93<br>Edition|My T. Vu<br>Eduard Bos<br>Ann Levin|November<br>1992|0. Nadora<br>31091|
|WPS101|7 Rural Poverty, Migration, and the<br>Environment<br>inDeveloping<br>Countrie<br>Three Case Studies|Richard E. Bilsborrow<br>s:|November 1992|WDR<br>31091|
|WPS101|8 Tariff and Tax Reform: Do World<br>Bank Recommendations<br>Integrate<br>Revenue and Protection Objectives|Anand Rajaram<br>?|November<br>1992|P. Pender<br>37851|
related government bodies can monitor water quality precisely and regularly.<sup>6</sup> The drinking water from those water treatment plants is supposed to meet a variety of standards, including general chemical, toxicological, bacteriological and radiative indices stipulated by Sanitary Standard for Drinking Water Quality (Ministry of Health, 2007). To avoid water contamination during transport, pipeline systems are also constructed to deliver water directly from plants to households.

The implementation of the rural drinking water program has been rather decentralized. While the central government stipulates general guidelines about the locations of water plants, safe drinking water standards and monitoring, local governments are responsible for program implementation. The financing of the program is shared among the central and local governments, villages, households, and international organizations. The specific split varies greatly across regions. The western areas, for instance, rely mostly on outside donors, which account for as much as over 50 percent of the total fund. In contrast, local governments in rich areas self-finance the program or partly rely on private capital. Overall, total spending for this rural drinking water program was about 8.8 billion U.S. dollars from 1981 to 2002, and the cost for the program was approximately 30 U.S. dollars per capita (Meng et al. 2004). The rural drinking water program is far from being complete, however. Even in 2008, only 41.9 percent of rural residents had access to plant water, and about 300 million rural people were still using untreated drinking water (Center for Health Statistics and Information, Ministry of Health of the People’s Republic of China, 2009).

Given the importance of drinking water quality and the sheer scale of investment in the rural drinking water treatment program in China, evaluations of its benefits are clearly important. So far, we are only aware of two studies that attempt to evaluate the program. They find that the quality of plant water for the sample villages after the implementation of this program is indeed better than that of untreated water (Zhang et al. 2009), and that this program has improved health of adults and children (Zhang 2012). Differing from the above studies, in this paper we focus on

> 6 Because of the diversity of natural conditions, deep well pumps and rainwater harvesting systems have been introduced as temporary substitutes in some areas.

7
bank flows, which we use to fill the missing data under other investment debt.<sup>22</sup> Here, we only use loan lending by BIS reporting banks, so as not to capture direct investment flows or debt securities holdings.<sup>23,24</sup> We then complement these loans with any other non-missing data from the BOP for particular instruments within other investment debt (trade credit, IMF credit, etc.) to get a more complete and accurate measure of other investment debt<sup>25,26</sup> While the BIS data has extensive coverage and captures a vast amount of capital flows, in some cases it may not match well with the BOP data.<sup>27</sup> In these cases, we rely first on measures derived from IIP, produced concurrently with the BOP data by the IMF, and the Quarterly External Debt Statistics (QEDS) data, produced jointly by the IMF and World bank. These data have the same sectoral and capital flow definitions and breakdowns, making them comparable to the BOP data. These are stock measures, which we first difference with a simple currency adjustment to approximate flows. While imperfect, these stock-derived measures often line up very well with reported BOP flow data and allow us to be more accurate as we fill missing data.

22The BIS bank data captures the overwhelming majority of cross-border banking activity (BIS, 2015), but some banking flows between non-BIS reporting EM may not be captured (e.g. Polish banks lending to Nigeria, etc.).

23Debt security flows would already be captured in portfolio debt (or the equivalent filling series). In principle, there could be an overlap between “direct investment debt” series and the “BIS loans” series if the loan is from a BIS reporting bank to an offshore non-financial entity in which the bank has at least a 10% ownership stake. In practice, we expect this to be small.

> 24A small number AEs exhibit some discrepancies between the BOP data and the BIS Bank data (e.g. Japan, Switzerland, and the US). These are isolated cases that have already been well documented. As a rule, we use BOP data, which is generally well reported for these cases, and other data sources first to avoid these issues.

25It is almost always the case that when the total is missing, the underlying instruments are also missing, except for, in some instances, IMF credit.

> 26In some cases, the flows of other investment debt, by sector or in total, are reported as coming from just one instrument (usually loans) even though in reality they reflect flows from other instruments as well (e.g. trade credit). So, summing these instruments can capture the proper total in such cases (this is almost always not necessary since other investment debt itself is reported when the underlying instruments have non-missing data). We thank Gian-Maria Milesi-Ferretti for bringing this to our attention.

> 27An important example is advanced economy government bonds, which are issued domestically and then traded abroad. These flows would not be captured by the BIS debt securities data, which captures exclusively bonds that are issued in international markets.

14
<!-- Start of picture text --> Policy Research<br>Poet Analysis and Policy<br>WPS 1081<br><!-- End of picture text -->

- This paper- a product of the Poverty and Social Policy Division, Africa Technical Department is part of the output of the research project "Poverty and the Social Dimensions of Structural Adjustment in C6te d'Ivoire, 1985-88: A Policy-Oriented Analysis" (RPO 675-26). Copies ofthis paper are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Elena Vitanov, room J2-24 1, extension 38400 (January 1993, 39 pages).

Over the years, household surveys have become a popular, valuable data source for empirical research in microeconomics. In developing countries, household survey data have become more available in the past decade, as a result of several intemational programs. This has spurred interest in the economics of the household in the context of development economics.

Many analysts give little attention to the sampling design of the surveys they use, taking the data produced by statisticians and survey practitioners "as is." At best, sampling weights are applied to ensure that the results are representative.

Demery and Grootaert illusL-ite the need to pay close attention to the sampung aspects of a household survey used in applied microeco-

- nomic analysis particularly for comparisons over time. This case study shows that observed changes in household welfare and in the incidence of poverty in Cote d'Ivoire between 1985 and 1988 vanish when corrections are applied to the data for changes in sampling procedures; even the direction of the trend is reversed. Similarly, the cross-sectional patterns of welfare and poverty observed in earlier analyses for 1985-86 prove to be incorrect.

The C6te d'Ivoire Living Standards Survey, conducted between 1985 and 1988, has provided a popular, fruitful data set for policy analysis. But according to Demery and Grootaert, the recorded decline in mean household size during this period is due to sampling bias in the early years of the survey. If this is txue, the robustness of the analyses based on these data is questionable.

The Policy Research Working Paper Series disseminates the fndings of work under way in the Bank. An objective of the series is to get these findings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusioas in these papers do not necessarily represent official Bank policy.

Produced by the Policy Research Dssemination Center
Figure **II:** Access to International<sup>Equity Markets</sup> <!-- Start of picture text --> Debt-Equity Ratios*<br><!-- End of picture text --> <!-- Start of picture text --> 3.5<br>3.O<br>2.5<br>2.OL<br>1993 1994 1995 1996 1997 1998<br><!-- End of picture text --> <!-- Start of picture text --> Short-Term Debt/Equity*<br>2.4<br>2.0<br>1.6<br>1.2<br>0.4<br>1993 1994 1995 1996 1997 1998<br><!-- End of picture text --> <!-- Start of picture text --> Long-Term Debt/Equity*<br><!-- End of picture text --> <!-- Start of picture text -->
1.6-<br>1.4-<br>1.2-<br>1.0.<br>0.8-<br>0.6-  ILEE.<br>0.4AL  MU-<br>93  94  95  96  97  98<br><!-- End of picture text -->

<!-- Start of picture text --> Short-Term Debt/Total Debt*<br>0.75<br>0.70<br>0.65<br>0.6a<br>0.5&<br>0.50<br>0.4a<br>1993 1994 1995 1996 1997 1998<br><!-- End of picture text --> Firms that Issued Equity in Foreign Maikets Firms that Did Not Issue Equity in Foreign Markets Source: **IFC** Corporate Finance Database and WorldScope ***** Ratios are averages across firms
pronounced when compared to just the rural youths in the country, but they increase when compared to both urban and rural youths at the national level.

In addition to comparing the characteristics of our study’s sample with the reference populations, it is also important to know the differences between the different subgroups comprising our sample. In table 2 some of the educational and labor characteristics of the youths are presented, disaggregating into subgroups of different sexes, ethnic backgrounds, migratory statuses and intensities of PROSPERA’s treatment received.<sup>30</sup> A comparison of sexes suggests that females have better educational characteristics (one more year of schooling and a higher school attendance rate), however, despite possessing occupations with higher status, their labor conditions are more precarious (they have labor income that is 20% lower, a higher proportion them are self-employed and without pay, and they have lower access to health services), which is consistent with the existing knowledge on gender segregation in the labor market and gender inequalities in labor remunerations.

> 30 The work sample for the study of the occupational attainment determinants was used since it presents a larger number of cases.

17
<!-- Start of picture text --> Effects of a Import Price S k In Prly Goods. StaUe m * Effect of amport Pri In PrC ood SrUc Ca<br>On Some Ma-Var.iables On Foreign Debt<br>111.2.Cam DdaauG Gowt b n - 1.00) (Fl. 3m ul *mcd ob -hs)a la)<br>RATIO RATIO<br>3.06 3.4<br>1.04<br>3.00 ____________________________<br>o .ua PK/PD9Co 0.2<br>(M-E)<br>0.04 S orI<br>0.92 0.92 ~ ~ - - ~ -- _ _ _ _ _ _ _ _ Expf Ex~~~~~~~~~kport _------------------______<br>O."<br>0.66<br>o. v . a * a a a ...................... a a * * * * * , * * ,. Q*<br>0 2 4 a 0 0 32 04 I6 18 20 0 2 4 t 0 12 14 to to to<br>TIME PERIODS TIME PERIODS<br>w 0~~~~~~~~~~~~~~~~~~~~~~~~~D~~~~~~~~~J0 Jt.2 a_<br>EMects of a Import Price Shock in Primary Coods. Static Case ffects of a Ihport Price Shock in Primary Goods, Stati Case<br>On Some Maro-Vaiables On Sectorat investnent<br>11usd 3Cem Balaced Growtb ha - 0.00) umzad eCm. Calancd Groft 2e - LO)<br>RATIO RATIO<br>1.0 1. 06<br>3.04 1.04 Pknoy<br>I.02<br>0.98 -------- --- PW@EP ------<br>0.96.<br>0.94 Export 0.96 MantocL-____<br>0.s2 _____________________ 0.96~ ~ ~ ~ ~ ~~~~~~~~~~~~~~~~~99 0.s _ -____<br>0.892 Eor 0.901<br>e 2 Is 6 4 10 12 14 O 16 20 0 a 4 G a 30 12 14 1 3l 20<br>TIME PERIODS TIME PERIODS<br><!-- End of picture text -->
<!-- Start of picture text --> Figure 1a: Ethiopia:<br>Firm Count & Employment by Firm Size<br>Number of Firms Share of Firms<br>1996 432 1996 0.69<br>10-49 Workers 2006 795 10-49 Workers 2006 0.69<br>2016 2,131 2016 0.76<br>1996 109 1996 0.17<br>50-199 Workers 2006 250 50-199 Workers 2006 0.22<br>2016 434 2016 0.16<br>1996 42 1996 0.07<br>200-499 Workers 2006 65 200-499 Workers 2006 0.06<br>2016 150 2016 0.05<br>1996 40 1996 0.06<br>> 500 Workers 2006 43 > 500 Workers 2006 0.04<br>2016 76 2016 0.03<br>Source: Ethiopia Survey of Large & Medium Scale Manufacturing Industries, Source: Ethiopia Survey of Large & Medium Scale Manufacturing Industries,<br>1996, 2006, & 2016. 1996, 2006, & 2016. Shares may not add to one due to rounding errors.<br>Total Employment Employment Share<br>1996 7,461 1996 0.09<br>10-49 Workers 2006 14,809 10-49 Workers 2006 0.14<br>2016 36,820 2016 0.16<br>1996 11,973 1996 0.15<br>50-199 Workers 2006 23,431 50-199 Workers 2006 0.23<br>2016 43,789 2016 0.19<br>1996 13,808 1996 0.17<br>200-499 Workers 2006 20,650 200-499 Workers 2006 0.20<br>2016 44,957 2016 0.20<br>1996 48,838 1996 0.60<br>> 500 Workers 2006 43,765 > 500 Workers 2006 0.43<br>2016 102,405 2016 0.45<br>Source: Ethiopia Survey of Large & Medium Scale Manufacturing Industries, Source: Ethiopia Survey of Large & Medium Scale Manufacturing Industries,<br>1996, 2006, & 2016. 1996, 2006, & 2016. Shares may not add to one due to rounding errors<br><!-- End of picture text --> 35
SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE the associated price tag from $1,785 million to $771 million. The overall savings from these measures would amount to $2 billion for Sudan (table 23).

If Sudan were unable to raise additional financing or reduce infrastructure costs, the only way to meet the targets identified here would be to take longer than ten years to do so. If the

**Table 23. Savings from innovation**

|$ millions|Before<br>innovation|After<br>innovation|Savings|Savings as %<br>of sector<br>funding gap|
|---|---|---|---|---|
|WSS appropriate technology|1,701|889|812|51|
|Roads appropriate technology|1,785|1,124|660|53|
|Total|3,486|2,014|1,472|51|

_Source:_ Derived from Foster and Briceño-Garmendia, AICD Flagship Report, 2009 _Note:_ WSS = water supply <u>and sanitation.</u> country were able to instantly realize all potential efficiency gains while holding spending at current levels, it could meet the identified infrastructure targets within 30 years. These simulations underscore the importance of making progress on the efficiency agenda, which could propel the country forward toward meeting its infrastructure targets.

> <sup>46</sup>
||**T**<br>|**able 7: Coun**<br>|**try-level po**<br>|**verty status a**<br>|**t $3.20 PP**<br>|**P**<br> <br>|
|---|---|---|---|---|---|---|
|Country|Survey<br>year|Poverty<br>rate (%),<br>per capita|Poverty<br>rate (%),<br>square root|<br>Millions<br>of poor,<br>per capita|Millions<br>poor, squ<br>root|of<br>are<br>Square-root<br>equivalent poverty<br>line (2011 USD PPP)|
|(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Nigeria|2018.75|71.0|71.0|141.8|141.8|7.602|
|Mali|2009.89|79.8|79.8|12|12|11.236|
|India|2011.5|61.7|61.7|775.9|775.9|7.193|
|Pakistan|2018.5|35.7|35.7|76.6|76.6|8.682|
|Tajikistan|2015|17.8|17.8|1.5|1.5|8.795|
|Indonesia|2017|24.6|24.6|65.2|65.2|6.721|
|Yemen,<br>Rep.|2014|51.2|51.2|13.2|13.2|9.155|
|Colombia|2017|11.1|11.1|5.4|5.4|6.713|

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD) Notes: The survey years with decimals are the cases where the survey spans two consecutive calendar years. By</u> convention, the decimal indicates the share of the survey conducted in the second year. For example, 75% of the Nigeria survey was conducted in 2019.

36