rafmacalaba/datause-encoder-data huggingface dataset:
**Table 3. Relative learning mobility**

||**Qua**|**ntitative reas**|**Depende**<br>**oning**|**nt variable: Z**<br>**Native**|**-scores in Sa**<br>**language (Sp**|**ber PRO**<br>**anish)**|**Foreig**|**n language (**|**English)**|
|---|---|---|---|---|---|---|---|---|---|
||**Ordinary**|**Non-**||**Ordinary**|**Non-**||**Ordinary**|**Non-**||
|**Variable**|<br>**Least**<br>**Squares**|**parametric**<br>**matching**|**Instrumental**<br>**Variables**|<br>**Least**<br>**Squares**|**parametric**<br>**matching**|**Instrumental**<br>**Variables**|<br>**Least**<br>**Squares**|**parametric**<br>**matching**|<br>**Instrumental**<br>**Variables**|
|**Teacher**|-0.4636***<br>(0.0161)|-0.3810***<br>(0.0333)|-1.2405***<br>(0.2353)|-0.1441***<br>(0.0166)|-0.1010***<br>(0.0316)|-0.9698***<br>(0.2188)|-0.0560***<br>(0.0184)|-0.0003<br>(0.0312)|-1.3436***<br>(0.2209)|
|**Saber 11 scores in**||||||||||
|Quantitative reasoning|0.5037***<br>(0.0047)|0.4408***<br>(0.0121)|0.5339***<br>(0.0106)|||||||
|Native language||||0.4967***<br>(0.0043)|0.4716***<br>(0.0109)|0.4893***<br>(0.0101)||||
|Foreign language|||||||0.6240***<br>(0.0055)|0.6105***<br>(0.0146)|0.6523***<br>(0.0114)|
|**Teacher * Saber 11 scores in**||||||||||
|Quantitative reasoning|-0.1554***<br>(0.0173)|-0.0391<br>(0.0336)|-0.8819***<br>(0.1873)|||||||
|Native language||||0.0322**<br>(0.0136)|0.0578*<br>(0.0323)|0.0054<br>(0.1225)||||
|Foreign language|||||||0.1025***|0.0812*|-0.8190***|
||||||||(0.0274)|(0.0454)|(0.2284)|
|Constant|0.5272*|-0.2659|0.5086*|0.5815**|0.3655|0.5849**|0.3366*|-0.2896|0.2944|
||(0.2831)|(0.3837)|(0.2764)|(0.2518)|(0.2917)|(0.2533)|(0.1997)|(0.5698)|(0.1975)|
|**Observable characteristics**|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|**Reliability of instruments†**||||||||||
|Kleibergen-Paap rk Wald F statistic|||193.1831|||167.6556|||130.4739|
|P-value|||0.0000|||0.0000|||0.0000|
|Hansen J statistic|||18.0750|||40.0966|||61.7321|
|P-value|||0.0001|||0.0000|||0.0000|
|Angrist-Pischke multivariate F test of<br>excluded instruments|||99.4|||102.22|||84.34|
|P-value|||0.0000|||0.0000|||0.0000|
|**R-squared**|0.3234|0.2751|0.2889|0.3045|0.3008|0.2807|0.5040|0.4327|0.4020|
|**Observations**|**50772**|**12431**|**50772**|**50772**|**12520**|**50772**|**50772**|**12463**|**50772**|

Robust standard errors in parentheses. * Significant at ten percent; ** significant at five percent; *** significant at one percent. Note: Observable characteristics include: gender; parents' education; family size; semester in which the student took Saber 11; and dummies by municipality where the student took Saber 11. Source: Authors’ calculations based on ICFES data.

19
allows us to control for baseline household fixed effects, offering identification of the effects of adult mortality via the different split-off households stemming from the same initial household. By thus exploiting the variation in outcomes across households whose members were all living together at baseline not at the time of the adult mortality event, we control more fully for heterogeneity in initial socio-economic conditions that may be correlated with subsequent adult mortality and the path of consumption growth. We also control for the characteristics of the 2004 households including household age fixed effects to account for the strong life-cycle patterns one may expect to observe in consumption.

The following pattern emerges: an affected household will see consumption drop by 7% within the first five years after the adult death. Meanwhile, as this is a period of rather high consumption growth, the average household continues to grow, at a rate that can be estimated to be about 13 percent over the same five year period, creating a 19 percentage point growth gap. For deaths that occurred 6-13 years ago, we consistently find negative but insignificant effects. This may be suggestive of no persistence in the impact of adult death and a full recovery. However it may well be that other events blur the difference between affected and non-affected households. A ‘casual observer’, and even our relatively detailed data set, may then only imprecisely measure differences in consumption between them, but a persistent effect, at least up to 13 years, seems plausible if unproven given our regression results. Further testing fails to find any evidence of risk-sharing for adult mortality shocks among the 2004 households which originated from the sample of initial households.

6
Also, enrollment rates at secondary level improved. During 1980-1999, Paraíba experienced an 8-percentage-points increase to 14.3 percent (or 117,271 students) in 1999, which is considerably lower than Brazil’s 19-percentage-points increase to 33 percent. An international comparison of the secondary enrollment rate reveals that Brazil lacks behind. In Chile and Mexico 70 and 58 percent are enrolled in secondary school and Korea tops the list with 98 percent enrollment rate. The low completion rate in eighth grade (see below) explains the very low entry rate in secondary education in Paraíba. In fact, student flow analyses have demonstrated that repetition, rather than physical access, is the principal impediment to the expansion of secondary education in Brazil.

**Figure 3.2: Primary and Secondary School enrollment rates, 1999**

<!-- Start of picture text --> 1 1 0<br>G ra d e 1 -4 G ra d e 5 -8<br>1 0 0 9 1 .4 9 1 .8 9 3 .5 9 2 .3 9 3 .2 9 3 .3 9 0 .6 9 0 .5 9 3 .9 9 2 .8 9 5 .4<br>9 0<br>8 0<br>7 0<br>6 0<br>%<br>5 0<br>4 0<br>3 2 .6<br>3 0<br>1 9 .2 2 0 .4 2 0 .5<br>2 0 1 3 .3 1 4 .3 1 3 .4 1 4 .1 1 5 .7<br>1 0 .8 1 1 .5<br>1 0<br>0<br>M a ra n h ã o P ia u í C e a rá R G N o rte P a ra íb a P e rn a m b u c o A la g o a s S e rg ip e B a h ia N o rd e s te B ra z il<br>Source: Based on data from INEP.<br><!-- End of picture text --> The most equitable way of increasing Brazilian educational attainment would be to focus investment at the fundamental cycle to help more of the poorest children complete 8<sup>th</sup> grade. International research indicates that at least 8 years of completed education is necessary for the workforce to be global competitive. Hence, Brazil needs more secondary graduates.

19
Improving Estimates of Mean Welfare and Uncertainty in Developing Countries<sup>∗</sup> Joshua D. Merfeld<sup>†</sup> Hai-Anh Dang<sup>‡</sup> David Newhouse<sup>§</sup> Originally published in the Policy Research Working Paper Series on _March 2023._ This version is updated on _June 2025._ To obtain the originally published version, please email prwp@worldbank.org.

**_Keywords_** : poverty, welfare, prediction, machine learning, geospatial **_JEL Codes_** : C53, C80, I32, O10

> ∗We thank the editor Tavneet Suri, three anonymous reviewers, Jed Friedman, Haishan Fu, Keith Garrett, Dean Jolliffe, Talip Kilic, and Bo Pieter Johannes Andree for comments and support. We thank Lina Cardona, Dilhanie Deepawansa, Carlos De Maia, Francis Mulangu, and Mario Negre for help obtaining data. This project was partially funded by the Knowledge for Change Program’s Phase IV-funded programmatic research project “Understanding Trends in Sub-National Differences in Economic Well-Being in Low- and Middle-Income Countries”.

> †KDI School of Public Policy and Management and IZA; merfeld@kdis.ac.kr

> ‡World Bank, IZA, Indiana University, and University of Economics, Ho Chi Minh city; hdang@worldbank.org

> §World Bank and IZA; dnewhouse@worldbank.org
## **Figure 5: Quadratic relationship between biomass and household consumption for the poor**

# Change in Welfare for Poor Rural

<!-- Start of picture text -->
1.2<br>39<br>1<br>0.8<br>0.6<br>0.4<br>0.2<br>0<br>0 20 40 60 80 100 120 140 160 180<br>Biomass m3 / Ha<br><!-- End of picture text -->

34
3 1989, due to the demise of the CMEA was offset by 1993 for the majority of the CEECs.<sup>5</sup> Per capita exports from thc CEECs to the EU nearly doubled. The growth rate of per capita exports to the EU is the highest for the Czech and Slovak Republics,' at 26 percent per year on average, followed by Bulgaria and Poland at 20 percent, and Hungary with 14 percent. The absolute value of per capita exports to the world is highest for the Czech Republic at $1,000 followed by Hungary at around $850 in 1993. Second, a marked shift in exports towards the EU occurs. Of the comparator countries, only China achieved similar performance.' The Czech and Slovak Republics also demonstrate one of the largest shifts in the geographic pattern of exports, by 30 percentage points.' The CEECs' trade structure has changed substantially in the last four years. Total exports to the EU rose by 14 per cent per year oii average between 1989 and 1993 (from ECU 12 billion in 1989 to 20 billion in 1993). In this period, total EU imports rose by only 2.1 per cent per year, while world trade increased by 3.7 per cent per year. The CEECs therefore substanitially increased their market share in the EU (Table 2).

Table 2: Market share in the EU **1%)**

||1989|1993|
|---|---|---|
|CEECs|2.7|4.1|
|FSU|3.0|3.2|
|Bulgaria|0.12|0.19|
|<br>CSFR|0.56|1.23|
|Hungay|0.58|0.31|
|<br>Poland|_0.85_|_1.54_|
|Romania|0.57|0.34|

For the six countries that have signed Association Agreements-Bulgaria, the Czech Republic, Hungary, Poland, Romania and the Slovak Republic-total import penetration rose from 2.7 per ce:it in 1989 to 4.1 per cent in 1993. Individual coumtry market share growth was often substantially higher with both Poland and the Czech and Slovak Republics doubling their share of the EU market, and Bulgaria almost doing so.

**Table** 3: Average annual growth in exports to EU

|1989-93 (%)<br>Bulgaria<br>16|
|---|
|<br>CSFR<br>24|
|Hungary<br>11|
|<br>Poland<br>18|
|Romania<br>-10|
|FSU<br>4|

The Czech and Slovak Republics are the 'star' performer on the basis of growth in exports to the EU (Table 3). In 1989 reported imnports by the EU from the Czech and Slovak Republics, Hungary and Romania were roughly equal at ECU 2.5 billion. By 1993, exports from the Czech and Slovak Republics had attained ECU 6 billion, Hungarian exports were slightly below ECU 4 billion, and those of Romania had fallen to ECU 1.7 billion (Figure 2). Exports of the Czech and Slovak Republics grew by 24 percent per year on average during the 1989-93 period, outperforming China. Poland and Bulgaria follow second and third in terms of export grcswth.

Table 4: Actual and **'long** run' predicted **exports to EU (ECU billion)**

||1993|Predicted<br>|
|---|---|---|
|Bulgaria|0.9|3.9|
|CSFR|6.0|12.5|
|Hungary|3.9|4.5|
|<br>Poland|7.5|8.5|
|Ronania|1.7|3.0|
|FSU|15.5|31.2|

How significant are these changes in trade patterns? Spatial trade pattems are usually
**Table 3: Representative PE Fund Managers by Target Investment Size**

<!-- Start of picture text --> US$1-5M US$5-20M US$20-100M+<br>Private Equity Funds<br>TBL Mirror Fund Ascent Capital Partners Abraaj Group<br>Fanisi Venture Capital Catalyst Principal Partners Actis Capital<br>Kibo Capital Partners Africinvest<br>BPI East Africa Phatisa Fund Managers Helios Investment Partners<br>Kibo Capital Partners<br>The Carlyle Group<br>KKR<br>Other Players<br>Familiy Offices<br>Chandaria Family<br>Listed Entities<br>Centum Investment<br>Transcentury<br><!-- End of picture text --> _Source: Primary Research Note: AfricInvest also invests in the $5-20mn category_ While many of the firms operating in Africa are generalist in nature, some managers have raised thematic funds focused on a particular investment thesis. Often, such funds are affiliated with established fund managers that are known quantities and leverage an existing track record as part of their fundraising story. AfricInvest raised its first generalist SME fund in 2004 and then raised the €61 million AfricInvest Financial Sector Fund in 2007. FMO served as the anchor investor for the fund with a €20 million investment. Other LPs including Proparco, Bio, the Lundin Foundation, and Desjardins, a Canadian Group, all joined subsequent closings between 2007 and 2014. The fund thesis included investments in less developed countries and post-conflict areas and encompassed greenfield and startup deals. Since these investments require greater time and flexibility than those included in a typical SME fund, AfricInvest expanded the fund's life from 10 years to 15 years. For the AfricInvest Financial Sector Fund II, a follow-on fund to be launched in 2017, AfricInvest has announced that it intends to raise a €100 million evergreen (i.e. permanent capital) vehicle.<sup>6,7</sup> AfricInvest is also raising the French Africa Fund, in conjunction with Bpifrance, which will target SMEs in both France and Africa seeking to expand among and between these geographies. Investors include Proparco and the Central Bank of Kenya Pension Fund.<sup>8</sup> Another manager, Phatisa, has also raised two thematic funds, the US$151 million African Agriculture Fund, closed in 2010, and the US$42 million Pan-African Housing Fund, closed in 2014. The firm is also currently fundraising for a US$200 million successor fund to the African Agriculture Fund, the African Food Fund II, which will target primary agriculture, primary food processing, FMCG, retail, supply chains, agri-inputs, and packaging. Finally, in additional to its generalist PE funds, global investor Actis Capital has raised three real estate funds since 2006.

> 6 Permanent Capital Vehicles (PCVs) are investment entities that do not have a fixed fund life.  They will be discussed in detail later in this report.

> 7 Private Equity International (October 28, 2016).

> 8 African Private Equity and Venture Capital Association (January 16, 2017).

7
"official' data, an adjustment was made to express the trade values in domestic prices, based on the IDS foreign trade data. Still, the adjusted GNP figures from IDS differ from the 'official" data. The reasons for that lie in the different estimations of military expenditures, private sector activities and also in the estimation of foreign trade in non-material services (for data comparisons, see Summary Table).' 6. The 1987 benchmark data from IDS were first extrapolated to 1988-90 via trend of the "official series. The extrapolation was done for all Republics. Then the extrapolated data were adjusted for re-evaluation of extra-Union trade from domestic to foreign prices.

7. For 1980-86, the foreign trade adjustments for 1987 were extrapolated backward via the trend of the foreign trade of the FSU.

8. The adjusted GNPs in constant prices were obtained by applying the deflators of the "official" time series to the current price data as obtained in paras 6-7 above. The results are shown in Tables 1-3.

**1** For the Baltic republics, the 1987 benchmark GNP figures were adjusted only for inter-Republic trade, to make them consistent with the data from the national authorities.

29
SOUTH SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 23. South Sudan’s existing infrastructure spending is average by African standards**

<!-- Start of picture text --> SSA<br>LIC-NoFragile<br>COMESA<br>MIC<br>LIC-Fragile<br>Resource-Rich Capex O&M<br>Ethiopia<br>Kenya<br>Uganda<br>Botswana<br>South Sudan<br>South Africa<br>North Sudan<br>Congo, Dem. Rep.<br>% of GDP 0 2 4 6 8 10 12 14 16<br><!-- End of picture text --> _Source:_ Foster and Briceño-Garmendia 2009. _Note:_ Values excludes irrigation.

LIC = low-income country; MIC = middle-income country; COMESA = Common Market for Eastern and Southern Africa; SSA = Sub-Saharan Africa; GDP = gross domestic product; O&M = operations and maintenance; CAPEX = capital expenditure.

Capital investments in the south have clearly been concentrated in transport infrastructure and financed by the public sector (figure 24).

**Figure 24. South Sudan’s pattern of capital investment in infrastructure**

<!-- Start of picture text --> Investment in infrastructure sectors as percentage of GDP, by source<br>Northern Sudan<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>Public ODA Non-OECD Private<br>% of GDP<br><!-- End of picture text --> _Source_ : Derived from Briceño-Garmendia, Smits, and Foster (2009). _Note:_ Private investment includes self-financing by households. ODA = official development assistance; OECD = Organisation for Economic Co-operation and Development; ICT = information and communication technology; GDP = gross domestic product; WSS = water supply and sanitation; LIC = low-income country.

42
# **7  Other Types of Programs**

This section describes other types of interventions that have been found to influence women‟s labor market outcomes.

## **7.1  Family Friendly Policies in Western Europe**

Most countries in Western Europe have relatively generous childcare and family leave policies in comparison with the rest of the world. Del Boca, Pasqua, Pronzato and Wetzels (2007) study the effects of family friendly social policies in European countries on the labor force participation, hourly wages and fertility of women aged 21-45. Their analysis is based on a random sample of households from 15 European Union member countries, drawn from the European Community Panel Survey. Their goal is to assess the impact of social policies on employment and fertility using a reduced form methodology that relates labor force participation and fertility decisions to a set of determinants of these decisions that include measures of countries‟ relative generosity in providing childcare services (for children age 0-3) and of countries‟ parental leave policies. They focus on the provision of childcare for young children, because there is a lot of variation across countries for the 0-3 age group. Del Boca et al. (2007) create indicators of childcare generosity by ranking countries according to their available programs and then grouping them into four generosity categories. A similar grouping is performed for parental leave. Denmark is the country with the most generous childcare programs for children age 0-3 and Italy is the country with the most generous parental leave policies. Spain is the least generous both in terms of childcare and parental leave.

Del Boca et al. (2007) then estimate a statistical model of the joint decisions to work and to have a child. The model is a bivariate probit model, with the outcomes modeled as a function of individual characteristics, such as education and age, household characteristics that include the husband‟s income, and the social policy generosity indicator variables. The study finds that the generosity indicators are highly predictive of fertility and employment outcomes and in some cases more predictive than are individual or household characteristics. Women who reside in countries in the two most generous childcare categories have a higher probability of working and of having a child. Similarly, the probability of working and having children is higher in countries with more generous parental leave policies. Thus, the authors conclude that family friendly social policies strongly influence women‟s decision-making with regard to fertility and labor 53
Araujo-Bonjean et al. (2006) suggest that supply responsiveness in WCA lags behind that of other major producers. In terms of variation over time, identifying a clear trend is difficult. While Gillson et al. (2004) produce some evidence that WCA supply has been become more elastic in the early 2000s; the conclusions of Shepherd (forthcoming) are more nuanced.<sup>29</sup> What happens, then if, in light of the evidence described above, the Goreux model is re-run assuming an elasticity of 0.1 instead of 0.5 for C-4 countries?<sup>30</sup> While in the original specification, the removal of industrialized countries subsidies led to a world price increase of 13.4 percent and a production increase of 6.5 percent in the C-4 countries, the production increase is of only 1.3 percent in the alternative scenario.<sup>31</sup> As a result, the yearly increase in total producer income is only of about 15 percent instead of 20. In absolute terms this means that the annual producers’ gain is just over US$ 90 million instead of over 123.<sup>32</sup> There can thus be no dispute that Africa’s ability to benefit from changes in rich countries’ cotton subsidies depends in part on the price responsiveness of its farmers.

# 3.2 Domestic regulation of cotton sectors in SSA<sup>33</sup>

The producers’ responsiveness to price incentives, and thus ultimately their competitiveness on the global market, is intimately tied to the question of domestic regulatory reform as sectoral institutions mediate between domestic and world markets and thus determine the speed and extent of price signal transmission.<sup>34</sup> Sectoral institutions also influence the elasticity of supply of producers in as much as they impact the supply-side constraints that producers have to deal with, among which, chiefly, difficulties to access inputs.

To grow cotton, farmers indeed need costly inputs but face strong cash constraints as credit markets are quasi non-existent in most of African rural areas. As a result, production overwhelmingly occurs through interlinked transactions whereby inputs are provided on

> 29 According to him, whereas the average elasticity in Mali between 1990 and 2004 is slightly higher than between 1969 and 1989, it has nonetheless been trending downwards sharply since 1996 and the results are even starker once improvements in technology are netted out.

> 30 The elasticity of 0.5 is maintained for the rest of the world.

> 31 The model is calibrated with price, production and subsidy data averaged over the 1999-2002 crop seasons.

> 32 The adaptation of the Goreux Model to multiple supply elasticities is given in Shepherd and Delpeuch (2007), Appendix 2, page 8.

> 33 This section heavily draws on Delpeuch and Leblois (2011).

> 34 The exchange rate between local currencies and the dollar, in which cotton is traded worldwide; transport costs and the efficiency of the companies which transform seed cotton into cotton lint (the ginners) also affect producer prices. See Baffes (2011) on the exchange rate and Delpeuch et al. (2011) as well as Tschirley et al. (2009) on transport and ginning costs.

11
index is greater than or equal to 3, (which is about the median), and a value of 0 if the index takes a value less than 3.9 Second we use a combination of the size of the public sector and corruption to create a variable of transparency. Kopits and Craig (1998) argue that the transparency in government behavior is reflected mainly in the structure and functions of the public sector, such as financing operations. Thus, we create a measure of public sector size as the ratio of credits directed to the government and the public sectors relative to total domestic.'<sup>0</sup> We define country with large (small) public sector if the share is greater (smaller) than the median. We combined this measure with our measure of corruption and define three levels of transparency. Poor transparency if a country is corrupt and has large public sector; Medium transparency if a country is corrupt but has a small public sector or is not corrupt but has a large public sector; Good transparency is a country, which is not corrupt and has a small public sector.

One caveat of using corruption as a proxy for transparency is that corruption by itself may affect the probability of a crisis. Financial liberalization may offer new opportunities for corruption that may lead to banking crisis. Thus, transparency may not affect banking crisis but corruption does. In order to test and address this issue, we use another proxy for transparency that is not based on corruption. Our third measure of transparency is from the World Competitiveness Yearbook. The variable is based on a survey of executives in 46 countries who were asked to state whether they agree or disagree to the following statement "the government does not communicate its policy intentions clearly". The variable takes values from 1, agree, to 8, disagree. Unfortunately, this data is available only for 1998. Thus, 9 The results are robust to the exact definition. The data is taken from IFS. The ratio is (line 32an + 32b + 32bx + 32c) / line 32.

19