rafmacalaba/datause-encoder-data huggingface dataset:
# **Abstract**

Major economic, political and social changes have taken place in Central America over the past decade. These changes have led to improvements in well-being and reductions in rural poverty rates, but the region is still characterized by persistent and stark inequalities in assets and incomes and high numbers of poor persons in rural areas. This inequality is most evident in terms of landholdings, but other key productive, social and locational assets are also unequally distributed among households and geographical areas.

The asset-based approach considers linkages between households’ productive, social and locational **assets** ; the policy, institutional and risk **context** ; their **behavior** as expressed in **livelihood strategies** , and **well-being** outcomes. For sustainable poverty reducing growth, it is critical to examine household asset portfolios and understand how assets interact with the context to influence the selection of livelihood strategies, which in turn determine well-being.

Policy reforms can change the context and income-generating potential of assets. Investments can add new assets or increase the efficiency of existing household assets, and also improve households’ risk management capacity to protect assets. After all is said and done, a household’s asset portfolio will determine whether growth and poverty reduction can be achieved and sustained over time.

The asset-based framework is amenable to different analytical techniques. It is suggested to combine quantitative and qualitative spatial and household level analyses (and linked spatial and household level analyses) to deepen understanding of the complex relationships between assets, context, livelihood strategies and well-being outcomes.

**Key Words** :  asset-based approach, drivers of growth, rural poverty, sustainable development, livelihoods approach, Central America, Latin America and Caribbean,

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**Table 2: Mining and Assessment of Public Services: Baseline Models**

|_Dependent Variables: Governmen_|_t improving _<br>|_living standa_<br>|_rds of thepoo_<br>|_r_<br>|||
|---|---|---|---|---|---|---|
||(1-LPM)|(2-LPM)|(3-LPM)|(4-LPM)|(5-Probit)|(6-Probit)|
|Active 50 km|-0.018***|-0.018***|-0.020***|-0.020*|-0.063***|-0.063*|
||(0.006)|(0.007)|(0.007)|(0.010)|(0.021)|(0.032)|
|Inactive 50 km|0.005|0.002|0.004|0.004|0.011|0.011|
||(0.004)|(0.004)|(0.004)|(0.006)|(0.011)|(0.017)|
|Interest in Public Affairs||0.045***|0.045***|0.045***|0.139***|0.139***|
|||(0.003)|(0.003)|(0.003)|(0.009)|(0.010)|
|Deprivation||-0.051***|-0.050***|-0.050***|-0.156***|-0.156***|
|||(0.001)|(0.001)|(0.002)|(0.004)|(0.005)|
|Natural Resources Rents (ln)|||-0.035***<br>(0.007)|-0.035***<br>(0.010)|-0.112***<br>(0.021)|-0.112***<br>(0.030)|
|Control of Corruption|||0.155***<br>(0.014)|0.155***<br>(0.022)|0.534***<br>(0.044)|0.534***<br>(0.071)|
|Constant|0.341***|1.301***|1.460***|1.460***|3.016***|3.016***|
||(0.010)|(0.118)|(0.119)|(0.125)|(0.358)|(0.375)|
|Difference in Differences|-0.024|-0.021|-0.023|-0.023|||
|F-test: active-inactive = 0|11.7|8.8|10.9|4.5|||
|P-value of F-test|0.00|0.00|0.00|0.03|||
|R-squared|0.05|0.07|0.07|0.07|||
|Pseudo R-squared|||||0.06|0.06|
|Observations|112,825|109,282|109,282|109,282|109,282|109,282|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Round FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Robust/Cluster std|Robust|Robust|Robust|Cluster|Robust|Cluster|

# **_Notes:_**

1: Significance level: * p<0.10, ** p<0.05, ***p<0.01. Robust or clustered (at the geo-localization of the mine) standard errors in parenthesis.

2: Individual-level control variables are added in specifications (2) to (6): age (ln), the square of the logarithm of age, gender, education, residential area (urban), employment status.

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# **Appendix B – Split Sample Regressions – OECD and Non-OECD Countries**

# **OECD Countries**

||**Column 1**|**Column 2**|**Column 3**|**Column 4**|
|---|---|---|---|---|
||**Aggregate**<br>**Paper**|**Newsprint**|**Printing**<br>**and**<br>**Writing**|**Household**<br>**and**<br>**Sanitary**|
|Internet|||||
|Pentration (per<br>100 people)|0.00142|0.00327|0.000687|-0.00326|
||(0.0011)|(0.0024)|(0.0028)|(0.0035)|
|GDP per capita (in<br>logarithm)|0.627***|0.629|0.197|3.917***|
||(0.2000)|(0.4200)|(0.4690)|(0.6710)|
|Percentage Post|||||
|High School<br>Students (over<br>whole population)|2.97|20.25***|16.60**|-24.56***|
||(2.6470)|(5.5680)|(6.8380)|(8.8640)|
|Workers in|||||
|Primary Sector<br>(percentage)|-3.621***|-1.798|-11.19***|0.815|
||(1.3730)|(2.8870)|(3.1870)|(4.2450)|
|Workers in|||||
|Manufacturing<br>Sector|3.353**|-2.928|-2.416|8.293**|
|(percentage)|||||
||(1.2920)|(2.7180)|(3.0500)|(4.1980)|
|Workers in the|||||
|Services<br>(percentage)|-0.413|5.210*|-10.16***|0.268|
||(1.3820)|(2.9060)|(3.1820)|(4.3280)|
|Constant|-1.417|-6.751|9.316|-37.77***|
||(2.6720)|(5.6200)|(6.2230)|(8.8160)|
|Observations|257|257|241|221|
|R-squared|0.97|0.935|0.904|0.944|

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Fund.<sup>3</sup> Table 1 Panel A lists the databases and specific search terms we used. In this phase, we looked for studies published before 2021 that stated an aim to estimate the effects of preprimary interventions. Keywords related to experimental and quasi-experimental evaluation designs effectively limit our attention to studies published after 2007.

In addition to database searches, we found studies through two other ways. We searched bibliographies of papers found through the database search. We also contacted experts and researchers who have frequently published on the impacts of preprimary programs in peerreviewed journals and asked for other relevant published studies to incorporate in our review.

This search process yielded a total of 270 studies, with 183 studies from database searches and 87 additional studies from other search methods. These studies included systematic reviews, experimental and quasi-experimental studies, and studies that did not employ experimental or quasi-experimental methods (for example, cost analysis, qualitative reports, etc.).

Table 1 Panel B describes the inclusion criteria we used to identify studies for review. We sought to include experimental and quasi-experimental studies of preprimary programs that provided group-based childcare in a center setting with a developmental or educational focus for children between the ages of 3 and 6 years. This definition of preprimary education encompasses the provision of formal preprimary, community-based preprimary, kindergarten, pre-k, and daycare with an educational component, as well as interventions that make preprimary education more affordable (such as subsidies) or increase its quality (such as teacher training, the introduction of a new curriculum or pedagogical approach, and provision of materials). We did not include programs that also provided services outside the preprimary period, such as childcare during infancy or classroom quality improvements after kindergarten, as it would be impossible to distinguish the impacts of interventions during the preprimary years from those of interventions that spanned a longer period. This restriction ruled out seminal studies of programs like the Abecedarian Project (Campbell et al., 2014; Conti et al., 2016) and Project STAR (Chetty

> 3 The Strategic Impact Evaluation Fund supports research that estimates the effects of programs and policies to improve education, health, access to quality water and sanitation, and early childhood development in low- and middle-income countries. See <u>here</u> for more information on the fund and a list of evaluations in its portfolio.

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trend continues in 2016. The ranking across provinces remains unchanged, with the larger increase in conflict intensity taking place in the regions where conflict levels were already high in 2014 (Figure 9).

The same assumptions on foreign aid and the presence of troops are used to forecast household expenditures beyond 2014, with inputs on conflict intensity coming from the simulation above. The exercise shows that during the transition households are subject to a triple shock: foreign aid declines, casualties increase, and whatever local demand there was from international troops quickly disappears. Admittedly this local demand from international troops is not as strong as that generated by Afghan troops, but it is not negligible. Although international troops may procure most of their supplies abroad, their construction and civil works most likely involve local labor. Furthermore, international troops’ withdrawal has a negative effect on household welfare through the increased conflict intensity it may generate.

**Figure 10. Household expenditure per capita declines relative to its 2013 peak**

<!-- Start of picture text --> Consumption per capita<br>110<br>105<br>100<br>95<br>2011 2012 2013 2014 2015 2016<br>Central East North Northeast<br>South Southwest West Westcentral<br><!-- End of picture text --> As a result of this triple shock, household expenditure per capita is expected to be 1.2 percent lower in 2016 than it was in 2011. Although this may seem a modest figure, the decline is much more pronounced (7.3 percent) relative to the time when the presence of troops and foreign aid flows were at their peak, a couple of years earlier. The magnitude of this triple shock is in fact comparable to that of a major recession.

Given the spatial concentration of the conflict—and consequently of foreign aid and international troops—the impact on household expenditures differs across provinces. According to the 23
_A Relatively Stable Macroeconomic Environment._ Recent studies have found that sector reforms are more likely when sector problems are combined with a macro-economic crisis (see, for example, Shirley and M6nard, 2002). Although the macroeconomic environment in Malawi was difficult in some respects in the mid-1990s, Malawi was not suffering from a major macroeconomic crisis. Inflation had accelerated in 1995, reaching 83%, but had slowed considerably by 1998 (see Figure 11). Furthermore, after a decade of unstable, but generally <!-- Start of picture text --> 90 20<br>80 15<br>70 - .- || 10<br>-50<br>o40 \- °22><§ 1 1 101 1 °^A° °<br>30 > -g 1l 0 / \ -5<br>20- . \<br>10- -10<br>0 - I I I I I I I I -15<br>Lo r 0) _ e n r- 0 _<br>G o am a0 0) 0 0 0 o<br>T 0) X 0 T 0) T T C04<br>---- Inflation, consumer prices (annual %)<br>-0- GDP growth (annual %)<br><!-- End of picture text -->

Figure 11: GDP Growth and Inflation in Malawi, 1995-2001 Source: World Bank (2002) modest economic growth (averaging 1.7% between 1985 and 1994), growth accelerated in the mid-1990s. After rapid growth of 16 percent in 1995, it < lslowed to 7 percent in 1996, but remained at over 3 percent per year through 1999. In summary, although the macroeconomic environment was not entirely stable - as discussed previously, the large government deficit (see Figure 9) meant that the government relied upon donors to finance most capital expenditures - it was far from a crisis situation, reducing pressure for immediate and radical sector reform.

_Weak support from domestic enterprises._ Although domestic private enterprises generally felt that Malawi's poor infrastructure was an obstacle to their competitiveness, pressure from this source was considerably less important than pressure from other sources such as donors.<sup>34</sup> Although private sector entities tended to view the reform favorably, few, including those broadly involved in the communications sector, seemed to have played a very active role

> **34** This is also in contrast to the experience in other case-study countries. For example, pressure from the private sector was one of the driving forces for reform in Senegal (Azam _et aL,_ 2002, p. 19)

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- colective support was more highly valued by the smaller leather firms with the avenge usefulness score declining monotonically from 3.0 for the smallest firms to 1.0 for the largest. In both lndonesia and Colombia, the field surveys uncovered substantial subsequent success,

.including export success, by these initially "marginal" firms.

On the supply-side, evidence presented in the next subsection suggests that it is reasonable as a first approximation to distinguish between three supply schedules:

* SISi, corresponding to Indonesia, whose institutional capabilities to supply .support appear weakest among the four countries studied;

* S252, corresponding to Colombia, which appears to have an intermediate level of collective supply capability; and

* M3M3, corresponding to Korea and Japan, where the institutional capabilities for collective supply are strongest.

As is illustrated in Figure 2, taken together the above propositions as to supply and demand IMPly that, among the countries and subsectors studied, the use of collective technical support will be highest for SMEs in Korea's three engineering-based subsectors, and will be lowest for Indonesian finns (especially the non-pribumi).22/ By and large, this pattem is evident in Table 12.

# M--.3: The Delivery of Collective SuDport

Colective technical support takes on one of two very different forms. It can be "broadbased", and work to facilitate the emergence of an "information-rich" environment. Alternatively, it can try to promote "high-intensity" technological learning by supplying technical inputs direcdy to firns. While it is reasonably straightforward to distinguish between the supply of "broad-based" and of "high-intensity" support for Colombia, Indonesia and Japan, the two tpes of support are provided jointly in Korea. Consequently, the Korean experience is presented separately.

22/ The relative importance of collective technical support among the remaning seven subsectors depends on the magnitudes of shifts in demand and supply schedules, and hence cannot be depicted unambiguously.
In order to objectively assess a farmer's knowledge, we administered an agricultural knowledge exam on each technique (see Appendix). The exam contained 23 questions. In some cases, the questions were multiple-choice. Other questions required numerical answers or multiple responses were possible. Sets of questions reect the knowledge of a particular technique, where approximately 3 questions per technique (with the exception of the placebo) were included on the exam. Our objective measure of knowledge, know by exam is created based on the responses to the exam questions: we consider the respondent knows a particular technique objectively if he answered at least one exam question pertaining to the technique correctly.<sup>4</sup> As with the knowledge measure, we create two sets of binary variables from the adoption information. During the indoor interview, farmers were asked whether they adopted each of the SLM techniques they knew by name in the 2011/2012 rainy season.<sup>5</sup> We use this to build our subjective adoption measure. To collect an objective measure of adoption, enumerators were eld-trained to identify SLM techniques in practice. The eld interview took place after planting and before harvest, so that enumerators could observe adoption and measure the area on which each SLM technique was practiced.<sup>6</sup> If the area exceeded zero, the objective measure of adoption indicator takes a value of one.

For the adoption analysis, we restrict the sample to respondents who manage only one plot in the household. Self-reported adoption is collected at the individual level, while objective adoption is only measured for the main plot of the respondents. For consistency, we restrict our analysis of both subjective and objective adoption to the sample of households with male and/or female respondents managing no more than one plot. In spite of slight dierences between the restricted and omitted samples in terms of demographics and crop choice,<sup>7</sup> the analysis remains informative in the search for methods that improve data collection eorts of agricultural production measures in similar settings in Africa.

> 4 Our knowledge exam provides a comprehensive set of questions that describe each technique. For the purpose of our study, it was important to measure how knowledgeable a farmer was about dierent techniques. However, testing dierent stringencies in the line of questioning could be of interest, particularly, for deriving the characteristics of an optimal knowledge exam.

> 5This implies that our analysis of adoption measures is conditional on knowing a technique by name.

> 6 Respondents were unaware of the eld visits until completion of the entire indoor survey.

> 7The sample used for the adoption analysis reects individuals at earlier stages in their life cycle with smaller households and a greater tendency to complete their primary education (Table A.1). A greater proportion of the restricted sample also produces maize, yet has similar inclinations to grow cash crops (Table A.1).

6
good floor material, having good roofing material, or owning a bicycle, computer, stove, television, washing machine, fan, refrigerator, car, and land. (For a complete list of assets used in each country, see Table 3.) PCA relies on the information from the covariance structure of these assets and the hypothesized latent variable that maximizes the explained variance in the set of assets. Table 3 shows the loadings for the first principal component which are treated as the relative weights assigned to each asset to construct the proxy for wealth, or the asset index. For example, in Nigeria, owning television is a strong positive contributor to household wealth (first factor loading of 0.82), while owning a bicycle is a negative contributor to household wealth (first factor loading of -0.02). For Mali, Pakistan, and Colombia, we have data on the ownership of only three assets, namely computer, cell phone, and landline. In these cases, _asset ownership_ variable is instead created as a binary/indicator variable of owning at least a computer or landline phone. The ownership of a cell phone has quite limited variation across households, hence it has a low weight in the asset index, while owning a computer or landline has greater variation across households and is more likely to indicate household wealth.

# **3. Results**

# _3a. Regional profiles and Reclassification of Poverty Status_

By using the same data from 162 countries on household resources as used for the per-capita poverty estimates, we examine how poverty profiles would change under the assumption of household economies of scale, as reflected by allocating household resources based on the square root of household size. Our focus is on how changing the allocation rule changes the composition of who is poor and not on the level of poverty. The IPL of $1.90 (in 2011 PPP US dollars) reflects typical values of national poverty lines expressed in per capita terms from some of the poorest countries in the world (Ferreira et al., 2016). Since the national poverty lines used to estimate the 11
household income, asset ownership (to be used for a wealth index); employment, educational, literacy and numeracy status of all members etc.

Data are weighted to account for the differences in probabilities of individual selection across counties and EAs. Weighted estimates are used when presenting descriptive statistics but not for regressions.

# **3. Descriptive Evidence**

## **3a. Adolescent Girl and Household Characteristics**

Mean age of the adolescent girls is 18.6 years. Fifty-six percent of these young women are not in training, education, or employment (NEET). Thirty-four percent are married. Most of them aspire to complete higher secondary schooling, while 86% of them want to work for pay outside home. Half of them know women running own businesses, and most feel moderately connected to the adults in their community.

In terms of characteristics of these adolescent girls’ households, most of them have low educated parents and fairly large household size. Almost 90% of them belong to SC/ST or backward castes, while close to 70% are Hindus. 41% of them live in households that own a BPL (Below Poverty Line) card, indicating their low-income status. As a measure of household resources, we use the log of annual household income, and use this to shed light on whether adolescent girls from wealthier households have higher aspirations.

**Table 1: Descriptive** **<u>Statistics for Individual and Household Characteristics of Adolescents (15-24 years)</u>**

||Mean|Std. Dev|Min|Max|Count|
|---|---|---|---|---|---|
|Education Aspiration|12.99|(2.84)|1|17|2106|
|Employment Aspiration|0.86|(0.35)|0|1|2388|
|Age (years)|18.57|(3.00)|14|24|2388|
|NEET (0/1)|0.56|(0.50)|0|1|2388|
|Married (0/1)|0.34|(0.48)|0|1|2388|
|Hope (z-score)|-0.02|(0.99)|-1|2|2103|
|Self-efficacy (z-score)|-0.00|(1.00)|-1|2|2388|
|Father's age (years)|46.47|(13.27)|15|100|2388|
|Mother's age (years)|38.34|(18.30)|0|642|2386|
|Father's education (years)|4.97|(4.88)|0|17|2388|
|Mother's education (years)|3.36|(4.52)|0|17|2376|
|Household size|5.97|(2.35)|1|21|2388|
|Log annual HH income|10.91|(0.96)|3|16|2193|
|Caste=SC|0.19|(0.39)|0|1|2381|
|Caste=ST|0.23|(0.42)|0|1|2381|
|Caste=OBC|0.47|(0.50)|0|1|2381|
|Caste=General|0.11|(0.31)|0|1|2381|
|Religion=Hindu|0.68|(0.47)|0|1|2378|
|Religion=Muslim|0.20|(0.40)|0|1|2378|
|Religion=Christian|0.04|(0.18)|0|1|2378|
|Religion=Sarna|0.09|(0.29)|0|1|2378|
|Has BPL card|0.41|(0.49)|0|1|2382|
|Lack of social support (0-9)|1.11|(1.50)|0|9|2388|
|Knows women in business (0/1)|0.51|(0.50)|0|1|2380|
|Adult connectedness (0-4)|2.57|(1.06)|0|4|2384|
|Urban|0.30|(0.46)|0|1|2388|
|Observations|2388|||||

6
# **Table 1. Low-income countries**

||**GNI per capita**<br>**used for 2020**<br>**income**<br>**classification**<br>**(2018, USD)**|**GNI per capita**<br>**used for 2001**<br>**income**<br>**classification**<br>**(1999, USD)**|**Share of**<br>**agriculture in**<br>**GDP**<br>**(percent)**|**Share of**<br>**primary**<br>**commodity**<br>**exports in GDP**<br>**(percent)**|
|---|---|---|---|---|
|**2001 LICs turned MICs**|||||
|Turkmenistan|6,740|660|...|...|
|Armenia|4,230|490|14.9|8.3|
|Georgia|4,130|620|6.9|6.2|
|Azerbaijan|4,050|550|5.6|30.2|
|Indonesia|3,840|580|13.1|5.9|
|Mongolia|3,580|350|10.3|46.7|
|Angola|3,370|220|10|28.4|
|Bhutan|3,080|510|17.4|...|
|Moldova, Rep.|2,990|370|...|3.5|
|Ukraine|2,660|750|10.2|13.8|
|Lao PDR|2,460|280|16.2|8|
|Vietnam|2,400|370|15.3|5.1|
|Ghana|2,130|390|19.7|7.1|
|Nicaragua|2,030|430|15.5|1.5|
|India|2,020|450|15.5|1.2|
|Uzbekistan|2,020|720|...|...|
|Solomon Islands*|2,000|750|...|27.8|
|Nigeria|1,960|310|20.8|11.4|
|São Tomé and Príncipe|1,890|270|11.5|...|
|Zimbabwe*|1,790|520|8.3|3.8|
|Bangladesh|1,750|370|13.4|0.1|
|Congo, Rep.*|1,640|670|6.4|44|
|Kenya|1,620|360|34.6|1.4|
|Côte d'Ivoire*|1,610|710|21.6|5.7|
|Pakistan|1,580|470|22.9|0.3|
|Sudan*|1,560|330|30.5|11.2|
|Cameroon|1,440|580|14.4|12.7|
|Zambia|<br>1,430|320|6.7|25.4|
|Senegal|<br>1,410|510|16|2.3|
|<br>Cambodia|1,380|260|23.4|1.1|
|Lesotho|1,380|550|6.1|...|
|Comoros*|<br>1,320|350|29.9|0|
|Myanmar*|1,310|...|23.3|...|
|<br>Kyrgyz Republic|1,220|300|12.3|4.1|
|<br>Mauritania|1,190|380|23.1|16.3|

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