rafmacalaba/datause-encoder-data huggingface dataset:
# **I. INTRODUCTION**

Countries in Sub-Saharan Africa often export narrow ranges of products (Collier, 1998). A recent study noted that in the late 1990s, 39 of 47 of African countries depended on two primary commodities for over half of their export earnings (Morrissey and Filatotchev, 2000). As a result, these countries are highly susceptible to terms-of-trade shocks. Diversifying exports away from primary commodities into labor-intensive manufacturing, which currently accounts for only a relatively modest share of GDP and an even more modest share of exports, could reduce this vulnerability.

In addition to reducing vulnerability to shocks, increasing exports might boost income by increasing economic growth.<sup>1</sup> Exporters tend to be more efficient than non-exporters— something that holds for the enterprises in this study.<sup>2</sup> If the enterprise-level correlation between exporting and efficiency is due to exporting improving productivity, increasing exports might increase income. There has been considerable debate over whether this is the case—the correlation could also simply be due to productive enterprises self-selecting into exporting. Under the first hypothesis, the _learning-by-exporting_ hypothesis, the discipline of competing in international markets encourages enterprises to improve their productivity and exposes them to foreign technologies and modes of production. Under the second hypothesis, the _self-selectivity_ hypothesis, only firms that are already efficient are able to export. Although inefficient firms are protected from international competition in domestic markets by natural barriers (e.g., high transportation costs) and policy barriers (e.g., government tariffs and quotas) to trade, they are unable to enter international markets. It is important to note that the two hypotheses are not mutually exclusive. Even if more productive enterprises self-selected into exporting, it would still be possible that exporting results in further productivity improvements.

> 1 Söderbom and Teal (2003) find that exports—although not manufacturing exports in particular—were associated with income growth in nine countries in Sub-Saharan Africa.

> 2 Several investment climate assessments, which calculate productivity data for the firms in this study have found evidence consistent with the idea that exporters are more efficient.  See, for example, World Bank (2004a) for Tanzania.  These studies are available on the World Bank’s website (http://www.worldbank.org/ privatesector/ic/ic_ica.htm ).  A similar relationship has been observed in many developing and developed countries.  The large literature on this topic is summarized in Tybout (2003).

2
Using the Ethiopia LSMS-ISA, Figure 7a also shows that response categories can be refined/condensed. Compared to other reported constraints, most enterprise owners overwhelmingly report “market access” as the biggest factor affecting enterprise growth — and within this group, distance and cost are the most common issues. Developing a more detailed set of response options around these market access issues is therefore useful, and conducting testing prior to survey implementation to tailor options to specific country contexts. Differences between men and women across all three categories of market-related constraints were also statistically significant, and Figure 7b also shows that reporting of these constraints followed markedly different trends for men and women by age (reporting of market-related constraints increases with men’s age, and falls — albeit with a flattening-out in between — with women’s age).

Figure 7a. Ethiopia 2015-16 LSMS-ISA: Reported constraints to non-farm enterprise growth (own-reporting sample) Across all reported constraints Within those reporting market-related constraints

# <u>Notes:</u>

There were 818 male and 811 female entrepreneurs in the own-reporting sample (and within those reporting market-related constraints, 489 and 511 men and women, respectively).

Figure 7b. Ethiopia 2015-16 LSMS-ISA: Reported constraints to non-farm enterprise growth (own-reporting sample) 21
For the past several years the WorldPop project,<sup>14</sup> based at the University of Southampton, has produced gridded population maps for dozens of countries, which detail the estimated population per 100 square meters.<sup>15</sup> WorldPop has also produced gridded poverty estimates at a 1 kilometer resolution for a small number of countries using a similar method based on household survey data. In collaboration with the Flowminder Foundation, WorldPop is now beginning to integrate CDR-derived features into the same framework. While relatively little information has been published on this approach, it has the advantage of being able to incorporate all of the relevant data into a single format. The resulting maps should not only be highly accurate, but also as consistent as possible with the underlying datasets. One caveat to the WorldPop approach is that the inclusion of the poverty dimension is still in an early stage, and it is not clear how these maps are validated, since by design they should closely correlate with survey-based poverty estimates.<sup>16</sup>

# **Using CDR Data for Poverty Mapping**

As technical approaches to using CDRs for poverty mapping continue to evolve, a number of challenges will need to be addressed in order to operationalize this methodology as a practical tool for policy analysis. Several of these challenges are described below. A range of other legal and ethical issues are considered in a separate World Bank/Data-Pop Alliance white paper.<sup>17</sup>

## **_Validation and Transparency_**

Validation is more complicated for CDR models than it is for survey-based techniques. Survey instruments are relatively transparent. They can be inspected, and quantitative and qualitative fieldwork performed both before and after data collection can build confidence. Spatial and temporal inconsistencies can be checked, and in rare cases overlapping surveys can be crosschecked. As a result, well-established survey programs such as the Demographic and Health Surveys and Living Standards Measurement Study are often treated as close to the ground truth. Validation is more difficult for big data research in general and for CDR models in particular. These data are not specifically collected for socioeconomic analysis, so inspection may not be useful and pre-testing is often not possible. Moreover, CDR models are usually designed to interpolate between conventional survey models in either time or space, so direct validation against a survey is not possible.

> 14 See <u>http://www.worldpop.org.uk/</u>

> 15 These maps are built by combining census data with physical covariates, such as climate, elevation, slope and water bodies, and human covariates, such as urban sprawl, based on satellite imagery, known settlements, roads, and points of interest under a Bayesian framework.

> 16 The World Bank Innovation Lab is presently sponsoring additional CDR analysis work with Flowminder/Worldpop in Haiti, which is expected to provide deeper insight into their methods.

> 17 Letouzé and Vinck (2015).

**18**
Table S.5: Impacts on children’s educational achievement: heterogeneity <u>(OLS)</u>

|Dependent variable:||||O|ne if pass|||||
|---|---|---|---|---|---|---|---|---|---|
|_X_:|Age|Education|Male|Household|No. of|Head|Head|Head|Land|
|||||size|male<br>members|age|education|male|size|
||(1a)|(1b)|(1c)|(1d)|(1e)|(1f)|(1g)|(1h)|(1i)|
|**December in 2013**||||||||||
|All products|-0.314|0.013|0.046|0.036|0.097|0.113|0.022|0.076|0.035|
||(0.284)|(0.115)|(0.041)|(0.121)|(0.071)|(0.172)|(0.036)|(0.095)|(0.034)|
|One product|-0.043|0.047|-0.022|0.059|0.097*|-0.010|-0.004|0.045|0.010|
||(0.245)|(0.091)|(0.047)|(0.119)|(0.057)|(0.149)|(0.029)|(0.123)|(0.025)|
|All products _× X_|0.027|0.002|-0.056|-0.003|-0.030|-0.002|0.000|-0.059|-0.001|
||(0.022)|(0.021)|(0.061)|(0.022)|(0.026)|(0.004)|(0.014)|(0.098)|(0.001)|
|One product _× X_|0.003|-0.011|0.046|-0.013|-0.043*|0.000|0.001|-0.053|-0.001|
||(0.020)|(0.022)|(0.065)|(0.026)|(0.025)|(0.004)|(0.012)|(0.136)|(0.002)|
|R-squared|0.326|0.325|0.326|0.325|0.327|0.325|0.325|0.325|0.326|
|No. of obs.|850|850|850|850|850|850|850|850|850|
||(2a)|(2b)|(2c)|(2d)|(2e)|(2f)|(2g)|(2h)|(2i)|
|**December in 2014**||||||||||
|All products|-0.701**|-0.253*|0.016|-0.309*|0.004|-0.123|-0.094*|-0.022|-0.054|
||(0.289)|(0.128)|(0.053)|(0.168)|(0.093)|(0.175)|(0.047)|(0.147)|(0.050)|
|One product|-0.281|-0.019|-0.020|-0.073|0.043|-0.207|-0.043|0.011|-0.042|
||(0.309)|(0.126)|(0.052)|(0.152)|(0.098)|(0.223)|(0.039)|(0.173)|(0.042)|
|All products _× X_|0.052**|0.042|-0.173*|0.051*|-0.025|0.002|0.028*|-0.039|-0.000|
||(0.023)|(0.028)|(0.093)|(0.030)|(0.032)|(0.004)|(0.015)|(0.143)|(0.001)|
|One product _× X_|0.021|-0.001|-0.005|0.010|-0.028|0.004|0.013|-0.039|0.002|
||(0.024)|(0.023)|(0.068)|(0.031)|(0.038)|(0.005)|(0.013)|(0.187)|(0.002)|
|R-squared|0.150|0.148|0.151|0.149|0.146|0.146|0.150|0.145|0.147|
|No. of obs.|845|845|845|845|845|845|845|845|845|
|Baseline control|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|School FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Village FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Notes: (1) Figures ( ) are standard errors. *** denotes significance at 1%, ** at 5%, and * at 10%. (2) Standard errors are robust to heteroskedasticity and clustered residuals within each school-grade. (3) The baseline controls include age (years); education (years); gender (dummy); household size; no. of males; head age (years); head education (years); head gender (dummy); and land size (natural number).

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Finally, to give a sense of overall marriage instability, it is informative to look at union survival rates by women’s ages. Among ever-married women, more than one-quarter have been through one marriage dissolution by age 45 (Figure 5).<sup>14</sup> Ever-widowed and ever-divorced women tend to differ primarily in two dimensions: ever-divorced women are more often urban dwellers and they are three times more likely to have ever been to a French school. This is perhaps not surprising as these characteristics permit a greater autonomy and are likely to facilitate divorce. In addition, for demographic reasons and because divorcees remarry more often than widows, widows are older, have more living children and are more often heads of their households (Table 3).

# **5. Determinants of current marital status**

We expect women to face various trade-offs upon marriage dissolution. Widowhood can be seen as a negative shock. In contrast, divorce, since it can be initiated either by the husband or the woman herself, may be anticipated, and hence, a positive or negative development for a woman.<sup>15</sup> When discussing remarriage, women emphasize the importance of consumption levels, autonomy and the degree of social pressure to be married.<sup>16</sup> Autonomy (or bargaining power) is linked to a woman’s position in the household: for example, a household head is more autonomous than the spouse of the head, or than wives of higher rank in a polygamous marriage. The benefits of independence and of not having to take care of a husband were clearly expressed in most of the interviews we conducted, as was the preference for a monogamous rather than a polygamous union. One’s position in the household also affects bargaining power and therefore, the share of household resources one can benefit from. Thus, the level of consumption accruing to a woman depends on both the household’s average consumption levels and the woman’s bargaining power. Finally, social pressure to remarry is mainly driven by age, and for widows, the gender of the progeny. Social norms make it largely unacceptable for a woman of childbearing age to remain unmarried. Living with an adult son may alleviate some of this pressure. On the other hand, having a young son can render a leviratic union unavoidable for a widow if the paternal lineage insists on keeping the child.

> 14 This number is comparable to the French case (29% of highly educated and 23% of less educated women have experienced one marital dissolution before age 45, 1999 data) (Lefèvre and Filhon 2005).

> 15 We interviewed both women who themselves chose divorce, and others claiming to have been surprised when their husband repudiated them. The latter were generally badly off (Lambert and van de Walle 2012).

> 16 Note that this ignores other dimensions of welfare often associated with widowhood and divorce, including bereavement, emotional loss, and changes in social status and identity.

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markets or where saturation is low.<sup>33</sup> Fortunately, the conditions under which local price spillovers arise are not widespread. By 2015 only 4.2 percent of villages in a nationally representative expenditure survey had a saturation level of 65 percent or higher. We are able to identify such impacts as roughly half the experimental village sample attained such a level of saturation. In addition, the Philippines has one of the highest incidences of stunting (Bhutta et al. 2013); a similar program might not have such severe consequences in countries where poverty is perhaps less linked with poor nutritional status. Indeed, another reason such negative spillovers have not been detected in other studies may be that cash transfer experiments began in middle-income countries, like Mexico and Brazil, where stunting was less of an issue.

As transfers are increasingly being introduced in poorer countries with higher levels of stunting, the possibility of adverse spillovers to young children in nonbeneficiary households through the local price mechanism merits more consideration. Might a different targeting scheme avoid such general equilibrium effects? The _Pantawid_ program is targeted to individual households based on a proxy means test score. An alternative is a village-based targeting scheme for the subset of villages that are particularly poor or remote. In such a scheme all households would be offered the program which would compensate everyone for any rise in local prices, thereby averting negative nutrition impacts.

Of course, area-based targeting or universal access, while averting spillovers, would also likely be more expensive. Using a limited number of assumptions, we consider the costs and benefits of such village-based or geographic targeting in poor areas (as detailed in Annex 5). Note that this is a conservative estimate of program benefit as we only consider the labor market impacts of stunting aversion on subsequent adult wages and not other possible detriments such as lowered intergenerational transmission of human capital. Given such parameter estimates as the average daily adult wages reported in our sample (US$6.3), the stunting differential attributable to _Pantawid_ , and the impact of stunting on adult wages (Hoddinott et al. 2013), we estimate that the

> 33 In extensions of our empirical analysis, we consider remoteness indicators as an additional measure for the integration, or lack thereof, of the local market with wider regional ones. Unfortunately, the _Pantawid_ evaluation was not designed to estimate differential impact by remoteness categories. Further, it is not clear what the relevant remoteness metric might be. Annex 4 explores the influence of two remoteness proxies on the main impacts and finds theoretically consistent but imprecise estimates. Given the unambiguous link between price changes, stunting, and program saturation, we believe that in this context the saturation metric is the most comprehensive available measure summarizing the channels through which a cash transfer can lead to general equilibrium impacts.

32
11 this single-year increase in 1986, and of the corresponding 40% proportional poverty decline reported in Table 2, are _not_ credible. Since there were no methodological changes to the survey, the explanation for this discrepancy – which is unique in the Brazilian time series, as an inspection of Tables 1 and 2 will attest – is unlikely to be found in statistical problems like those which have recently featured in the literature on poverty in India. More likely, it reflects the disconnect between monetary incomes and welfare that resulted from the widespread rationing that became prevalent throughout the Brazilian economy in late 1986. Rationing arose in most consumer-goods sectors as continued monetary growth made the price freezes, on which the failed Cruzado stabilization plan hinged, unsustainable. Under widespread rationing, of course, real monetary incomes (calculated with respect to the prevailing frozen prices) are no longer a reliable guide to welfare, or to poverty, since goods are not necessarily available to meet demand.

As the price freeze became unsustainable, and black markets proliferated, the Cruzado Plan was abandoned, and an upsurge in inflation in 1987 restored equilibrium prices. The results can be seen in the “return to normalcy” of median, mean and poverty indicators for 1987, in Tables 1 and 2. Crucially, during September 1986 – the reference month for the PNAD survey – the price freeze (which was decreed on February 28<sup>th</sup> , 1986, and effectively abandoned with the “Cruzado II” announcement of November 21<sup>st</sup> of the same year) was still firmly in place, but rationing and black markets were already commonplace.<sup>18</sup> In sum, while we are confident that the time-series for poverty and inequality presented in Tables 1 and 2 are reasonably accurate for all other years, they clearly overstate mean incomes and understate poverty for 1986. Since there is no obvious reason why rationing should be distributionally neutral, the inequality numbers for 1986 must also be treated with circumspection.

Be that as it may, poverty was higher in 1993 than in 1981 for all six poverty series in Table 2 indicating that, at least in terms of income poverty reduction, the 1980s really were a lost decade for Brazil. All of the overall reduction in poverty between 1981 and 2004 was therefore achieved between 1993 and 2004, a period marked by the restoration of macroeconomic stability, some modest resumption in growth, and sustained – if unspectacular – declines in inequality. While poverty reduction in this latter sub-period still falls short of the aforementioned Asian miracle 18 See Lara Resende et. alli (1987).
Table S.30: Checking on spillover effects - GPA z-scores: Case A <u>(OLS)</u>

|Dependent variables:|||GP|A z-scores|||
|---|---|---|---|---|---|---|
||Bengali|English|Math|General<br>science|Islam<br>studies|Bangladesh<br>general<br>studies|
||(1a)|(1b)|(1c)|(1d)|(1e)|(1f)|
|**December 2012**|||||||
|Share of treated students and their|-4.573*|-3.062|-4.573*|-3.494|-4.011|-2.085|
|siblings in each school-grade|(2.568)|(2.290)|(2.366)|(2.395)|(2.852)|(1.734)|
|Share of students in each school-grade that|3.451|2.352|3.349|2.844|2.444|1.690|
|had no access to electricity at baseline|(2.389)|(2.424)|(2.451)|(2.386)|(2.751)|(1.638)|
|All products|-0.018|-0.035|0.005|-0.032|-0.095|-0.078|
||(0.080)|(0.078)|(0.070)|(0.073)|(0.072)|(0.089)|
|One product|-0.019|0.006|0.022|-0.004|-0.107|-0.086|
||(0.096)|(0.081)|(0.096)|(0.105)|(0.087)|(0.095)|
|R-squared|0.232|0.220|0.218|0.217|0.231|0.225|
|No. of obs.|849|849|849|849|849|849|
||(2a)|(2b)|(2c)|(2d)|(2e)|(2f)|
|**December 2013**|||||||
|Share of treated students and their|-1.546|3.335|-4.084*|-6.019***|-3.535|0.317|
|siblings in each school-grade|(2.009)|(2.385)|(2.187)|(1.892)|(2.155)|(1.678)|
|Share of students in each school-grade that|0.324|-3.599|2.413|3.287*|3.441*|-0.075|
|had no access to electricity at baseline|(1.889)|(2.429)|(2.054)|(1.752)|(1.999)|(1.715)|
|All products|-0.044|-0.062|-0.034|0.062|0.061|0.065|
||(0.085)|(0.063)|(0.058)|(0.084)|(0.081)|(0.069)|
|One product|0.029|-0.145**|-0.068|0.059|0.033|0.128|
||(0.085)|(0.069)|(0.071)|(0.078)|(0.078)|(0.089)|
|R-squared|0.260|0.263|0.398|0.318|0.266|0.272|
|No. of obs.|849|849|849|849|849|849|
||(3a)|(3b)|(3c)|(3d)|(3e)|(3f)|
|**December 2014**|||||||
|Share of treated students and their|0.499|0.273|1.239|1.548|0.714|2.043*|
|siblings in each school-grade|(1.033)|(1.052)|(1.359)|(1.126)|(1.010)|(1.170)|
|Share of students in each school-grade that|0.360|0.340|-0.133|-0.791|0.207|-0.819|
|had no access to electricity at baseline|(0.972)|(0.914)|(1.127)|(0.995)|(0.897)|(0.908)|
|All products|-0.085|-0.035|-0.103|-0.104|-0.059|-0.129|
||(0.082)|(0.085)|(0.087)|(0.081)|(0.088)|(0.090)|
|One product|0.051|0.041|0.033|-0.073|0.032|-0.036|
||(0.092)|(0.086)|(0.089)|(0.092)|(0.100)|(0.092)|
|R-squared|0.115|0.099|0.164|0.150|0.109|0.130|
|No. of obs.|845|845|845|845|845|845|
|Baseline control|Yes|Yes|Yes|Yes|Yes|Yes|
|School FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Village FE|Yes|Yes|Yes|Yes|Yes|Yes|

Notes: (1) Figures ( ) are standard errors. *** denotes significance at 1%, ** at 5%, and * at 10%. (2) Standard errors are robust to heteroskedasticity and clustered residuals within each school-grade. (3) The baseline controls include age (years); education (years); gender (dummy); household size; no. of males; head age (years); head education (years); head gender (dummy); and land size (natural number).

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# **Figure 9**

Decomposition of the Fall in Monetary Poverty Rate in Argentina and Chile during the Golden Decade

# Panel A. Argentina

<!-- Start of picture text --> 100%<br>90%<br>20.0<br>80%<br>10.4 Cyclical income 44.1<br>70%<br>15.0<br>60%<br>2.0 Permanent income 8.6 50%<br>10.0<br>40%<br>30%<br>Redistribution<br>11.1 47.3<br>5.0 20%<br>10%<br>0.0 0%<br>In absolute terms As percentage<br>Fall in Poverty Rate (in percentage points)<br>Fall in Poverty Rate (as percentage of total fall in poverty)<br><!-- End of picture text --> <!-- Start of picture text --> Panel B. Chile<br>16.0 100%<br>90%<br>14.0<br>80%<br>12.0<br>10.0 62.1 70%<br>Permanent income<br>10.0<br>60%<br>8.0 50%<br>40%<br>6.0<br>30%<br>4.0<br>6.1 Redistribution 37.9 20%<br>2.0<br>10%<br>0.0 0%<br>In absolute terms As percentage<br>Fall in Poverty Rate (in percentage points)<br>Fall in Poverty Rate (as percentage of total fall in poverty)<br><!-- End of picture text --> Notes: Poverty line of 5.50 dollars per person a day (2011 PPP). See Appendix B for details. Sources: Authors’ estimations based on SEDLAC (CEDLAS and World Bank) and WEO (October 2018).

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