rafmacalaba/datause-encoder-data huggingface dataset:
learning algorithms.

These findings are also policy relevant, particularly regarding providing more accurate inputs into designing cost-effective poverty targeting and budget transfers. For example, recent World Bank guidelines strongly encourage the adoption of EBP models where census microdata is available (Corral et al. 2022). These results offer more nuanced insights into the performance of EBP and other methods when applied to geospatial data, which presents policy makers with different modeling options for the best decision-making outcomes. If additional information is available for within-community targeting (Elbers et al., 2007), more accurate poverty estimates would translate into significant poverty reduction. Furthermore, since small area estimation methods are generally less costly than additional data collection, they offer appealing policy choices in a poorer country context.<sup>4</sup> The rest of the paper is organized as follows. In section 2, we provide a brief overview of the data, the estimation methods, and the method utilized to validate estimates for accuracy and uncertainty. Then, in section 3, we review detailed results of simulations, including distinguishing between in and out of sample performance, performance in poorer or richer areas, using alternative metrics, and different specifications. Section 4 concludes.

# **2 Methods**

This paper evaluates four different methods for generating predictions of district-level poverty rates: linear empirical best predictor (EBP) models (Battese et al., 1988; Jiang and Lahiri, 2006; Molina and Rao, 2010); cubist regression models (Quinlan et al., 1992; Wang and Witten, 1997), which we refer to as Cubist throughout the results section; extreme gradient boosting (Chen and Guestrin, 2016), more commonly known as XGBoost; and boosted regression forests, or BRF (Friedberg et al., 2020; Tibshirani et al., 2018). Table 2 summarizes key features of these methods, which we describe in greater detail below. Importantly, we evaluate these methods in the context of developing countries, where census data is often unavailable or dated. One of our goals is to improve the estimation of key development outcomes in such contexts. As such, we propose using data that is widely available across the globe: remote sensing and geospatial data. In addition, we adapt and apply a two-stage residual bootstrap procedure to estimate uncertainty for the machine-learning models.

These techniques may improve on existing methods but require rigorous evaluation of their accuracy and

> 4Data gaps are a well-known challenge affecting poverty measurement in poorer countries. While census data are less available in these poorer countries, fewer household surveys are also infrequently collected. Even where household surveys are available, they likely display inconsistent quality over longer time periods. Data imputation methods have been increasingly used to help fill these data gaps (Beegle et al. (2016); Dang et al. (2019); Dang and Lanjouw (2023)).

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6 some of these differences (see Oster, 2005), the dominant view is that this unusual discrepancy reflects son-preference in these societies, implemented through selective abortion and post-natal care (see Sen,1990, and Klasen and Wink, 2003).

Opportunities continue to depend on morally irrelevant, predetermined circumstances even if you survive your first year. Access to basic health care, such as immunization services, is strongly correlated with parental wealth (Figure 2). Even a child’s cognitive skills seem to develop at different rates depending on family background, for children as young as 3-5 years old. Figure 3, drawn from Paxson and Schady (2004), shows the evolution of vocabulary recognition test scores (TVIP) for two groups of children from Ecuador: those whose parents have 0-5 years of schooling, and those whose parents have 12 or more years of schooling. By the time these children enter primary school, at age 6, they have markedly different learning abilities, shaped in large part by differential family backgrounds.

**Figure 2 Access to childhood immunization services depends on parent’s economic status**

<!-- Start of picture text --> 70<br>60<br>50<br>Poorest<br>40<br>30<br>20<br>10<br>Wealthiest<br>0<br>EgyptJordan (*)ColombiaRwandaSouth AfricaPeru KenyaMalawiZambia (*)BrazilVietnamTurkeyGuatemalaTanzaniaIndonesisiaTurkmenitan (*)MoroccoGhanaPhilippinasBeninBangladeshComorosBoliviaKazakhstan (*)ParaguayBurkina FasoYemenCameroonUgandaMauritaniaIndiaCentral African RepublicHaitiTogoEthiopiaMadagascarMozambiqueGuineaCambodiaMaliPakistanEritreaNigerChad<br>Percentage not covered<br><!-- End of picture text --> _Source:_ World Development Report 2006, from Demographic Health Survey (DHS) data. _Note:_ The continuous dark line represents the percentage of children without access to a basic immunization package in each country, while the endpoints of the whiskers indicate the percentages for the top and the bottom quintile of the asset ownership distribution.

* Indicates that the poorest quintile have higher access to childhood immunization services than the wealthiest quintile.

These statistical associations do not establish causality, of course. While endogeneity should not be a concern in the associations presented above – because access to immunization rates or child mortality today can not cause parental education decades earlier – omitted variables clearly exist. The descriptions presented above are essentially
<!-- Start of picture text --> 1<br>75<br>ia<br>Oo<br>.25<br>0<br>2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022<br>Year<br>—e*— Construction sector —e— Other sectors<br><!-- End of picture text --> <!-- Start of picture text --> 1<br>75<br>iva<br>.25 eee see<br>0<br>2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022<br>Year<br>—e— Health and social work services —e— Other<br>—e*— Medical equipments, pharmaceuticals products<br><!-- End of picture text -->
19 Table 6: Trade Measures Appied to Sdected Iteu of tho "QboIlvYer-Delaedroe Trde" group

||-FCS|Hungary|PoLand|<br>NFNs(lumLOeverage<br>tariff rate In|
|---|---|---|---|---|
|_?srngwr Newr VekWdex (S_<br>_.3.7J12)_||||lO|
|Tariff Quota CmiLlIon of US$. 1992<br>Imports (miLlion of USS$, 1992<br>1991|91<br>m<br>210|141<br>25<br>3|141<br>Z59<br>39||
|<br>qltiliztiun<br>retito in prmt).<br>1992<br>Duty free cawonentCin<br>percent),1M|<br>299<br>33|<br>la<br>100|l<br>5S||
|_Polykae_<br>_(SriC.r.3.57112)_|||||
|TariffQuota(miLlion ofUSS),<br>1992<br>[Worts<br>mtillion of US$3. 1992<br>1991|15<br>20<br>34|15<br>30<br>31|15<br>1<br>1||
|<br>qUtiLizntiun ratio<br>(Sin<br>purmtu.<br>1992<br>Duty free camnet<br>(in percent), 1992|<br>IN<br>76|<br>201<br>50|7<br>100||
|_Fksul*n(SITC.Rv.3.8213)_||||_LA_|
|Tariff Quts (milLiLon of US$3,1M<br>Ieqorts (millionof US$3 1992<br>1991|7B<br>121<br>61|0<br>66<br>54|75<br>242<br>192||
|<br>UUtiLiz.tio<br>ratiosCin**perct),1992**<br>Duty free coonent (in percent),1M|156<br>64|**P/a**<br>0|311<br>32||
|_Appwd andCloSingmui of_**_monxlu_**<br>_(SI TC.e3.UI1+ I_|_E 477)_||||
|Tariff Guots CmiLLion of US$3, 1992<br>Imports (million ofUSS$,<br>1992<br>1991|5<br>11<br>8|8<br>29<br>24|a<br>6<br>10||
|<br>ftiLizatimn ratio<br>Cinpec<br>t) 1992<br>Duty freecomponent(Cnpercent), 1992|<br>23Z<br>43|<br>3C6<br>26|aZ<br>100||
|_Glaze Cemmlcs (SlltRet.3SZdS)_|||||
|Teriff Quota (miLlion of US$), 1992<br>Imports (millionof US$), 1992<br>1991|4<br>Z<br>13|4<br>7<br>7|4<br>3<br>.7||
|qUtiLizntim ratio"Cinpero nt), 1992<br>Duty free cowponentCinpercent),1M|6w3<br>16|152<br>66|7<br>100||
|VFWi_of Zi_<br>_ornwxn-aey Med (SflYZ.Zev34fh1Z-12)_|||||
|TariffQuota (millionof USS3 , 1992<br>|2.2<br>|2.2<br>|2.2<br>||
|leports(millionofUS$3, 1992<br>1991|35<br>33|2<br>3|5<br>6||
|<br>UitiLization ratio" (in pucuit), 1992<br>Duty free couponent Cin percent),1M|<br>1644<br>6|<br>80<br>10W|_VA8_<br>40||
|_Mersmundaim_<br>_(A.)_Share of the sample in:|||||
|- total imports of the grcoup (in percent). 1992<br>1991|30<br>29|i8<br>16|27<br>16||
|<br>-total quotas for the group (in percent) 1992|<br>51|<br>35|43||
|,<br>(B3Share of duty free iWportsCinpercnt).<br>1992|<br>40|<br>34|47||

Source: see Table 5.
personalized coaching sessions.<sup>2</sup> Students could then graduate by writing a business plan instead of a traditional undergraduate thesis. Upon graduation, participants were invited to submit their business plans to a competition ( _concours des meilleurs plan d’affaires “entreprendre et gagner”_ ). The fifty winners of the competition became eligible to receive seed capital to establish their business.

In this paper, we identify the impact of the entrepreneurship track on beneficiaries’ labor market outcomes by relying on randomized assignment. Half of the 1,702 students who applied to the program in 2009/10 were randomly selected to participate in the entrepreneurship track while the other half were assigned to the standard curriculum. Students graduated in June 2010 and were re-interviewed between April and June 2011. We show that the entrepreneurship track significantly increased the rate of self-employment among university graduates approximately one year after graduation, but that the effects are small in absolute terms, ranging from 1 to 4 percentage points. Given the low prevalence of self-employment in the control group, these small absolute effects imply that program participants were on average 46 to 87 percent more likely to be self-employed compared with graduates from the control group.<sup>3</sup> However, the employment rate among beneficiaries remained unchanged, which in partial equilibrium points to a substitution from wage employment to self-employment. The evidence suggests that the program affected employment outcomes through a range of channels. In particular, it fostered business skills, expanded networks, and shaped behavioral skills. Participation in the entrepreneurship track also heightened graduates’ sense of opportunities and optimism towards the future shortly after the Tunisian revolution.

To our knowledge, the analysis of the Tunisian entrepreneurship track provides the first experimental evidence on the effectiveness of entrepreneurship education targeted to university students<sup>4</sup> , as well as the first evidence on entrepreneurship-support interventions in the Middle East and North Africa (Angel-Urdinola et al., 2010). Furthermore, we analyze the effectiveness

> 2 The program was implemented in all Tunisian universities delivering _licences appliquées_ , including Ez-Zitouna, Jendouba, Gabès, Gafsa, Tunis, Kairouan, Mannouba, Monastir, Carthage, Sfax, Sousse, Tunis, and Tunis El-Manar. 3 As we will discuss below, the rate of self-employment in the control group is 4%. This is in line with the low rate of self-employment among university-educated in Tunisia in general.  Among 25 – 34 year old with a university degree, 4.6 percent were classified as independent workers and 5.6 percent as employers according to the Labor Force Survey of 2010.

> 4 Oosterbeek et al. (2008) analyze the impact of entrepreneurship education on students’ entrepreneurship competencies and intentions in the Netherlands. They find few positive effects, but their study used a quasiexperimental approach with a small sample size and high attrition rates.

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than two types of crises in one period, and therefore these periods are included in a crisis episode. Although period (D), period (E) and period (F) has less or equal to one crisis identification in one period, the gap between period (C) and period (G) is less or equal to three quarters. Then, period (D), period (E) and period (F) are included in one crisis episode together with period (C) and period (G). On the other hand, the gap between period (G) and period (L) is four periods and then the second condition is not met. Therefore, period (L) is recognized as a different crisis episode.

**Fig. 2: Examples of the Relationship between Crisis Period and Crisis Episode Identifications**

|Period|A|B|C|D|E|F|G|H|I|J|K<br>L|M|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Liquidity type banking crises|?||||||||||||
|Solvency type banking crises|||?||||||||?||
|Balance of payments crises|||||?||?||||||
|Currency crises|||?|||||||?|||
|Debt crises|||||||?|||||?|
|Growth rate crises||||?|||||||||
|Financial crises|||||||?||||?||
|**Crisis episode**|**×**|**×**|**?**|**?**|**?**|**?**|**?**|**×**|**×**|**×**|**×**<br>**?**|**×**|

_Note: (? ) denotes crisis periods; ?: crisis episode, ×: non-crisis episode Source: Author_ Indicators are derived from the following sources.

- Liquidity type banking crises (demand deposits to total assets ratio): IFS line 24 divided by the sum of IFS line 24-27;

- Solvency type banking crises (capital account to total assets ratio): IFS line 27A divided by the sum of IFS line 24-27;

- Balance of payments crises (year-on-year growth rates of gross international reserves): total reserves minus gold (IFS line 1L.d);

- Currency crises (end of period nominal exchange rates against the US dollar): IFS line ae;

- Debt crises (external debt outstanding to exports ratio): external debt outstanding data is obtained from CEIC data base, countries’ publications and GDF, and outstanding is divided by IFS line 70;

- Growth rate crises (year-on-year real growth rates): Data is obtained from CEIC data base, countries’ publications and interpolated WDI (World Bank’s World Development Indicators) data if quarterly data is unavailable;

- Financial cris es (capital flights): IFS line 78 BC (capital account), line 78 BJ (financial account), line 78 CA (net errors and omissions) minus line 78 BD-E (FDI), line 78 BP-BT (government other investments).

# **2.5 Result**

This exercise produces two sets of crisis identifications: crisis period and crisis episode. The four steps identify which quarters experience what types of crises, and the two crisis episode conditions are applied to identify crisis episodes. As a result, eight hundred twenty-seven crisis periods and fifty-nine crisis episodes are identified in fifteen emerging economies between Q1 1980 and Q4 2002. There are three dimensions to observe the results, namely country, crisis type and period.

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The results are quite similar when we look at the gender wage gap in terms of monthly wages where we control not for the usual hours of work, but for the actual hours of work, as we do in panel (b). In this case, a gender gap emerges, although not highly statistically significant, also among the teenagers, because we consider here the much smaller actual number of hours worked by women. It may be argued that early marriages force women in this stage to adjust simply the number of hours worked (intensive margin) and only at a later stage, when children come, their availability to work (extensive margin).

The gap in terms of hourly wages presented in panel (c) is much lower mirroring the lower number of weekly working hours of women with respect to men. The gap is now not statistically significant among older age segments, suggesting the importance of controlling for the hours of work, which clearly correlate to the effort women can put in productive activities as a consequence of their increasing effort in reproductive activities. In the estimates in panel (c), the hours of work are the contractual hours.

The above findings suggest the existence of strong variability of wages by years of age. Figure 9 provides measures of the gender wage gap from different equations that we ran for individuals belonging to a particular age group using monthly wages. Panel (a) reports the unconditional estimates only, while panel (b) also reports conditional estimates based on basic earnings equations. The unconditional estimates confirm the impression that there is a gender gap not statistically different from zero for the youngest segment, while the gap has a clear tendency to rise over the years, with the exception of the individuals aged 26 and 27. The trend estimated by OLS and reported in the figure suggests of an increase of the gender gap by about 1.3% per year of age.

Panel (b) confirms this trend for estimates conditional on log hours of work, while the estimates conditional also on education tend to move closer to the horizontal axis, still from below, as a consequence of the slightly higher level of education of employed women.

# **3.3. Earnings Equations**

Table 2 presents the results of extended earnings equations estimated by OLS for different age groups and by gender. As already noted above, there is no conditional gender wage gap among the teenagers, though the gap is quite high among the young adults, about 18%, and the prime-aged, about 26%. This is quite a large gap, considering the relatively low wages of young workers in Azerbaijan.

The role of work effort in terms of hours of work on monthly wages is confirmed by the coefficient of the weekly hours. The coefficient is statistically significant especially for men and among the young adults and the prime-age workers, a finding that Pastore, Sattar and Tiongson (2013) report relative to Kosovo, using the same types of data.

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generally, we interpret these results as evidence of a large remaining amount of measurement error in the self-reported outcomes. Results with other proxy respondents are broadly similar but are less significant and robust, possibly because the response bias cannot be cancelled out. Hence for proxy information, there appears to be a benefit of asking one well-informed and connected person about many people in the village, rather than using several proxy respondents.

# **4.6 Differences in the factor structure**

The factorial analysis of the noncognitive skills in section 3 raised concerns because it often did not pool items that were expected to belong to the same sub-constructs into the same factors. To formalize this finding, a congruence test (explained in more details in the appendix) tests the degree of correlation of the factor loads of similar items obtained in different contexts. We restrict the analysis to the 23 items from the Big Five included in our study, as we want to compare constructs based on the same set of items. Table 13 presents the congruence with respect to the same items administrated in the United States where it has been validated multiple times. It shows an average congruence across the five factors that is only .40. For comparison the congruence using the factor loads of the same items administrated in Spain, Holland and Germany show congruence coefficients ranging between .76 and .93.

This finding could indicate that the underlying factor structure is different for this population than for populations on which it was previously validated. But it could also be that the lack of understanding of some items or response patterns did not allow detecting the same factor structure even if the true latent factors are similar. If the main problem was lack of understanding, we would expect the congruence with the US to be higher among individuals that are above the median of cognitive skills compared to the ones below the median, but this is not what we find. Further investigation is needed to better understand the lack of congruence.

Our calculation of improved indexes used the factorial analysis of items corrected for acquiescence bias. For comparison, the factor loads of the items non-corrected for acquiescence bias are presented in Appendix Table A7. They generally show far less consistency in how the items are sorted, making it very difficult to attribute a dominant interpretation to the factors. Instead, specific factors appear to be pooling questions with the same answer types and phrasing. The first, fifth and sixth factors are only pooling items that are not reversed, and second, third and fourth factors are pooling reversed items together. Almost all factors that are not in a one to five Likert-scale sorted themselves together in the sixth factor. In sum, without the correction of acquiescence bias, the share of the variance in the responses driven by acquiescence bias and 29
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