rafmacalaba/datause-encoder-data huggingface dataset:
vast majority of households (96 percent) did not have any electricity source.

**Table 2: The data**

||**Burkina Faso**|**Rwanda**|**Senegal**|
|---|---|---|---|
|**Year**<br>**of**<br>**Data**<br>**Collection**|2010|2011|2011|
|**Type of Sample**|Non‐electrified HHs in<br>villages selected for<br>electrification through<br>Solar Home Systems<sup>1</sup><br>before intervention|A random sample of<br>HHs in villages<br>selected for<br>electrification through<br>grid extension before<br>intervention|The random sample<br>of HHs in villages<br>selected for<br>electrification<br>through mini‐grids|
|**Location**|Rural area with slightly|National|Remote rural area,|
||above‐average income|representative|partly|
||opportunities due to||above‐average soil|
||cotton farming||fertility|
|**Sample Size**|553|1008|361|
|**The share of HHs with**|0|0.03|0.25<sup>2</sup>|
|**electricity access**||||

_Notes:_<sup>1</sup> Households within these villages that already have a pre‐electrification source (mainly solar panels) have been excluded.<sup>2</sup> Most common access technology in Senegal is a solar panel; there are also very few car batteries and generators.

The Senegal data are random samples of households in villages that the Senegalo‐ German Energy Access Program PERACOD<sup>7</sup> selected for electrification through solar‐diesel village grids or Solar Home Systems (see Bensch et al. 2011). All the households were asked about their WTP for electricity services. A considerable share of households to be provided with grid electricity had already had a Solar Home System. The data in all countries provide detailed information about households’ socio‐economic situation and especially details on their electricity and energy sources, as well as expenditures, household appliances, and lighting patterns.

> 7 For more information on the program, see www.peracod.sn.

15
# **7.9. Item-test and Item-Rest correlations including Fluency as an item in the ECE test**

**Table 32. Item correlations including Fluency 45 wpm  in the ECE test (N=475)**

|**Item (equivalent**<br>**in the test)***|**Sign**|**Item-test**<br>**correlation**|**Item-rest**<br>**correlation**|**Average**<br>**inter-item**<br>**covariance**|**Alpha**|
|---|---|---|---|---|---|
|I1 (Items 1+2)|+|0.528|0.485|0.024|0.744|
|I2 (Items 3+4)|+|0.479|0.427|0.024|0.745|
|I3 (Items 5+6)|+|0.517|0.471|0.024|0.744|
|I4 (Items 7+8)|+|0.495|0.448|0.025|0.745|
|I5 (Item 9)|+|0.527|0.478|0.024|0.743|
|I6 (Item 10)|+|0.461|0.350|0.023|0.744|
|I7 (Item 11)|+|0.494|0.386|0.023|0.741|
|I8 (Item 12)|+|0.431|0.307|0.023|0.748|
|I9 (Item 13)|+|0.323|0.196|0.025|0.757|
|I10 (Item 14)|+|0.390|0.261|0.024|0.752|
|I11 (Item 15)|+|0.530|0.417|0.022|0.739|
|I12 (Item 16)|+|0.476|0.376|0.023|0.743|
|I13 (Item 17)|+|0.575|0.486|0.022|0.734|
|I14 (Item 18)|+|0.500|0.383|0.023|0.742|
|I15 (Item 19)|+|0.374|0.246|0.024|0.753|
|I16 (Item 20)|+|0.369|0.244|0.024|0.753|
|I17 (Item 21)|+|0.339|0.216|0.024|0.755|
|I18 (Item 22)|+|0.303|0.183|0.025|0.757|
|I19 (Item 23)|+|0.308|0.193|0.025|0.756|
|**Fluency 45 wpm**|**+**|**0.514**|**0.406**|**0.023**|**0.740**|
|**Test Scale**||||**0.024**|**0.757**|

* Items were re-numbered when calculating of a new total score over 19 that involved merging the pairs of connected items into single items.

** Test scale = mean(unstandardized items)

# **7.10. Difference of means between fluent and less fluent readers**

**Table 33. Mean comparison at different cut points of fluency**

|**Cut points of**||**Rasch**|**Raw**|
|---|---|---|---|
|**fluency**|**Mean and differences**|**score**|**score**|
|45 words er|Mean for 45 wpm or more|44.5|10.0|
|p<br>minute|Mean for less than 45 wpm|51.6|12.6|
||**Mean difference**|**7.1**|**2.6**|
|60 words er|Mean for 60 wpm or more|46.0|10.5|
|p<br>minute|Mean for less than 60 wpm|52.5|12.9|
||**Mean difference**|**6.5**|**2.4**|
|75 words er|Mean for 75 wpm or more|47.1|10.9|
|p<br>minute|Mean for less than 75 wpm|54.0|13.5|
||**Mean difference**|**6.9**|**2.6**|
|**Standard Dev**|**iation (all)**|**7.8**|**2.9**|

82
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22 unionization more than ten years before the sample started—also raised the elasticity of poverty reduction with respect to growth (in manufacturing).

Other policies can also affect the pattern of distributional change (and thus of poverty reduction), even after one controls for differences in the pattern of growth. A repeated finding is that higher rates of inflation result in lower rates of poverty reduction (in Brazil, China and India). The Brazilian case study revealed two important changes in the policy environment which contributed to greater success against poverty: a dramatic reduction in the country’s previously massive rate of inflation (in 1994), and a substantial increase in the amount of social security and social assistance payments, accompanied by some improvements in targeting, during the period 1988-2004.

A second branch of literature is even more micro-oriented, and takes the individual household, rather than a state or province, as the unit of observation. This approach is exemplified by the various chapters in Bourguignon et al. (2005) and can be thought of as a set of statistical decompositions of the _growth incidence curve_ , as given by _g_ ( _p_ ) = _d_ ln _y_ ( _p_ ) (where it will be recalled that _y_ ( _p_ ) is the quantile function).<sup>34</sup> _g_ ( _p_ ) is the income growth rate at percentile _p_ of the distribution (for example, _g_ (0.5) is the growth rate of the median income). In these studies, a small set of models for key economic relationships—such as earnings regressions, participation equations, or education demand functions—is estimated for both the initial and terminal years of the period under study. Then various counterfactual income distributions can be simulated by importing sets of parameters from either date into the corresponding models for the other date. The spirit of the exercise follows that of Oaxaca (1973) and Blinder (1973) and the results, like the original Blinder-Oaxaca decomposition, are best interpreted as a statistical decomposition of changes in the distribution, rather than as measures of causal effects.

> 34 On the properties of the growth incidence curve see Ravallion and Chen (2003). When making distributional comparisons over time, the growth incidence curve can be calculated from any two cross-sectional surveys (which do not need to be panel surveys, given the usual anonymity assumption).  Alternatively, one of the two quantile functions can be a counterfactual distribution. It can also be shown that the changes in most commonly used poverty and inequality measures can also be written as functionals of the corresponding growth incidence curve, usually with weights that can be interpreted as the sensitivity of the particular measure to changes in the distribution at each percentile. This is particularly simple for the Watts index of poverty; it can be readily shown that the change in this index is given by the area under the growth incidence curve up to the headcount index of poverty (Ravallion and Chen, 2003).
# **_Summary statistics_**

The main outcome variables of interest in this study are (1) the proportion of project playgroups that remained open in 2016, three years after the end of project funding, and (2) the proportion of project playgroups that remained open in 2016 with two teachers that were trained as part of the project. This latter measure captures a more rigorous definition of sustainability compared to our first measure because it requires the continued employment of teachers who were trained under the project.

Of the full sample of 432 project playgroups, 92% were still operating in 2013 at the end of the project and 86% remained open in 2016, see Appendix Figure 1. As noted above, data on teacher training were not available for the full sample of 432 project playgroups so the second measure of sustainability cannot be assessed for this group.

For the main analytic sample, the 245 project playgroups with data from all three time points, about 96% were still open in 2013 and by 2016 92% remained in operation (shown as a solid line in Figure 1). Because the project was rolled out in phases and the training was provided in two blocks of 100 hours each, in 2010 our first survey of playgroups showed that only about 29% of the 245 project playgroups reported that they had two trained teachers. However, the proportion of open playgroups with two trained teachers _increased_ between 2013 and 2016 even though the teacher training component of the project ended in 2013 (shown as a dashed line in Figure 1).<sup>11</sup> We argue that open project playgroups with two trained teachers in 2013 and 2016 were those that were particularly successful at attracting and hiring teachers from other project playgroups who had also been trained under the project. This view is supported by the fact that the proportion of open project playgroups with at least one trained teacher _declined_ between 2010 and 2016 (shown as a dotted line in Figure 1). Thus, our second measure of sustainability – the proportion of open project playgroups with two trained teachers – captures those that were most successful in providing quality early childhood education after external funding ended.

> 11 Since the rollout of the project was not complete, this is understandable. However there is also anecdotal evidence to suggest that there were issues with the fidelity of implementation of the teacher training component which also have contributed to the low share of playgroups with two trained teachers by 2010.

6
Table I Deemnantst of Sprea Over Risk-Free RAte Dependent Variable **is** Spread

||DoUar Bo<br>|nds<br>|Stering BoDnds<br>|<br>|
|---|---|---|---|---|
||CoefflcieS|<br>,|Q4|3,~an|
|Constant.|2.61|0.31|1.97|0.46|
|Value/Exports|-0.07|0.24|0.0007|0.0007|
|Municipal|0.11|0.18|-|-|
|State *|0.07|0.17|||
|Corporate|0.76|0.16|||
|National Bank|0.04|0.34|-|v|
|Other Bank|0.14|0.28|-|-|
|Dominionetc.Central|-|-|-1.52|0.38|
|Dominion etc.Local|-|-|-1.25|0.69|
|Foreign Corporate Stocks|-|-|-0.45|0.25|
|Trade Surplus|-0.20|0.25|-0.56|0.40|
|Budget Surplus|-0.80|0.40|-0.12|0.41|
|1921|-).12|0.26|-|-|
|1922|0.04|0.25|||
|1923|-0.01|0.36|-|-|
|1924|0.17|0.29|0.18|.0.48|
|1925|0.19|0.27|0.39|0.52|
|1926|0.40|0.26|0.09|0.44|
|1927|0.38|0.26|-0.01|0.45|
|1928|0.07|0.27|0.22|0.45|
|1929|0.06|0.28|0.20|0.48|
|Western Europe|-0.73|0.18|0.33|0.26|
|Eastern Europe|1.21|0.26|1.15|0.33|
|Canada|-1.38|0.19|-0.69|0.85|
|Central America|-0.79|0.29|-|-|
|South America|0.50|0.21|0.38|0.67|
|Japan|0.05|0.36|-0.03|0.38|
|Australia|-0.91|0.45|-0.29|0.67|

Note: for dollar bonds 1920 is the omitted year, while for sterling bonds 1923 is omitted. The omitted country is Germany, while national government is the omitted category of borrower.

|R-squared|0.69|0.91|
|---|---|---|
|<br>S.E. of Regression|0.00654|0.3876|
|<br>Number of Observations|207|43|

_7_
Because of the potential use of poverty maps for targeting purposes, there has been a growing literature on the use of the maps for policy. The World Bank recently came up with a collection of papers showing how poverty maps can be used for policy (Bedi et al., 2007). In this collection, country studies include work on Albania (Carletto et al., 2007), Bolivia (Arias and Robles, 2007), Bulgaria (Gotcheva, 2007), Cambodia (Fujii, 2007), China (Ahmad and Goh, 2007a), Ecuador (Araujo, 2007), Indonesia (Ahmad and Goh, 2007b), Mexico (Lopez-Calva et al., 2007), Morroco (litvack, 2007), Sri Lanka (Vishwanath and Yoshida, 2007), Thailand (Jitsuchon and Richter, 2007), and Vietnam (Swinkels and Turk, 2007). While East Asia, Eastern Europe, Latin America, Northern Africa and South Asia are all represented, sub-Saharan Africa is notably absent. This does not mean that there has not been any work on poverty maps in sub-Saharan Africa. Indeed, poverty maps have been constructed among others for Ghana (Coulombe, 2008), Madagascar (Mistiaen et al., 2002), South Africa (Alderman et al., 2002), as well as Uganda (Emwanu et al., 2006; Hoogeveen and Schipper, 2005). Still, Africa remains under-represented in terms of work done in this area, and this is especially the case for West and Central Africa.

A key reason for the lack of poverty maps available for West and Central African countries has been the fact that good census and survey data were not available until recently. This has now changed, as more data has become available. This has enabled the World Bank’s Africa Region unit launched a few years ago a West and Central Africa poverty mapping initiative that has helped in constructing poverty maps in 16 countries (Coulombe and Wodon, 2007). For most of the countries, the poverty map is close to being finalized.

In the context of the recent food price crisis, poverty map can also be used to document the likely impact of the crisis on various regions and areas within a country. Indeed, telling a policy maker that poverty will increase due to the crisis is not very useful. By contrast, telling the policy makers where the impact is likely to be the larger is better, so 3
Table 11

# Relative Usefulness of Various Types of Support From Public or Other Non-Profit Agencies to the Non-Electrical Machinery **SMI** Exporter Firm in Penetrating Export Markets

|Type of Support||Percen<br>of Firms<br>Using<br>Service|t Averag<br>Score<br>Among<br>Users|e<br>Averag<br>Score<br>|e A<br>3|F<br>ssign<br>4|irm<br>ing<br>a<br>5|s<br> <br>Score<br>of<br>(Percent)<br> <br>4 or5|
|---|---|---|---|---|---|---|---|---|
|Information on Export<br>Marketing Opportunities|All|40.9|3.22|1.91|1|7|3|<br>20.5|
|Sent Buyers to the Firm|All|31.8|2.64|1.52|6|4|0|<br>9.1|
|Organized<br>Fair at Home|All|63.6|3.86|2.82|10|8|9|<br>38.6|
|Facilitated<br>Participati<br>in FairCs)<br>Abroad|on<br>All|56.8|4.12|2.77|3|10|10|<br>45.5|
|Assisted<br>in Marketing<br>or<br>Research<br>or Exploration|All<br>|18.1|3.37<br>-|1.43|2|5|0|<br>11.4|

43
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- Wanzirah, H., Tusting, L. S., Arinaitwe, E., Katureebe, A., Maxwell, K., Rek, J., Bottomley, C., Staedke, S. G., Kamya, M., Dorsey, G. et al. (2015), ‘Mind the gap: house structure and the risk of malaria in uganda’, _PLoS One_ **10** (1), e0117396.

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- **URL:** _https://data.worldbank.org/indicator/SH.MLR.INCD.P3_

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- Zhang, W., Chintagunta, P. K. and Kalwani, M. U. (2021), ‘Social media, influencers, and adoption of an eco-friendly product: Field experiment evidence from rural china’, _Journal of Marketing_ **85** (3), 10–27.

- Zimmerman, R. S., Xiao, Z., Mehrotra, P. and Roy, C. (2016), ‘Models of health behavior change’, _Introduction to Global Health Promotion_ **65** .

40
# 6 Proposed way forward

Estimating monetary poverty trends in Syria post 2011 is severely constrained by data quality and availability issues. Evidence presented in this paper suggests that – among possible distribution neutral nowcasting approaches – the one in which baseline poverty estimates based on 2009 grouped data and average consumption growth based on per capita GDP in current prices, deflated by the CPI, would likely lead to the most sensible trend profile of poverty over the conflict period. Moreover, given data constraints, the analysis of consumption data based on the HNAP 2022 Demographic and WASH survey does not evidence any concern regarding the possibility to use these data for the purpose of poverty measurement.

Based on these considerations, the proposed way forward is to use 2022 HNAP-based poverty estimates to anchor the most recent estimates to the best available evidence, and to interpolate the poverty evolution obtained from back-casting 2022 and nowcasting 2009 poverty estimates over the 2009-2022 period using the growth rate of per capita GDP in current prices, deflated by the CPI with a passthrough of 0.7 (Figure 17).<sup>36</sup> <!-- Start of picture text --> Figure 17: Poverty trends 2009-2022<br>$2.15,2017 PPP $3.65,2017 PPP<br>80 80<br>70 70<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>Nowcasting,base 2009 Interpolation 2009-2022<br>Nowcasting,base 2009 Interpolation 2009-2022<br>Backcasting, base 2022 Backcasting, base 2022<br>Source : World Bank staff calculations based on CBS and HNAP 2022<br><!-- End of picture text --> The proposed approach has three main advantages. First, it makes the best use of the best available evidence in a simple and transparent way, therefore substantially improving on current World Bank poverty estimates available in PIP. Second, it is grounded in the standard methodology used in PIP for the analysis of poverty trends in other countries, including in cases of non-comparability of poverty estimates between baseline and end-line surveys. Lastly, it will allow an updating of poverty estimates moving forward even in absence of new household survey rounds.

> 36 See <u>https://datanalytics.worldbank.org/PIP-Methodology/lineupestimates.html#nationalaccounts</u>
self-assessment of performance ranking in class but also with their performance in the test that was conducted under the survey. However, while more homework time is associated with student selfassessment of performance ranking in class, it is actually negatively correlated with the test score produced by the survey. While we believe student self-assessment of their performance standing in class is largely accurate—as it is a common practice that schools regularly rank students by their examination scores—these results may shed some light on the likelihood that different sets of tests tend to generate different rankings.

Figure 3.19 Factors associated with the student’s hours of sleeping and homework per day (marginal change in probability)

- a) Determinants of sleeping hours per day

<!-- Start of picture text --> Mother's education post-secondary<br>Mother's education sr. sec<br>Mom's education jr sec<br>Family economic condition-well off (student response)<br>Family economic condition-middle (student response)<br>Student performance ranking - high (self assessment)<br>student performance ranking - midde (self assessment)<br>Student standardized test score<br>School is in rural area<br>school is in city<br>-0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15<br>b) Determinants of homework hour per day<br>Mother's education post-secondary<br>Mother's education sr. sec<br>Mom's education jr sec<br>Family economic condition-well off (student response)<br>Family economic condition-middle (student response)<br>Student performance ranking - high (self assessment)<br>Student performance ranking - midde (self assessment)<br>Student standardized test score<br>School is in rural area<br>school is in city<br>-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5<br><!-- End of picture text --> _Source_ : Authors’ calculation based on the CEPS.

The system of examination, ranking, selection, and promotion has often been viewed as meritocratic. However, the limitation of this system serving as a mechanism for social mobility is well recognized. Children from disadvantaged background tend to already lag when they reach Grade 9. The CEPS data set also reveals another layer of hidden cost related to the selective system for achieving social equity. According to the survey, students from a more disadvantaged background appear to sacrifice more to go through the highly competitive and selective process of participating in senior secondary education. Not only do they have higher pressure and spend more time on homework and sleep less, they are also less likely to be exposed to other activities— enrichment or entertainment—such as visiting museums and exhibitions, going to movies, or participating in sports (Figure 3.20). This layer of cost can be manifested by the situation that even when they are able to continue to the higher levels of education, children from disadvantaged background can have difficulties adjusting and adapting to a new environment where a lot more outside academic studies matter for a fulfilling life.

42
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18