rafmacalaba/datause-encoder-data huggingface dataset:
Table 1: Summary statistics

||Mean|S.D.|Min|Max|
|---|---|---|---|---|
|_Individual characteristics_|||||
|Standardized Saber 11|0.940|1.099|-8.2|7.1|
|Female|0.573|0.495|0.0|1.0|
|Age|26.591|2.414|21.0|35.0|
|Ethnic origin|0.010|0.099|0.0|1.0|
|Birth order #1|0.489|0.500|0.0|1.0|
|Birth order #2|0.276|0.447|0.0|1.0|
|Birth order #3 or higher|0.235|0.424|0.0|1.0|
|_Parental characteristics_|||||
|Very high household income|0.212|0.409|0.0|1.0|
|<br>High household income|0.196|0.397|0.0|1.0|
|<br>Moderate household income|0.219|0.413|0.0|1.0|
|Low household income|0.287|0.452|0.0|1.0|
|Very low household income|0.086|0.281|0.0|1.0|
|<br>Mother, college degree|0.305|0.460|0.0|1.0|
|<br>Mother, associates degree|0.210|0.407|0.0|1.0|
|Mother, high school degree|0.279|0.448|0.0|1.0|
|<br>Mother, less than high school degree|0.207|0.405|0.0|1.0|
|_Educational background_|||||
|<br>Attended college|0.834|0.372|0.0|1.0|
|<br>Attended a technological school|0.158|0.364|0.0|1.0|
|<br>Attended at&tinstitute|0.009|0.094|0.0|1.0|
|Attended a public college|0.386|0.487|0.0|1.0|
|Attended a public high school|0.427|0.495|0.0|1.0|
|In a big city during high school|0.504|0.500|0.0|1.0|
|In a medium city during high school|0.205|0.404|0.0|1.0|
|<br>In a small city during high school|0.291|0.454|0.0|1.0|
|_Higher education access_|||||
|<br># of public colleges within 50km|3.458|2.685|0.0|8.0|
|<br># of private colleges within 50km|11.632|10.779|0.0|26.0|
|<br># of technological schools within 50km|26.302|27.933|0.0|64.0|
|<br># oft&tinstitutes within 50km|15.295|16.595|0.0|39.0|

_Notes_ : Number of observations is 212,023. t&t for technical and technological.

offspring in their families. The average ability score (Saber 11) is 0.94 standard deviation above the national average. While a fifth (21.2%) of individuals come from households with very high income (i.e., annual income is more than five times the annual minimum wage), only 8.6% come from households with very low income (i.e., with annual incomes below the annual minimum wage). A high share (30.5%) of individuals also have mothers with a college degree. These characteristics are what we would expect given that the individuals in our sample are all college graduates in a developing country setting.

We note, too, that half of individuals are in a big city during high school, while under a third 6
||**(1)**<br>**Accountability**|**(2)**<br>**Accountability**|**(3)**<br>**Accountability**|
|---|---|---|---|
|**OGD Average**|0.375**|0.336*|0.289|
||(0.156)|(0.171)|(0.180)|
|**Political Agency**||0.203**<br>(0.0770)||
|**Publicity**|||0.294***<br>(0.110)|
|**Log GDP**|9.778***|7.213***|7.809***|
||(1.437)|(1.420)|(1.626)|
|**_cons**|-35.19***|-26.13***|-33.41***|
||(10.66)|(9.792)|(11.40)|
|**_N_**|86|86|86|
|**_R_**<sup>**2**</sup>|0.421|0.469|0.486|

In addition to the overall accountability index, positive and significant correlations are found with respect to other accountability sub-indices, including those produced by the World Bank and the African Development Bank—thus adding to the validity of the purported relationship. In addition, greater levels of OGD are positively associated with with less abuse of office (Bertlesmann) as well as less diversion of public funds (WEF). However, OGD does not seem to have a significant effect on popular corruption indexes, including Executive Corruption (VDem), Corruption in Public Officials (EIU), and Corruption in Bureaucracy (WB), which is a counterintuitive finding in the context of the present research.

#  **_Premise 2: More accountable governments enable improved service delivery._**

To test the second premise, education and health service delivery—in terms of both provision and outcomes—are regressed on the accountability index. First, in the education sector, no significant effect of accountability is found on education service delivery in terms of provision or outcomes.<sup>124</sup> Second, when controlling for the public management condition—which again, is the index used to measure administrative capacity—no significant association is found with respect to either education provision or outcomes. Finally, it should be noted that the coefficient for accountability becomes marginally negative and significant when controlling for public management, which is an unexpected result given that higher levels of public administration capacity would be expected to correlate with improved education outcomes.

||**(1)**<br>**Education Provision**|**(2)**<br>**Education Outcomes**|**(3)**<br>**Education Provision**|**(4)**<br>**Education Outcomes**|
|---|---|---|---|---|
|**Accountability**|0.0658|-0.140|0.162|-0.205*|
||(0.115)|(0.0915)|(0.144)|(0.111)|
|**Public Mgmt.**|||-0.232<br>(0.194)|0.157<br>(0.150)|
|**Log GDP**|8.975***|17.12***|9.429***|16.82***|
||(1.952)|(1.447)|(1.872)|(1.487)|
|**_cons**|-19.36|-82.99***|-15.54|-85.58***|
||(11.70)|(8.614)|(12.84)|(8.452)|
|**_N_**|86|86|86|86|
|**_R_**<sup>**2**</sup>|0.428|0.741|0.437|0.743|

124 As explained subsequently, this is not to suggest that accountability has no import in the education sector; rather, given the particular political economy of how education services are produced and consumed, formal accountability and social accountability (Client Power) mechanisms may not translate _in the same ways as expected_ in other service delivery sectors.

25
the SUBSIM direct version in that it automatically provides a set of results in Excel tables and graphs that can be readily used for analysis. The difference is that this version provides results for individual products across countries instead of results for individual countries across products. As part of the distributional analysis, we look first at the importance of subsidies and subsidized products for households. We then determine who are the main beneficiaries of subsidies, as well as the potential dilemmas for reforming subsidies.

When we talk about the importance of subsidized products, we should distinguish between _absolute_ and _relative_ importance. For absolute importance, we refer to the monetary values of subsidies or subsidized products in USD at PPP values. For relative importance, we refer to subsidies or subsidized products as a share of total household expenditure.

# The Absolute Importance and Distribution of Subsidies

Table 4 compares the per capita expenditure of the four main energy and food products considered across countries in US$-PPP values. Looking at energy products and on average, households spend $19.7 per capita, per year on LPG, $85.5 on electricity, $54.2 on gasoline, and $9.5 on diesel. These amounts vary widely across countries. For example, Moroccans spend (in PPP values) the largest amount on LPG, electricity, and diesel. Libya has the lowest expenditure for electricity and one of the lowest for gasoline and diesel. As expected, because Libya has high subsidies and Morocco has low subsidies, it is clear that expenditures for crude oil products are partly driven by the level of subsidies. But other factors must be considered, including the desirability of these products and the exchange rate used in PPP values.

Subsidies on food are much less widespread in terms of countries and products. Libya has the largest variety of food subsidies, and a few other countries subsidize flour, bread, sugar, or vegetable oil, which are the four products that we analyze across countries. The largest subsidies go to flour and bread. The distinction between flour and bread is not always clear cut in the data. Some countries subsidize the price of flour for mills and then impose regulated prices on the sale of bread. What we observe in expenditure data are direct purchases of flour or bread on the part of households. Therefore, we need to estimate the flour subsidies received by households via the purchase of bread using conversion factors between these two products. As a consequence, the estimates on bread and flour should be taken with some caution. Sugar is also an important subsidized item in three countries, and vegetable oil remains subsidized in two countries.

8
pointing to a positive effect of incidents on the poverty gap and a deterioration of perception of police competence.

Table 5: DiD and IV estimates for the effect on employment and earnings

||Proportion of household members<br>(aged 15-50) employed in<br>the previous week|Proportion of household members<br>(aged 15-50) earning income<br>in the previous week|
|---|---|---|
||DiD<br>(1)<br>IV<br>(2)|DiD<br>(3)<br>IV<br>(4)|
|DiD or IV|-0.147**<br>-1.952***|-0.150*<br>-1.682***|
|coefficient|(0.066)<br>(0.326)|(0.086)<br>(0.285)|
|Fixed effects|District<br>Region|District<br>Region|
|Full set of controls|Yes<br>Yes|Yes<br>Yes|
|Observations|1,525<br>2,221|1,525<br>2,221|

Source: Authors’ calculations based on data from the SHFS and ACLED. Note: Estimated coefficients from an OLS regression for Mogadishu and IV regression for urban areas with exposed and control households in Wave 1 and 2. Estimates obtained from our most complete specification including household size, receiving remittances, age, sex and literacy of the household head, tenure, roof and floor material of the dwelling, water source and sanitation type. Drought affected status and humanitarian assistance are not included in the OLS regression due to the lack of variation in the data within Mogadishu. Humanitarian assistance is not included in the IV regression due to collinearity with the region fixed effects. Standard errors considering sampling weights in parenthesis; *** p<0.01, ** p<0.05, * p<0.1.

We further explore the mechanisms through which a terrorist attack from Al-Shabaab against civilians can lead to a decrease in consumption. Table 5 presents the results for the impact on the proportion of household members aged 15 to 50 that were employed and earned income after an incident. The DiD coefficients are obtained from the sample of households in Mogadishu, while the IV coefficients from urban areas with exposed and control households in Waves 1 and 2; both from our most complete specification. All estimates are negative and statistically significant. Similar to other outcomes, IV point estimates are larger and have bigger standard errors. However, the IV estimates also reinforce the DiD findings, which indicate a decrease of around 15% in the share of both, household members working and earning income between 1 and 7 days after an incident. A reduction in employment and income could lead to a decline in consumption and exacerbate poverty and vulnerability among households exposed to an incident.

There are other supply-side mechanisms, such as limited availability of food items and higher prices, which could help explain how a terrorist attack disrupts the economy and affects welfare conditions of the population. To assess this, we compare the cost of the consumption basket –made out of 38 core food items– of exposed households from Mogadishu in Wave 2 against that from a group of households also in Wave 2 located within 1 kilometer from incidents in Mogadishu but that were interviewed before the 18
29 Economic Parameters of Deforestation <!-- Start of picture text -->
> o  i  -f f  P<br>di"'  W:2  rp  (8)<br>d_  <  O  f' "f  I  Pt<br>erp<br><!-- End of picture text -->

Hence, the result that deforestation proceeds more rapidly with higher timber prices does not hold unambiguously, and an additional condition on the production function is required to hold for this effect. At least theoretically, a rise in the timber price level can lead to a rising appreciation of the timber stock that more than offsets the increase in foregone harvest prfirts. This can happen if the profit function is highly convex in the ilmber price and the increase in profits with higher prices is relatively low (i.e., if L is dose to zero, and f is small). In this case, logging profits wull rise faster over time at a higher price level. As a result, it could become profitable to delay logging.

In order to analyze the robustness of this paper's main result, it is important to gain some appreciation whether the condition for dtr/dk **c** 0 is likely to be met. If the rate of timber price increase was higher than r, no timber would currently be harvested at all. Hence it is reasonable to assume that: Hence, with (8) and (9): This condition is also sufficien **t** o establish earlier logging with price level increases at the margin of unmanaged and managed forest (see appendix). Now consider a Cobb-Douglas production function:
and can have practices that are detrimental to the environment as opposed to formal firms which presumably adhere to environmental regulations (e.g. see Basbay et al (2016) or Biswas et al (2012)). To study these effects, the paper builds a theoretical model wherein the economy consists of formal and informal workers and the impact on countries’ total emissions depends on the difference in emissions per worker between the formal and informal sectors and the share of informal workers in total.

The literature analyzing the relationship between the informal sector and emissions is very scarce and is limited only to a few papers. For example, Elgin and Oztunali (2014) use cross-country panel data to analyze this relationship by applying fixed effects regressions and system estimations. They find an inverted-U relationship between the size of the informal economy and environmental pollution: small and large sizes of the informal economy are associated with lower environmental pollution and medium levels of informality are associated with higher levels of environmental pollution. This inverted-U shape is also called the environmental Kuznets curve (e.g. see Cole et al. (1997), Dinda (2004), or <mark>Stern (2004)).</mark> Estimating the relationship between the environmental quality and informality for a sample of 58 countries, Swain et al. (2020) find that the informal sector measured as a share of value added positively influences the emission of local pollutants but has no impact on global pollutants such as CO2 per capita and CO2 intensity. The contribution of our study to the literature is three-fold. First, it adds to the literature by studying the impact of informality on emissions across developing countries using more recent data and helps understand to what extent the differences in production technologies and compliance with the environmental policies drive the differences in the emissions. Second, it uses a new measurement of informal economy – the share of informal workers in total employment which is based on data and not on estimates which are sensitive to approaches and assumptions that could potentially bias the results. Finally, the paper studies the impact of informal employment on emissions using country- and sector-level data and estimates the aforementioned relationship on the sector level. This is important given the considerable differences between sectors in terms of their productivity, capital intensity, and competitiveness, which might affect the aggregate country-level results.

The estimation results using country and sector fixed effects show that there is no significant impact from informality on either CO2 or non-CO2 emissions. This implies that emissions per value added in the informal sector are greater than in the formal sector as shown in the theoretical model.<sup>8</sup> Conducting these regressions separately for nine sectors - agriculture, manufacturing, trade, mining, construction, finance,

> 8 The model uses a decomposition approach to derive the relationship between the estimated coefficients and informal emissions per value added.

4
# **Figure 20**

# **Time Fixed Effects of Momentum Statistics**

This figure presents the time fixed effects obtained from the regression of the Grinblatt et al. (1995) momentum statistics on time and PFA fixed effects. Panel A presents the Contemporaneous Momentum Statistic L1M and Panel B presents the Lagged Momentum Statistic L0M. Coefficients that are statistically significant at the ten-percent level are marked on the figure. T-tests are one-tailed. Numbers represent percentages (coefficients are multiplied by 10,000 - weights and returns are in percentages). Significant events that occurred during this period of time are also highlighted on the figure: the Asian crisis (July 1997), the Russian crisis (August 1998), the introduction of Fund E and the widening of the minimum return band (October 1999), and the establishment of the multi-fund regime (September 2002).

Panel A. L1M Time Fixed Effects (1996 - 2005) <!-- Start of picture text --> 40<br>L1M<br>30 Significant at ten percent<br>20<br>10<br>0<br>-10<br>-20<br>-30<br>-40<br>Asian Crisis Russian Crisis Fund E Multi-Fund<br>-50<br>Jul-96 Mar-97 Nov-97 Jul-98 Mar-99 Nov-99 Jul-00 Mar-01 Nov-01 Jul-02 Mar-03 Nov-03 Jul-04 Mar-05 Nov-05<br>Panel B. L0M Time Fixed Effects (1996 - 2005)<br>80<br>L0M<br>Significant at ten percent<br>Asian Crisis<br>60<br>Russian Crisis<br>Fund E<br>Multi-fund<br>40<br>20<br>0<br>-20<br>Multi-Fund<br>Asian Crisis Russian Crisis Fund E<br>-40<br>Jul-96 Mar-97 Nov-97 Jul-98 Mar-99 Nov-99 Jul-00 Mar-01 Nov-01 Jul-02 Mar-03 Nov-03 Jul-04 Mar-05 Nov-05<br> Time Fixed Effects from Momentum Regression<br> Time Fixed Effects from Momentum Regression<br><!-- End of picture text -->
technologies to offer a TaRL approach to low-literate populations, contributing to filling the evidence gap pointed out by (GEEAP 2020) about the evidence of the impact of studies using such innovations and their cost-effectiveness.

The rest of this is as follows. Section 2 a of paper organized provides background northern where our takes Section 3 an overview of en- Nigeria, study place. provides tertainment education. Section 4 describes the interventions and explains the design rationale behind them. Section 5 describes the evaluation design and data collection and Section 6 presents results on main outcomes and mechanisms, spillover effects on other household members, treatment effects by gender, and robustness checks. This section also includes an effectiveness and cost-effectiveness analysis, comparing our learning results with those of other educational interventions. Section 7 concludes.

# **2 Study Setting**

This study took place in Kano and Jigawa states, located in the North West region of Nigeria. Based on model-based estimates, Nigeria has the third largest out-of-school population in the world and the largest in Sub-Saharan Africa with approximately 20 million children out-of-school (UNESCO 2022). This adds up to over 10 percent of the global outof-school population (Antoninis 2014; Bertoni et al. 2019). The problem is particularly severe in northern Nigeria, which accounts for a disproportionate number of out-of-school children in comparison to the south of the country. In 2018, only 51 percent of the schoolage children attended school, versus 84 percent at the national level (The World Bank 2021). Moreover, gender gaps in educational attainment are particularly salient in North West Nigeria, where 64% of 15-49-year-old women have no education in comparison to 38% of men of the same age, according to the Demographic and Health Survey 2018 (NPC and ICF 2019).

In Nigeria, schooling attendance does not necessarily translate into increased learning, and educational indicators for the north substantially lag behind the national average. According to the Reading and Access Research Activity report (2019), in northern Nigeria, less than 3 percent of second grade pupils in government primary schools could read a text in Hausa with 80 percent comprehension or greater (Kano: 2% girls and 3% boys; Jigawa: 0% girls and 3%). One factor that may contribute to lower school enrollment and learning outcomes in the north is the inter-generational persistence of low rates of educational attainment. Overall, in the North West of the country, only 29% of 15-49 year-old women and 59% of men are literate (NPC and ICF 2019). A number of analyses show that parental educational attainment is a strong determinant of educational outcomes among 5
|AFCC2/RI-Great Lakes Trade Facilitation<br>Charles Kunaka|
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|AFCC2/RI-Great Lakes Trade Facilitation<br>Charles Kunaka|
|AFCC2/RI-Great Lakes Trade Facilitation<br>Charles Kunaka|
|AFCC2/RI-Great Lakes Trade Facilitation<br>Charles Kunaka|
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|Vietnam EnergyEfficiencyfor Industrial Enterprises(VEEIE)<br>Thi Ba Chu|
|Vietnam EnergyEfficiencyfor Industrial Enterprises(VEEIE)<br>Thi Ba Chu|
|Vietnam EnergyEfficiencyfor Industrial Enterprises(VEEIE)<br>Thi Ba Chu|
|Vietnam EnergyEfficiencyfor Industrial Enterprises(VEEIE)<br>Thi Ba Chu|
|West Bank and Gaza: Finance for Jobs<br>Abdalwahab Khatib|
|West Bank and Gaza: Finance for Jobs<br>Abdalwahab Khatib|
|West Bank and Gaza: Finance for Jobs<br>Abdalwahab Khatib|
|West Bank and Gaza: Finance for Jobs<br>Abdalwahab Khatib|
|West Bank and Gaza: Finance for Jobs<br>Abdalwahab Khatib|
|West Bank and Gaza: Finance for Jobs<br>Abdalwahab Khatib|
|West Bank and Gaza: Finance for Jobs<br>Abdalwahab Khatib|
|West Bank and Gaza: Finance for Jobs<br>Abdalwahab Khatib|
|West Bank and Gaza: Finance for Jobs<br>Abdalwahab Khatib|
|Integrated Forest Ecosystem Management<br>Drite Dade|
|Integrated Forest Ecosystem Management<br>Drite Dade|
|Integrated Forest Ecosystem Management<br>Drite Dade|
|Integrated Forest Ecosystem Management<br>Drite Dade|
|Integrated Forest Ecosystem Management<br>Drite Dade|
|Integrated Forest Ecosystem Management<br>Drite Dade|
|Integrated Forest Ecosystem Management<br>Drite Dade|
|Integrated Forest Ecosystem Management<br>Drite Dade|
|Integrated Forest Ecosystem Management<br>Drite Dade|
|Integrated Forest Ecosystem Management<br>Drite Dade|
|Improvement of CIS Statistical Infrastructure and Development of C Marko Olavi Rissanen|
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|CAMEROON - StrengtheningPublic Sector Effectiveness and StatisticMarieta Fall|
|CAMEROON - StrengtheningPublic Sector Effectiveness and StatisticMarieta Fall|
|CAMEROON - StrengtheningPublic Sector Effectiveness and StatisticMarieta Fall|
|CAMEROON - StrengtheningPublic Sector Effectiveness and StatisticMarieta Fall|
|BiH Floods EmergencyRecoveryProject<br>Ko Takeuchi|
|BiH Floods EmergencyRecoveryProject<br>Ko Takeuchi|
|BiH Floods EmergencyRecoveryProject<br>Ko Takeuchi|
|BiH Floods EmergencyRecoveryProject<br>Ko Takeuchi|
|BiH Floods EmergencyRecoveryProject<br>Ko Takeuchi|
|BiH Floods EmergencyRecoveryProject<br>Ko Takeuchi|
|BiH Floods EmergencyRecoveryProject<br>Ko Takeuchi|
|BiH Floods EmergencyRecoveryProject<br>Ko Takeuchi|
|BiH Floods EmergencyRecoveryProject<br>Ko Takeuchi|
|BiH Floods EmergencyRecoveryProject<br>Ko Takeuchi|
|Supportingthe economic inclusion ofyouth<br>Ana Paula Fialho Lopes|
# **Measuring Empowerment in Practice:**

**Structuring Analysis and Framing Indicators**

Ruth Alsop Nina Heinsohn

## World Bank Policy Research Working Paper 3510, February 2005

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ 1