rafmacalaba/data-use-mentions-extended huggingface dataset:
accessible to researchers, for example through data sharing agreements or by boosting data literacy.

Our results can help countries better understand if their statistical system is supporting the needs of researchers. Given that research is at the heart of evidence-based policy making, a statistical system oriented around making data available and easily accessible for these purposes is important. Countries could use the results to facilitate an understanding of the maturity of their statistical systems in supporting researchers, point to other countries that a particular country could learn from as it seeks to improve, and track progress or assess return on investment for funding related to statistical capacity building.

Our results face some limitations. First, the s2orc database does not currently contain non-English language articles. While the dominance of the English language in academic publications has been well-documented (Altbach 2007), the database omits articles published in other languages. Second, because of computational limitations, only 1 million articles were analyzed out of a sampling frame of around 10 million articles. Future work could bring in these additional articles to improve precision. Third, while the natural language processing model was able to classify whether or not the articles made use of data with 87% accuracy, the model could be extended in ways to extract more information from the articles, including more details about the subject of study and which particular data sources may have been used (surveys, censuses, administrative data, geospatial information etc.).

# **References**

Altbach, Philip G. “The imperial tongue: English as the dominating academic language.” _Economic and political Weekly_ (2007): 3608-3611.

Cameron, Drew B, Anjini Mishra, and Annette N Brown. 2016. “The Growth of Impact Evaluation for International Development: How Much Have We Learned?” _Journal of Development Effectiveness_ 8 (1): 1–21.

Cameron, Grant J., Hai-Anh H. Dang, Mustafa Dinc, James Foster, and Michael M. Lokshin. 2021. “Measuring the Statistical Capacity of Nations.” _Oxford
Regressions for labor market participation in table 5 allow us to explore the extent to which wage changes affected labor supply and demand. In the aggregate, we find insignificant impacts on the incidence of agricultural wage work but a significant increase (by 3.4 points) in non-farm casual work including NREGS jobs. There is evidence of a program-induced 5 point increase in on-farm self-employment, either to substitute for lower availability of wage labor or as a result of intensification that may partly be attributed to a greater ability to bear risk by households who, in program areas, can rely on the implicit safety net provided by the program. Allowing heterogeneity of effects by gender (panel B) points towards an increase of labor supply to the agricultural sector by males but a shift away from farm to non-farm employment and to some extent even the salaried sector, by females. Allowing for heterogeneity of program impacts by size of households’ land endowment (panel C) suggests that increases in labor supply to the non-farm sector are concentrated among the landless and small or medium sized farmers who are the MNREGS’ main target group. While the landless increase supply of labor to the farm as well as the non-farm sector, significant increases in labor supply to agricultural self-employment emerge only for small and to some degree medium farmers, possibly because some MNREGS-investment can be performed on farm. We also find evidence of a reduction of non-farm self-employment and an increase in salaried work by the largest land owners.

To appreciate MNREGS’ effects on agricultural labor markets and to assess particularly if concerns about program-induced wage increases having caused labor shortages and prompted premature mechanization are supported by the evidence, we explore data on labor demand and supply. Estimates of program impacts on the number of hired (panel A) and family worker days (panel B) used in agricultural production by the about 3,200 farmers in the sample in table 6 lead to a number of conclusions. First, the program is estimated to have prompted a significant reduction of hired labor use which is estimated to
work safety and professional development (lifelong learning). Second, labor and household surveys in many countries only have partial coverage of the main indicators used to produce the proposed JQM. There is insufficient information on key characteristics of good jobs for workers beyond salaried employment. Given these limitations, the paper focuses on estimating job quality for wage employment in 40 developing countries with adequate data. However, future research is required to expand the coverage of countries as well as to examine job quality in other types of employment, such as the selfemployed, which constitute a large share of labor in developing countries.

27
and covers emotional, conduct, hyperactivity, peer, and prosocial problems. Count of non-adult dependents under 18 reports household members under 18. Share of non-adults attended school all of last week reports the share of children aged 6-17 who attended 5 days of school last week. Days child slept hungry last week reports the number of days any child under 18 went to bed hungry because there wasn’t enough food to eat.

- The regressions also have assessment month-by-year fixed effects, enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), and household-level controls (vulnerability-assessment quartile, shelter program, baseline number of children, baseline number of children plus adults, respondent gender, and respondent age). In the parentheses are robust standard errors clustered at the locality level. Regressions are weighted by the number of people interviewed in each household. Statistical significance represented by * (10%), ** (5%), and *** (1%). Q-values are calculated per Anderson (2008) and correspond to Family xxx in the preanalysis plan.

36
Note the difference between  _j_ and  _j_ .  _j_ is the simple difference in the net present value (NPV) of labor earnings for those with _j_ years of schooling compared to those with _j_ –1 years. Later, we will call this the “one year” gain to completing year _j_ .  _j_ is a probability weighted recursion of the gains to completing years of schooling beyond _j_ . We will call this the “forwardlooking” gain to completing year _j_ .

Note that either  _j_ or  _j_ is an estimate in cedis of the NPV of completing year _j_ of schooling. As such, we can use these estimates directly to compare to the costs of providing that year of schooling to obtain a benefit-cost ratio. We draw information for the government’s costs of providing schooling from the Ghana Ministry of Education.<sup>13</sup> We also include private costs incurred using the average amount reported for tuition and fees, expenditure on books, uniforms and sports clothes, transport to school, and parent-teacher association (PTA) dues in the 2016/17 GLSS for students in public schools, by level.

# _Data and specification_

The data for the regressions come from the 2016/17 GLSS. We use the labor earnings of workers ages 15 to 45 because older workers attended school in a prior regime that included many more years (15) of primary and secondary education than the current regime. We include only wageemployed workers as the 2016/17 GLSS does not ask about the labor earnings of self-employed workers.<sup>14</sup> The 2016/17 GLSS also does not ask about years of work experience in either one’s current job or one’s working life, so we assume that experience is equal to age less the number of years spent in school plus five, a common assumption in the literature. See appendix A for precise definitions for these variables as well as for years of schooling and mother’s and father’s level
- Start of picture text --> NATIONAL 10%<br>AVERAGE 75%<br>13%<br>Djibouti City<br>89%<br>Urban (except 7%<br>capital) 70%<br>0%<br>Rural<br>25%<br>Computer Cellphone<br><!-- End of picture text --> Source: Calculations based on EDAM4 The lack of mobile phone infrastructure (and associated coverage) in rural areas – combined with less than optimal transport infrastructure and low penetration of telephone service– helps explain why a large share of the rural population remains offline. Only one third of the rural population is less than 10 minutes away from a location where a phone call can be made, to two-thirds of the urban population. Even worse, a third of the rural population is more than an hour away from such infrastructure, compared to less than 5 percent for the urban population (Figure 6). There are no official coverage maps from Djibouti Telecom, but crowd-sourced coverage maps provided by 6
# **INTRODUCTION**

The Arab Republic of Egypt is the most populous country in North Africa and the Arab World, with a population of approximately 95 million inhabitants. It is rich in natural resources, with the oil and gas sector making up approximately 15 percent of the country’s gross domestic product (GDP). Its population has been rapidly increasing in recent years—at a pace of 2.6 percent since 2006—and due to the fact that the majority of the country is desert lands, the population density has also been increasing, as most of the population is settled along the Nile.

Egypt experienced a period of robust growth from 2005 to 2009, averaging an annual rate of 4.2 percent in real GDP per capita. The global financial crisis, and the political turmoil experienced in the wake of the Arab Spring in 2011, led to a slowdown of the economic activity and an average growth in real GDP per capita (in 2011 PPP) of 0.66 percent between FY10 and FY13. This rate has picked up since then, reaching 2.18 percent in FY15 and 2.26 percent in FY16.

Economic growth in the past few years has had limited success in lifting the population out of poverty. According to official figures, 27.8 percent of the Egyptian population was considered poor in 2015.<sup>3</sup> Moreover, important disparities in welfare across regions is an enduring feature: in Metropolitan Egypt about 15 percent of the population was considered poor, whereas in rural areas of Upper Egypt this share was almost four times higher. In terms of consumption inequality measured using the Household Income, Expenditure, and Consumption Survey (HIECS), Egypt is among the countries with lowest levels (as measured by the Gini index). It is notable, however, that a slight uptick has been found in the periods 2012 and 2013–15, with the Gini index being 30.8.<sup>4</sup> The
period. Out of 35 countries that have two poverty estimates over the ten-year period, the poverty estimates were more than five years apart for 20 countries, resulting in poverty monitoring efforts being dated. Thus, among the 155 countries for which the World Bank monitors poverty data, a total of 77 countries faced challenges in producing timely or any poverty estimates during the period of 2002-2011. Serajuddin et al. (2015) refer to such lack of poverty data as “data deprivation.” On October 15, 2015, WBG President Jim Yong Kim announced the WBG’s commitment to address this form of data deprivation by supporting the 78 poorest countries<sup>5</sup> to collect the household survey data needed to estimate poverty and shared prosperity every three years between 2016 and 2030:

> 5 The 78 poorest countries are those with IDA or Blend (i.e. IDA/IBRD) lending status that are eligible for World Bank grants and concessional financing, inclusive of India. The list is available at: <u>http://ida.worldbank.org/about/borrowing-countries. Although India graduated from IDA in Fiscal Year (FY) 2014,</u> the country will receive <mark>transitional support on an exceptional basis through the IDA17 period (FY2015 through FY2017).</mark> While the number of countries identified as data deprived by Serajuddin et al. (2015) is coincidentally close to the number of countries in which the World Bank committed to support data production, their compositions are somewhat different. The former identifies countries without sufficient number of data points to effectively monitor poverty, regardless of income status, and it includes some upper middle income countries. See Section 2 for more details.

3
4 The OECD’s PSE can be compared with the extent of the distortions inserted into global economic models used to calculate the economic welfare and other consequences of these (and non-agricultural) trade-distorting measures. For more than a decade now the Global Trade Analysis Project (GTAP) at Purdue University has coordinated the compilation of a global database of trade and agricultural subsidy interventions by governments. This GTAP database has become the standard and is used in dozens of different models by hundreds of modelers throughout the world. The most recent and by far the most comprehensive release, which relates to 2001 policies, is Version 6 (Dimaranan and McDougall 2005). It incorporates all three components of support for agricultural production -- producer subsidies, import tariffs and export subsidies – and thereby also the effect of the latter two on raising food consumer prices.

How do the OECD’s PSE numbers compare with those in the most recent version of the GTAP database (that is, for 2001)?<sup>3</sup> To allow easy comparison, we present them in Table 1 on the same subsidy-equivalent basis as the OECD numbers. To do this, we estimate the domestic subsidy amounts in the GTAP database by adding the subsidies paid to output, inputs, land and capital. We compare this with the subsidy equivalents of border measures, which are calculated by multiplying the rate of assistance assumed in the GTAP database by the value of agricultural output.<sup>4</sup> For domestic support in OECD countries we find that the OECD and the GTAP numbers are within 1 percent of each other (a total of $89 billion reported by the OECD compared with $90 billion in the GTAP database). This is not surprising because the OECD estimates are the source for that part of the GTAP protection database. To that 3 A side issue is how the OECD’s PSE compares with the Aggregate Measure of Support (AMS) that members notify to the WTO as part of their commitments under the Uruguay
municipalities (out of 2,457 municipalities) and 6,849 AGEBs.<sup>11</sup> Table A1 presents additional details on the geographic structure of Mexico.

Importantly, the publicly available data contains an AGEB-level identifier, which can be linked with the official AGEB shapefile. For rural areas, the data and associated shapefile identify localities, which are points rather than polygons. These were grouped together using the same AGEB shapefile into rural AGEBs. The survey collects income per capita, which is the welfare metric used to calculate the official poverty rate. With the AGEB-level identifiers and merged shapefile, we can link the survey data with predicted poverty rates and land classifications derived from satellite imagery.

To predict poverty for the entire country, we need data on the location of all households. For this, we create a “synthetic census” based on the 2010 census. For each AGEB, we estimate the number of households by dividing the total AGEB population in the 2010 census by the average household size, at the state-urban/rural level, in the 2014 household survey. We round to the nearest integer and use this to construct a synthetic census that links the “households” in an AGEB with the AGEB-level satellite indicators, which serve as the auxiliary data for the household model. This resulting synthetic census consists of just under 28 million records. In addition to the satellite imagery – which serves as the auxiliary data for the prediction exercises – we also include average household size at the state-urban/rural level, estimated with the 2014 MCS-ENIGH survey, which we use as population weights when aggregating across households to generate municipal poverty estimates.

# 3.2. Benchmark CONEVAL estimates

We compare the estimated poverty rates with a benchmark to evaluate the ability of the satellite imagery to improve poverty predictions. The benchmark used for evaluation is the municipal income poverty estimates, generated by CONEVAL using a combination of the 2014 MCSENIGH household survey and the 2015 Intercensus survey. The Intercensal survey is conducted every 5 years between two censuses
relying on non-parametric reasonable and avoids that a food commodity measured in bags of 100 _kg_ dominates the food basket price, but is obviously not smart enough to deal with all situations effectively. A specific rule is specified to deal with eggs, which may be described in some countries as 12 _units_ , while in other countries eggs are measured as 1 _dozen_ . In the case of ‘12 units’, a conversion to 1 dozen is made. All the price estimates from the paper are provided for analysis, and the index weights are provided with the index estimates. If it is suspect that conversion factors may have an impact on the final inflation rate estimates, researchers are encouraged to construct their own indexes relevant to the studies at hand using the individual food price estimates.

> 27For example, 1 kg of salt and 1 kg of sorghum are given equal weight in the simple index generated here, whereas the latter may carry more dietary importance. The reason for using equal weights is that it takes expert judgment to define weights based on dietary needs, which is not easily automated given the heterogeneity of the data. In an ideal world, future price survey programs would be accompanied with surveys on household expenditure shares or food specific trading volumes.

> 28First, minor gaps of up to 3 consecutive months are filled with a univariate Seasonal Kalman smoother using a Basic Structural Model. Then food-market combinations with at least 67% data coverage are completed using the same method. Next, all markets trajectories completed in this way are averaged to construct a preliminary country mean. All other market level series with _>_ 50% data coverage are then imputed using predictions from a Generalized Additive Model with observed price points as dependent variable and the preliminary country mean as the predictor. The parameters are estimated by Restricted Maximum Likelihood. The country average is recalculated, if there are any further markets with missing data, but _>_ 20% data coverage, the same method is again applied. Any market which has _<_ 20%
# **1. Introduction**

Countries in Sub-Saharan Africa (SSA) have experienced stable economic growth averaging 5 percent over the past decade. However, growth has not been inclusive, and poverty as well as unemployment have remained very high. The challenge of inclusive growth is made even more daunting by unmanaged urbanization that has led to millions living in substandard housing.

Africa has the highest rate of urbanization growth, but the least developed housing finance in the world (World Bank 2014). UN Habitat reports that 46 African cities now hold more than one million people and that every day for the next fifteen years, Africa’s cities will have to accommodate an extra 40,000 people. Over the next 25 years, more and more people will be added to the number of urban dwellers in Africa. This new challenge is emerging in a context of already widespread poverty and inequality in cities, where many people are living in slums without adequate basic services. Better housing finance systems are needed to meet the growing demand for housing that African countries face.

However, the low capacity of the construction industry and the absence of a strong housing finance sector are likely to exacerbate stresses on the housing market, raising rents and further widening income inequality. Despite a positive trend, the ratio of mortgage debt to GDP remains extremely low in Africa at 3 percent, compared to 70 percent in developed countries. Africa’s mortgage markets are nascent and small by international comparisons. However, according to data from Badev et al. (2013), the average proportion of the population with the minimum income required for a prudent mortgage is 16 percent in Africa.

So far, efforts of African governments, international partners, and financial institutions have barely reduced the gap between high housing demand and insufficient housing supply in Africa’s urban areas. As in other parts of the world, this gap is even more important for low- and middle-income households. In fact, currently most low- and middle-income households in Africa only have
.8|
|Central Asia|7|40.6|39.1|42.1|
|Asia|12|28.8|29.0|30.8|
|East Asia|7|30.1|30.6|32.7|
|South Asia|5|26.8|26.8|28.6|

_Source_ : Author’s calculations from GWP (Gallup 2011) self-reported food insecurity prevalence rates. _Note_ : * “Large and fast growing” includes India, Indonesia, Brazil, Pakistan, Bangladesh, Nigeria, Mexico, and Vietnam but excludes China.

Although the results in table 8 cover the majority of the developing world’s population, there are still sizeable omissions. Although the GWP surveys cover China, we excluded the 2005–06 rounds due to specific concerns about biases in the responses to the food insecurity question. However, a number of other countries are lacking the requisite data for 2005–06 or 2008. China, of course, has a population of over a billion people, but 16 other omitted developing countries 28
# 2.7.1 Student progression

Rates of student progression are low in Malawi, as a result of high rates of dropout and repetition, driven in part by large class sizes and limited teacher effort. We anticipate improvements in class sizes, as a result of improved allocation of teachers to lower grades. We also anticipate improvements in teacher effort. As a result of these, we anticipate improvements in student progression, particularly in lower grades.

Our primary measures of student progression are dropout and repetition rates.

**Dropout Rate:** This is measured by the number of students in each grade dropping out in the most recently completed school year, divided by the total number of students enrolled in that grade for that year.

**Repetition Rate:** This is measured by the number of students in each grade repeating a grade in the most recently completed school year, divided by the total number of students enrolled in that grade for that year.

As these measures are drawn from school records, we measure them at the school level and report them overall across grades, and for lower and upper primary (Grades 1-4 and 5-8 respectively).<sup>11</sup>

# 2.7.2 Student test scores

As a result of improvements in class sizes, teacher effort, and school learning environments as described above, we anticipate improvements in the test performance of sampled students in learning assessments in mathematics and English. We utilize data from norm referenced assessments that cover competencies from Grade 1 through Grade 6 (see section 2.8. We measure these outcomes at the student level through a longitudinal sample (see section 2.9.4). Raw scores are converted to _knowledge scores_ on a mean-centred scale using Item Response Theory (see section A).

**Mathematics Knowledge Score:** This is measured by the knowledge score of each student in the mathematics test for each school.

> 11 Owing to the fact that our data is not collected annually (see section