rafmacalaba/data-use-mentions-extended huggingface dataset:
_The World Bank Economic Review_ 23, no. 3 (2009): 481-507.

Ministry of Health and Population [Nepal], New ERA, and ICF. “Nepal Demographic and Health Survey 2016.” (2017).

Ministry of Health and Population [Nepal], New ERA, and ICF. “Nepal Demographic and Health Survey 2022.” (2023).

Ministry of Labor, Employment, and Social Security [Nepal]. “Nepal Labor Migration Report 2020.” (2020).

Muñoz Boudet, Ana María, Tasmia Rahman, Nour Nasr, and Abigail Dalton. "Addressing Social and Gender Norms to Promote Gender Equality." _World Bank Group Gender Thematic Policy Notes Series: Evidence and Practice Note_ (2023).

Nepal Rashtra Bank. “Fifth Household Budget Survey Nepal 2014/15.” (2016).

Paudel, Anil. "Girls' Transitions to Work through Higher-Quality TVET Programs in Nepal. Echidna Global Scholars Program, Policy Paper." _Center for Universal Education at The Brookings Institution_ (2019).

Peet, Evan D., Günther Fink, and Wafaie Fawzi. "Returns to education in developing countries: Evidence from the living standards and measurement study surveys." _Economics of education review_ 49 (2015): 69-90.

Phadera, Lokendra. “International migration and its effect on labor supply of the left-behind household members: Evidence from Nepal.” No. 333-2016-14201. Annual Meeting, July 31-August 2, Boston, Massachusetts, _Agricultural and Applied Economics Association_ (2016).

Raju, Dhushyanth, and Jasmine Rajbhandary, eds. “Youth employment in Nepal.” _The World Bank_ , (2018).

Ram Mohan, Rohini, Ranjitha Puskur, D. Chandrasekar, and Harold Glenn A. Valera. "Do gender dynamics in intrahousehold decision making shift with male migration? Evidence from rice-farming households in Eastern India." _Gender, Technology and Development_ 27, no. 2 (2023): 157-183.

Ruppert Bulmer, Elizabeth, Ami Shrestha, and Michelle Marshalian.
where Ui is the information (from administrative records) on whether student i completed entrepreneurship trainings and received coaching during academic year 2009/2010, Ti is an indicator variable for randomized assignment to the treatment group, Xi is a vector of controls, πis are strata fixed-effects, and εi is a mean-zero error term. The coefficient β indicates the causal effect of being selected for the entrepreneurship track via lottery on the likelihood of actually completing the entrepreneurship track.

In a second stage, the effect of increased take-up of _business training and coaching sessions_ on employment (and other) outcomes is estimated. Randomized assignment to the entrepreneurship track generates a large and exogenous increase in program take-up in the treatment group. The exogenous variation in take-up predicted in the first stage (equation (2)) can be used to estimate the TOT estimates per the following equation: where Yi is the outcome of interest at follow-up of individual i and _Û_ i is the predicted program take-up from the first stage. The coefficient θ gives the TOT, which is the impact of actually taking up entrepreneurship training or coaching _for students who complied with their assignment to the treatment or control group_ .

TOT estimates are local average treatment effects and should be interpreted as such. They are estimated for students who complied with their assignment to the treatment group and actually took up the program because they were selected to participate. The previous section showed the characteristics of the compliers and revealed that students who dropped out had some different characteristics. Very few students in the control group took the entrepreneurship training after graduation. In this sense, TOT estimates essentially produce the average impact of the program for students who had the characteristics of compliers.

Importantly, almost all the results below are robust across ITT and TOT estimates, with TOT estimates of larger magnitude as would be expected. Given the consistency of the results across both sets of estimates, we mainly focus on discussing ITT estimates since they are more easily interpreted and directly policy-relevant.

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targeted to populations in greatest need and tailored to local conditions, thereby promoting the efficiency and impact of program efforts.

The emergence of standardized cross‐sectional household surveys such as the Demographic and Health Surveys (DHS) Program,<sup>19</sup> which now routinely include georeferencing (i.e., latitude and longitude coordinates), provides a new opportunity to understand subnational geographic variations in a wide range of socioeconomic, health and demographic indicators, including stunting. A recent DHS survey has been undertaken in Tanzania<sup>15</sup> and, like nearly all others, is configured to provide representative estimates of stunting rates at the first subnational unit level (regions in Tanzania), but not below. However, geospatial methods have been established that can use the georeferenced survey data alongside relevant spatially gridded covariate data to reliably estimate DHS indicators at much finer levels of spatial disaggregation.<sup>20,21</sup> The use of georeferenced survey data with geospatial models to generate interpolated surfaces is perhaps most established in the field of infectious diseases.<sup>22,23,24,25,26</sup> In recent years, these methods have also been developed and applied to a broader set of social, demographic, and health contexts. This includes the use of both DHS and World Bank Living Standards Measurement Survey data, among other sources, to create high‐resolution poverty maps for a range of Sub‐Saharan African countries.<sup>27,28,29,30</sup> Most recently, the method has been applied to mapping water and sanitation indicators in Nigeria based on the 2015 Nigeria Water and Sanitation Survey.<sup>31</sup> This study describes the use of such an approach to generate a new high‐resolution map of stunting prevalence in Tanzania.

# **Methods**

# _Data_

Data on stunting were obtained from the Tanzania DHS 2015‐16.<sup>15</
paper spans the period 1998 to 2018<sup>7</sup> while data on 3G/4G coverage covers the period 2006 to 2018.

The mobile coverage maps provide granular data on spatial coverage of the mobile networks at a 1km _×_ 1km grid-cell. However, it does not indicate the share of people in the respective grid-cells with access to the network. I, therefore, combine this data with 1km _×_ 1km grid-cell population density<sup>8</sup> data to calculate the share of population across subnational districts living in places covered by the mobile networks. Specifically, for a given subnational district _J_ with underlying grid-cells, I calculate network coverage as: where 1 {Covered by network} turns on if a grid-cell _j_ is covered by mobile network and 0 otherwise. Mobile coverage, therefore, implies the share of the population in a given district living in areas covered by mobile networks. Three main coverage rates are computed and used in this paper: mobile coverage (all),<sup>9</sup> 2G coverage, and 3G coverage.<sup>10</sup> Figure 2 shows the global distribution of 2G and 3G mobile network coverage during 1999– 2018 and 2007–2018 respectively. Figure 3 also shows the temporal growth in penetration of the respective mobile technologies. The figure shows a significant increase in coverage rates for 2G over the past two decades with the coverage rate for the average second administrative district increasing from 25% in 1999 to 91% in 2018, while 3G coverage also increased from 13% in 2007 to 74% in 2018.

# **2.2 Nightlights**

Since the pioneering works of Henderson et al. (2011) and Henderson et al. (2012), satellite data on nighttime lights have gained popularity in the economics literature as a proxy for measuring economic activities, particularly, in districts where high frequency and disaggregated data on economic outcomes are scant. These nightlight data come from NASA’
BENIN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 6. Shares of different factors involved in the time and cost of importing through gateways in West Africa**

<!-- Start of picture text --> a. Cost b. Time<br>100 100<br>90 90<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>Border Administrative Transport Port Border Administrative Transport Port<br>Share (%) Share (%)<br><!-- End of picture text --> _Source_ : Ocean Shipping Consultants 2008; AICD ports database; Teravaninthorn and Raballand 2009. Ports data are based on indicators from 2006/07.

Similarly, lengthy travel times in the Cotonou–Niamey corridor are mainly associated with inefficient operations in the Port of Cotonou, which is responsible for more than 70 percent of the time required to import to Niamey, similar to the time consumed at the Port of Abidjan within the context of the Abidjan– Ouagadougou corridor. Comparatively, the time spent at the Port of Lomé accounts for 53 percent of the total time involved in transporting goods using the Lomé–Niamey corridor. It is estimated that the delays in the Port of Cotonou impose a cost of around $180 per container. Time-consuming regulatory processes related to customs clearance and technical controls further augment the total time (figure 6b), accounting for about 23 percent of the total time. The average time to clear imports through customs in Benin is more than twice the time required in Sub-Saharan Africa (World Bank and IFC 2009).

# Roads

## **Achievements**

Benin’s has a well-developed road network with relatively high levels of traffic. The density of the country’s classified road network (75 km/1,000 km2) is comparable with that of the
@1800 MHz and 2100MHz and 4G: 10MHz@ 700MHz and 1800MHz) and Uganda (3G: 10MHz@1800 MHz and 2100MHz, and 4G: 10MHz@800MHz and 1800 MHz) using available country information (Frequency Check, 2021). To reiterate, these bands constitute an average MNO spectrum portfolio, therefore representing a fraction of the total nationally available spectrum portfolio for each country.

The backhaul for the sites in each region is estimated based on statistics reported by GSMA on the existing composition of technology types by global region (GSMA, 2019b). For Sub-Saharan Africa the 16
- Collier, Paul (2007) _The bottom billion: why the poorest countries are failing and what can be done about it_ , Oxford: Oxford University Press.

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- Deutsche Gesellschaft f ̈ur Technische Zusammenarbeit (2009) _International Fuel Prices 2009_ , Eschborn, Germany: Deutsche Gesellschaft f ̈ur Technische Zusammenarbeit, 6th edition.

- Donaldson, Dave (2010) “Railroads of the Raj: Estimating the Impact of Transportation Infrastructure,” NBER Working Papers 16487, National Bureau of Economic Research, Inc.

- Duranton, Gilles and Matthew A. Turner (2011) “Urban growth and transportation,” mimeo, University of Toronto.

- Elvidge, Christopher D., Kimberly E. Baugh, Eric A. Kihn, Herbert W. Kroehl, and Ethan R. Davis (1997) “Mapping of city lights using DMSP Operational Linescan System data,” _Photogrammetric Engineering and Remote Sensing_ , Vol. 63, No. 6, pp. 727–734.

- Elvidge, Christopher D., Jeffrey M. Safran, Ingrid L. Nelson, Benjamin T. Tuttle, V. Ruth Hobson, Kimberly E. Baugh, John B. Dietz, and Edward H. Erwin (2004) “Area and position accuracy of DMSP nighttime lights data,” in Ross S. Lunetta and John G. Lyon eds. _Remote Sensing and GIS Accuracy Assessment_ , Boca Raton, FL: CRC Press, pp. 281–292.

- Elvidge, Christopher D., Daniel Ziskin, Kimberly E. Baugh, Benjamin T. Tuttle, Tilottama Ghosh, Dee W. Pack, Edward H. Erwin,
total portfolio is measured by the investments made between the year of their implementation and the end of 2020.<sup>13</sup> A somewhat different picture emerges once the portfolio size is expressed as a percentage of GDP (figure 6). Brazil, with its large PPP portfolio, and Albania rank at the top, with active PPP portfolios representing 22.6 percent and 18.6 percent of their GDPs, respectively. The Solomon Islands’ single active PPP represents about 15.2 percent of its GDP. Peru, Ghana, Bhutan, South Africa, Argentina, Kosovo, Georgia, and Indonesia follow, with active portfolio sizes reaching at least 5 percent of their GDPs. The PPP portfolios of Ethiopia and Burundi are below 1 percent of their GDPs.

**Figure 6 Size of public-private partnership portfolios as percentage of GDP**

<!-- Start of picture text --> 25<br>20<br>15<br>10<br>5<br>0<br>AlbaniaArgentina Bhutan BrazilBulgariaBurundiEthiopiaGeorgia GhanaIndonesia Kenya Kosovo PeruRomaniaSolomon IslandsSouth AfricaUkraine<br>PPP Portfolio as percent of GDP<br><!-- End of picture text --> _Source_ : Authors’ calculations based on data from PPI and WDI databases.

# **_5.2 Predicted Probabilities of Early Termination in Sample of Developing Countries_**

We predict the probabilities of early termination using equation (2), assuming all active projects survived until the end of 2020. We tried to fill in missing values for project-specific variables essential for prediction; when such efforts proved fruitless, we imputed these values using the

> 13 To remain true to the spirit of the PPI database, we collected each year’s investment amount using the financial statements of the special purpose vehicles whenever possible. The financing composition was determined using the last five years of statements, considering that each new debt contributes to financing of seven years of investment. The investment commitments of these projects do not reflect potential future investments
As it is clear from the left panel in Figure 2, the vast majority of the interviewed people did not leave <!-- Start of picture text --> Figure 2: Household members left behind<br>All sample Somebody left behind<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Percentage hh Percentage hh<br>Source: LDPS 2014-15<br>80 25<br>20<br>60<br>15<br>40<br>Percent Percent<br>10<br>20<br>5<br>0 0<br><!-- End of picture text --> any household member behind. However, there were some individuals who decided or were forced to migrate without other members of the household. Indeed, the right panel in Figure 2 shows that some households were split in two and that some migrants left without anybody else.

We can deepen our analysis by breaking apart the previous data by migrant status. Indeed, almost everybody (86%) among the refugees did not leave anybody behind, while the same figure for IDPs and returnees was around 57%. Furthermore, the household head or the spouse remained in the North during the crisis more frequently for returnees, while the same was very uncommon among refugees.

These migration patterns have caused deep changes in the composition of households. While 184 of the surveyed individuals were household heads before the conflict who managed to maintain their role during the crisis, 47 individuals completely changed status and effectively became ‘visitors’ (Table D1). This happened also to some other individuals, e.g. spouses or offspring, suggesting that some forcibly displaced individuals merged into recipient families, losing their original family role in the process.

**Table D1: Role inside the household before and after the crisis**

||||**Son/**|**Role Aft
Policy Research Working Paper 6436

# **Abstract**

Employment and earnings statistics are the key link between the size and structure of economic growth and the welfare of households, which is the ultimate goal of development policy, so it is important to monitor employment outcomes consistently. A cursory review of employment data for low-income Sub-Saharan African countries shows both large gaps and improbable variation within countries over time and among countries, suggesting that low quality data are routinely reported by national statistics offices. Unfortunately, policies are formed and projects developed and implemented on the basis of these statistics. Therefore, errors of measurement could be having profound implications on the strategic priorities and policies of a country.

This paper explains the improbable results observed by using data from Uganda, where the labor module contains variation both within and across surveys, to show the sensitivity of employment outcomes to survey methodology. It finds that estimates of employment outcomes are unreliable if the questionnaire did not use screening questions, as labor force participation will be underestimated. Likewise, surveys that use a seven-day recall period underestimate or potentially misrepresent employment outcomes, owing to seasonality and multiple jobs. Common multivariate analysis applied on household survey data will be affected, as the errors in measurement in the dependent and independent variables will be correlated. Corrections to reduce measurement bias in existing data are tested with the survey data; none are found to be completely satisfactory. The paper concludes that there is a knowledge gap about employment outcomes in Sub-Saharan Africa that will continue unless collection techniques improve.

This paper is a product of the Regional Poverty Reduction and Economic Management Unit, Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at opimhidzai@worldbank.org and lfox@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work
households, which might explain the absence of differential impacts by poverty status.<sup>9</sup> Conditional cash transfers increase the number of routine preventative health care visits for children in those households and the household’s poverty status does not alter this relationship (columns 1 and 4). There are no significant improvements on health clinic visits for the regressions when we group transfers to mothers or transfers to fathers. As before, conditionality is driving our observed results and the gender of the recipient does not matter, and the estimated impacts are similar between extremely poor and less poor households.

# **5. Conclusions**

This paper presents evidence of the health impacts from a cash transfer pilot program in rural Burkina Faso, the Nahouri Cash Transfer Pilot Program (NCTPP). Our evaluation focuses on routine preventative health clinic visits for children 0 to 5 years old. The NCTPP incorporated a random experimental design to evaluate the relative effectiveness of the following modalities for delivering cash transfers: conditional versus unconditional and transfers to mothers versus fathers. Families under the conditional cash transfer schemes were required to obtain child growth monitoring at local health clinics for all young children.

Our results indicate that children in families that received conditional cash transfers had an additional 0.43 routine preventative health clinic visits during the previous year compared to children in the control households. These results contrast with those for unconditional cash transfers given to either fathers or mothers, which showed no beneficial impacts. As long as the transfers were conditional, we did not find any significant difference between whether the money was given to fathers or to mothers, indicating that at least for routine health clinic visits, the cash transfer recipient’s gender is not a critical factor influencing outcomes. One limitation of our

> 9 To make this additional distinction in Table 7, extremely poor households are defined as being below the median household per capita expenditure level in the baseline survey.

17
gains and losses arising from higher food prices in such an economy? Can income gains for agricultural workers offset their losses as net food buyers? Are they sufficiently large and widespread to attenuate or reverse increases in poverty? What is the overall effect on inequality? Can government policy, through targeted cash transfers for example, help mitigate some of the negative shocks? When all these effects are taken together, are the poorest hardest hit, as is often claimed?

This paper reports on a first attempt to address these questions for the case of Brazil, where food price inflation in the twelve months to July 2008 reached 18% (while overall inflation was 7%). We combine monthly, regionally-disaggregated data on (consumer-level) food prices with household consumption expenditure and income information to estimate the net effect of the price shock at
**Table 11. Interactions: female empowerment**

|Dependent variable||Female en<br>|trepreneur<br>||
|---|---|---|---|---|
||Empowerme<br>Fem. labor part|nt indicator:<br>icipation rate|Empowerme<br>Fem. Primary c|nt indicator:<br>ompletion rate|
||(1)|(2)|(3)|(4)|
|F_cj|-0.628**|-0.571***|-0.218|0.152|
||(0.254)|(0.183)|(0.413)|(0.384)|
|F_cj * Empowerment indicator|0.010**|0.010***|0.001|-0.002|
||(0.005)|(0.003)|(0.005)|(0.004)|
|F_c|0.476||0.462||
||(0.299)||(0.359)||
|F_c * Empowerment indicator|-0.002<br>(0.006)||0.001<br>(0.004)||
|Empowerment indicator|0.002***<br>(0.000)||0.001***<br>(0.000)||
|Firm controls|Yes|Yes|Yes|Yes|
|City BE controls|Yes|Yes|Yes|Yes|
|Country controls|Yes|No|Yes|No|
|Country FE|No|Yes|No|Yes|
|Industry, Year FE|Yes|Yes|Yes|Yes|
|Observations<br>|45,182|45
measurements and analysis of personality traits in empirical studies (Dal Bo, Finan, and Rossi, 2013; Callen et al., 2015).

The insights from this paper are also relevant for various strands of the wider literature. In light of the large debate about whether the worldwide increase in schooling is leading to measurable and sustained gains in learning (Pritchett and Beatty, 2015), having widely comparable measures of cognitive abilities, that can be measured for adults, outside of the classroom, and in a variety of settings, is arguably key. Certain large data collection efforts covering wide and heterogeneous populations, such as the Young Lives Surveys or the World Bank STEPS surveys (Pierre et al., 2014), are now including measures for noncognitive abilities and personality traits. Increasingly such measures are also included in targeted surveys for impact evaluation purposes, most directly when interventions aim to change noncognitive traits (Bernard et al., 2014; Groh, McKenzie, and Vishwanath, 2015; Blattman, Jamison, and Sheridan, 2016; Ghosal et al., 2016; Adhvaryu, Kala, and Nyshadnam, 2016) but also when changes in noncognitive abilities are seen as potential mechanisms to explain changes in final outcomes (Blattman and Dercon, 2016). Other recent work focuses on the long-term impact of external factors during childhood on noncognitive outcomes of adults (Leigh, Glewwe, and Park, 2015; Krutikova and Lilleor, 2015). Finally, there is a renewed interest and widening literature on learning, technology adoption and agricultural productivity in developing countries (Jack, 2011; de Janvry, Sadoulet, and Suri, 2016), for which having reliable measures of agricultural knowledge and learning is key.

The paper is organized as follows: the next section provides more information about the context, the instrument and the implementation of the survey experiment. Section 3 provides the description of, and rational for, the calculation of the improved constructs, and discusses reliability and internal consistency. It also shows predictive validity results, using