rafmacalaba/data-use-mentions-extended huggingface dataset:
99. https://doi.org/10.1016/S2214109X(22)00367-9.

The Food and Land Use Coalition. 2019. “Growing Better: Ten Critical Transitions to Transform Food and Land Use.” https://www.foodandlandusecoalition.org/global-report/.

UNICEF. 2022. “UNICEF Global Databases: Infant and Young Child Feeding.” New York. <u>https://data.unicef.org/topic/nutrition/infant-and-young-child-feeding/</u> Vadiveloo, Maya, L. Beth Dixon, and Niyati Parekh. 2013. “Associations between Dietary Variety and Measures of Body Adiposity: A Systematic Review of Epidemiological Studies.” _The British Journal of Nutrition_ . https://doi.org/10.1017/S0007114512006150.

Vega-Solano, Jaritza, Karol Madriz-Morales, Adriana Blanco-Metzler, and Eduardo Augusto Fernandes-Nilson. 2023. “Estimation of the Economic Benefits for the Public Health System Related to Salt Reduction in Costa Rica.” _PLOS ONE_ 18 (2): e0279732. https://doi.org/10.1371/JOURNAL.PONE.0279732.

- Verger EO, Agnes Le Port, Augustin Borderon, Gabriel Bourbon, Mourad Moursi, Mathilde Savy, François Mariotti and Yves Martin-Prevel. 2021. Dietary Diversity Indicators and Their Associations with Dietary Adequacy and Health Outcomes: A Systematic Scoping Review. _Advances in Nutrition_ , 12(5): 16591672.

- Verger EO, Savy M, Martin-Prével Y, Coates J, Frongillo E, Neufeld L et al. Healthy diet metrics: a suitability assessment of indicators for global and national monitoring purposes. Geneva: World Health Organization; 2023.

- Walters, Dylan D, Linh T H Phan, and Roger Mathisen. 2019. “The Cost of Not Breastfeeding: Global Results from a New Tool.” _Health Policy and Planning_ 34 (6): 407–17. https://doi.org/10.1093/heapol/czz050.

Wang, Huan,
not actively seeking work<sup>5</sup> because of unpaid work burdens, lack of suitable economic opportunities, not being allowed to work/other barriers to seeking work, or other social/cultural norms affecting their time allocation (see Section V as well). The ILO LFS study found, for example, that a much higher percentage of women cited family responsibilities as a reason for not searching for employment (Walsh, 2018). Wording can also matter — the ILO LFS study found that more women tended to report job search when the question on search was split into two (seeking a job, and seeking to start their own business) as opposed to a single question on seeking work for pay or profit (Benes and Walsh, 2018c).

> 5 19th ICLS defines the concept of unemployment as “active job search by persons not in employment who are available to work.”

12
al. (2011) conclude that proxy reporting has no effect on the female employment rate, but substantially lowers the male employment rate. Exploiting variation within surveys, Comblon and Robilliard (2015) compare the effect of several survey design issues, including the wording of questions, on labor market statistics. Although the effects are substantial, they do not differ by sex. Interviewing the same population of women in the Arab Republic of Egypt, Langsten and Salen (2008) show that the female employment rate is substantially higher when an activity list is used rather than identifying workers with a single keyword question about their ‘main’ occupation. Finally, Gaddis et al. (2018) study recall error in labor input at the plot level in agriculture. Recall error substantially affects measurement of labor input for both male and female workers, but – contrary to their expectations ‐ the authors do not find larger recall bias for women.

This background paper aims to take stock of current approaches used in nationally representative household surveys to collect individual‐level employment data and attempts to evaluate how survey design features affect women’s and youth labor market statistics. It is structured along four questions: (1) what should we measure, (2) what do we measure, (3) how do we measure and, to conclude, (4) how and what will we measure in a changing labor market?

# 2. What should we measure?

Before delving into the specifics of household surveys, we need to agree on a common set of labor market indicators that are indispensable to study labor markets. While there are many 3
iberia’s 14-year civil war left much of the country’s infrastructure shambles. The country’s 170 megawatt power generation capacity and national grid were completely destroyed. In Monrovia, L just 0.1 percent of households had access to electricity. According to the 2008 National Census, access to piped water fell from 15 percent of the population in 1986 to less than 3 percent in 2008. The national road network was left in severe disrepair.

Peace brought many positive developments. The Freeport of Monrovia is now privately managed and has resumed normal operations. Essential rehabilitation work has been carried out, and the port’s performance now matches that of neighboring ports along the West African coast. Liberia has also successfully liberalized its mobile telephone markets, with access surging to 40 percent in 2009, at some of the lowest prices in Africa.

But Liberia still faces critical infrastructure challenges. Perhaps the starkest lies in the power sector. The country’s installed power generation capacity is little more than 2 megawatts per million people, barely one-tenth of the benchmark level for other low-income countries in Africa. The cost of generating power is $0.77 per kilowatt-hour, exceptionally high by any standard. Furthermore, the power tariff of $0.43 per kilowatt-hour is about three times the average for Africa, which is already very high by global standards. Investment in more cost-effective power generation is therefore an important strategic objective for the country if electrification is to reach the wider population.

Although good progress has been made in securing donor finance for road reconstruction, existing capital spending is about half the level that would be required to complete the rehabilitation process within five years, and existing maintenance spending is about half what would be needed to preserve these new investments into the future. Many countries have addressed this problem by establishing road funds financed by user charges. That approach is being considered in Liberia.

Addressing Liberia’s public infrastructure needs will require sustained expenditure of between $350 million and $600 million per year, depending on
**Table 3:** Effects of Turnovers on Morale and Effort

||||**Bureaucrat survey**||
|---|---|---|---|---|
||Enthusiasm|Motivation|Interacts dailyw/ citizens|Frequencyindex|
||(1)|(2)|(3)|(4)|
|New village head|0.487<sup>_∗∗∗_</sup>|22.868|0.197<sup>_∗∗_</sup>|0.405<sup>_∗∗_</sup>|
||(0.143)|(15.03)|(0.088)|(0.156)|
||[0.165]|[18.495]|[0.102]|[0.183]|
|Observations|1064|1062|1064|1064|
|Control mean|-0.057|100.6|0.57|0.32|
|Robust p-value|0.001|0.153|0.029|0.012|
|<br>Bandwidth size (%)|20.8|21.5|18.5|16.3|
|Effective obs.|522|533|487|441|

_Notes_ : This table reports RD estimates of _γ_ in equation (1) obtained via the non-parametric method from Calonico et al. (2014). Units of observation are bureaucrats in all columns. The dependent variable is: in column 1, a standardized z-score of self-reported enthusiasm; in column 2, a continuous measure of motivation anchored at 100 at baseline and winsorized at the top 1%; in column 3, a dummy variable equal to 1 if the bureaucrat reports interacting with citizens on a daily basis; in column 4, a standardized z-score of the frequency of
location premiums as an alternative performance indicator. The location premium is computed as the fraction of local expenditure per capita that cannot be accounted for by observable household characteristics, following Li and Rama (2015b). The growth rate of the location premium provides a good proxy of local productivity growth.

We consider three types of locations: states, districts and places. The latter result from a breakdown of the district into up to four types of agglomerations: small rural (fewer than 5,000 inhabitants), large rural, small urban (fewer than one million inhabitants) and large urban. The methodology to process this disaggregation, developed by Chatterjee et al. (2015), exploits the fact that NSS 2004–05 and NSS 2011– 12 follow a stratified multistage sampling design. Each district of a state or union territory is stratified into rural and urban areas. Within each stratum, first‐stage units are ordered by their population and then further stratified into small rural, large rural, small urban and large urban places.

As for the potential drivers of local growth, we construct 30 indicators to measure the potential drivers of growth identified above, making sure that there are several of them for each of the nine conceptual buckets. To construct these indicators, we draw primarily from the Spatial Database for South Asia compiled by the World Bank (Li et al. 2015). The detailed metadata is available on request.

Of these 30 indicators considered, 27 are measured at the district level and three at the state level:

1. _Geography_ (3). We capture climate through temperature and precipitation.  To reduce the impact of short‐term fluctuations we use averages in the preceding decade. We capture topography through the average altitude of each location.

2. _Infrastructure_ (4). Indicators cover electricity, connectivity, irrigation and housing. The connectivity measure is the density of railway and metro stations. Data on road density, based on Open Street Maps (a crowdsourcing platform) is also available, but it
Table 4 reports the results of the spatial DiD regression model specified in equation (2). The dependent variable in all columns is the nighttime radiance in each grid cell measured using data from satellite images. In columns (1) and (2), a simple DiD specification based on only two years (pre and post program) is reported (where the years correspond to those of the household-survey data used for estimating the results in Tables 2 and 3). In columns (3) and (4) of Table 4, the regression model with all available years (2012 to 2020) as specified in equation (2) is reported. For each model, results for two different treatment indicators are reported. The treatment indicator in columns (1) and (3) equals one for the grid cells corresponding to project areas at the most disaggregated level of information about project locations, which is the subdistrict/city level (administrative level 3). The treatment indicator in columns (2) and (4) equals one for all grid cells belonging to the next higher level of administrative division (administrative level 2, i.e. district) of each project location, to capture potential spillover effects on the area surrounding each project location.

According to the results in column (1) in Table 4, project areas (defined at the administrative level 3) experienced an increase in nighttime radiance between the years 2013 and 2019 that was 0.21 units larger than the increase in non-project areas (from the baseline of 1.64 in 2013 in treated areas). When all years between 2012 and 2020 are considered in column (3), the coefficient decreases to 0.048 but remains statistically significant at the 10-percent significance level. In addition, the significant results in columns (2) and (4) suggest that the project also had positive effects on radiance in areas around the cities (or subdivisions) in which the project components were located.

Various studies have shown that remote-sensing data can
5.5%|8.9%|
|US $5PPP a day<br>27.3%|6.1%|6.0%|9.2%|
|US $2.5PPP a day<br>19.0%|2.6%|1.9%|2.9%|
|**Poverty gap**||||
|National legal<br>20.5%|3.5%|3.0%|4.7%|
|National extreme<br>19.6%|2.9%|2.3%|3.6%|
|US $5PPP a day<br>19.1%|2.9%|2.4%|3.7%|
|US $2.5PPP a day<br>14.3%|1.6%|1.3%|1.9%|
|**Poverty severity**||||
|National legal<br>17.1%|2.3%|1.9%|2.9%|
|National extreme<br>16.5%|2.0%|1.6%|2.4%|
|US $5PPP a day<br>16.0%|2.0%|1.7%|2.5%|
|US $2.5PPP a day<br>12.4%|1.3%|1.2%|1.7%|
|Source: Own estimates based on HBS 2014.||||

11 The extreme poverty rate reported by GUS in 2014 was 7.4 percent, while the legal poverty rate was 12.2 percent. The discrepancy is largely on account of the fact that these are measured using expenditures instead of income (see GUS: <u>http:
# **1. Introduction**

_“Responding effectively to irregular migration is hampered by a serious lack of verifiably accurate data, making it difficult to identify trends or compare the scale of the phenomenon in different parts of the world.” IOM (2010, p. 31)_ Illegal or irregular migration is a topic of policy interest in much of the world.<sup>1</sup> Globally it is estimated that between 10 and 15 percent of the World’s migrant population, or 20 to 30 million people, are individuals without the appropriate documentation (IOM, 2010). In the United States, it is estimated that 11.2 million people, or 28 percent of all foreign-born (Passel and Cohn, 2011) and 56 percent of all Mexican immigrants (Hanson, 2006) are illegal, while in the European Union estimates range from 7 to 13 percent of the immigrant population (Clandestino, 2009).

However, since illegal migration is by definition migration that occurs without the proper documentation, measuring the extent of it is difficult and the above estimates involve considerable uncertainty. Several approaches are typically used to estimate these stocks. The “residual method” used in the United States involves comparing numbers of individuals recorded as foreign-born in surveys to numbers on official admissions files (Passel and Cohn, 2011). Other estimates come from border apprehension data, re-inspections of travelers, and regularization or amnesty program applications (Morrel-Samuels 2002; IOM, 2010). However, such data are often not available for all countries, and in most cases provide, at best, an estimate of the overall incidence of illegal migration without any socioeconomic or demographic information about these migrants.

An alternative is to try and directly measure illegal migration through survey approaches (Massey and Capoferro, 2007). There are two key challenges here. The first is getting a representative sample of illegal migrants when no sampling frame is available. In countries with high emigration or immigration, a random sample of dwellings might be
.63|23.91|24.66|24.85|
|School/college|9.21|7.46|10.93|9.9|8.65|
|Unemployed/not in labor<br>force|12.81|15.15|10.53|12.23|13.29|
|<br>No. of observations|38714|19147|19567|17398|21316|
||,|, <br>**Panel C: Village**|, <br>**level characteristi**|, <br>**cs (8 states only)**|,|
|No of households|508||508|704|400|
|.<br>MNREGS. days total/hh|<br>21.39||<br>21.39|<br>6.53|<br>38.79|
|<br>MNREGS spdg tot (Rs/hh)|5762||5762|2312|9734|
|<br>MNREGS. days irrig/hh|9.34||9.34|2.72|17.11|
|MNREGS spdg irr. (Rs/hh)|2347||2347|1278|3513|
|No. of obs.|94||94|40|54|

_Source:_ Own computation from REDS 1999/00 and 2007/08. Village level data are available for 2007-08 only. _Note:_ Irrigation related days are those relating to work related to irrigation facilities (including micro and minor irrigation works); renovation of traditional water bodies (including de-silting of tanks); water conservation and water harvesting (digging/maintenance of ponds, watersheds, etc.); and drainage works (cleaning and digging of new main and field drains). Works not included were those for drought proofing (including afforestation and tree plantation)
provides information on how services’ value added is distributed among economic activities and how services linkages relate to trade over time.

The GTAP database represents the most comprehensive, convenient, and internationally‐comparable source of sector‐specific data across countries. Of the 129 regions in GTAP v.8, 112 represent individual countries and 17 represent composite regions. In the case of individual countries, the social accounting matrix (SAM) for each country relies on the most recent input‐output data available from national sources for each country (see Aguilar and Walmsley, 2012). These are harmonized to a standard 57‐ sector format for ease of comparison. Limitations of the GTAP data include infrequency of updates (the most recent GTAP 9 pre‐release takes the data only to 2011) and the fact that some input‐output data may be adjusted to provide consistency with merchandise trade and macroeconomic data also used in the SAM. Therefore, results should be interpreted cautiously and should be seen as a first attempt to understand trade performance in developing countries.

(2) For the Trade in Services database, the World Bank merged and reconciled all available sources of data from the OECD, Eurostat, the United Nations, and the International Monetary Fund (IMF) to create a database on bilateral services trade. By mirroring flows, it provides a best available estimate of bilateral flows and their evolution in recent years for 200 countries. Data are reported in US$,<sup>3</sup> millions for 1981–2009. This database makes it possible to carry out a wide range of sophisticated assessments, such as estimating gravity models, measuring of the trade potential of country, trade diversification, and other analytical techniques.

(3) Finally, the report uses firm‐level data for eight aggregated regions in Russia and at the much disaggregated regional level. The data come from the RUSLANA database, which contains balance sheet information on companies in Russia, such as stock data and exports revenue. Although RUSLANA distinguishes between domestic
Policy Research Working Paper 10845

# **Abstract**

This paper presents evidence of the impact of a five-year package of interconnected interventions intended to improve learning environments in eight disadvantaged districts in Malawi. The intervention, which was implemented over five years, provided additional finance to schools to support the hiring of additional teachers and construction of learning shelters to improve class sizes in lower primary, along with constructing classrooms and providing results-based finance to reward improvements in staffing. The interventions were targeted to eight districts with longstanding disadvantages in staffing, learning environments, and learning outcomes, particularly for girls. Employing administrative data and data from a nationally representative independent sample of public primary schools, the analysis finds that these investments closed the gap in learning outcomes between the targeted districts and the rest of Malawi. There is also suggestive evidence that the program reduced learning gaps between girls and boys. The findings suggest that even in a low-income environment with significant constraints, targeted efforts to reduce class sizes can close district-level gaps in learning.

This paper is a product of the Education Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at sasim@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._
in the last 30 days there was a health complication related to disease or chronic pain, accident, tooth pain, surgeries, or pregnancy complications (for females older than 11 y.o.a). Standard errors are clustered at the household level *** * p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

# **V.B Cognitive development: Peabody vocabulary assessment**

Cognitive development is measured using the Peabody Vocabulary Test Score (PVTS) for children and adolescents 5 to 17 years of age in our sample. This is an untimed test of receptive vocabulary for standard Spanish and is intended to provide a quick estimate of the test taker’s receptive vocabulary verbal ability. It includes 125 questions, each containing four picture options with only one correct answer. Items are organized in order 35
_Business_ represents characteristics of the business that operates in tandem with FINCA agent activities. Like agent age, we expect that the age of the agent’s business could be positively linked to agent transactions to the extent that more firmly established businesses inspire trust and/or already have experience with a wider number of potential clients. We also hoped to explore how the nature of the business affects agent transactions. To proxy for interactions between an agent’s established and banking businesses, we tried a number of variables including the number of days per week (and hours per day) that the business is open, the number of employees of the business, and the economic sector of the agent’s business.<sup>22</sup> We were unable to derive robust results for those variables, and thus rely on the value of stock (meaning inventory) in the retail business as a simple summary indicator of how demanding are an agent’s duties to maintain that business. To the extent that demands from the retail business on an agent’s time are high, it might make it harder (and relatively less financially lucrative) for her/him to fully pursue banking, and thus we would expect a negative relationship between retail stock value and agent transactions.

_Market_ represents a set of variables that describe the demographic characteristics of the municipality including population, population density, commercial development, and average income level. Both population and population density are taken from official sources for the most recent year for which data are available.<sup>23</sup> We expect both to be positively linked to the number and volume of agent transactions. Average income levels and the level of commercial development were derived by Experts SARL Consulting, as described above. Lacking a census of businesses and a representative survey of households, we asked Experts SARL to sort municipalities into one of three categories for these variables – high, medium, and low. Our hope was to derive coarse,

> 22 Unfortunately, we were