rafmacalaba/data-use-mentions-extended huggingface dataset:
- (a) High frequency sample surveys using smartphone and cellular technologies. For example, a high-frequency survey initiative in Somalia employs a dynamic questionnaire loaded onto smartphones, which enables data to be collected from household interviews in 60 minutes. This approach was developed to overcome the challenges of insecurity, limited data gathering capacity and budgetary constraints.

- (b) The use of mobile phones to conduct surveys or follow up interviews following face-to-face household surveys. For example, a Bank paper on the impact of the 2012 crisis in Mali on IDPs, refugees and returnees used information from a face-to-face household survey as well as follow-up interviews with its respondents via mobile phones. This combination provided a mechanism to monitor the impact of conflict on hard-to-reach populations who at times live in areas inaccessible to enumerators. And in Sierra Leone and Liberia, the Bank supported the use of mobile phones to collect key socio-economic data on the effects of the Ebola virus.

- (c) Crowdsourcing data on displacement. Platforms such as the Kenyan Ushahidi has crowdsourced data on displacement in Kenya and eastern DRC by encouraging IDPs and host communities to report incidents using their mobile phones or the internet, including information about living conditions. The platform references these reports geo-spatially.

- (d) Geo-mapping of data on displaced populations and affected host communities. Geographic Information Systems (GIS) and geospatial analysis can be used to map, monitor and analyze data on forced displacement. Triangulation of this information with socio-economic and other indicators can provide a rich source of data and enable insights into underlying patterns and trends over time.

- (e) Use of big data (mobile phone data, news scraping, social media). IDMC is pursuing big data approaches to capturing displacement data in real time in order to report on displacement situations as they are happening and to provide updates on how they are evolving (IDMC 2015). These data are not necessarily representative but can be used in conjunction with other methods to triangulate trends. For example, the Swedish NGO, Flowminder, has pioneered
Inequality indices are calculated for each income category to assess the redistributive effect of taxes and transfers. Inequality is measured using the Theil index, since the index provides the advantage of additive decomposability, i.e., aggregate inequality can be decomposed into 1 <u>i i</u> inequality within- and between- sample subgroups. Theil index, = ∑ l , where � � NN QQ QQ is the Theil index of the income _Q_ , is the income of individual QQ _i_ , is the average income ii=1 _Q_ QQ, TT NN QQ<sup>�</sup> QQ<sup>�</sup> TT and _N_ is the sample size. The index varies from 0, perfect equality, to ln( _N_ ), perfect inequality. ii h Total inequality is the sum of within-gender inequality: QQ = ∑ QQ<sup>�</sup> , and between-gender ∑ 2 <u>g</u> = <u>j</u> ww gg=1 gg gg inequality, = ∑ , where NNgg is gender g’s income share of total income, TT SS TT �l �SS ~~�~~ � ∑ j=1 QQjj <u>g</u> is the share of the gender g’s population of the total population (Andrei et al. 2017). TTbb gg=1 SSgg PPgg SSgg NNii=1 QQii NN gg= PP **3.** NN **Data and Assumptions**

# **3. Data and Assumptions**

## **3.1. Data**

The primary source of data for the study is the 2018/19 Ethiopia Socioeconomic Survey (ESS). Basic demographic characteristics, health care utilization, school enrollment status, and labor market outcomes are available at the individual level. Others, such as consumption expenditure, other income, property taxes, business taxes, land use fee and agricultural income
# **1. Introduction**

The drivers of labor market participation and employment outcomes have been well documented in the literature (Baah-Boateng, 2014; Contreas et al., 2010). These determinants range from observed household and individual characteristics (Fadayomi et al., 2014), to the geographic location of an individual (Naudé and Serumaga- Zake, 2010), to the type and distribution of employment options (labor demand) in the local area. In addition, individuals’ cultural setting as well as their inherent traits play a significant role in shaping their decisions regarding labor market participation and employment choices (Reimers, 1985; Attasoy, 2017; Wichert and Pohlmeier, 2010). For instance, wage employment tends to increase with age in Nigeria, but is non-linear, slowing down as workers approach retirement (Aminu, 2010; Anyanwu, 2013).

In Ghana, unemployment rates are higher among women (Baah-Boateng, 2014). Broeck and Kilic (2018), using cross-country data from Ethiopia, Malawi, Nigeria, Tanzania, and Uganda, similarly found that women are underrepresented in wage and self-employment, likely due to local norms that differentially affect their opportunities. Aminu (2010) also identified a positive relationship between and wage employment. Urban residence is associated with greater employment, owing to the larger capacities and job opportunities in these settings (Naudé and Serumaga-Zake, 2010). Male household headship, marital status and household size have all been found to improve employment outcomes (Sackey, 2005; Fadayomi et al., 2014). Giannetti and Andrei (2004) noted that household wealth serves as a positive driver of self-employment, as individuals may encounter fewer liquidity constraints when starting a business.

This study examines the drivers of labor market participation and employment choices, and how individual gender role attitudes and personality traits shape these decisions. We achieve this by pursuing the following specific objectives. First, we explore the gender differences in labor market outcomes. Second, we examine the determinants of labor market participation
the baseline survey included all the variables necessary to estimate the proxy-means targeting score for all households in the baseline sample. On that basis, we can replicate the targeting process and

> 19 We also provide a robustness check by considering actual beneficiaries from the baseline sample only (comparing observations in cells D and E in Table 1).

> 20 We report the randomization-t test as Young (2018) shows it is superior to the alternative randomization-t statistic. Furthermore, he shows that there are little changes in rejection rates beyond 2,000 simulations.

14
**Figure 2. Services Value Added and Level of Country Development, 2013**

<!-- Start of picture text --> 100<br>80<br>ZAF BRA<br>60<br>RUS<br>IND<br>CHN<br>40<br>IDN<br>20<br>0<br>6 8 10 12<br>log GDP per capita PPP<br>Services value-added as % of GDP<br><!-- End of picture text --> Source: World Development Indicators.

Notes: GDP = Gross Domestic Product; PPP = Purchasing Power Parity. BRA = Brazil, CHN = China, IND = India, RUS = Russia, ZAF = South Africa.

27
The poor in EICV3 can be identified using: and the poor in EICV4 can be identified using: Inequalities (12) and (12’) give a poverty rate of 62.4 percent in EICV3, and 56.9 percent in EICV4. Therefore, if spatial price variations and region level monthly inflation rates are properly controlled, the international poverty rates also show a significant reduction between EICV3 and EICV4.<sup>12</sup>

# **V. Comparison of Price Indices**

The above analysis clearly shows the centrality of adequate price adjustments in poverty measurement, especially when establishing the comparability of poverty statistics over time and across space/regions. In Rwanda, two different sets of price data are potential candidates for poverty measurement – the price data used in the estimation of the official CPI, and the price index developed through household survey data, for instance the COLI in NISR (2016).

Figure 2 shows monthly inflation rates from EICV3 survey months to January 2014 for each of these price indices. The dotted line is a population weighted average of inflation rates calculated from COLI of NISR (2016) for each month from November 2010 (1011) to October 2011 (1110) while the solid line is calculated from monthly CPI (national) data. It is evident that CPI data show much higher inflation rates for almost all survey months. Below we provide some possible explanations underlying these differences.

The first major difference in CPI and COLI comes from the data sets and the reference group used to get item budget shares. A typical price index is made up of item budget weights and prices. In Rwanda, price data for the selected food items included in the NISR (2016) COLI are the same as CPI price data. The budget weights used for the two indices however, differ in two ways. First, in the year 2014, the official CPI base year was set at 2011 and CPI item weights were derived from EICV3 data. In contrast, item weights for the COLIs
Policy Research Working Paper 5829

# **Abstract**

This paper takes a bibliometric tour of the past 40 years of health economics using bibliographic “metadata” from EconLit supplemented by citation data from Google Scholar and the authors’ topical classifications. The authors report the growth of health economics (33,000 publications since 1969—12,000 more than in the economics of education) and list the 300 mostcited publications broken down by topic. They report the changing topical and geographic focus of health economics (the topics ‘Determinants of health and illhealth’ and ‘Health statistics and econometrics’ both show an upward trend, and the field has expanded appreciably into the developing world). They also compare authors, countries, institutions, and journals in terms of the volume of publications and their influence as measured through various citation-based indices (Grossman, the US, Harvard and the JHE emerge close to or at the top on a variety of measures).

This paper is a product of the Human Development and Public Services Team, Development Research Group. 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 author may be contacted at awagstaff@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._ Produced by the Research
period under the Kyoto Protocol.

The first step in the analysis is settling on a definition of what constitutes an agricultural JI or CDM project. There is some confusion in reported studies since the classification systems used by the IPCC in the mitigation reports discussed earlier differ from how the United Nations Environment Programme Risoe Center on Energy, Climate and Sustainable Development (Risoe) reports projects in their widely used project data base.

Indeed as of July 1, 2011, there were no JI projects and only two active CDM projects classified by Risoe as agricultural.<sup>28</sup> This is superficial since many of the projects do relate to agriculture; however Risoe‘s classification choices are also indicative of the energy-sector focus of climate change policy discussions. Projects related to land use, the primary focus of the IPCC chapter on agriculture, are included under afforestation/reforestation projects, which we refer to as land-use-forestry projects.<sup>29</sup> Other projects related to agriculture – for example systems to manage animal wastes or projects that use agricultural waste products to generate energy -- fall into other aggregate categories in the Risoe classification system, such as biomass energy or methane avoidance; however, these groups include non-agricultural projects as well. Consequently, we are left to sort the projects in a way that better suits our purpose.

In this we follow the lead of the United Nations‘ Food and Agriculture Organization (FAO) and define an agricultural project as a project that uses agricultural residuals, outputs or agricultural processes to directly or indirectly reduce greenhouse gas emissions (FAO, 2010a). This definition is broad enough to include projects that sequester carbon in soils. We include projects that reduce methane emissions from composting agricultural waste products and also include waste water projects when the projects are linked to agricultural products – for example palm oil, cassava or distilleries. In general, the use of ethanol or biodiesel made from

> 28 The two projects are, using Risoe project identification numbers, CDM6938 and POA0011.

> 29
endogenous US city selection being present by 1980, it is important to recall that the city distribution does not itself provide identification given the first differencing and fixed effects included in the first-differenced specification. What matters instead is the differential exposure across industries and technologies that a country’s US spatial footprint generates. Thus, for a bias to exist, it must be that a country’s spatial placements by 1980 sought out specific city-industry combinations in the United States that would become

> 5The top industries for each country in Columns 7-8 are determined through USPTO patent filings made by inventor teams working in each country and industry during 1975-1984. For Columns 9-10, the procedure regresses country-industry labor productivity on a set of country, industry, and year fixed effects. For each country, the one or five industries with the highest average residual labor productivity are excluded.

14
# Individual Wealth Inequality: Measurement and Evidence from Low- and Middle-Income Countries

Ardina Hasanbasri<sup>†</sup> , Talip Kilic<sup>‡</sup> , Gayatri Koolwal<sup>#</sup> and Heather Moylan<sup>*1</sup> JEL Codes: C81, C83, D31, J16, O12.

Keywords: Asset Ownership, Wealth, Inequality, Gender, Respondent Selection, Household Surveys, Cambodia, Ethiopia, Malawi, Tanzania.

1 † University of Michigan and Independent Consultant, Development Data Group (DECDG), World Bank, ardinah@umich.edu; ‡ DECDG, World Bank, <u>tkilic@worldbank.org;</u><sup>#</sup> Development | Science and Independent Consultant, DECDG, World Bank, <u>gkoolwal@worldbank.org;</u><sup>*</sup> DECDG and Gender Group, World Bank, hmoylan@worldbank.org. The research was funded by the World Bank Living Standards Measurement Study – Plus (LSMS+) program. The authors are grateful for the ongoing support from the survey teams and national statistical offices of Malawi, Ethiopia, Tanzania and Cambodia in implementing these national surveys. The authors are grateful to participants at the 2021 Northwestern-IPA Methods and Measurement Conference for their comments on the study. The authors would also like to thank Cheryl Doss, Agnes Quisumbing and Abhay Pasupathy for their feedback, as well as participants in the 2021 International Association for Feminist Economics Conference, and the 2021 International Conference of Agricultural Economists.
# 6 Robustness Checks

I do several robustness checks to test the main specication (Table 3). Column 2 uses an estimate of imported incidence without weighting the mobility data to be representative of the full population. This assumes that the only movement in the country is the movement in the Sonatel data, which would be an underestimate. Weighting the data represents an upper bound for the level of movement. The actual movement that occurs, and therefore, imported incidence, is then somewhere in between these lower and upper bounds. Thus the real eect should also lie between these upper and lower bounds of 2.1 and 1.7. In addition to weighting the movement, Column 3 scales both imported incidence and total incidence by health post utilization at the region level. The results are similar and not signicantly dierent from the results in the main specication (Column 1). Utilization is relatively uniform throughout the country; therefore, incorporating utilization does not signicantly change the results. I rerun the specication using Conley standard errors that account for spatial autocorrelation and serial autocorrelation over time (Column 4) (Hsiang 2010).<sup>29</sup> The results remain signicant.

In Column 5, I use net imported incidence, subtracting expected infected travelers leaving the health post catchment area. Both the coecients on imported and lagged imported do not change signicantly and they remain signicant. Results from additional robustness checks are available in Appendix B. These include (1) using cases rather than incidence; (2) adjusting the population that incidence is based on by the number of people entering and leaving the area each month; (3) loosening the assumption that the susceptible population is equal to 1 and scaling lagged incidence by a calculated susceptible population; and (4) restricting the movements included in calculating imported incidence by removing any movement where the SIM card had no calls or texts for more than two weeks before or after.

One limitation of the
*|**(2)**|**(3)**|**(4)**|
|---|---|---|---|---|
||**ANC**|**OVA**|**Poo**|**led**|
||**5 weeks**<br>**PHQ‐ADS score**|**3 months**<br>**PHQ‐ADS score**|**Pooled**<br>**PHQ‐ADS score**|**Clustered SEs**<br>**PHQ‐ADS score**|
|Treatment|‐0.736|‐1.358*|‐1.070*|‐1.070|
||(0.801)|(0.788)|(0.560)|(0.672)|
|Baseline score|0.434***|0. 429***|0.432***|0.432***|
||(0.0460)|(0.0457)|(0.0323)|(0.0591)|
|Round‐ ERKF|1.249|‐0.714|0.309|0.309|
||(0.826)|(0.830)|(0.583)|(0.635)|
|Age|‐0.00995|‐0.0197|‐0.0153|‐0.0153|
||(0.0383)|(0.0374)|(0.0267)|(0.0308)|
|Education|0.0326|0.00303|0.0164|0.0164|
||(0.0991)|(0.0984)
3 share of nonfarm income varies inversely with both size of land owned and total rural income. On the other hand, studies in Africa have generally produced very different results. For instance, Reardon, Delgado and Matlon (1992) in Burkina Faso, Collier, Radwan and Wangwe (1986) in Tanzania and Matlon (1979) in Nigeria find that nonfarm income has a negative impact on rural income distribution because it is mainly large landowners who receive nonfarm income.

Part of this inconsistency may be explained by differences in the key factor noted above, namely, the distribution of land. In other words, in land-scarce, labor rich countries - like Pakistan and much of Latin America - inadequate access to land may tend to "push" poorer rural households out of agriculture and into the nonfarm sector. Thus, in these countries, nonfarm income may have a positive impact on inequality and poverty. The obverse, then, could hold in land-rich, labor-scarce countries - such as Africa - where ample land access may tend to keep most people in agriculture and to "pull" only richer households into the nonfarm sector.

This paper proposes to clarify the impact of nonfarm income and unequal land distribution on rural income inequality by analyzing the results of a new, nationallyrepresentative household survey in Egypt. The paper seeks to make three contributions. First, it uses decomposition techniques to pinpoint the contribution of five different sources of rural income - including nonfarm income - to overall rural inequality. This is useful because few past studies have used disaggregated, household-level income data to analyze the contribution of different types of income to rural income inequality. Second, the analysis shows how small, exogenous changes in income from different sources affects overall rural inequality and welfare. Such information is useful to policymakers,
|7|Conditional cash<br>transfer|Philippines|EAP|RCT|Enrolled in school 15‐<br>17yrs|**‐0.098**|Evaluation<br>report|Chaudhury et<br>al. 2013|
|---|---|---|---|---|---|---|---|---|
|8*|Sanitary products|Nepal|SA|RCT|Attendance|**‐0.083**|Journal|Oster and<br>Thornton<br>2011|
|9|Conditional cash<br>transfer|Uruguay|LAC|DID|School attendance,<br>18‐month follow up<br>School attendance,<br>30‐month follow up|**‐0.056**<br>**‐0.047**|Journal|Amarante et<br>al. 2013|
|10|Unconditional Cash<br>Transfer|South Africa|SSA|DID|Attendance|**‐0.043**|Working<br>Paper|Santana 2008|

Note: Interventions marked with an asterisk are girl‐targeted. 1 Adjusted negative value for comparison.

**Table 5: The 10 most effective interventions to improve learning for girls**

||**Program**<br>**description**|**Country**|**Region**|**Evaluation**<br>**design**|**Outcome**|**Effect**<br>**size(SD)**|**Type of**<br>**publication**|**Reference**|
|---|---|---|---|---|---|---
Policy Research Working Paper 5907

# **Abstract**

International migration has increased rapidly in recent decades and this has been accompanied by a remarkable increase in transfers made by migrants to their home countries. This paper investigates the effect of the rural economic growth brought about by migration and remittances on Nepal’s Himalayan forests. The authors assemble a unique village-panel dataset combining remote sensing data on land use and forest cover change with data from the census and multiple rounds of living standards surveys to test various inter-relationships between population, economic growth and forests.

The results suggest that rural economic growth spurred by remittances has had an overall positive impact on forests. The paper also finds that remittances caused an increase in rural wages and an increase in income, but a decrease in land prices. Considered together, however, the relationship between forests and remittances is driven largely through the income channel, indicating that the demand for amenities provided by forests in the rural Nepali setting may have been more important than factor prices in influencing land use changes for the period of the study.

This paper is a product of the Poverty Reduction and Equity Unit, Poverty Reduction and Economic Management Network. 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 author may be contacted at stiwari@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,