rafmacalaba/data-use-mentions-extended huggingface dataset:
located the center of the assigned block, they had to identify the majhee who was in charge of the majority of households surrounding the point and list all households under said majhee.

UNHCR provides a publicly available camp navigation application that is primarily used by site management and NGOs operating inside camps. A customized version of the tool, containing the NPM R12 majhee mapping system and outlining the CBPS sample blocks, was shared with the team in the second half of the camp listing period. After the tool was received, the teams used the application to locate and list within demarcated sample blocks.

# _Ground truthing and developing the listing methodology for hosts_

The host mauza segmentation was conducted digitally using an algorithm that divided each host mauza into segments of approximately 100-150 households based on population counts from the Bangladesh Population Census 2011 and the number of buildings within mauza boundaries visible on OSM. The proposed listing methodology instructed field teams to locate the selected segments using maps generated on Google Maps and do a full listing within those segments. However, owing to the issues faced during the camp listing, the suboptimal quality of the imagery, and heterogeneous settlement patterns across host communities, it was judged necessary to carry out a ground validation exercise before the actual listing could start. Non-sample mauzas were selected for a two-day validation exercise which highlighted that the actual number of households in some of the selected segments was significantly higher or significantly lower than the expected 100-150 households. For these segments, a number of field protocols were developed in addition to the core listing guidelines:

- _For segments with fewer than the expected number of households (that is, fewer than 100 households)_ , if the border of the segment cut through a settlement cluster belonging to another nonsample segment, the field team was instructed to continue listing along the border of the segment until they had listed approximately 100 households.

- _For segments with fewer than 100 households and with no avenue for expanding_ listing into clusters along the segment borders, field teams were instructed to complete listing among the households inside
|
|P-value equal coef.|0.826|0.911|0.839|0.993|0.586|0.248|0.235|0.296|
|Observations|567|567|542|542|585|585|572|572|
|Mean Y (Control)|0.377|0.377|0.586|0.586|0.297|0.297|0.858|0.858|

Notes: Heteroscedasticity-robust standard errors in parentheses. Controls are described in the notes to Table 2. * p < 0.10, ** p < 0.05, *** p < 0.01 Table A7: Short-term impact on attitudes towards gender norms

|_Dep. Var. (Y):_|_Wrong to _|_follow norms_|_Women virg_|_in at marriage_|_Ban kitche_|_n during period_|_Wear whate_|_ver they want_|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Drama|0.081<sup>_∗_</sup>|0.072<sup>_∗_</sup>|-0.086<sup>_∗∗_</sup>|-0.070<sup>_∗_</sup>|-0.091<sup>_∗∗_</sup>|-0.083<sup>_∗∗_</sup>|0.076<sup>_∗∗∗_</sup>|0.060<sup>_∗∗_</sup>|
|
. Sweeney, S. Takao, P. P. Tans, H. Tian, B. Tilbrook, H. Tsujino, F. Tubiello, 999 G. R. Van Der Werf, E. Van Ooijen, R. Wanninkhof, M. Watanabe, C. Wimart-Rousseau, D. Yang,

- 1000 X. Yang, W. Yuan, X. Yue, S. Zaehle, J. Zeng, and B. Zheng. Global Carbon Budget 2023. _Earth Sys-_ 1001 _tem Science Data_ , 15(12):5301–5369, Dec. 2023. ISSN 1866-3516. doi: 10.5194/essd-15-5301-2023. 1002 URL `https://essd.copernicus.org/articles/15/5301/2023/` .

- 1002

- 1003 D. Fullerton and G. E. Metcalf. Environmental controls, scarcity rents, and pre-existing distortions. 1004 _Journal of Public Economics_ , 80(2):249–267, May 2001. ISSN 00472727. doi: 10.1016/S0047-2727(00) 1005 00087-6. URL `https://linkinghub.elsevier.com/retrieve/pii/S0047272700000876` .

- 1006 K. Gillingham and J. H. Stock. The Cost of Reducing Greenhouse Gas Emissions. _Journal of Economic_ 1007 _Perspectives_ , 32(4):53–72, Nov. 2018. ISSN 0895-3309. doi: 10.1257/jep.32.4.53. URL `https:` 1008 `//pubs.aeaweb.org/doi/10.1257/jep.32.4.53` .

- 1009 M. Golosov, J. Hassler, P. Krussel, and A. Tsyvinski. Optimal Taxes on Fossil Fuel in General 1010 Equilibrium. _Econometrica_ , 82(1):41–88, 2014. ISSN 0012-9682. doi: 10.3982/ECTA10217. URL 1011 `http://doi.wiley.com/10.3982/ECTA10217` .

- 1012 J
12 types of disaster risks, that can be classified across four disaster risk categories as follows: meteorological (tropical cyclones / hurricanes, severe storms, and extreme rainfall), geophysical (earthquakes, volcanic eruptions, and tsunamis), climatological (heat waves, droughts, and wildfires) and hydrological (floods, landslides, and high swells).<sup>6</sup> In Suriname, after detailed incountry consultations and reflecting the local context, households were only asked about

> 3 - - See the ITU’s digital development dashboard: https://www.itu.int/en/ITU <u>D/Statistics/Dashboards/Pages/Digital Development.aspx</u> (consulted on February 12, 2024).

> 4 See World Development Indicators: https://data.worldbank.org/indicator/IT.CEL.SETS.P2 (consulted on February 12, 2024).

> 5 For Suriname, data were collected later, in June 2022.

> 6 These four disaster risk categories are not entirely mutually exclusive. For instance, floods (which we classify as hydrological risks) may be caused by heavy rains (which we classify as meteorological risks). See, for instance, Below et al. (2009) for discussion.

4
# **3.5 Contemporary Predictors of Rural Non-Farm Employment**

## **3.5.1 Individual Predictors of Rural Non-Farm Employment**

We close our analysis of the predictors of rural non-farm employment by exploring the role of education, gender and community identity in predicting non-farm employment with contemporary data. As the last NSS employment survey was undertaken in 2011, we rely on household survey data from the Consumer Pyramids (CP), which began its household surveys in 2014. Unlike the NSS which is a repeated cross-section, undertaken once every 5 years, the CP tracks households over time in a panel framework, with each household surveyed thrice every year. We select the year 2015 for our analysis, as starting towards the end of 2016, a number of policy interventions affected the functioning of the economy.

Unlike the NSS, the CP data provides only a coarse mapping of individuals to their respective occupations. Thus, instead of precise sectoral employment outcomes, we explore whether the individual is a) employed as a wage laborer or industrial worker; b) operates a small business; c) engaged in formal business activity; d) a qualified self-employed entrepreneur; or e) a white-collar professional. The empirical specification is similar to equation (1), with flexible controls for district-level factors through district and town/village fixed effects.<sup>12</sup> We also use survey-month fixed effects to account for any seasonality based on the timing of the survey. All specifications include sample weights to make the data representative.<sup>13</sup> Standard errors continue to be clustered by district.

We start by studying the predictors of LFP, unemployment and participation in nonfarm activities (Table 11). The outcome of interest in the first three columns is a dummy equaling 1 if the individual participates in the labor force; in columns (4)-(6), a dummy equaling 1 if the rural worker is unemployed; and in columns (6)-(
_Table 2.2. 2SLS Regression Results: Explaining Wage Growth_

|||Model A|||Model B|||ModelC||
|---|---|---|---|---|---|---|---|---|---|
||Total|Unskilled|Skilled|Total|Unskilled|Skilled|Total|Unskilled|Skilled|
|∆Export Index|.024<br>|.047*<br>|.0072<br>|.0076<br>|.028*<br>|-.027<br>||||
||(.021)|(.027)|(.017)|(.011)|(.016)|(.023)||||
|∆Export US Index|||||||.010|.042*|-.029*|
||||||||(.017)|(.023)|(.017)|
|∆Skilled/unskilled status|-.056|.11|.028|-.083|.055|-.060|-.083|.055|-.064|
||(.062)|(.093)|(.062)|(.064)|(.090)|(.077)|(.064)|(.090)|(.077)|
|∆Marital status|-.13*|-.16**|-.017|-.10|-.14**|.0069|-.099|-.14**|-.00035|
||(.070)|(.078)|(.092)|(.061)|(.068)|(.091)|(
Because the BRMS only covers households with return migrants, we complement it with a nationally-representative survey carried out in Bangladesh: the Household Income and Expenditure Survey (HIES, 2016-2017 wave). This survey collected detailed data on the socio-economic characteristics and outcomes of all members in a representative sample of Bangladeshi households. These include labor market outcomes, including entrepreneurship activities, which are recorded in a similar way to the BRMS.

# **2.3 Descriptive Statistics**

Table 1 summarizes the outcomes of return migrants (who worked overseas in the past) compared to non-migrants (who never worked overseas). First, return migrants are considerably more likely to be self-employed (without paid employee) or employer (with paid employees) than non-migrants. Return migrants who are self-employed (with or without other paid employees) also earn significantly more on average than returnees who are wage employees. Since entrepreneurs are on average older than wage workers, we also compare the earnings gap by age: the income gap between entrepreneurs and wage employees is observed at all ages (Figure A3). This suggests that self-employment is indeed an attractive option for return migrants to Bangladesh. Return migrants who become business owners also stayed longer at a destination - and presumably accumulated more savings - compared to those who become wage employees. Relative to return migrants who are wage workers and to non-migrant entrepreneurs, return migrants who are business owners are disproportionately concentrated in the service sector, and in particular in the retail/hotel/restaurant sector. Finally, return migrants who employ other workers generate higher incomes than comparable non-migrant entrepreneurs.

8
provincial poverty rate, the structure of the economy (i.e. the relative shares of the industrial and service industries), the share of younger persons in the relevant age group (i.e. youth aged 15 to 24 or prime-aged persons aged 25 to 54) and the share of persons with a primary education or less. The variable γt is a set of year dummies to control for economic conditions in a given year. Given the small sample size and the inclusion of numerous province-specific, time-varying explanatory variables, province dummies are excluded to preserve degrees of freedom.

**Variations to the basic model include analyses by gender, education level, and sector of employment.** The relationship between the formal employment rate of older and the labor market outcomes of younger persons (including the formal employment rate of younger persons) is also analyzed. The motivation for this is because the planned increase in the retirement age is likely to affect workers who are formally employed rather than those who are informally employed. It would therefore be important to understand the relationship between the formal employment rates of older and younger persons.

**The model used in this paper is similar to that used by Munnell and Wu (2013) for the United States and China.** The reason is because like Munnell and Wu (2013), this paper uses household survey data to compute the variables of interest at the province level within a country and to study the relationships between the employment rates of older persons and the labor market outcomes of younger persons. A similar approach was taken by Jasmin and Abdur Rahman (2021). Gruber et al. (2010) and Apella (2024) utilized the same variables of interest, but both papers looked at the relationship between the employment rates of older persons and labor market outcomes of younger persons across several countries. Other papers such as Martins et al. (2009) and Kondo (2016) analyzed the relationship at the firm level.

> 10 This model is similar to that
# **2 How to measure management in schools?**

Until the early 2000s, management was typically viewed as an unmeasurable productivity shifter, to be relegated to the residual in any performance regression [Bloom and Van Reenen, 2007]. Since then, improvements in survey methodology and data access have allowed for advances in measurement. The current “state of the art” approach uses a dedicated survey—the World Management Survey (WMS)—to measure establishments’ adoption of structured management best practices. While the WMS offers uniquely rich information about management practices, it costs approximately USD400 per interview and takes about 4 months to conduct a single country wave [Bloom et al., 2016]. In view of these costs, it may not be well-suited to every context.

In this section, we propose an alternative three-step approach than can, in principle, be used with any existing public dataset containing information on management practices. The first step is to use the original WMS phone survey as a benchmark, and to look for questions in the public survey that elicit information on the management practices already measured by the WMS.<sup>4</sup> The second step is to code answers in line with the WMS methodology. And the final step is to create a management index. In Section 2.1, we provide a brief overview of the WMS questions and coding. In Section 2.2, we describe our approach using two existing public datasets as examples: PISA and the Brazilian school census survey, Prova Brasil. Since Brazil and several other PISA countries are part of the Bloom et al. [2015a] sample, we can compare the (within-country) distribution of each index with the corresponding (within-country) distribution of the WMS index. Both indices are well-validated and can therefore be used by researchers interested in studying management across a wider range of countries and schools than was previously possible.

> 4Our approach follows the spirit of the re-casting of the original phone-based World Management Survey into the
percent to 71 percent, while the attendance rates increased only slightly for the richest quintile. Gaps, however, persist: in 2005 67 percent of children in the poorest quintile attended school compared to 95 percent of the children in the top quintile.

In Figure 8, we disaggregate the attendance figures by preschool, primary, and secondary for 2000 and 2005.<sup>11</sup> This figure reveals an important aspect of Haitian education: a very large number of students are “over-age” for their school level. In 2005, a large proportion of older children were attending preschool: 33 percent of children age 8, 17 percent age 10, and 8 percent

> 11 The 1994 data is excluded from this analysis because the 1994 survey did not capture complete information on the level and grade of students attending school.

8
strategy, while scaling up targeted transfers, engaging the private sector, and encouraging its growth by passing laws that aim to improve the business regulatory environment as well as promote competitiveness.

To contribute to the policy dialogue, this study assesses the redistributive impact of fiscal policy in Egypt using an internationally recognized methodology developed by the Commitment to Equity (CEQ) Institute (see Lustig 2018). The study’s objective is to estimate the impact of fiscal revenue collections (taxes) and fiscal expenditures––direct cash and near-cash transfers, in-kind benefits, and subsidies––on household-level inequality and poverty. It then provides evidence to help policy makers and Egyptian stakeholders understand the trade-offs inherent between the government’s current fiscal policy priorities (such as energy policy) and other, social goals (such as poverty reduction). The assessment is based on the fiscal policies as of 2015, since that is the most recent year for which a household survey is available and allows for estimating the incidence of all the policies analyzed.

Using the standard approach of the CEQ model, the impact of the fiscal system in the Egyptian population is described via an estimation of prefiscal and postfiscal income measures. The prefiscal measure comprises market income before any transfers (including, for instance, public spending on health and education, farming inputs, fuel and energy subsidies, and unconditional cash transfers) or taxes (including personal income taxes, general sales tax, property taxes, and so forth) of any kind have been received or paid by the household. Postfiscal income<sup>5</sup> takes prefiscal income and adds to it a subset of executed fiscal policies: subsidies and direct transfers received, direct and indirect taxes paid, and in-kind transfers received through use of services.<sup>6</sup> Poverty and inequality measures then are derived under pre- and postfiscal income measures. Thus, by comparing these welfare indicators for the distribution of each

> 5 Postfiscal income concepts include net market Income, disposable income,
card expenditures (which corresponds to the total expenditures we observe in administrative data). Specifically, respondents were asked: “Do you expect your credit card spending to go up, stay the same, or go down during the next 3 months?” We code this and other similar questions on a simple three-step scale. the variable takes the value -1 if the individual responded “go down,” 0 if the individual responded “stay the same,” and +1 if the individual responded “go up.” Another pair of questions uses similar language to elicit total spending, not limited to spending on credit cards, and spending on groceries for comparison.

The next set of questions on expected spending asks about spending on durable goods and uses wording that closely follows the Michigan Survey of Consumers. We first ask respondents: “Do you think now is a good time, a bad time, or neither a good nor a bad time to buy household items, such as furniture or a refrigerator?” We code responses using the same _{−_ 1 _,_ 0 _,_ +1 _}_ scale 13
womenheaded households than the state average, and about the same as rural localities with forest coverage.

Finally, for localities with data on the marginalization index of 2010<sup>3</sup> , we show descriptive statistics in Table 4 using the official groups “Very High” and “High” based on official cutoffs of the continuous index. Most localities in the sample are marginalized, with 68% in the “High” group and 29% in the “Very High” group. We can also assess the differences between localities using the continuous marginalization index: The sample localities ( _M_ =.36, _SD_ = .78) are, on average, less marginalized than other rural localities with forest coverage ( _M_ =.72, _SD_ = .87); but virtually equally marginalized with those across the state ( _M_ =.33, _SD_ = .85).

> 2 Localities in these two states had, according to the 2010 census, at least 27 indigenous languages spoken. Our sample had at least 10 of these indigenuous languages. Rural localities with forest coverage had at least 15. Nevertheless, in terms of proportion of people speaking indiginous languages, the sampled localities had a higher average proportion of people speaking an indigenous language (0.62) than the average locality in these two states (0.45) . Rural localities with forest coverage had 0.59, which is statistically equivalent to our sampled localities.

> 3 CONAPO (2010) Indice de marginalización por localidad 2010. Taken from <u>http://www.conapo.gob.mx/es/CONAPO/Indice_de_Marginacion_por_Localidad_2010</u>

> The marginalization index combines a series of socio-economic indicators: percentage of population 15 years or older illiterate or without completing primary education, precentage of inhabited houses without a toilet, electricity, refrigerator or piped water, or with dirt floor, and average number of inhabitants per room.

8
Table 3. Population Supply<sup>a,b,c</sup> (robust standard errors in parentheses)

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||GMM-IV|OLS|GMM-IV<br>(1980)|GMM-IV<br>(1991)|GMM-IV<br>(2000)|
|Ln(incomeper capita)|2.370***|1.813***|1.830***|2.636***|2.886***|
||(0.683)|(0.378)|(0.569)|(0.704)|(0.933)|
|Ln(rural income opportunities:|-5.151***|-4.152***|-4.821***|-5.316***|-5.624***|
|marketpotential)|(1.454)|(0.819)|(1.457)|(1.354)|(1.824)|
|Ln(ruralpop. supplymarket|5.851***|4.878***|5.559***|5.978***|6.317***|
|potential)|(1.368)|(0.752)|(1.378)|(1.281)|(1.705)|
|time dummies|Yes|Yes|No|No|No|
|R<sup>2</sup>||0.745||||
|Hansen J statistic<br>(overidentificationtest)|1.909||1.
. Individuals were eligible for participation in the multi-faceted productive inclusion components of the Sahel Adaptive Social Protection (ASP) program on the basis of a Proxy Means Targeting (PMT) score and other methods seeking to identify poor and vulnerable households.<sup>15</sup> Full details can be found in Bossuroy et al. (2022).

_Treatments_ . A summary and additional details are as follows. The multi-faceted productive inclusion components of the Sahel Adaptive Social Protection program were targeted to women in low-income households to encourage and support them in expanding and diversifying microenterprises, which is a primary pathway for reducing poverty and food insecurity in this highly climate-affected region. The design of the program was multi-faceted, including various economic components such as regular cash transfers, savings groups, coaching, and business trainings. It also tested innovative psychosocial programming, such as media to role model success in the program, community-wide discussions to shift gender norms, and life skills trainings. Full details can be in Bossuroy et al. (2022) Supplementary Information.

The community sensitization was designed to introduce participating communities to the program. Both the film and discussion prompted a construal of women’s micro-entrepreneurship, a target outcome of the program, as a way to advance local values of social harmony, respect, and generosity. The 20-minute film modeled the story of a woman named Amina starting a new micro-enterprise and overcoming economic and interpersonal challenges as she strives toward

> 15 The selection process is detailed here: http://documents.worldbank.org/curated/en/387791524060631076/pdf/WPS8412.pdf.

33
||||0.876|0.899|0.022|
|Owns anylivestock||||0.882|0.895|0.013|
|Operates nonfarm enterprise|0.380|0.381|0.001|0.157|0.174|0.017|
|Anymember has financial account|0.773|0.799|0.026|0.305|0.370|0.065**|
|Income from remittance (int'l &<br>domestic)|0.215|0.213|-0.001|0.111|0.124|0.013|

†The results of an adjusted Wald test comparing weighted means are presented (*** p<0.01, ** p<0.05, * p<0.10).

# <mark>4.1.2 Sample representativeness</mark>

The Nigeria HFPS included a sample selection performed on the frame (GHS-Panel households with a phone number) to reach the phone survey sample (see Section 3.1.1 above for more details on the Nigeria sample selection). This is in contrast to Ethiopia, Malawi, and Uganda where the entire frame served as the phone survey sample in those countries. As a result of this additional sampling step in Nigeria, it is important to examine just how representative the sample selected is. Unlike the issues of coverage and response, no distortions in the representativeness of the sample are expected from this sampling selection, but it is nonetheless useful to confirm this is the case. Table 4.2 presents the mean characteristics for the full GHS-Panel sample, the frame (households with contact information), and the selected sample for the Nigeria HFPS (columns 1, 2, and 3, respectively). Column 4 also contains the difference between the mean characteristics observed in the sample and the frame (with significance 17