rafmacalaba/data-use-mentions-extended huggingface dataset:
We rank clinics at baseline and endline, using baseline data collected at clinics and from households residing in a catchment area around clinics. Key performance indicators include measures of utilization for ante-natal care, childbirth, and vaccinations; as well as measures related to users’ experiences, including absenteeism, staff attitude, and charging fees for services that should be free. Importantly, we do not reveal these indicators publicly to avoid having staff reallocate their effort toward these tasks at the expense of other important tasks.

Although staff do not know what indicators are used in the ranking, we also worry that competing clinics might manipulate their records to exaggerate their performance. To encourage truthful reporting, we inform all clinics (not just those in NFA) that we will audit their registers at baseline and endline and disqualify clinics with fraudulent entries. Each audit involved randomly selecting 30 patients from the clinic register (corresponding to 15 patients per study community) and then visiting these individuals to verify their recorded visit date and purpose. None of the audits uncovered ghost patients or manipulated entries in the clinic register.<sup>10</sup> In the NFA arm, the competition is announced and extensively advertised at the clinic. As in the CM intervention protocol, facilitators revisit clinics three additional times during the intervention to sustain interest in the competition. Awards are given to the best and the most-improved clinics in each district, with winners announced after the endline survey concludes. Winning clinics receive a plaque or wall clock to display inside the clinic at a public ceremony.

The awards are “non-financial” from the government’s perspective, as that they do not involve any monetary compensation. Yet, workers may associate winning with a longer-term financial payoff. For example, workers may have anticipated that being on staff at a wining clinic would facilitate promotions or transfers to attractive locations. We are agnostic about which element of the award, recognition or potential future gains, motivated workers; our aim is to test whether it had any discernible effects on health outcomes
Table 5: Nigeria- correlations between IPV and respondent characteristics across methods

||Q1: emoti|onal violence|Q2: physic|al violence|Q3: sexu|al violence|Method<br>from|difference<br>tests of di|s: P-values<br>fferences|
|---|---|---|---|---|---|---|---|---|---|
|Variable|(1)<br>List|(2)<br>F2F|(3)<br>List|(4)<br>F2F|(5)<br>List|(6)<br>F2F|(1)-(2) <br>Q1|(3)-(4)<br>Q2|(5)-(6)<br>Q3|
|Vulnerability index|0.02|-0.08***|0.09|-0.05***|0.03|-0.05**|0.14|0.05|0.25|
||(0.06)|(0.02)|(0.07)|(0.02)|(0.07)|(0.02)||||
|Unequal village gender norms index|0.06|0.08***|0.09*|0.05***|0.08*|0.05***|0.61|0.43|0.50|
||(0.04)|(0.02)|(0.05)|(0.02)|(0.04)|(0.02)||||
|Relationship quality index|-0.07|-
can be used to identify (observed) achievement vectors as poor or non-poor, i.e., determine whether u(z) > u(ˆxi).<sup>68</sup> Ideally, the proposed method selects the parameter values (w, k, z) that best approximate the inter-personal well-being comparisons following from the information on preference u. Formally, for the AF identification method, which relies on dichotomous status vectors si, these parameter values solve the problem where Bias quantifies the deviations from<sup>69</sup> In practice, some parameters value can be constrained by the data, which is typically the case for some cutoffs zj. An equivalent problem can be defined for PX identification when replacing status si by deprivation di.<sup>70</sup> It could be interesting to develop such method so as to derive the weights of a global multidimensional poverty indicator from subjective well-being data and then compare the bias associated to the derived weights with the bias associated to equal-weights.

The method outlined above readily extends to more general well-being indicators, which compare any two achievement vectors.<sup>71</sup> One natural way of selecting the parameters defining the individual well-being indicator uˆ is to select them in a way that maximizes the correlation between uˆ(ˆxi) and u(ˆxi), where u stands for the information on preference u (see footnote 68).

thresholds z<sup>e</sup> j<sup>.</sup> 68 For instance, preference u can be assumed to be the preference that is most in line with the subjective well-being data, as is done in Decancq et al. (2019), or with the preference elicitation survey data, as is done in Decancq and Nys (2021).

69 Examples of how to quantify “Bias” are proposed at the end of Section 3.2.

> 70Observe that both AF and PX methods are linear identification methods. This linearity constrains the
decision on whether a question was coded by promotion or prevention was made by the first author, who did not have access to the startup’s founder gender when making these decisions. We constructed a binary measure at the investor-dyad level to measure the incidence of a prevention focus, and used the same type of binary incidence measure for a promotion focus. Simply put, if an investor question to a startup did not have a promotion nor prevention focus, both variables would equal zero. If an investor question to a startup included both a promotion focus and a prevention focus, both variables would equal one.<sup>7</sup>

**5.5. Results.** We analyzed 1,341 decisions taken by 278 investors – combining 198 professional investors and 80 trainee investors – on 87 startups. As shown in Table 2, we assessed differences across all investor characteristics across treatment and control groups. As expected, given our randomized treatment assignment, here were no significant observable differences – using raw numbers, percentages or the p-value taken when regressing each characteristic on treatment.<sup>8</sup> As a result, any differences in evaluation practices between treatment and control groups are likely to be caused by our randomized treatments – not by the types of investors in treatment and control groups.

Investors in the control group scored startups with female founders significantly lower than those with all male teams. Startups with female founders received an average score of 3.7 out of 6, while startups with all male teams received 4.1. When including startup controls, investors in the control group gave startups with female founders significantly lower scores than startups with all male

> 7 Following Kanze et al. (2018), we also used a computerized method on English responses, utilizing a dictionary of 27 promotion and 25 prevention words developed and validated by Gamache et al. (2015), and uploaded these dictionaries into Linguistic Inquiry and Word Count (LIWC) software to determine their frequencies. Similarly
26 falls, (Humphrey (1984) p241)<sup>12</sup> .

We noted from the WFS data that for large numbers of women in the lower educational subgroups in Asia and Africa more maternal education increased the tempo of child bearing in the earlier years. This results from the conjucture of shorter post-partum infecundability and the earlier termination of child bearing consequent upon contraceptive use. This is important for the relationships between fertility and nutrition explored below.

# 3.2 Education and Maternal and Child Well-being

We now examine in what ways maternal education can affect the welfare of the child and its mother after birth.

As far as survivorship is concerned, there is overwhelmingly strong evidence that maternal education is almost everywhere a major determinant, if not the major determinant, of infant and child mortality in any given community (Mosley (1985), Caldwell (1979), UNICEF (1984), Preston (1978a), DaVanzo and Habicht (1986), Ware (1984), Taha (1977), Jayachandram and Jarvis (1986), Beenstock and Sturdy (1986)). The purpose of our framework is to deconstruct and categorise this association into four routes by which education may be acting on welfare.

12The association between greater female employment and reduced fertility is not always causal. In Puerto Rico, for example, both were contemporaneously induced by distinct public policies, (Pearson (1986b) p.77).
there are a few items in the adaptive test where the fit is poor (for instance, items 62, 96, and 107 in Urdu). Re-estimating the model after eliminating these poorly fitting items does not alter the overall estimated score.

One concern with the LEAPS assessment is that it may not adequately reflect functional literacy and numeracy as documented by Banerjee et al. (2022). Therefore, we designed a separate assessment to capture respondents’ proficiency in everyday arithmetic and literacy skills. The math questions in this assessment of functional numeracy asked respondents to read an electricity bill, compute the correct amount given arrears (easier) and then recompute the right amount given electricity consumption and non-linear pricing (harder). Another math question assessed competency in a marketplace transaction where respondents purchased multiple items and collected change. To assess functional literacy skills, we asked respondents to read several messages written in Urdu and Roman Urdu (Urdu but using roman language script). We also assessed whether the individual knew how to use a phone by asking them to save a contact on a phone. Appendix C.3 details these items. We argue that someone who can complete these tasks can be considered “functionally” literate; accordingly, we use principalcomponent factor analysis to aggregate these questions into a single “functional literacy and numeracy” index.

# **II.2. SOCIOEMOTIONAL SKILLS**

Measuring socioemotional (SEM) skills in low-income countries has proven difficult, with evidence of non-classical measurement error for self-reported instruments (Laajaj and Macours, 2021). Consequently, we developed the instruments included in the Pakistan survey through an iterative process. We started with data collection in Cambodia for a related project in 2017 (we will present comparative results from that study in Section III.2.2). As in Pakistan, the sample in Cambodia also consisted of young adults from rural regions. The data collection incorporated a comprehensive assessment of socioemotional skills. However, despite our efforts to mitigate the issues later discussed by Laajaj and Macours (2021
many people with refugees status have left their home country each year. We focus on these numbers as they appear to be the most comparable across time and countries. However, this is likely to capture only the tip of the iceberg in some cases. The number of IDPs is extremely high in some instances but cannot be captured with the same level of confidence as refugees generally.<sup>23</sup> Cross-country data about conflict is provided by the UCDP/PRIO. As for the index of country-level economic activity, we use again information provided by the Penn World Table and World Bank databases. As mentioned above, our aim is to explore the dynamics of refugees during conflicts. In other words, we attempt to answer several questions. Do people run away from their home country when a conflict breaks out? Does the seriousness of the conflict matter in this decision? In which phase of the conflict do they leave? When do refugees come back to their home country?

In order to answer these questions, we look at the impact of conflict incidence on

> 21We will return to these issues in the policy section.

> 22According to the UNHCR definition, this category includes “individuals recognized under the 1951 Convention relating to the Status of Refugees; its 1967 Protocol; the 1969 OAU Convention Governing the Specific Aspects of Refugee Problems in Africa; those recognized in accordance with the UNHCR Statute; individuals granted complementary forms of protection; or those enjoying temporary protection; and people in a refugee-like situation”.

> 23The UNHCR Global Trends Report 2014 provides evidence that confirms this hypothesis. About 59.5 million people were forcibly displaced worldwide by the end of year 2014. Among them, 19.5 million were refugees and 38.2 million were IDPs.

28
.058|0.058|0.060|
||(0.1049)|(0.1048)|(0.106)|(0.106)|(0.106)|(0.106)|(0.105)|(0.105)|
|Const|-0.8822***|-1.0625***|-0.897***|-1.025***|-0.926***|-1.052***|-0.912***|-1.085***|
||(0.2274)|(0.3754)|(0.221)|(0.396)|(0.218)|(0.378)|(0.215)|(0.365)|
|N. Obs|9,802|9,802|9,802|9,802|9,802|9,802|9,802|9,802|
|R2|0.1480|0.1499|0.148|0.151|0.149|0.151|0.148|0.150|
|CY FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|IQis|||Control of|Corruption|Rule o|f Law|Government|Effectiveness|

*** p<0.01; ** p<0.05; * p<0.1

31
**<mark>Table 1 Share of employed population working NFEs (non-wage only)</mark>**

|_Country_|Burkina Faso|Ghana|Mozambique|Rwanda|Tanzania|Uganda|Weighted<br>Average|
|---|---|---|---|---|---|---|---|
|Primary employment|11.8|25.9|9.2|9.3|15.7|13.5|16.8|
|Primaryor secondaryemployment|28.8|37.5|20.1|21.4|28.9|21.0|27.6|

_Source: Annex table 1._ In sum, the HE sector in low income countries in SSA is likely to provide employment, either as a primary or secondary activity, to a substantial share of the labor force for the foreseeable future. There is no way to change this picture in the medium term. Instead of expecting this segment to disappear (or even actively discouraging it), policymakers and development professions will need to seek job creation solutions through improvements in the opportunities offered in this sector. In the following sections, we analyze the characteristics of this sector in more depth, focusing on owners of HEs and their households. Individuals for whom HE is either a primary or a secondary activity are included as owners, and when we analyze household livelihoods and incomes in section 5 we also include all economic activities of all members to obtain a full indication of the importance of HEs in household income generation.

# **4. Household enterprises and their owners**

A full understanding of how to promote employment and income growth through the development of HEs requires several perspectives – an understanding of HEs as business, and an understanding of HE ownership as an employment choice. This section presents our analysis on HEs from both perspectives using the survey data.

## **HE
**TABLE 1. DISTRIBUTION OF WORKERS’** **<u>CHARACTERISTICS</u>**

|Gender||Training||
|---|---|---|---|
|Women<br>Men|27.2%<br>72.8%|None<br>In-house training|41.1%<br>39.8%|
|Education||External formal training|19.1%|
|Lower secondary or less|71.2%|Belongingto a union||
|Upper secondary|18.6%|No|57.8%|
|Universityor more|10.2%|Yes|42.2%|
|Experience in the firm||Type of Contract||
|Three years or less|47.6%|Temporary|10.7%|
|From 3 to 10 years|37.5%|Permanent|89.3%|
|More than 10years|14.9%|||

Source: Calculations based on ENESTYC 2001.

According to the survey, we estimate that the majority of employees receive some form of training, though most was internal. The share of employees receiving formal inhouse-sponsored training was 40 percent, while the number not receiving any training was roughly the same at 41 percent, with only one-fifth of employees receiving external formal training. The differences in education and training (both in-house and external) are very small between women and men in Mexico. In-house training was 40 percent for men and 36 percent for women, while external training was 20 and 16 percent, respectively.

Less than half of these employees, 42 percent, were unionized. Also, 89 percent were employed via permanent forms of contracts.

The characteristics of a firm used in this analysis are size, division of activity, region, whether the foreign capital of the firm exceeds one-half, if the firm exports over 50 percent of its output
Policy Research Working Paper 7763

# **Abstract**

This study combines high-resolution, geo-spatial data and household data from the Vietnam Living Standard Measurement Surveys in 2010, 2012, and 2014 to investigate the relationship between environmental risks and poverty. Using recently developed data on air pollution, tree cover loss, land degradation, slope, rainfall and temperature variability, and flood and drought hazards, the study shows: (i) at the district level, there are hotspots of high poverty and environmental risks; (ii) ethnic minorities and poor households are much more exposed to multiple environmental risks than other groups, and also within rural and urban areas poorer households live in communes exposed to higher environmental risks; and (iii) environmental risks relate to lower consumption levels, but less so to lower consumption growth over time. Altogether these findings suggest that Vietnam’s poor are disproportionally exposed to environmental risks, which can result in livelihood impacts that in many ways go beyond consumption. In light of growing pressures due to population growth, economic development and climate change, green growth actions, ecosystem-based adaptation, and land-use planning could be important strategies to reduce the environmental burden on poor people.

This paper is a product of the World Bank Environment and Natural Resources Global Practice Group and the Climate Change Cross-Cutting Solutions Area and is a background paper for the World Bank work on “Climate Change and Poverty in Vietnam.” 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 unarloch@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
, 652–667.

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17
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- FAO; The World Bank; UN-Habitat. (2019). Measuring Individuals’ Rights to Land: An Integrated Approach to Data Collection for SDG Indicators 1.4.2 and 5.a.1. Washington, DC: World Bank. © FAO, The World Bank, and UN-Habitat. License: CC BY-NC-SA 3.0 IGO

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account (direct transactions) for the bank or mobile account and bank or mobile payroll treatment groups. Figure 5, panel (b) reports the share of workers who make at least one sendmoney transaction that is intermediated by a mobile money agent for the bank or mobile account and bank or mobile payroll treatment groups, respectively. The figure illustrates that, at endline, workers in the payroll account treatment condition are significantly more likely to make direct transactions. They are 42 percentage points more likely to make send-money transactions through their own account than workers in the account only group (p-value _<_ 0.001) and that, at the same time, they are 32 percentage points less likely to make transactions that are intermediated by a mobile money agent (p-value _<_ 0.001).

Table 4, panel B reports treatment effect estimates on direct versus intermediated transactions. For this exercise, we use survey data, which –unlike administrative data– also allows us to observe changes in _intermediated_ transactions that are made through a channel other than the respondent’s own account. The results confirm that workers in the payroll account treatment condition reduce the share of transactions intermediated by an agent and learn how to transact through their own account. The results in columns (5) and (6) confirm that, at endline, study participants in the payroll

> 39We report results using administrative data in the appendix for comparison and confirm that the results on direct transactions are consistent with estimates using self-reported data.

18
75<sup>th</sup> percentile level. We achieve national level representation by multiplying the resulting normalized weights with the rural and urban population populations of each state. The population estimates are obtained from the NFHS-2015 for 2015 and 2016 rounds; and from the PLFS 2017 to 2019 for the remaining periods. Finally, the household level weights are reconstructed by dividing the adjusted individual level weights by the household size observed in the survey.

17 We use the following set of target indicators for reweighting at the first step: dummy variables for ownership of air conditioners, cars, computers, refrigerators, television sets, two-wheelers, washing machine; dummies for household sizes 1 and 2, sizes 3 and 5; dummy variables for hindu, muslim, scheduled caste, schedule tribe, other backward classes households; total number of members less than 10 years old, over 60 years old; and, total members with below primary level of education, primary level and secondary level of education.

18 We use the following set of target indicators for reweighting at the second step: dummy variables for female headed household; scheduled caste, scheduled tribe and other backward classes households; dummy variables for household sizes 1 to 5; total members working in casual, salaried and self-employed jobs; total number of members less than 10 years old, over 60 years old; and, total members with below primary level of education, primary level of education and secondary level of education.

12