rafmacalaba/data-use-mentions-extended huggingface dataset:
house. In other cases, women may not have the financial autonomy to afford the cost of travel or emotional autonomy to move alone. A 62-year old woman from Centro depended on her father to take her because she was not familiar with public transportation or with the city center.

# 3.4 **Facilitators in the search for formal support**

The following section presents a set of facilitators identified in the qualitative data that support women living in situations of violence to overcome personal and external barriers and seek help. It will explore how both informal institutions – family, friends and religious institutions – and formal institutions facilitate women’s help-seeking behaviors in Brazil within the context of this research.

3.4.1 Support from family, friends and community – Informal institutions Data from Brazil points to an increasing mobilization of society to support the victims of aggression. DataSenado’s 2017 national survey on domestic and family violence against women found that 90 percent of those interviewed would report a witnessed act of aggression against a woman. Recent research from Research Institute Avon (2016)<sup>21</sup> explores the role of men in the deconstructing of “ _machismo_ ,” finds that the majority of respondents recognize and reject inequality between men and women, 79 percent believe that machismo is something negative in Brazilian society, and 84 percent believe that all should fight for a world that is less “ _machista._ ” Importantly, the research also finds that 34 percent of men recently ceased to practice some sexist behavior.<sup>22</sup> While the study does not identify the drivers of attitude and behavioral change relative to VAW in Brazil, a growing body of global research has documented and analyzed the effectiveness of prevention programs targeting men, women and communities to reduce VAW in the midto long-term (see Alexander-Scott, Banjeree et al. 2017, Bell and Holden 2016, Cares et al. 2014, Ellsberg et al. 2015, Jewkes et al. 2015, Promundo 2012, UN Women 2015b
# **6 Analysis of Potential Constraints to Growth**

**Recent enterprise survey data**<sup>**28**</sup> **for Togo offers an important source of information about the main business constraints faced by firms in Lomé, as declared by interviewed firm representatives**<sup>29</sup> . However, as discussed in Section 2, perception data has a number of caveats and limitations so the reliability and interpretation of each of these will be discussed below, when combined with other types of data.

**Figure 23 suggests that Political Instability, Access to Finance, Corruption, and Informal practices of competitors, are the main constraints to businesses in Togo.** Other constraints often mentioned are Trade and customs, Electricity and Tax rates and administration. Interestingly, potential problems such as Crime/Theft/Disorder, Court, Labor regulations and Labor Skills are more or less never mentioned as major or severe constraint. Even Access to land, Business licensing and permits, and Transport are mentioned by a surprisingly low number of firms, but this result will be further discussed below. When breaking down the ICA result by size of firms (Figure 24) we see that micro and small firms see political instability as the main constraint, while medium firms mostly complain about access to finance and large firms about political instability.

**Figure 23: Main Constraints to Business (ICA), Togo, 2009. Percent of Firms perceiving a category as the main constraint.**

<!-- Start of picture text --> 30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br><!-- End of picture text --> _Source_ : ICA data, World Bank.

**Figure 24: Main Constraints to Business (ICA), by Size of Firms, Togo 2009. Percent of Firms perceiving
# _Implications for Poverty Analysis_

The fact that both mean consumption and distributional measures vary substantially across the modules implies that there will be complex comparability problems when poverty estimates from different modes of consumption measurement are combined. For example, consider a poverty monitoring survey that switched from 14-day to 7-day recall. This switch would raise reported mean consumption (see Table 3) and lower measured inequality (see Table 7). In a setting where the poverty line is set below median consumption, these effects will work in concert to make poverty look lower than it would if the method of consumption measurement had not changed (as occurred with changes in India’s NSS). Conversely, if a switch was made from a disaggregated recall list to a collapsed list (as the Indonesian _Susenas_ does every 3<sup>rd</sup> year), it would lower the mean (implying higher measured poverty) and lower inequality (lower measured poverty) with ambiguous implications for poverty measures. Hence there are unlikely to be simple correction factors that can be used to enforce comparability on different consumption modules when attempting to measure poverty consistently over either time or space. Similarly, the literature that reacts to possible survey understatement by combining survey measures of inequality with national accounts measures of mean consumption (such as Bhalla, 2000, and Sala-i-Martin and Pinkovskiy, 2010) is likely to be misguided since survey design changes will also affect estimated inequality.

We attempt to quantify the implications of survey design for poverty analysis in a setting like Tanzania by presenting aggregate poverty numbers based on an international poverty line, as well as the characteristics of those households deemed poor by each module. Figure 1a presents the poverty headcount rate based on a common international poverty line of $1.25/person/day converted into 2008 Tanzanian shillings on a PPP basis.<sup>25</sup> With this absolute poverty line fixed at the same value regardless of module, we see dramatically different headcount rates depending on how consumption is measured. The benchmark personal
approximately 0.95. The second statistic is the total width of the confidence interval, which is an indicator of the extent to which estimated uncertainty varies across estimation methods.

Coverage rates for the four estimators vary quite a bit depending on the context and the method. On average, BRF is associated with the highest coverage rates, averaging 88.4 percent for assets and 88.8 percent for poverty. This is partly because BRF is less accurate than the other ML methods on average, leading to wider confidence intervals due to the use of a residual bootstrap. For assets, XGBoost has the second-highest coverage rates at 87.2 percent, followed by Cubist at 84.7 percent. For poverty, the ranking is the same, as XGBoost has a colverage rate of 85 percent with Cubist following at 82.3 percent. Coverage rates vary by context, however. Uncertainty estimates when predicting assets in Tanzania, for example, appear to be systematically underestimated for all estimators, especially for EBP at 39.1 percent. Figure A2 in the Appendix shows that part of the reason for this may be a systematic underestimation of poverty across the entire distribution, leading to decent correlation but poor coverage rates. We also note that the Tanzania census is from 2012, while some of the geospatial features such as land classification are from 2019. As such, we hesitate to put too much weight on the results from Tanzania. The coverage rate for Sri Lanka is also low for EBP (68.4 percent). We encourage further research to better understand why coverage rates are 22
was how to achieve multiple goals with one sampling design. As mentioned above, the objective of the HIES 2016/17 is three-fold: (i) producing reliable annual poverty and welfare statistics at the district level; (ii) producing reliable quarterly poverty and welfare statistics at the national level; and (iii) producing reliable

> 1 In addition to collecting detailed household consumption and income data, the HIES collects detailed information on household demographics, education, health, livelihoods and economic activities, quality of housing, asset ownership, migration, social safety-nets, micro-credit, access to services, and household shocks and coping strategies.

> 2 Divisions are the higher level of geographical divisions in Bangladesh. As of 2010, there were a total of seven divisions in the country, including Dhaka, Chittagong, Barisal, Khulna, Sylhet, Rangpur, and Rajshahi.

3
materials. The shortage of foreign exchange tends also to fuel the contraband trade through Somaliland because the birr is an unofficial currency there and can more easily be used to purchase products from around the world.

# 3.5. Underdeveloped and fragile financial system

Ethiopia has one of the least developed financial systems in the world. In the 2018 Global Competitiveness Report, Ethiopia ranked 126 of 140 countries in terms of financial system development.<sup>76</sup> With total assets of 46 percent of GDP, Ethiopia’s financial system is shallow and does not serve well the needs of a transforming economy. Access to finance is identified as the main obstacle to business by 40 percent of firms.<sup>77</sup> While the share of adults having a bank account has increased significantly in recent years, it remains at a low 35 percent in 2017 (as compared to 43 percent on average in Sub‐Saharan Africa). The number of bank branches per capita is low and branches are concentrated in major towns and cities.<sup>78</sup> Nearly one‐third of branches are located in Addis Ababa. The rural poor have little access to formal banking instruments and rely on microfinance institutions (MFIs), informal financing like saving and credit associations, moneylenders and friends. In the absence of formal banking, MFIs have developed and operate a network of around 1,900 branches in September 2018. Some of the biggest MFIs outperform most of the banks in the country in terms of branch network and capital.<sup>79</sup>

> 75 U.S. Department of State, Bureau of Economic and Business Affairs, Investment Climate Statements for 2018.

> 76 World Economic Forum (2018). Global Competitiveness Report 2018.

> 77 World Bank Enterprise Survey (2015).

> 78 Gashayie, A. and M. Singh (2016). “Development of Financial Sector in Ethiopia: Literature Review”, Journal of Economics and Sustainable Development
HBS.

> 9 Adult Equivalent: children aged below the age of six have been assigned a weight of 0.2, children aged between 7 to 12 have been assigned a weight of 0.3, children age 13 to 17 have been assigned a weight of 0.5, and a weight of 1.0 if the household member is older than 17 years. The equivalence scale is designed to account for differences in ‘need’ due to age and sex. It does not however account for economies of scale of household size as each adult carries the same weight on 1.0. The World Bank's 2003 Poverty Profile for Azerbaijan undertaken within the Programmatic Poverty Assessment, proposes a different per adult-equivalent consumption measure that assumes a scale parameter of 0.8 and that the cost of a child (individuals of age 18 and below) is 70% of the cost of an adult. This study was completed earlier and therefore did not use this definition.

36
’ education. This systematic review complements the existing literature in two ways: first, it incorporates a wide range of general education interventions that have not been included in previous studies of what works to improve girls’ learning and access to education; second, it converts the point estimates of all included studies into standardized effect sizes to allow easier comparison across studies.

# 3. Method

We gathered a large collection of studies that report education outcomes, either access or learning. For each of the studies, we identified whether or not they separately report impacts for boys and girls. For studies that separately report impacts for boys and girls, we extract those data, standardize the estimates, and use them to compare the impacts for boys versus girls and across programs for girls. For studies that do not separately report, we contacted the authors and asked them either to share the data or to provide the separate estimates themselves. In this section, we report on each step in detail.

## Literature search

We began with a comprehensive database of education impact evaluations compiled for Evans and Popova (2016) and subsequently updated it. The database consists of 495 studies that were

> 4 Vote counting consists of categorizing the results of studies by their results: negative and statistically significant, negative and statistically insignificant, positive and statistically insignificant, and positive and significant. Evans and Popova (2016)  includes a discussion of different types of systematic reviews and the respective advantages and disadvantages.

5
|11.8|14.3|14.6|
|Expenditure Poverty||||||||
|2001|Urban|-- -|0.3|2.5|7.6|16.2|24.3|
||Rural|0.2|2.3|10.1|25.1|40.9|55.6|
||Total|0.09|1.2|5.8|15.3|27.1|38.1|
|2007|Urban|-- -|0.07|1.2|4.8|9.9|17.5|
||Rural|0.04|1.2|5.7|14.4|25.9|38|
||Total|0.02|0.6|3.2|8.9|16.8|26.4|
|Difference|Urban|-- -|0.2|1.3|2.8|6.3|6.8|
||Rural|0.18|1.1|6.9|10.7|15|29.2|
||Total|0.07|0.6|2.6|6.4|10.3|11.7|

Source: NSLS 2006/07. Totals are simple weighted averages of the urban and rural statistics.

37
As noted above, the Federal Reserve capped dividends and banned stock buybacks by large banks from June until the end of the year, which may have helped keep bank capital ratios from falling below healthy levels. Similar to the full sample, the mean liquidity ratio increased after the start of the COVID-19 crisis from 47.71 to 55.13 percent and the minimum rose from 28.03 to 39.14 percent, indicating no liquidity problems.

# **4.1.2. Bank Credit Performance**

We scrutinize several statistics to evaluate bank credit performance during the crisis. We examine credits that are recognized as current losses, represented by charge-offs; nonperforming loans with payment delays that may turn into credit losses, represented by the past due of at least 90 days plus nonaccrual ratio; and whether banks have enough funds set aside for expected losses, measured by loan and lease loss allowances. To the extent that current and impending losses are high and there are insufficient allowances for them, the industry could be in trouble. We again note that charge-offs and nonperforming loans could be biased downward for some institutions during the crisis due to forbearance rules on some bank debt.

Examining the credit performance data for the industry, mean charge-offs remain low throughout the time interval, and are actually lower after the COVID-19 crisis begins than their local peak of 0.14 percent in 2019:Q4. There is a minor upward movement in nonperforming loans from 0.61 percent in 2019:Q4 to 0.65 percent in 2020:Q1, but the ratio fell from there to 0.54 percent in 2020:Q4. We acknowledge that these data means may obscure the view of some banks with more severe credit performance problems – researchers find that nonperforming loans problems are more concentrated where banks more exposed to the pandemic and lockdown policies (e.g., Beck and Keil, 2021). Switching to the more adverse values of these performance issues, however, does not raise alarm bells. The P90 for charge-off
suggests that this assumption may not always hold, since the tasks of each occupation tend to change with economic development (Caunedo et al., 2023; Lewandowski et al., 2019; Lo Bello et al., 2019).<sup>25</sup> This paper tries to mitigate this issue in Section 4.2 by adjusting these exposure measures with measures of computer use at work.

> 24 Argentina‘s labor force survey covers only larger cities, which is why the exposure to GenAI is significantly larger than for other countries (44%). It was removed from the plot to avoid confusion. Data for Grenada, Guyana, Montserrat, and Suriname are based on ILO micro data, all other countries based on SEDLAC. Total employment for Grenada and Montserrat based on 2020 surveys, for all other countries based on ILO modelled estimates for 2023. 25 However, since the patterns of task specialization across countries differ along several dimensions, it is difficult to assess whether our method tends to under- or over-estimate the exposure to GenAI in the developing world. For example, Caunedo et al (2023) find that occupations tend, on average, to be more intensive in non-routine interpersonal tasks in richer countries. If such tasks are less prone to be automated by GenAI, then our method would be underestimating the exposure to GenAI in developing countries relative to developed ones. On the other hand, occupations tend to be more intensive in non-routine manual tasks in poorer countries. If such tasks are less likely to be automated by GenAI, this would imply that our method would over-estimate the exposure to GenAI in developing countries relative to developed ones.

18
|19.1%|
|Mexico|100%|53.8%|6.4%|39.9%|100%|54.0%|6.7%|39.2%|
|Panama|100%|50.6%|12.6%|36.8%|100%|49.8%|13.2%|37.0%|
|Paraguay|100%|43.4%|6.8%|49.8%|100%|43.7%|7.5%|48.8%|
|Peru|100%|59.9%|9.7%|30.4%|100%|59.9%|9.5%|30.6%|
|Uruguay|100%|53.5%|3.4%|43.1%|100%|53.8%|3.5%|42.7%|
|Total region|100%|59.4%|7.9%|32.7%|100%|59.4%|8.0%|32.6%|

Note: Percentages of individuals. Source: Own elaboration based on household survey data, CCKP historical data, ThinkHazard! data, EM-DAT occurrences, and SPEI.

21
test this assumption, and we provide empirical evidence to show that the assumption holds.</mark> Malawi is not an exception when it comes to grain market controls. Historically, developing countries, including Malawi’s neighbors (Zambia and Tanzania), have intervened in grain markets whenever they experience significant food inflation or face a server production deficit for their major staples. In the wake of the 2007-08 food price spike, these countries renewed their efforts to intervene and regulate agricultural markets with the objectives of ensuring domestic food security and price stability. Such measures included a slew of trade of controls such as temporary restrictions on exports of food grains and other staples (Sharma, 2011) as well as active interventions in the grain markets through price regulation, direct purchases, storage, and other discretionary policies (Chapato and Jayne, 2009). Maize has been the focus of these policies within SSA, given that maize is of strategic importance for both producers and consumers in these countries. These policies generally aim to maintain affordable prices for consumers of maize, and simultaneously also attempt to ensure viable prices for maize farmers and seek to reduce the volatility of prices. Given the multitude of objectives, governments often engage in a mix of policies, and measuring the net impacts of such policy measures on prices and welfare can be challenging, both theoretically and empirically. An additional empirical challenge arises due to the simultaneous existence of restrictive trade regimes in neighboring countries.

<mark>The data and the empirical strategy that we use in this study allows us to overcome these challenges and isolate the short-run effects of the trade policies on maize prices and, subsequently, the welfare implications for maize producers and consumers in Malawi. We assemble a panel dataset of monthly market prices from 152 markets in Malawi and neighboring Tanzania, Mozambique, and Zambia from 2006 to 2018. We</mark> 2
date, only a handful of approaches have been proposed to measure refugee self-reliance, each of which comes with its own limitations. These measures are either at odds with the concept of self-reliance – in that refugees who do depend on humanitarian assistance can be considered self-reliant – or where a proposed measure is consistent, the data required to measure it is not (yet) available (e.g. the measure proposed by Hoogeveen and Hopper, 2024).

In this paper, the aim is to measure self-reliance using a simple one-dimensional indicator: income. Here, a refugee is considered self-reliant if their self-earned income exceeds the poverty line relevant to their context. It is thus not sufficient for refugees to survive on humanitarian assistance; self-reliance instead requires that refugees have the ability to be independent of assistance and generate the income necessary to attain a – locally relevant – acceptable minimum standard of living.

This approach has several advantages. Practically, it anchors refugee self-reliance within existing poverty measurement, including the monitoring of SDG1.1 (elimination of extreme poverty). Empirically, it allows one to explore the determinants of refugee self-reliance through a quantifiable and widely understood metric: income generation. Theoretically, it offers an appropriate measure with which to conceptualize refugee needs, capturing both income and, at least partially, non-income factors in its measurement, as this paper will demonstrate, using estimates of self-reliance drawn from 11 consumption surveys, which present the universe of data that can be used for such purposes. As far as we can tell, this paper is the first to present a set of global descriptives on refugee self-reliance.

The remainder of the paper is organized as follows. The next section reviews the literature on, and current measurements of, self-reliance. Section 3 outlines the paper’s proposed measure self-reliance and 2
to zero. Thus, given the effectiveness of the interventions (for this outcome), there is no space for complementarities. Interestingly, for physical abuse in the last year, where neither intervention has an effect as large as the control mean, the interaction term is very close to zero (although none of the coefficients are statistically significant). There is also no evidence of complementarities in Tables 4 and 6.

# **7 Attrition**

Tables 7 and 8 presents analysis of sample attrition using baseline data for the females and males, respectively, to confirm that attrition does not vary by treatment status or baseline characteristics in a way that may be driving our results. The outcome in all panels is an indicator equal to 1 if the female or male left the sample (attrited).

In both tables, Panel A shows estimates from a regression of attrited on treatment indicators, including the _Goal_ interaction with each clustered treatment arm. For both females and males, there is no evidence that there is differential attrition by treatment assignment. In Panel B, we further test for differential attrition across treatment arms by demographic characteristics. These estimations also show little evidence of differential attrition. We provide a more detailed breakdown of the outcome means at baseline according to attrition status in appendix figures A2 and A3, which broadly show no evidence of differential attrition, aside from evidence that goal setting participants who experienced physical abuse at baseline are less likely to attrit. If anything, this would imply positive bias in our estimate of the impact of goal setting on physical abuse, which would bias us away from finding the negative impact of goal setting on physical abuse that we see in Table 2.

# **8**

We now present evidence on the cost-effectiveness of our interventions. The per-female cost of the _Boys_ treatment is $41 and the per-female cost of the _Goal_ treatment is $38. To ease comparison across treatments and studies, we also normalize the cost of each treatment in terms of a 0.25 standard deviation