rafmacalaba/data-use-mentions-extended huggingface dataset:
components subject to congestion costs (population for health capital, output itself for infrastructure, and the stock of raw labor for education). This disaggregation is consistent with the evidence suggesting that inadequate public capital is among the main variables that account for the lack of per capita growth experienced by many low-income countries in sub-Saharan Africa. Fifth, constraints on absorptive capacity are accounted for by introducing a nonlinearity in the relationship between nonfood aid and total public investment, with foreign assistance being positively related to public capital outlays up to a certain level of aid, and negatively related thereafter. Thus, depending on parameter estimates, aid may entail significant diminishing returns. Although it does not explicitly account for all the factors (institutional or otherwise) that may constrain the ability of a country to absorb greater aid flows, this specification is tractable and quite intuitive.<sup>17</sup> Sixth, poverty effects are calculated in two ways: first, by linking the model to a household survey, and second, by using partial growth elasticities. The link with a household survey follows the approach first proposed by Agénor, Izquierdo, and Fofack (2003) and further developed by Agénor, Chen, and Grimm (2004). The growth rate of nominal consumption per capita is applied uniformly to each household in the survey, whereas the poverty line is updated with the price projections of the model. Thus, implicitly, growth is taken to be distribution neutral. The use of partial growth elasticities, by contrast, allows the user to assume that growth is non neutral, by using values that are either greater or lower than unity. Finally, the model is dynamic, and this allows the user to analyze the potential dynamic trade-offs that adjustment policies may entail regarding the impact of policy reforms, for instance between the short-run impact of higher public spending on education and health (on the budget, aggregate demand, and the real exchange rate) and the long-run effects on the productivity and supply of educated labor, and thus on long-run
39 involving a three-year panel/cohort study among more than 4500 young respondents and at least three rounds of data collection--in 2003, 2004, and 2005(Solorzano, Peña et al. 2004). This study will measure the program’s success in changing attitudes and behavior around gender, stigma, HIV/AIDS and GBV.

# **2.3.6  Policy recommendations**

It is clear that the health sector can play a key role in the identification and support of survivors of violence. Based on existing evidence, the following approaches are recommended for strengthening the capacity of health providers to address GBV: _Integrate violence issues horizontally into health care services, especialy sexual and reproductive health services._ The accumulated experience in the region has shown that GBV cannot be addressed as a vertical stand-alone program. Vertical programs are likely to reach only a fraction of women who are in need of support. In contrast, horizontal integration of violence into primary care services permits the expansion of basic support for abused women and children throughout the health sector; moreover, because of the serious impact of violence on women’s sexual and reproductive health, it is particularly important that providers of services for pre-natal care, family planning, sexually transmitted infections and other reproductive health services routinely ask women about violence and be prepared to address the specific needs of abused women. The ongoing processes of health sector reform throughout the region provide an excellent opportunity for integration of services for GBV into health services more generally.

_Use a systems approach for institutional change._ Numerous studies have demonstrated that training of health providers to address GBV has no appreciable effect on provider behavior unless it is accompanied by changes in procedures and systems. Even in institutions where successful screening programs have been implemented, gains are quickly lost in the absence of continuous followup and supervision. Institutional change must include implementation of new procedures with regard to patient flow, documentation, measures to ensure privacy and confidentiality, and the creation of referral networks. Although norms and protocols cannot
.6|–3.62|–1.45|0.19|–26.6*|–5.37|–7.95|–2.91|
||(7.21)|(3.81)|(3.38)|(2.90)|(15.7)|(10.4)|(7.21)|(5.23)|
|Mean of dep. var.|0.25|0.25|0.11|0.12|0.42|0.30|0.18|0.18|
|No. of municipalities|153|153|153|153|153|153|153|153|

_Source:_ Data for the study comes from field reports of the Armed Forces of the Philippines (AFP) and election returns from the Philippines Commission on Elections (COMELEC).

_Note:_ The running variable of the RD design is the incumbent margin of victory. All regressions are weighted by a triangular kernel with a bandwidth of 0.05 and include the same control variables as in Table 3. Reported values are marginal effects, calculated at the sample means. The dependent variables in columns marked “AFP” are based on incidents initiated by the Armed Forces of the Philippines. The dependent variables in columns marked “Rebel” are based on incidents initiated by insurgent groups. Standard errors are in parentheses. *, ** and *** denote statistical significance at the 10%, 5% and 1% levels.

32
|Parameter|Data source|
|---|---|
|No impediments to natural flow upstream|FAO Africa dams; Meridian Global Dam Database<br>(2006);andpowerplants - CARMA(www.carma.org)|
|Gauging station data: location, discharge, and<br>year(minimum of 4 in between 1988-2009)|Global Runoff Data Centre|
|Greater than 100 Km from major water bodies|Global Lakes and Wetlands Database(2004)|
|Sufficient amount of rain for detection|SSM/I|
|International River Basin|Transboundary Freshwater Dispute Database (TFDD,<br>2008)|
|Catchment area upstream of gauging station is<br>as large as possible to provide many<br>observations and degrees of freedom for the<br>model<sup>12</sup>|15 second accumulation and flow direction grids<br>(HydroSHEDS, 2006)|

**Table 1 : Selection of river basins criteria and catchment upstream of gauging station data source.**

<!-- Start of picture text --> Mekong Zambezi<br>Length (km) 4,350 2,574<br>Area (km 2 ) 787,836 1,390,000<br>WB Region EAP SSA<br>Population<br>71 21<br>density / km 2<br>Population 13 55,800,000 28,800,000<br>Treaty with<br>water Yes Yes<br>quantity<br>River basin Zambezi<br>Mekong River<br>organization River<br>Comission<br>Authority<br>Riparians Zambia,<br>Angola,<br>China, Burma,<br>Namibia,<br>Thailand,<br>Botswana,<br>Laos,<br>Zambia,<br>Cambodia,<br>Zimbabwe,<br>and Vietnam<br>
20.<br>15.<br>10.<br>5.<br>0.<br>Slovakia Slovenia Czechia Belgium Netherlands Poland Finland Hungary Sweden Denmark Austria Ireland Croatia Germany Cyprus Luxembourg EU-27 France Malta Greece Estonia Spain Portugal Romania Italy Latvia Lithuania Bulgaria<br><!-- End of picture text --> _Source: World Bank estimates using Eurostat. These estimates represent the reference year, not the survey year. For example, estimates for 2021 reflect data collected in 2022, whose reference period is 2021._ _<u>https://ec.europa.eu/eurostat/databrowser/view/ILC_LI02/default/table?lang=en</u> Note: Estimates are based on adult equivalents of disposable income. More recent estimates are not yet available._

**Some groups continue to be left behind, reflecting in part uneven service delivery across regions and groups, consistent with the recent deterioration in inequality indicators.** Public service provision is adequate in the capital city and several other well-developed areas, but in other regions, especially the Northwest region, more limited access to quality social and municipality services has undermined both the inclusiveness and strength of growth (World Bank, 2021). There is a significant variation of poverty by district. In the 2021 income year, the districts with the most significant proportion of individuals facing a risk of poverty are Stara Zagora at 33.9%, Dobrich at 27.0%, Lovech at 26.8%, and Pernik at 25.5%. Conversely, the districts with the lowest percentage of people at risk of poverty are Shumen at 12.9%, Veliko Tarnovo at 15.8%, Silistra at 16.1%, and Targovishte at 16.2%. In the 2021 income year, about 81.6 percent of Roma were at risk of poverty and social exclusion, compared to only 32.2 percent among the overall population, and more
main results depend on the assumption of oligopolistic competition. Applying the theoretical framework of Bagwell and Staiger (2001), who analyze strategic trade policy under perfect competition, we re-examine the e¤ect of a rainfall shortage on optimal import tari¤s when ...rms are competitive rather than oligopolistic. We ...nd, once again, that the key predictions of our basic model remain qualitatively similar.

We then examine empirically the impact of rainfall shocks on e¤ectively applied tari¤s. Using detailed panel data on applied tari¤s and rainfall for 70 nations over 1988-2006, we ...nd that rainfall shortages generally lead to lower applied tari¤s on agricultural imports. The positive relationship between rainfall and import tari¤s is robust to several alternative empirical speci...cations, tends to be stronger in countries with greater access to irrigation, and applies fairly widely across the set of agricultural products and regions considered. The main exception refers to nations in Africa, for which we ...nd no evidence of signi...cant tari¤ adjustment in response to rainfall shocks. This lack of response may plausibly re‡ect the fact that governments of African nations tend to depend more heavily on import tari¤s for revenue (Devarajan et al., 1999). In this case, tari¤ revenue is likely to carry a larger weight on the governments’objective function, and thereby o¤set opposite incentives via pro...ts and/or the consumer surplus. Overall, however, our empirical ...ndings suggest 3
# **III.B.3 Cox’s Bazar Panel Survey**

The Cox’s Bazar Panel Survey (CBPS) tracks social, economic, and health outcomes among a representative cohort of the displaced Rohingya population after August 2017, alongside Bangladeshi households residing close to the refugee camps in Cox’s Bazar and Bandarban. The survey covers 5,016 Bangladeshi households from six _upazilas_ (subdistricts) in Cox’s Bazar and one _upazila_ in Bandarban that also hosts Rohingya.

The CBPS is representative of two primary demographic segments in Cox’s Bazar: Rohingya living in camps (comprising newly arrived as well as previously settled individuals) and the host population, encompassing both native households (with heads born in Bangladesh) and non-natives, including Rohingya.

The survey protocol includes two main components. The first is a household interview administered to one adult member that encompasses household demographics, food security, consumption patterns, assistance utilization, asset ownership, household income, and anthropometric measurements of a randomly selected child under five. The second is an adult interview conducted with three randomly chosen adults (18 years and older) that covers topics such as labor market participation, migration history, health care utilization, experiences of crime and conflict, and overall well-being. Across both rounds of data collection (2019 and 2023), CBPS conducted household interviews with 5,020 households (constituting 25,421 individuals) and adult interviews with 9,386 adults.

In the 2023 round, the same set of adults was reinterviewed, with a response rate of 97 percent of households and 80 percent of adults, corresponding to 4,996 households and 7,486 adults. The second round retained the core components of the household questionnaire and added a credit and coping module. The adult questionnaire was modified; the sections on labor market history, migration history, and experiences of crime and conflict were omitted, and three new sections were added to assess individuals’ intentions to return to Myanmar, host population cohesion with the refugees, and
analyzed will be distribution neutral or, conversely, that there is a specific, quantifiable form for<br>the distributional change. Changes in employment distribution are accommodated by reweighing the<br>sample households.|
|**Data/information:**<br>**ents**|SimSIP Poverty uses grouped household data, typically grouped by income; the mean income or<br>consumption by group and the share of these groups are required. In addition, SimSIP Poverty requires<br>macroeconomic data at a nationally aggregated or sectorally disaggregated level. This includes, for<br>example, past or expected growth rates of output, employment, and population by sector. Finally, the<br>population size and growth and a poverty line are necessary for calculating poverty incidence.|
|**Time:**<br>**quirem**|One day to gather the data on population shares and mean income or consumption by group, check the<br>credibility of scenarios, and enter the data into the software.|
|**Skills:**<br>**Re**|Familiarity with Excel.|
|**Supporting**<br>**software:**|Excel.|
|**Financial cost:**|–|
|**Limitations:**|SimSIP does not capture second-round effects. These may be captured by CGE models.|
|**References and**<br>**applications:**|�<br>For an overview of the technique, see Datt and others(2003).<br>�<br>Wodon, Ramadas, and van der Mensbrugghe (2003).<br>�<br>Ramadas, van der Mensbrugghe, and Wodon (2002).<br>�<br>Software available at www.worldbank.org/simsip.|

60
reinterviewed in early 1992, a total of 9,036 were still operating businesses.

Our empirical approach is to seek patterns of participation that accord with the predictions detailed in the previous section. However it should be emphasized (again) that there is a fundamental identification problem faced _both_ by our methodological approach and by other approaches that assume enforcement efforts are the sole determinants of participation. We are aware of no data set with the requisite information on both costs and benefits of participation to evaluate the relative importance of each approach."<sup>7</sup> Our limited goal in this section is to document empirically the heterogeneity and depth of participation; show the importance of firm size, age, and mode of production as correlates of participation; and (partially) establish a role for firm dynamics and life cycle considerations as key concerns for modeling participation (in both developing and industrialized countries).

# _1. Heterogeneity of Participation_

Though the data set is bounded above at five workers (fifteen in manufacturing), even within this narrow firm size range informality is clearly not an all or nothing proposition. The summary statistics in Table 1 show that there are high participation rates in societal institutions for even these small firms: 41.7 percent are registered with the federal treasury, 25.2 percent are registered with the local treasury (including Mexico City), 34.6 percent pay some taxes to one or both treasuries, 34.6 percent of firms with paid workers have them registered with IMSS (Mexico's social security administration), 22.5 percent are members of a business guild or association, 15.6 percent pay dues to a business organization, and 33.1 percent of firms that existed in 1989 were enumerated in the Census of that year.

Table 2 presents cross tabulations along several dimensions of participation and shows that participation along one dimension need not imply participation along others. For example, '' Such a data set would have to identify exogenous variation in government policy that is
EAP productivity, using country-level data. Focusing on labor, one of the factors of production driving the results in Section 2. Section 3 employs newly available sector-level information and examines how labor movement is linked to labor productivity differential across sectors over the last ten years. This allows us to examine if labor has moved from sectors with low productivity to sectors with high productivity, i.e., better resource allocation across sectors. The allocation of resources within an industry has implications for aggregate productivity; Section 4 digs deeper into this using manufacturing data for four developing EAP countries (Malaysia, Philippines, Indonesia and Vietnam). It first presents evidence on misallocation and its dynamics and then explores possible determinants.

# **2. Aggregate trends for EAP’s productivity**

Notwithstanding significant volatility and heterogeneity, developing EAP total factor productivity (TFP) is gradually catching up to the frontier (the United States) since 2000 (Figure 2 ). In the aftermath of the 1997-1998 East Asian financial crisis, a sharp drop in aggregate productivity is observed in many EAP countries. Since then, the recovery process has been gradual and slow. Yet, there is heterogeneity in the speed of convergence across countries. On the one hand, China and Malaysia have fully recovered and high catch-up speed has increased since the crisis. On the other hand, relative TFP is below the pre-crisis levels in Indonesia and, to 4
other alternative has been to work with somewhat larger or growth-oriented firms, and offer one-on-one virtual coaching (Anderson et al., 2022), or live online sessions combined with one-on-one coaching (Cusolito et al., 2023). This allows for more interaction and tailored content, but is far more expensive and less likely to scale. Our study compliments these approaches by testing the feasibility, cost, and effectiveness of offering synchronous business training content with a poorer and more typical set of microentrepreneurs.

# **2 Context, Content, Sample, and Data**

We partnered with the Mexican NGO Crea Comunidades de Emprendedores Sociales, which has been providing programs in Mexico since 2008 for women entrepreneurs in economically marginalized areas. They typically offer in-person training courses to women, funded by a range of government and private sector partners. When the COVID-19 pandemic hit, inperson training was no longer feasible, and they were interested in seeing whether they could instead deliver training to women online.

## **2.1 Recruitment and Enrolment in the Program**

CREA launched their program under the name _Fortalece tu negocio_ (Strengthen your business). The program was advertised as a free online course where microenterprise owners could learn resilience, costing, prices, marketing, e-commerce, and making their business plan. Recruitment took place in ten waves spread between November 2020 and November 2021. Recruitment mainly took place through social media channels, the principal one being paid Facebook advertising. In addition the program was advertised on the social media pages of CREA and some of its funders, through SMS messages and emails sent to a sample of firms in a Mexican government database, and through flyers in Mexico City. Guatemala was added as a second country after five rounds of recruitment had taken place, in order to test the feasibility of further geographic expansion. Overall, 65% of the Mexican sample and 86% of the Guatemalan sample were recruited through Facebook.

Facebook usage is high in Mexico, with an estimated 90 million users in 2022,
0.079]<sup>_∗_</sup>|(0.054)<br>[0.463]||
|(1) ANC/PNC visits index|0.000|_−_0.038|0.008|_−_0.079|887|
||(1.000)|(0.064)|(0.082)|(0.068)||
|||[0.386]|[0.861]|[0.968]||
|(2) Birth in western medicine facility|0.834|0.048|0.094|0.006|877|
||(0.373)|(0.025)<sup>_∗_</sup>|(0.028)<sup>_∗∗∗_</sup>|(0.030)||
|||[0.114]|[0.002]<sup>_∗∗∗_</sup>|[0.968]||

_Notes:_ Treatment effects are estimated using Missing-Indicator ANCOVA, controlling for the community-level average of the outcome family index at baseline and matching-triplet fixed effects. Robust standard errors, clustered by clinic, are shown in parentheses. Multiple-inference corrected q-values that adjust for the false discovery rate within treatment arm are shown in square brackets. Significance: * is significant at the 10% level, ** is significant at the 5% level and *** is significant at the 1% level.

A16
and a cubic for age when multiple survey waves are included. Column 1 combines data from the Ethiopian census (2007) and the DHS (2005, 2011, and 2016); each data source is used separately in columns 2 and 3. Standard errors are clustered at the zone level, and Anderson and Rubin (1949) confidence sets of the 2SLS estimate are shown along with the _p_ -value from the associated chi-squared test, given in square brackets.

A.7
Those organizations are armed with details about potential victims and “scripts” instructing fraudsters on how to persuade potential victims to buy their stories and make money transfers (Levi, 2008). However, it remains unclear why fraud victims are victimized. In this paper, we investigate fraud victimization from the perspective of household financial conditions. Specifically, how and why do household credit constraints affect fraud victimization when facing fraud schemes?

Though our data have both urban and rural households, our investigation of the relationship between credit access and fraud victimization relies on the urban sample for several reasons. First, we have a credible identification strategy for the effects of credit constraints only for urban households, with details to be explained later. Second, previous research has shown that rural and urban access to credit differ greatly. Rural residents have much less access to bank finance and depend to a much greater extent on informal finance (Cull et al., 2019). Finally, rural and urban residents also differ greatly in their fraud victimization behavior, with rural residents living in more sparsely-populated areas, facing less social interaction, and using the internet less (i.e., a major source of fraud). We thus focus only on urban residents in this paper.<sup>6</sup> Using the urban sample of a novel nationally-representative data set on fraud victimization and household finance, we find that households facing credit constraints is a key determinant of fraud victimization. The baseline regression results show that being credit constrained is associated with 2.3 percentage points increase in the probability of becoming a victim, and 20.4 percent increase in the total amount of subsequent economic losses for those being approached, after controlling for other household characteristics and regional fixed effects. To deal with potential omitted variable bias and reverse causality issues, we employ the instrumental variables (IV) approach where the exposure to a nationwide property privatization reform, which happened only in the urban areas, and the local

> telemarketing and other forms (for more detail
.3<br>.6<br>.6 .5 .25<br>.5<br>.5 .2<br>.4 .4 .4 .15<br>Wage Premium, High School-Dropouts<br>.8 .6 .3 .45<br>.55 .25 .4<br>.6 .45.5 .2 .35<br>.4 .4 .15 .3<br><!-- End of picture text --> Source: own computations based on SEDLAC (CEDLAS and World Bank).

49
Figure 4.1: _While firm formation is key for job creation, starting a business is not easy in many ECA countries_ <!-- Start of picture text --> Start-up cost index<br>12<br>10.0<br>10<br>8<br>6 5.0 5.25.55.55.65.86.16.26.36.46.56.66.66.66.86.97.07.57.77.77.98.18.28.28.38.48.9<br>4 3.4 3.54.1<br>2<br>0<br>Source: World Bank, Cost of Doing Business Database (2003); and Bank staff<br>calculations<br>Figure 4.2: Good access to finance is one of the main contributors to a good<br>employment performance<br>Access to finance index<br>8.5<br>9<br>8 7.4 7.7<br>7 6.0 6.3 6.4 6.7 6.7 6.9 7.0<br>6 5.0 5.0 5.6 5.6 5.7 6.0<br>5<br>3.8 3.9 4.0 4.1 4.3 4.5 4.6 4.8 4.9<br>4<br>3 2.4<br>1.6<br>2<br>1<br>0<br>Belarus Bosnia i Herz. Ukraine Hungary Albania SEE Azerbaijan Poland Serbia and Mont. Slovak Rep. Czech Rep. Turkey Macedonia, FYR ECA Croatia CIS Moldova Slovenia Georgia CEE Bulgaria Uzbekistan Russia Kyrgyz Rep. Kazakhstan Europe Lithuania Armenia Latvia Romania US Estonia<br>Romania Georgia Turkey Kyrgyz Rep. Kazakhstan Armenia CIS