rafmacalaba/data-use-mentions-extended huggingface dataset:
. Therefore, in order to improve outcomes it is essential to address both structural issues and individual behaviors. As Charter and Loewenstein (2022) highlight, policies focusing on the individual (i-frame) should be seen as complementing policies addressing the system in which individuals operate (s-frame).

This paper documents the structural and behavioral barriers that discourage pregnant women from attending institutional care during their pregnancy and delivery in Haiti. It builds on earlier research by Gage and Calixte (2006) and Wang et al. (2017), which examined the impact of physical access to health services on the use of ANC and delivery care services. Using data from Haiti, Gage and Calixte (2006) found that limited access to obstetric services and limited use of existing facilities discourage delivery at a hospital.

This paper uses data from the 2017 Haiti Demographic and Health Survey (DHS), the 2017 service provider assessment (SPA), and qualitative data collected during fieldwork in May 2018 to shed light on other factors that influence women’s decisions and emphasizes the importance of the quality of health services in shaping these decisions. The quantitative analysis uses a multilevel model, which accounts for the fact that women are nested within geographic clusters with similarly available health services, to identify determinants of women’s decision to seek and reach care and to receive adequate care. This is then complemented by the analysis of perceptions and attitudes through the qualitative data.

We find that structural factors, including difficult access to healthcare centers, can be significant for women seeking care. Many women have rational concerns about the impact of these barriers on their health. For example, traveling on rough roads by motorcycle during pregnancy and labor can be frightening and dangerous. Additionally, uncertainty about the

> 1During the pregnancy, the World Health Organization (WHO) recommends four visits providing essential evidence-based interventions such as identification and management of obstetric complications (preeclampsia), and of infections (HIV, syphilis...) as well as promoting the use of skilled attendance at birth
<br>only 2<br>1 & 2<br>1 & 2|
|Better “flter”<br>available data|3.5 Use most fned-grained unit of analysis<br>(take advantage of individual-level data)<br>3.6 Select weights from information on preference<br>(SWB data or preference-elicitation surveys)|1 & 2<br>1 & 2|
|Beyond dim-by-dim<br>policy making|3.7 Develop best practices for MPM analysis|1 & 2|

<!-- Start of picture text --> 3.4.3 Total consumption 1 & 2<br>Better “filter” 3.5 Use most fined-grained unit of analysis 1 & 2<br>available data (take advantage of individual-level data)<br>3.6 Select weights from information on preference 1 & 2<br><!-- End of picture text --> <!-- Start of picture text --> (SWB data or preference-elicitation surveys)<br>Beyond dim-by-dim 3.7 Develop best practices for MPM analysis 1 & 2<br><!-- End of picture text --> <!-- Start of picture text --> policy making<br><!-- End of picture text --> Many opportunities presented in this review are complementary and are thus worth pursuing in parallel. Some opportunities are substitutes for one another, like the three alternative methods to deal with the missing joint distribution between monetary and non-monetary outcomes. Among these three methods, the first (overlap) is the simplest and the second (imputation) is conceptually the most promising. This does not disqualify the third method (total consumption), which could be very useful if the second method proves impractical or unreliable.

For what it is worth, our own expectation is that the largest increase in signal-to-noise ratio will come from the integration of additional data. This could in particular
9|107.9|
|South|15.2|14.7|9.5|-4.6|101.4|107.8|
|Center-West|7.1|16.0|4.8|-5.0|119.4|121.3|

Source: Authors’ calculations.

Due to the regional differences both in factor endowments and specialization patterns, we might expect poverty reduction patterns to differ substantially between the regions for the BaU as well as for the trade shocks. The reduction in the headcount for the BaU confirms this expectation, as poverty declines more strongly in the Northeast, South, and CenterWest, the regions with the highest shares in agricultural employment. The Doha round has negligible effects across all regions although the figures in Table 19 suggest a different story for the Center-West. Yet, a look at the changes of P1 and P2 (not reported) demonstrates that this strong effect is due to many households being just below the poverty line in this region.<sup>38</sup> The Northeast, the region with the highest incidence of poverty where more than 50 percent of the Brazilian poor reside, benefits most from the Doha liberalization and about 50 000 individuals are lifted out of poverty in this region. In the same region, full liberalization helps about 175 000 individuals to escape poverty. The poor in the North, another region with worryingly high poverty rates, gain relatively

> 38 This is a case that illustrates why we usually report not only the headcount index, as this indicator can be quite misleading in some instances.

48
.00029|
|Summer crop GDD 2022|1343|1313|1343|0.185|0.00023|
|No. ofobs.|19,660|23,|343|||

_Source:_ Own computation from SAR records and cadastral data as described in the text Note: Pre refers to before entropy balancing and post refers to after entropy balancing that adjusts for differences in the first and second moment of all the covariates and reweighting.

31
2.

# **5 Results**

## **5.1 Main estimates of poverty and inequality**

Both approaches yield qualitatively similar levels and trends in headcount poverty estimated at the $1.90 line: poverty is about 12.3 percentage points lower in 2019 than 2011 (see Figure 15). Estimates of poverty obtained using observed CPHS consumption data are seen to be up to 3.5 percentage points higher when compared to estimates obtained using NSS-compatible measures of consumption. By the same token, our estimates of poverty are notably higher than previous estimates obtained by the World Bank’s Povcalnet database and other scholars, see e.g. Edochie, et al. (2022); Newhouse and Vyas (2019) and Gupta, Malani and Woda (2021b). Estimates from World Bank’s 38
<!-- Start of picture text --> Decided with decision makers, experts, and and key<br>stakeholders<br>[| | available)Source project data (CBASource project data (CBA project data (CBA data (CBA (CBA elements incorporated incorporated if<br>Using statistical and and mathematical techniques techniques<br><!-- End of picture text -->

Decided with decision makers, experts, and and key stakeholders [| | available)Source project data (CBASource project data (CBA project data (CBA data (CBA (CBA elements incorporated incorporated if Using statistical and and mathematical techniques techniques PerformanceSerformance IndicesIndices | (05 combinere selected variablesto create SEI [ fie se kta, | Combine SEI, FEI, and budget constraint SEI, FEI, and budget constraint FEI, and budget constraint and budget constraint constraint se alltel rine to create a four-quadrant four-quadrant matrix boos Based on informed deliberation on informed deliberation informed deliberation deliberation

<!-- Start of picture text --> Indices 5 re<br>[ fie se kta, | Combine SEI, FEI, and budget constraint SEI, FEI, and budget constraint FEI, and budget constraint and budget constraint constraint<br>se alltel rine to create a four-quadrant four-quadrant matrix<br>boos Based on informed deliberation on informed deliberation informed deliberation deliberation<br><!-- End of picture text -->
# **Credit Constraints and Fraud Victimization: Evidence from a Representative Chinese Household Survey**<sup>**1**</sup>

Nan Gao Wenlan School of Business Zhongnan University of Economic and Law Yuanyuan Ma Wenlan School of Business Zhongnan University of Economic and Law Lixin Colin Xu The World Bank Group

**Key words** : fraud victimization, credit constraints, privatization reform, financial coverage. **JEL codes** : K14, D14, D12.

> 1 The paper represents the views of the authors and do not implicate the World Bank and its membership countries.
the growth rate. Bloom and Canning (2004) find that 1 percentage point growth in the share of working age population leads to an increase by 1.4 percentage points in growth in income per capita.<sup>11</sup> Kelley and Schmidt (2005) developed a framework to explain the

> 10 Using cross-sectional data for 78 countries, from 1965 to 1990, Bloom and Williamson (1998) find that demographic forces appear to have contributed 0.6 percentage point to the East Asian miracle via labor inputs per capita and 1 percentage point via capital accumulation per capita.

> 11 The OLS estimates of Bloom and Canning (2004) suggest that an increase of 1 percentage point in the share of working age population leads to 1.0 percentage point increase in income per capita.

7
>consumer durables|
|Other Individual<br>Questionnaire Modules<br>Notes: LSMS+ data are pub|Employment, non-farm<br>enterprises, education,<br>health, savings<br>licly available. More informa|Employment, non-farm<br>enterprises, education,<br>health, subjective well-<br>being<br>tion on the data and question|Employment, non-farm<br>enterprises, education,<br>health, 24-hour time use<br>diary; domestic and<br>international migration<br>naire can be found in thislink.|

Notes: LSMS+ data are publicly available. More information on the data and questionnaire can be found in this link.

# **2.2 Paradata collection and cleaning**

The Cambodia, Ethiopia, and Tanzania surveys were conducted using the World Bank Survey Solutions CAPI software, which automatically includes a supplemental paradata that contains timestamps of all “events” associated with each interview. Table 2 provides an example of what a paradata file looks like. There is a row for each event followed by the timestamp of when the event occurred. The “parameters” column provides input associated with the event. For example, the first row shows an _AnswerSet_ event for the question hh_a01_1 where the person answered “53”.<sup>5</sup> Each row in the paradata follows the previous row sequentially in time which is shown in the column order.<sup>6</sup> Our analysis includes data on events initiated by the interviewer (variable role==1)

> 5 The variable “parameters” is important since it consists of key information concerning the event. For example, for an _AnswerSet_ event, the parameters value contains the question that is answered, what was the answer, and who answered the question. In some cases, there are multiple IDs in the paradata to uniquely distinguish specific assets or agricultural plots. Parsing these types of information
plaining observed changes in specialization than factor endowments. Over longer periods, factor endowments become relatively more important and account for most of the observed variation in specialization. This evidence is consistent with the idea that changes in relative factor abundance occur gradually and take time to affect the structure of production.

Morrow (2010) builds on Romalis (2004) to augment the model with Ricardian TFP differences, and estimates the model using panel data across 20 developed and developing countries, 24 manufacturing industries, and 11 years (1985–1995). He finds that both productivity differences and the interaction of factor abundance with factor intensity play a role in determining international specialization patterns with little evidence that relative productivity levels are systematically higher or lower for skilled labor abundant countries in skilled labor intensive sectors. Furthermore, he finds that differences in factor abundance are more potent than differences in Ricardian productivity in determining patterns of specialization.

Our paper contributes to the existing literature in at least two ways. First, we examine a large number of countries with considerable heterogeneity in their endowments of capital, skilled and unskilled labor. The existing literature has focused on mostly homogeneous groups of countries such as OECD countries or a small number of countries due to limited availability of production data. Since we are using trade data, we are able to expand our sample to include more than 100 countries. Second, we allow for the possibility of changes taking place both in export-oriented and import-competing industries, thus controlling for potential general equilibrium effects, by relying on Leamer (1995).

The paper proceeds as follows. Section 2 discusses the empirical strategy and describes the data. The results are presented in Section 3. Section 4 concludes.

5
time _t_ as dependent variables.<sup>16</sup> Results are reported in Table 8 and show no significant effect on the number of bonds or in the size of corporate bond financing at the same time as, and in the two years after, MDB participation in a syndicated loan (columns 1 and 5). In the remaining columns we show that results are robust to the inclusion of controls for the presence of the top 10 banks and ODA in the previous 2 years. Only when we control for Chinese lending (columns 3 and 7) do we observe a marginally significant increase in the size of bond financing at _t+1_ . The cumulative effects are never significantly different from zero.<sup>17</sup> Overall, there is no evidence of crowding-out in the corporate bond market.

A further concern could be that MDBs’ entrance in a given country-sector pair may attract the private sector, but at the same time, crowd-out other sources of development financing. To mitigate this concern, we run the same exercise explained above but using either the number of projects financed by ODA flows or the number of officially-financed Chinese projects as dependent variables. In both cases, we do not find any statistical association between MDB participation and changes in official aid flows by OECD’s Development Assistance Committee (DAC) countries and official Chinese lending (see Table A9).

# **6 Conclusions**

Filling the investment gap to achieve the SDGs by 2030 is a major development challenge. As foreign aid and domestic revenue mobilization alone cannot meet the target, leveraging private investment is essential to make progress towards inclusive growth. In this respect, MDBs can play an important role in attracting private capital flows towards investment in developing countries.

We use granular data on international syndicated lending to evaluate whether MDBs can mobilize private capital flows. Our results indicate that once an MDB enters a country-sector pair through the participation in a loan syndication, the number of total syndicated loans and the associated bank lending flows
Policy Research Working Paper 10548

# **Abstract**

This study demonstrates systematic bias against women in public perceptions of the fairness of wages. In nationally representative survey experiments across more than 70,000 individual vignettes posed to 4,500 respondents in three Central Asian countries, respondents were 13 percent more likely to say wages were “too high” when the randomly assigned person described in the vignette (subject) was a woman, and 34 percent more likely to say they were “too low” when the subject was a man. The pattern of bias favoring higher wages for men is statistically significant at conventional levels in all three countries, among both male and female respondents, and in each of the eight occupations studied. The results also demonstrate the presence of significant bias in favor or older workers, specifically for white-collar occupations, and the absence of this relationship for the blue-collar occupations included in the experiment. The findings reinforce the importance of bias as a contributing factor to the gender pay gap, and the value of equal pay regulations to prevent gender discrimination in wage setting.

This paper is a product of the Poverty and Equity Global Practice. 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://www.worldbank.org/prwp. The author may be contacted at wseitz@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations,
# **Appendix D: Robustness checks**

To further scrutinize the robustness of our results, we conduct a battery of sensitivity analyses. These include checks with using the linear FE and IV-FE models instead of the logit FE and IV-logit-FE models, analyzing the original non-standardized mental health scores, removing workers with zero incomes, addressing panel attrition, and examining potential endogeneity of some covariates, whether the impacts are mitigated by other health channels other than mental health, alternative and disaggregate measures of mental distress, a different modeling approach, considering previous work experience, as well as implementing various stress tests on the IV.

First, we estimate the linear FE and IV-FE models for employment status and other binary outcomes variables instead of the logit FE and IV-logit-FE models and find qualitatively similar results. The results are presented in Table A3 (Appendix A). Second, our findings remain consistent when using the original non-standardized mental health scores, as shown in Table A4 (Appendix A). Third, for the labor income regressions in Table 1, refugees who do not work are assumed to have zero incomes. Alternatively, we also analyze a subsample of workers that have positive incomes and find consistent effects of mental illness (see Table A5, Appendix A).

Fourth, the panel attrition rate from wave 1 to wave 5 is approximately 22% and relatively lower than those in other surveys in Australia such as the HILDA or the Longitudinal Study of Australian Children (LSAC) (Department of Social Services, 2018). To address panel attrition issues, we create a balanced sample and replicate our main analysis in Table 1. The results confirm the negative impacts of mental illness on both employment status and labor income; furthermore, the estimates are of similar magnitudes (Appendix A, Table A6). In addition, we also account for the survey’s stratified sampling design and nonresponse rates by using the longitudinal weights provided by BNLA. Again, the results of weighted regression reaffirm our previous findings, as shown in Table A7 (Appendix
Standardization leads to a slight change in food expenditures in all countries. With non-food nondurables, standardization slightly increases the share of non-food non-durables in Bangladesh, Bhutan, India, and Sri Lanka, but decreases in other countries. Standardization does not affect health care expenditures, and the proportion of housing expenditure changes little with standardization. However, Indian housing expenditure is much higher than that seen in the national consumption aggregate, and this is driven mainly by the inclusion of imputed rent among urban households. To study this point further, we reproduce Tables 8 and 9, but only for the poor in each country. The tables are presented in the Appendix and show that much of the increase in consumption expenditure among the poor in urban India is driven by imputed rent.

While standardization of consumption aggregates does not affect international poverty rankings for most countries, it has a significant effect on the poverty numbers for India. Table 11 shows the international extreme poverty rates calculated using national consumption aggregates and standardized consumption aggregates. In some cases, the standardization increases the poverty rate, like in Bangladesh, Bhutan, Pakistan, and Sri Lanka. However, in the cases of India, Maldives, and Nepal, the standardization process decreases the international extreme poverty rate.

**Table 10. Budget shares** **<u>by categories</u>**

||**B**|**anglad**|**esh**|**Bhutan**||**Ind**|**ia**|**Maldives**|**Nepal**|**Pakistan**|<br>**Sri**<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|
||**All**|*
# **Appendix I. Database on debt restructurings with Chinese and other creditors**

This appendix section presents a detailed overview on how the data sets on restructurings with private, Paris Club and Chinese creditors were constructed.

**Restructurings with state-owned Chinese creditors:** The starting point for our data compilation is our own work in Horn et al. (2021), which we cross-check, supplement, and update by drawing on a variety of new academic sources and policy reports. We make extensive use of AidData’s newly released Chinese Official Finance Database 2.0 (Custer et al. 2021; Malik et al. 2021), which provides data on Chinese debt restructurings between 2000 and 2017. To update the database until 2021, we draw on academic work by Acker et al. (2020, 2021) on Chinese debt restructurings with African countries, on the case studies presented in Bon and Cheng (2021) and Gardner et al. (2020) as well as on two reports by the Rhodium Group (Kratz et al. 2019, 2020). To verify and complement the information found in this literature, we also consult IMF and World Bank reports.

<u>Rescheduling agreements under the Debt Service Suspension Initiative (DSSI):</u> As part of the G20, China has joined the DSSI. In contrast to Paris Club members, however, it has not disclosed with which debtor countries it has concluded DSSI rescheduling agreements. To identify Chinese rescheduling agreements under the DSSI, we therefore need to rely on information provided by the World Bank and the Paris Club. For 2020, the World Bank’s DSSI website provides detailed information on debt service payments deferred by each bilateral creditor. For 2021, no such data is available yet. To identify Chinese DSSI agreements in this year, we make use of the fact that the DSSI requires comparability of treatment from all bilateral creditors. More specifically, we assume that all eligible countries with debt service obligations toward China Ex-Im Bank
End of picture text -->

# D. Maturity of government debt

<!-- Start of picture text --> Years<br>17 Interquartile range Mean<br>15<br>13<br>11<br>9<br>7<br>5<br>2007 2018<br><!-- End of picture text -->

# F. Cyclically-adjusted primary fiscal balance

<!-- Start of picture text --> Number of economies<br>100<br>2018 2007<br>80<br>60<br>40<br>20<br>0<br> < -5 -2.5 to -5 0 to -2.5 > 0<br>Percent of potential GDP<br><!-- End of picture text --> Source: International Monetary Fund, Kose et al. (2017), World Bank.

A. Unweighted average of government debt ratios for 85 EMDE commodity exporters and 60 EMDE commodity importers.

B. Based on data for 146 EMDEs.

C. Unweighted averages of foreign currency sovereign credit ratings for 54 EMDE commodity exporters and 40 EMDE commodity importers. Whiskers denote interquartile ranges.

D. Unweighted averages of the average maturity of government debt based on 38 EMDEs.

E. Unweighted average of cyclically adjusted primary balance-to potential GDP ratios for 91 EMDE commodity exporters and 64 EMDE commodity importers. F. Based on data for 149 EMDEs.