rafmacalaba/data-use-mentions-extended huggingface dataset:
29 effectiveness of condoms, suppositories, and oral contraceptives and could be distributed to customers making contraceptive purchases (Bailey and Zambrano, date). Sales data on these three products were collected before<sup>and after the</sup> introduction of the pamphlets at 60 drugstores. The pamphlets<sup>were introduced at</sup> only 30 of the drugstores and the other 30 vere paired with the<sup>30 experimental</sup> drugstores as a way of measuring substitution toward the experimental drugstores. Comparing the sales at the experimental drugstores before and after the pamphlets vere added, the researchers found that sales of condoms and<sup>suppositories</sup> increased significantly while pill sales increased only slightly. The control drugstores experienced no significant change in sales over this period.

Analysis of the sales of the paired control drugstores<sup>indicated that in 2</sup> of 16, 4 of 10 and 6 of 11 pairs, sales of suppositories, oral contraceptives and condoms respectively fell but that overall, there vas only a net decrease in the condom sales of the control drugstores. Because the data are<sup>strictly from</sup> retail sales, it is difficult to evaluate how much switching<sup>across methods or</sup> across supply sources occurred and therefore, it is difficult<sup>to conclude vhat</sup> the net impact this sort of program has on overall contraceptive use and births prevented, but it does seem that the use of pamphlets can increase<sup>use of condoms</sup> and suppositories. This suggests that retailers could play a bigger<sup>role in</sup> providing contraceptives if they gave customers method-specific information.

Legal Constraints. Obtaining product approval from the<sup>local country's Food</sup> and Drug Administration equivalent is a precondition for -being able to sell a product in most local markets. This can be a long process,<sup>
- Cross-sectional, also known as survey or prevalence studies:

- Serological survey

- Knowledge, attitudes and practices survey

- Demographic and health surveys

- Cross-sectional studies of other topics

- Case series studies

- Others

# **4. Priority population or universe**

This item takes into account the population engaged by each study:

- People living with HIV

- Men who have sex with men

- Female/male sex workers

- Injection drug users

- Prisoners

- Migrant population

- Pregnant women

- General population

- Youth

- Other populations as defined by the country

# **Evaluation of methodological content**

ASAP defined eight items or characteristics linked to “best practices”. Analysis of these methodological characteristics yields important information, such as the existence of bias, statistical significance, or the feasibility of generalizations based on the findings. For each criterion, a question with yes/no answer has been defined. If the answer is positive, one point is awarded; if it is negative, no points are awarded. Three of these items are doubleweighted because of their critical importance. If the answer to a double-weighted criterion is positive, it is multiplied by a factor of two. These double-weighted criteria are: (i) sample size; (ii) sampling technique; and (iii) techniques to reduce data collection errors. Finally, the results are added up, to obtain a total score that is regarded as the quality index of the methodological content. The score scale has a range of 0 to 11. Two cut-off points have been defined in this scale, establishing that a score of 8 or more represents high methodological quality; a score of 5 to 7 represents intermediate quality; and a score of 4 or less represents limited quality.

# **1. Objective of the study**

Is/are the objective(s) of the study clearly defined? Do the development and results of the study respond to the objective(s)? The answer to both questions should be yes.

**Yes=
# 2. Direct debt data disclosure

Public debt data in LIDCs are disseminated through different channels and in different formats. National authorities may publish debt statistics in dedicated bulletins or broader monetary / macro publications, they may also share their databases with the general public. This channel of dissemination of public debt statistics can be defined as _direct_ reporting _._ In parallel, national authorities also make data available to a number of external agents (e.g., WB/IMF, credit rating agencies, etc.) for their review, compilation and disclosure on their own publications or websites. This second form of dissemination is defined as _indirect_ reporting.

National authorities have a responsibility to provide timely information on debt statistics to their citizens as well as to current and potential investors, the lack of full disclosure may have important financial consequences and hinder trust in governments. The extent of a country’s debt transparency can be evaluated based on the degree of information that it shares on its public debt; particularly by examining a country’s active reporting of debt data vis-à-vis the expected level of reporting according to its legal and institutional frameworks, and its actual debt portfolio.<sup>2</sup> Despite the relevance and increasing interest in debt transparency, there is no available methodology to measure data disclosure standards at the national level while being able to make cross-country comparisons. This paper sets this agenda forward by providing a simple tool to facilitate this assessment in LIDCs, the Public Debt Reporting Heatmap.

## _The Public Debt Reporting Heatmap_

The heatmap evaluates debt information available on national authorities’ websites to assess direct reporting standards. The tool is intended to show strengths and weaknesses at the country level. The heatmap is updated on an annual basis and published on the WB’s website,<sup>3</sup> with the aim of identifying existing gaps by country, fostering countries’ efforts to improve data dissemination, and informing technical assistance strategies.

A country’s performance is evaluated across nine indicators and
line with cross-product pricing strategies. While the credit reallocation and credit risk channels might also explain these outcomes, such alternative hypotheses generate additional predictions. For instance, under the credit reallocation channel, we would expect to observe a simultaneous decline in working capital loan volume to recipients of earmarked loans; a behavior that should be more pronounced for riskier firms and banks with market power. In the case of credit risk, we would expect to observe an increase in loan provisions, precisely for firms that pay the highest mark-ups in new working capital loans.

# **4. Empirical Methodology**

# **4.1. Test of cross-product pricing strategies (Hypothesis 1)**

We first examine whether banks adjust the pricing of non-earmarked products after a borrower receives an earmarked loan. The empirical specification is summarized as follows: l l 0 1E E E l l l l l l l (1) In Equation (1), l l denotes the interest rate spread of a working capital loan ll _l_ issued at yy = αα + αα EE + XX + γγ + γγ +uu month _t_ to firm _f_ by bank _b_ . We introduce the variable E E E l to capture the start of an yy earmarked credit relationship of a firm-bank pair. This indicator variable equals one after the first ll earmarked loan is issued to firm _f_ by bank _b_ and zero otherwise. Thus, our test of Hypothesis 1 is EE

> 13 In Brazil, information sharing through the credit registry helps banks distinguish loan applicants with earmarked loans; hence, it is expected that all banks internalize this information and price new free-market loans accordingly.

9
out of the 40 LGAs (16 project and 20 comparison).

In Figure C1 we plot the mean per-capita revenues for both project and comparison LGAs. (We normalize for LGA population size in 2011.) The trends follow a very similar path between 2007 and 2009, starting to show some divergence after 2009. Given the paucity of data, we cannot run a more thorough exercise which would include an event study (including the periods before and after the start of the program). However, when we run a simple statistical test of the difference between the mean per-capita revenue in project and comparison groups, we cannot reject the null of equality of the means.<sup>17</sup> To further explore the parallel trends assumption, we also use nighttime lights data. Nighttime lights data are highly granular and are available over a long period of time and so enables us to complement the pre-trend analysis of revenues. We focus on the period to 2000 to 2018.<sup>18</sup> However, while a good proxy of local economic activity,<sup>19</sup> nighttime light data might not be a good proxy for local state capacity. With this important caveat in mind, we show that trends in nighttime light are remarkably similar over the period 2000 to 2013 (which is when the program came into effect) between project and comparison areas (see panel (a) in Figure C1). This find-

> 17 Coefficients obtained estimating equation OwnPercapitaRevenue = α + β(ULGSP)+ year + ε with standard errors clustered at the LGA level.

> 18Details are in Appendix C.

> 19See Gibson et al. (2021) for a recent review.

19
# **<u>Figure 6 continued</u>**

# (b) By agricultural and non-agricultural sources of income

<!-- Start of picture text --> 100%<br>8 5<br>17<br>19 25<br>80%<br>60% 43<br>44 33<br>40%<br>22<br>16 28<br>20%<br>17 20<br>10<br>0% ‐2<br>National Urban Rural<br>Remittances Agricultural income<br>-20% Non-agricultural income Household demographics*<br>Other factors**<br><!-- End of picture text -->

* Household demographics are a sum of the share of adults, the share of workers, the share of agricultural workers, and the share of non-agricultural workers out of total number of a household’s members **Other factors include <u>consumption-to-income ratio and capital income.</u> _<u>Source</u>_ <u>: World Bank staff calculations based on data from Nepal Living Standards Survey 1995 and 2010</u>

An important caveat in this analysis is that one cannot attribute causality of the results of the decomposition results on poverty reduction. For example, an increase in remittances (as a result of a household member migrating abroad) could simultaneously lower labor income for the rest of the household members if other household members do not participate in the labor market. A growth in labor income driven by higher labor market participation might make the household ineligible for other public transfer programs. Accordingly it would be incorrect to interpret the changes in labor income or remittances as strictly “causing” the observed poverty reduction. Nevertheless, this accounting exercise is useful in identifying empirical regularities and focusing attention on factors that are quantitatively more important in describing distributional changes. The rest of the section attempts to put into
services – can alleviate the pressure on women due to activities (such as housekeeping, and caring for children and the elderly) that traditionally keep them detached from the labor market. Finally, when we explore the impact across different education groups, we again fail to find any statistically significant effect. Even though the employment effect is positive for the highly educated and negative for those with lower levels of education, the effects are not precisely estimated.

Finally, we examine the impact of immigration on natives’ wages, the focus of many papers in the literature (see World Bank 2018 for a review). Results in Table 17 show that we again fail to find any significant impact of migration on natives’ wages. As a caveat to these findings, we underscore that the EU Labour Force Survey only provides information on the wage decile of workers, not their actual wages. In order to circumvent this problem, we construct a variable that measures the gap between the actual and expected probability of being in the top three income deciles given individual-level characteristics (age, gender, education levels), and that varies by country and year. The impact of migrants’ presence on the wage-gap variable, however, remains not statistically different from zero, a result that holds when looking at the share of top three wage deciles of teleworkable, essential, income-safe and health-safe jobs. These results are robust to alternative versions of the dependent variable, such as the share of employees in the bottom three deciles, or the income decile estimated using ordered probit regressions in the initial stage. Therefore, the adjustment in the labor market after the arrival of migrants seems to happen through the composition of employment but does not affect the relative or absolute wages of the different types of occupations in line with a more competitive and mobile labor market.

# 6. Conclusion

This paper provides evidence that exposure to negative economic shocks and health risks associated with the COVID-19 pandemic is greater among immigrant workers than among nativeborn people in high-income Western European countries. Using data from
on destination choice, as well as how they change given key migrant characteristics. Section 4 presents the empirical results and interprets them with respect to the observed migration patterns of Kagera’s population (more to towns than to cities). Section 5 concludes.

> 3Ravenstein’s first law of migration, formulated in 1885 and derived from observing place of birth in the British 1871 and 1881 censuses, states that most migrants move only a short distance (Lucas, 2001). The importance of distance for migration has further been generalized into a gravity model of migration, in which the number of people migrating is inversely proportional to the square of the distance between origin and destination and proportional to the product of their populations (Stewart, 1941).

3
istry was open between April 6 and June 8 of 2018, meaning that migrants who arrived in Colombia after June 8 could not register in RAMV and thus were ineligible for PEP. Furthermore, the RDD takes advantage of the fact that PEP was enacted unexpectedly, was available to all migrants registered in RAMV, and was not paired with other eligibility requirements or policies, which enabled us to rule out behavioral and anticipatory effects as well as simultaneous treatments that have precluded the analysis of similar programs. Specifically, the fuzzy RDD compares eligible and ineligible migrants on each side of the RAMV cutoff date under the following two-stage specification: Equation 1 models the likelihood of receiving PEP based on whether Venezuelans migrated to Colombia before the RAMV registry closed, while equation 2 models the effects on the outcomes of interest _Yij_ as a function of the predicted likelihood of having PEP. Specifically, 1[ _PEPi_ = 1] is an indicator variable that takes the value of one for migrants with PEP, _Ti_ and _T_ ̄ are the date of migration to Colombia and the date when the RAMV registry closed, respectively, and 1[ _Ti < T_<sup> ̄</sup> ] is an indicator variable for whether the migrants arrived in Colombia when the registry was still open. Therefore, the treatment is equal to one for migrants who arrived in Colombia before RAMV closed and had the opportunity to register in RAMV and, subsequently, in PEP. As many PEP benefits are defined for the individual holder (e.g., the right to work), the PEP treatment variable 1[ _PEPi_ = 1] is defined at the individual level for the majority of outcomes. For other outcomes, however, the treatment is defined at the household level because access is legally defined at this level (e.g., access to Sisb ́en and government transfers), or because outcomes were measured at the household level (e.g., consumption) in the survey.

The running variable in the fuzzy RDD is _di_ ,
</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01.

literature has established that the pandemic had a detrimental impact on migrants from rural to urban areas in India. As the unexpected lockdown measures were implemented, workers returned to their rural homes and experienced declines in salaries, consumption, and wellbeing; female workers were particularly vulnerable to this abrupt displacement (Allard et al., 2022). While affected men were, on average, able to subsequently remigrate and recover all of their pre-pandemic income, female re-migrants continued to experience lower income and employment (Arora et al., 2023). To the extent that young and inexperienced job-seekers form their wage expectations and aspirations on the basis of prevailing labor market conditions, the pandemic’s negative impact on female migrants could be a potential channel for our findings. Therefore, in column (7) of Table 3, we analyze whether exposure to the pandemic reduced women’s willingness to migrate for work, especially for rural women. We observe that the pandemic reduced rural women’s willingness to migrate for work by 65 percent, whereas the effect on urban women was indistinguishable from zero. This decline in the desire to migrate could be driven by a decrease in the expected net benefit from migration due to greater uncertainty, fear of job loss, lower chances of reintegration, and lack of social security in the event of another COVID-19 wave.

# **4.1 Robustness Checks**

Table 5 demonstrates the robustness of our findings to using a more aggregate measure of pandemic exposure, i.e., the district-level COVID-19 death rate. These regressions are based on a modified version of specification (1) where we replace the district fixed effects with the following district-level controls from the 2011 Census of India: population density, share of population
was administered to a sample of 1,000 households with an HIV+ positive member, and 600 randomly sampled neighbor households in the catchment area of the facility. We identified HIV/AIDS patients either by contacting the health facility where they received care or via association of PLWHA. We selected them randomly proportional to the number of HIV/AIDS patients attending each facility. We obtained informed consent from the patients before interviewing their household. In the follow-up survey, 85 percent of the baseline households were re-interviewed. The rate of attrition from the baseline sample was not statistically different between the treatment and comparison groups (15 percent each).

The outcome measures are constructed using data from the household surveys. The outcome for individual HTC is an indicator variable for whether the individual has ever been tested for HIV. For the purpose of the HTC analysis, we exclude identified HIV/AIDS patients who by definition are HIV positive and are aware of their HIV status. The sample is further restricted to individuals aged 15 or older. At baseline, the sample is comprised of 438 individuals in the treatment group and 445 in the comparison group. Individuals present at follow-up but not at baseline were not different based on standard socio-demographic characteristics.

For the analysis of couple testing, we create an indicator using the question of whether or not the most recent sexual partners the respondents had in the 12 months prior to the survey had ever been tested for HIV. We further combine the responses about each respondent’s individual testing and the testing of their sexual partners to create an indicator variable for whether both partners in the couple/sexual partnership have ever been tested. We then restrict the sample to individuals living with their sexual partners and who self-reported having had sex in the 12 months prior to the survey. For this analysis, the unit of observation is the couple and we include only one report by couple to avoid double-counting.

# _3.3. Summary statistics and balance at baseline_

Table 2 reports the baseline means of facility characteristics in 2006.
text --> <!-- Start of picture text --> 16<br><!-- End of picture text --> <!-- Start of picture text --> 20<br><!-- End of picture text --> <!-- Start of picture text --> Note : Data is extracted from the World Bank Enterprise Surveys. Number of Observations: 6,345<br><!-- End of picture text --> Figure 3 shows the median survival time by country, which ranges between 7 (Ukraine) and 16 weeks (Peru). Kazakh and Kenyan firms are as cash constrained as Ukrainian firms (also at 7 weeks) and have a survival time that is less than half that of the median Colombian firm (15 weeks). The median business in Italy, Jordan, and the Russian Federation can last 10 weeks, one week longer than the median business in Portugal and Turkey (9 weeks).

9
Caregivers: Examining the Global Relationship between Gender Norms and Economic Behavior." _World Bank Policy Research Working Paper_ 10692 (2024).

Hainmueller, Jens, Dominik Hangartner, and Teppei Yamamoto. "Validating vignette and conjoint survey experiments against real-world behavior." _Proceedings of the National Academy of Sciences_ 112.8 pp.2395-2400 (2015).

Halim, D, E Perova, and S Reynolds. “Childcare and mothers’ labor market outcomes in lower-and middle- income countries,” _The World Bank Research Observer_ , 38: 73–114. (2023a).

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