rafmacalaba/datause-encoder-data huggingface dataset:
single and married individuals, and married individuals were issued invitations for their spouse to join the study as well (in the same intervention arm).

Regarding age group, we initially sought to enroll 15-24 year olds (a particularly high risk age group), but due to ethical concerns we decided not to enroll minors, thus our final target range was 18-30 year olds (plus all spouses ages 16 or older).

For recruitment we chose a random sample of village residents from the demographic surveillance site computer registry, and went to each of their houses to explain the study and invite them to participate by reporting to a study station set up in their village the following week. Of individuals who were reached in their houses for recruitment, approximately 70% came to the study station and enrolled. Thus the enrolled sample is highly representative of the general population of 18-30 year olds in these villages. Table 3 further shows that only 6.5% of enrollees attrited or were lost to follow-up during the one-year intervention. Attriters were younger and more likely to be single, but overall means of those choosing to complete the study were quite similar to attriters, except for the fact that attriters were substantially more likely to be HIV positive at baseline (with 21% of HIV positives attriting, possibly due to confusion about continued study eligibility). With this exception, the study sample was highly representative of the underlying population.

# **_Conditionality Design_**

A primary goal of the study was to reduce risky sexual behaviors. But because these were not directly verifiable, we instead opted to link cash payments to objective measures – STI test results – that could serve as (imperfect) proxies for risky sexual behavior. Ideally we wished to condition on only STIs which have been incontrovertibly linked to solely risky sexual activity. We also needed to balance cost considerations with the imperative to have enough tests so as to sufficiently capture risky behavior. In addition, we needed to consider local knowledge of STIs, testing capacity, and availability of appropriate drugs with low rates of treatment failure. Because RESPECT was designed as a ―proof of concept‖ to detect whether or not individuals appeared to responded to this type of incentives related to sexual behavior, we chose to conduct an expansive set of tests. These were: Chlamydia, gonorrhea, trichomonas, _Mycoplasma_ 16
# **Table  B.6. Variable importance scores in Random Forest, Viet Nam**

|**Variable Importance**||
|---|---|
|Head`s age|0.0086|
|Head`s ethnicity|0.2932|
|Primary education|0.0069|
|Lower secondary education|0.0072|
|Upper secondary education|0.0102|
|College|0.0309|
|<br>Household size|0.0493|
|Dependency Ratio|0.0218|
|<br>Gender Ratio|0.0099|
|Household Head worked for a wage, salary, or commission in the last 12 months|0.0094|
|<br>Household Head worked as self-employed in the last 12 months|0.0083|
|Household Head engaged in casual/ganyu labor in the last 12 months|0.0086|
|Household Head employed in industry 1|0.0080|
|<br>Household Head employed in industry 2|0.0110|
|Household Head employed in industry 4|0.0119|
|<br>Household Head employed in industry 3|0.0115|
|Household Head employed in industry 5|0.0137|
|Proportion of adult males worked for a wage, salary, or commission|0.0136|
|<br>Proportion of adult males that engaged in casual/ganyu labor in the last 12 months|0.0103|
|Proportion of adult males that worked in farm in the last 12 months|0.0121|
|<br>Proportion of adult males that worked in industry 1|0.0094|
|Proportion of adult males that worked in industry 2|0.0107|
|Proportion of adult males that worked in industry 3|0.0120|
|<br>Proportion of adult males that worked in industry 4|0.0126|
|Proportion of adult males that worked in industry 5|0.0138|
|log of residential area|0.0212|
|<br>Roof: cement|0.0087|
|Roof: cement|0.0140|
|Wall: bricks|0.0144|
|Wall:cement|0.0161|
|Improved water source|0.0208|
|Improved toilet source|0.1869|
|<br>Lighting source- Electricity|0.0115|
|<br>Dwelling_cookfuel|0.0089|
|Household owns a car|0.1665|
|Household owns a motorbike|0.0253|
|Household owns a bicycle|0.0109|

76
GDP. These averages induce a strong decline in trajectories of foreign debt.<sup>12</sup> These estimated long run values, however, do not seem reasonable. For example, the implied long run value for the debt shock is highly aected by the debt relief episode associated with the HIPC initiative. In addition, it is dicult to believe that the yearly rate of appreciation of the real exchange rate in Senegal will be, on average, 5 percentage points for the indenite future. Therefore, it is important to be particularly careful with the long run values of the determinants of debt implied by the proposed country-specic intercept. To make this point more clear, the lower panel of Figure 6 displays results analogous to those in the top panel, but setting the long run values of the endogenous variables in Senegal equal to the pooled averages across time and countries.<sup>13</sup> Results are quite dierent from those in the top panel. For example, the median debt trajectory now decreases to only 29 percentage points of GDP. Moreover, the probability that debt will increase to 60 percentage points of GDP by 2020 is now over 20 percent, more than 12 percentage points larger than the probability displayed in the top panel.

In sum, this exercise highlights the importance of having reasonable and accurate forecasts for the long run values of the main determinants of debt. Historical data is probably not the best way to do that, given the changing environment in low income countries. Here, the insight of experts knowledgeable of the country's idiosyncrasies could be extremely valuable. Alternatively, one could perform debt sustainability analysis under dierent long run scenarios to assess the robustness of the results.

# 4.1 Comparison with the IMFWorld Bank DSA

This subsection explores the consequences of ignoring the co-movements between the determinants of debt by comparing the predictions of the stress tests of a typical IMFWorld Bank DSA (IDA and IMF, 2011) with those obtained using the methodology proposed in

> 12The non-interest current account is -6 percentage points of GDP, inducing an increase in foreign debt. This force, however, is not enough to neutralize the strong debt-reducing force of the other variables.

> 13The implied country-specic intercept follows from equation (3.4).

22
makes good intuitive sense and is consistent with findings about the returns to skills and gender during the transition (see Orazem and Vodopivec, 1995). More skilled and younger workers have significant advantages in finding a job. Similar results are found for Hungary by Micklewright and Nagy (1994b). The differences among the groups are systematic, with increases in education and experience, and reductions of age, consistently yielding higher success rates in job finding. The only exception are the unemployed with less than three years of experience, whose hazard is larger than the hazard for those with three to four years of experience. The least experienced have been helped by targeted job-subsidy programs. The effects of experience and age work against each other, with the effects of age prevailing. Surprisingly, the results show that women have been equally likely to find a job. Non-Slovenians, however, have had significantly lower escape rates than Slovenians. In the climate of growing ethnic tensions that tore the country apart, ethnic discrimination is not unexpected. The hazard of jobfinding is also strongly influenced by the source of unemployment. Among the groups with a much larger success rate are labor market entrants, helped by governmentsponsored internship program, and the unemployed whose previous employer went bankrupt.

The unemployment insurance variables, the focus of our analysis, tell a clear and consistent story. The probability finding a job is significantly lower for recipients than for non-recipients. The coefficients of the three variables indicating the receipt of different types of benefit (one for unemployment compensation, one for unemployment assistance following the expiration of unemployment compensation, and one for unemployment assistance for former interns) are all negative and strongly significant. Moreover and of utmost importance, the spline dummies. which capture the effects of the remaining potential duration of unemployment compensation. show that the hazard dramatically increases just before the exhaustion and equally dramatically falls immediately after the exhaustion of benefits.

More precisely, holding other variables constant, the effects of approaching and moving 17
# 4 Results

## 4.1 Instrumental Variable Strategy: First Stage and Specication Checks

To clarify the identifying assumptions needed for our IV strategy, Figure I illustrates how the existence of private schools and the binary instrument covary with the 1981 village population (the relationship with 1998 population is similar). Here, we plot Rulei for all villages in our sample and the non-parametric relationship between private school location and village population. There are both eligible (Rulei = 1) and ineligible (Rulei = 0) villages at all population levels. We can thus compare two villages with the same population, one of which was eligible to receive the GSS and another that was not, allowing us to exclude the direct eect of population on private school existence. Further, the non-parametric relationship between private school existence and village population is approximately linear; it is therefore likely that linear and quadratic population terms in the regression specication suciently control for the underlying relationship between village population and private school existence.

Table II, Columns (1) and (2) present regression estimates using the eligibility rule as a predictor for the location of GSS. Column (1) runs a probit specication with linear and quadratic controls for population, and shows that an eligible village was 1.24 percentage points more likely to receive a GSS. Column (2) augments the rst stage with other villagelevel public goods and PC xed eects, resulting in similar point estimates that are signicant at the 1 percent level of condence: Villages with Rulei = 1 were 1.6 percentage points more likely to receive a GSS. Although the point estimate seems small, this is because few girls' secondary schools were constructed. In fact, this estimate represents an almost 100 percent increase over the fraction of ineligible (instrument = 0) villages that had received a GSS by 2001. In addition, both the basic and the more demanding rst stage are at or above the 17
- Wolf, S., J.L. Aber, J.R. Behrman, and M. Peele. 2019b. "Longitudinal causal impacts of preschool teacher training on Ghanaian children’s school readiness: Evidence for persistence and fade‐out." _Developmental Science_ 22 (5): e12878.

- World Bank. 2013. _What Matters Most for Early Childhood Development: A Framework Paper._ Washington, D.C: World Bank. SABER Working Paper Series.

- World Bank. 2018. _World Development Report 2018 : Learning to Realize Education's Promise._ Washington, DC: World Bank.

- Yoshikawa, H., C. Weiland, J. Brooks-Gunn, M.R. Burchinal, L.M. Espinosa, W.T. Gormley, J. Ludwig, K.A. Magnuson, D. Phillips, and M.J. Zaslow. 2013. _Investing in Our Future: The Evidence Base on Preschool Education. Society for Research in Child._ Society for Research in Child Development.

- Yuan, S., and C. Fisher. 2009. "Really? She blicked the baby? Two-year-olds learn combinatorial facts about verbs by listening." _Psychological Science_ 619-626.

- Zelazo, P.D., U. Müller, D. Frye, S. Marcovitch, G. Argitis, J. Boseovski, J.K. Chiang, et al. 2003. "The development of executive function in early childhood." _Monographs of the society for research in child development_ i-151.
_Use the regional distribution._ Finally, for close to 3% of the world’s population (50 countries), we have no prior household survey data. For those countries, we generate the 2020 distribution in the same way as we generated the 2019 distribution: by applying the regional distribution to those countries. This follows the methods used by PIP for countries without data.

# **4. Results**

In what follows, we first report within-country changes in poverty and inequality comparing across the four preferred methods – actual household survey data, data from NSOs, data from phone surveys, and data from the literature.<sup>15</sup> Then, we report the changes in global inequality and poverty. Finally, we look at the drivers of these global changes.

## **4.1 Impact of COVID-19 on inequality and poverty within countries**

Figure 1 shows the net percent change in the Gini index in 2020 for all countries with data from our four preferred methods. The net impact is calculated as the difference between the Gini index in the observed 2020 distribution and the counterfactual distribution.<sup>16</sup> The countries are ranked according to their mean income or consumption expenditure on the horizontal axis. In general, changes in the Gini index in 2020 are mixed and, for most countries, small. Note that some of the larger declines in the Gini – for instance in the US, Canada, Australia, Brazil, and South Africa – can be linked to the wide-ranging social assistance programs that were implemented in 2020. Across countries, inequality is just as likely to increase (in 43 countries) as decrease (in 48 countries).

Whereas the changes in inequality were mixed, extreme poverty has largely been increasing (Figure 2). We find large increases in extreme poverty for countries that already had high poverty rates. This could either be because these countries experienced larger shocks to average incomes, or because they have more people just above the international poverty line. India experienced the largest increases in the extreme poverty rate, which appears to be for both of these reasons. South Africa and Brazil, two countries that had substantial pandemic support (see World Bank 2022), report the largest declines in poverty.

15 This does not include the last group of countries which relies only on national accounts growth rates or the regional average. While the sectoral growth rates allow for some distributional effects, these are very limited.

16 Note that the change from 2019 to 2020 is equivalent to the net COVID-19 impact on within-country inequality because the country inequality is held fixed at the 2019 level for the counterfactual distribution. This does not apply to the global Gini which depends on both within- and between-country differences.

14
<!-- Start of picture text --> Burkina Faso Ethiopia Malawi<br>650 65 1400<br>g @ 60 ° S 1350<br>8 Fy ? g / 8 1300 \<br>a d \ a / a \<br>8<a 50> iF,/ =8a 6 / 8=o 1250 \ Pp<br>[Ys \ ing / in &<br>500 45 é 1200<br>CT as st. ua 0. a rr a: a a: cs: | GC«aac eu<br>pre-COVID Jan20 Jan21 Jan22 Jan23 Jan24 pre-COVID Jan20. Jan21 Jan22 Jan23 Jan24 pre-COVID Jan20. Jan21 Jan22 Jan23 Jan24<br>Date Date Date<br>Tanzania Uganda<br>re 12000<br>2 1750 z<br>77<br>3g 10000<br>&8 1700- , &3 r<br>8 3 8000 !<br>5 1650 5 /<br>1600 a es ee es | 6000- rr re a a |<br>pre-COVID Jan20 Jan21 | Jan22 Jan23 Jan24 pre-COVIDJan20 Jan21 Jan22 Jan23 Jan24<br>Date Date<br><!-- End of picture text -->
_likely to happen than not to happen. If you put 10 beans on the sheet of paper, it means you are sure the event will happen._ A practical issue is how many beans respondents have been given. The most common choices in the existing literature have been 10 and 20 beans (Delavande et al, 2009), although exceptions exist, such as the use of 12 stones by Luseno et al. (2003). Ten and twenty beans are easily interpreted as probabilities, with the trade-off being between less respondent burden (both in terms of time and cognition) with ten beans against the potential for allowing more precision with twenty beans. The 10-bean format forces respondents to round their probabilistic beliefs to nearest 10 percent while the 20bean format forces them to round to the nearest 5 percent. Manski and Molinari (2010) find that in the U.S. Health and Retirement Study, answers to expectations asked in percent chance format tend to be rounded to the nearest 5.

To evaluate how much difference the number of beans makes, half the sample of respondents (4 of the 8 treatment groups) were randomly chosen to receive 10 beans, and the other half 20 beans. For those given 20 beans, the introduction paragraph above was modified accordingly. After reading the introduction, the interviewer illustrated the concept further by using an example: they were told to suppose 5 black beans and 5 white beans were placed in a box, and then asked how likely it is that they would pick a black bean without looking.<sup>4</sup>

# **2.3. Questions to Measure Understanding of Probability**

The expectations module then began with five simple questions to measure whether boat owners understand the concept of probability. The five questions were as follows:

- 1) Imagine I have 5 fishes, one of which is red and four of which are blue. If you pick one of these fishes without looking, how likely it is that you will pick the red fish?

- 2) How likely are you to go to (nearby town) sometime in the next two days?

- 3) How likely are you to go to (nearby town) sometime in the next two weeks?

> 4 Again this was modified for those respondents given 20 beans.

8
through of taxes to informal prices.<sup>30</sup> The results are partially affected but our key findings remain. First, we find that scenario #1 (exemption of informal varieties) remains very progressive, with the richest quintile paying over 70% more in taxes than the poorest quintile on average. Second, the de facto exemption of the informal sector continues to be more progressive than de jure exemption of food (scenario #2), although the difference in progressivity has decreased. Third, the progressivity impact of the de jure exemption, conditional on allowing for informal consumption, remains smaller than that of the de facto exemption.

**Distributional savings rates** Our baseline results use total expenditures to proxy for household income, assuming households do not save. Intuitively, allowing for savings both decreases effective tax rates (as savings are not taxed) and decreases the progressivity of all tax scenarios if saving rates increase with income.<sup>31</sup> The distribution of savings across income levels is hard to obtain from expenditure surveys, especially in developing countries where income is hard to measure. To assess how savings could affect our results, we use data from the US Consumer Finance Survey, in which savings rates range from 0% for the poorest households to 15% for the richest quintile.<sup>32</sup> Results are presented in Table A6: allowing for distributional savings decreases the progressivity of all scenarios, as expected, but our main findings are unchanged.

**Alternative formality assignment** Finally Table A6 presents our progressivity results under the two alternative rules for of as formal or in- categorizing places purchases formal, as described in Section 2.2 (the probabilistic formality assignment based on the 2013 Mexican Census of retailers and the assignment of specialized stores to the informal sector). Our three main take-aways are unchanged.

> 30 The share of formal inputs used by informal firms is likely to be an upper bound. First, the 10% number is applied to both fixed and non-fixed establishments, while the latter category (which includes street stalls) is likely to source fewer inputs from formal firms. For this reason, we maintain a 0% passthrough for home production. Second, in Mexico, it is the large informal stores which use formal inputs: The use of formal inputs for smaller stores is closer to 5% on average. Third, segmentation between formal and informal firms is likely to be even larger in poorer countries, leading to lower average pass-through in those countries than in Mexico.

> 31Calculations based on annual income may overstate the regressive nature of consumption taxes since consumption depends on lifetime income, which is less volatile than annual income (Poterba, 1989; Caspersen and Metcalf, 1994).

> 32Source: 1985 US Consumer Finance Survey. Savings rates are in the same range in the few developing countries (China and Chile) for which similar consumer finance surveys are available.

19
<!-- Start of picture text --> Figure 4: Percentage of children stunted by slum/non-<br>slum residence, 4 metropolitan cities, India 2005-06<br>60<br>51<br>50 47<br>42<br>38<br>40<br>33<br>30 28 Slum<br>25<br>23<br>Non-slum<br>20<br>10<br>0<br>Chennai Delhi Mumbai Kolkata<br><!-- End of picture text --> Source: Arnold et al (2009:12), derived from India’s NFHS-3 survey.

**Table 1. Sanitation rankings of metropolitan cities**

||2009-10<br>Rank Order<br>(_among 423 cities, using_<br>_19 parameters of_<br>_sanitation_)|2014-15<br>Rank Order<br>(_among 476 cities, using_<br>_2 parameters: open_<br>_defecation_<br>_and solid waste management_)|
|---|---|---|
|**Delhi:**|||
|Municipal Corporation of Delhi|**168**|398|
|Delhi Cantonment|5|-|
|New Delhi Municipal<br>Corporation|4|16|
|**Chennai**|13|61|
|**Kolkata**|25|56|
|**Mumbai:**|||
|Navi Mumbai|11|-|
|Greater Mumbai|45|140|

**_Source:_** Government of India. National Urban Sanitation Policy (NUSP) for the 2009-2010 rankings, and Swacch Bharat for the 2014-15 rankings.<sup>56</sup>

Subsequent Swachh rankings are not directly comparable to these surveys, as they give half the weight to self-assessed performance by municipal bodies (50% weight in 2016 and 45% in 2017), 25-30% weight to direct observation, and the rest to citizens’ reports.<sup>57</sup> 17
however, the business model of the international ratings companies relies on global reputation rather than any regulatory requirements. Traditionally, auditors were chiefly responsible to the shareholders (especially in common law countries), though law may increasingly impose certain other responsibilities including reporting to the prudential supervisors.

# _Political economy_

Because rating of banks is typically conducted by international firms, the process can be thought of as largely independent of national political pressures in the host country. It would be a different matter if rating agencies were brought in formally to the regulatory process inasmuch as they would require accreditation, which could suck them into compliance with political pressures. Auditing firms are usually accredited through a selfregulatory process, though there is a recent trend toward official regulation in several countries following recent US accounting scandals.

Table 1: _Number of banks rated by Fitch in each developing country_ <u>(October 2003)</u>

|_Sub-Saharan Africa_|_East Asia_|_Middle East_|
|---|---|---|
|Benin 1|China 16|_& North Africa_|
|Kenya 4|Indonesia 14|Egypt 9|
|South Africa 16|Malaysia 11|Jordan 3|
||Philippines 14|Lebanon 5|
|_Eastern Europe_|Thailand 19|Morocco 5|
|_& Central Asia_|Viet Nam 7|Oman 3|
|Azerbaijan 1||Pakistan 5|
|Belarus 1|_Latin America_|Saudi Arabia 9|
|Bulgaria 2|_& The Caribbean_|Tunisia 15|
|Croatia 4|Argentina 6|Turkey 29|
|Czech Republic 4|Bolivia 1||
|Estonia 2|Brazil 50|_South Asia_|
|Georgia 1|Chile 5|Bangladesh 5|
|Hungary 8|Colombia 5|India 34|
|Kazakstan 7|Dominican Rep. 5|Sri Lanka 5|
|Latvia 5|Ecuador 2||
|Poland 12|El Salvador 5||
|Romania 5|México 9||
|Russia 30|Panama 4||
|Slovakia 7|Trinidad & Tobago 4||
|Ukraine 3|Uruguay 8||
|Uzbekistán 1|Venezuela 8||

Source: www.fitchratings.com

# _Market pre-requisites_

Many of the ratings they provide are unsolicited and unpaid-for by the banks. Nevertheless, it has apparently been profitable for several international ratings firms to rate the largest banks in upper-middle-income countries and in the largest of the lower11