rafmacalaba/data-use-mentions-extended huggingface dataset:
Hale, Shadia Mohammed, Nada Mustafa Ali, and Harry Verhoeven, who recommended particular scholars’ work. The literature review includes academic monographs, peer reviewed journal articles, and reports from the World Bank and UN organizations, in combination with descriptive survey data from the World Bank, the Arab Barometer, and others. Selected online news sites, such as Dabanga, were also referenced.

The qualitative research element of the study provides a deeper and more nuanced understanding of the binding constraints on women and youth from both the supply and demand side of jobs by conducting focus group discussions (FGDs) and key informant interviews (KIIs) in three different states of Sudan. A survey firm (Consulsat) was hired to develop and implement qualitative research instruments to improve the understanding of the constraints both women and youth face in joining and staying in the labor force, finding a job, and accessing self-employment. A total of 33 FGDs with different segments of youth and women (urban/rural, employed/unemployed, and so on) were carried out regarding issues such as access to information, decision-making power and processes, and challenges to enter and stay in the workforce. The FGDs, 12 each in Khartoum and Madani and 9 in Nyala, were conducted separately with men, women, male youth, and female youth. The focus group participants had varying levels of education—primary school to post-secondary education. In-depth interviews with employers (28) include employers’ concerns, policies, and experiences related to hiring and retaining young people and women. The KIIs and FGDs were conducted in Arabic and then translated into English. The data were coded and then compiled, according to themes, in Excel spreadsheets. In addition to shedding light on urban/rural divides, the qualitative work highlights regional differences in three different states where services, agriculture, and industries are the predominant employment sectors: Khartoum (Khartoum State), Madani (Al-Gezira 5
of indirect ownership is more ambiguous.<sup>22</sup> We test this potential relationship to address this important empirical question.<sup>23</sup> We argue the government’s ability to influence strategic and managerial decisions of BOSs is higher when it acts as a direct owner, whereas it may diminish as the state has participation through other entities or chain of entities. For instance, when acting as a direct shareholder, the government can have voting rights and exercise the decision powers of the board, which then can be reflected in different risk-taking decisions and financial outcomes of the firm.

Therefore, we explore how the degree of proximity to the public authority as a shareholder can explain differences in performance among BOSs. For this purpose, we create an indicator of the degree of proximity to the state based on the ownership structures provided by the WB BOS database. We differentiate between BOSs owned directly by the government (i.e., public authority) and those in which it holds participation through another firm or set of firms. For the latter, we identify how many firms -denoted as layersparticipate in the ownership path and differentiate among BOSs with 2, 3, and 4+ layers of ownership (Table 13).<sup>24</sup> _<u>Table 13. Degree of proximity to the state</u>_

|**Category**|**Definition**|
|---|---|
|**Direct ownership**|Firms owned by a central or subnational public authority with 10% or more<br>participation.For instance,Ministry of Finance owns25% of firm A.|
|**Indirect (2 layers)**|The state is an indirect owner through another firm/entity. In this case, B is<br>an indirectly owned BOS as the firm A (with 25% direct participation of the<br>MoF) owns 10%
sup>|-0.020<sup>***</sup>|-0.018<sup>***</sup>|-0.011<sup>***</sup>|
|_(Herfindahl Index)_|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Natural Resource exports|0.007<sup>**</sup>|0.008<sup>***</sup>|0.006<sup>***</sup>|-0.005<sup>***</sup>|-0.002<sup>***</sup>|-0.003<sup>***</sup>|
|_(% of GDP)_|(0.002)|(0.000)|(0.000)|(0.000)|(0.000)|(0.001)|
|Secondary enrollment rate||||0.008<sup>***</sup>|-0.001|0.002<sup>***</sup>|
|_(initial level, logs)_||||(0.000)|(0.800)|(0.000)|
|ICRG Political Risk Index||||0.006<sup>***</sup>|0.001<sup>***</sup>|0.005<sup>***</sup>|
|_(principal components)_||||(0.000)|(0.000)|(0.000)|
|Credit to private
tion generates greater effects for the less violent children.

These results are consistent with a body of microlevel evidence, such as papers on random assignment of freshmen or students (Thiemann, 2013); on elite exam schools (Abdulkadiro ̆glu et al., 2014; Dobbie and Fryer Jr, 2014; Lucas and Mbiti, 2014); and programs for gifted individuals (Bui et al., 2014). Additional evidence on academic and labor contexts is presented by Hoxby (2000); Zimmerman (2003); Angrist and Lang (2004); Rao (2019); Griffith and Rask (2014); Lafortune et al. (2016); Chetty et al. (2016); Oreopoulos et al. (2017). Overall, these papers find positive impacts of exposure to a very different set of peers. They argue that the integration effects occur due to the interaction between different individuals within groups, supporting the rainbow model of peer effects (Hoxby and Weingarth, 2005).

As I briefly noted before, my results relate mostly to those of Rao (2019), who provides the first evidence of how changes in peer composition at school can shape a student’s social preferences through an improvement in her generosity, prosocial behavior, and equity. My paper contributes to these results by providing additional experimental evidence that is especially relevant for the developing world. I test how exposure to diversity regarding violence positively impacts additional noncognitive outcomes such as violence, approval of peers’ antisocial behavior, misbehavior, and attitudes toward school and learning. An additional outstanding characteristic in Rao (2019) is that the author uses well-constructed measures of social preferences. In my study, I collected measures of noncognitive outcomes from students’ self-reports and administrative data provided by schools. These two sources of information enable me to contrast and validate the results.

Additional evidence from my experimental design regards the tracking effects for marginal participants. For example, an individual at the median in the violence distribution who is assigned to a high-violence group
<!-- Start of picture text --> Stunting prevalence by region (% of infants/children)<br>0-5 completed months 6-18 completed months 19-60 completed months<br>50%<br>Autonomous Region<br>40 inin Muslim(ARMMMindjanao<br>30 utonomous Region<br>in Muslim Mindanao<br>(ARMM)<br>20 National Capital<br>Region (NCR)<br>Region Ill: Central<br>Luzon<br>10 ° NationalRegion (NCR)Capital<br>Region III: Central<br>National Capital Region|X: Mindanao Luzon<br>Region (NCR) Autonomous Region<br>0 Region II: Cagayan in Muslim Mindanao<br>Valley (ARMM) $$ SS —<br>0 10 20 30 40 50% 0 10 20 30 40 50% 0 10 20 30 40 50%<br>Poverty rate (% of population)<br><!-- End of picture text -->

Source: Author's calculations using 2015 NNS and official poverty statistics. Notes: Poverty rates are based on the Philippines national poverty line. The 95% confidence intervals for regional estimates of stunting prevalence are displayed. In addition, the line indicates the average relationship between regional poverty and stunting prevalance based on ordinary least squares (OLS) fit of the data points, the shaded area represents the 95% confidence interval of the OLS line.
by comparing the number of poor under the two growth scenarios.<sup>20</sup> Denoting the number of poor people in year

> 19If the same number of deaths were to be distributed at random in the population, they would generate 4.5 as many lost-years as the current distribution. This factor is equal to the average residual life-expectancy in Sweden (43 years) divided by the average residual life-expectancy of those dying (9.5 years).

> 20In all projections, it is assumed that only 85 percent of growth in GDP per capita is passed through to growth in welfare observed in household surveys in line with historical evidence (Lakner et al., 2020). In contrast to the data from PovcalNet which are expressed in 2011 PPPs and in per capita terms, the national poverty lines for Pakistan, Peru and the Philippines are expressed in local currency units and per adult equivalents. To convert the national poverty lines into 2011 PPPs and per capita terms we follow the approach of Jollie and Prydz (2016), which is to nd the poverty line in per capita 2011 PPPs that gives the same poverty rate as the national poverty line in local currency units and per adult

9
**The fees charged in the good years seem relatively high compared with other countries with similar pension systems** . Assuming a real rate of return of 5 percent and an inflation rate of 8 percent, pension funds will be able to charge a fee of approximately 2 percent of the assets on a permanent basis. While most countries are trying to find the equilibrium fee in the range between 0.5 and 1.0 percent of the assets, targeting fees in the range of 2 percent seems excessive. As shown in Figure 3, most of the CEE countries plus Chile are far below the barrier of the 2 percent. Although it took nine years for Chile to go down to this threshold, the technology available in the 1980s was less sophisticated than the one available right now.  The structural design of the Russian pension pillar should be able to operate at lower costs than Latin American and Central European countries, and closer to the 50 basis points (or less) of the system in Sweden.

**Figure 3 Total Fees in Selected Countries**

(as a percentage of the Assets) <!-- Start of picture text --> Total Fees<br>(% Assets)<br>4%<br>2%<br>0%<br>1 2 3 4 5 6 7 8 9 10 11<br>Years of operation<br>Chile Hungary Poland Slovak R. Lithuania Croatia<br><!-- End of picture text --> Source: The World Bank It is interesting to note that in 2007 the asset managers did not charge the maximum fee allowed in the Law. Figure 4 shows the fees charged by different asset managers of pension funds. As seen in Figure 4, the distribution of fees goes from 0 to 0.9 percent among different asset managers, VEB being one of the least expensive.

23
after the provision of TMDs. The distribution of WS also imposed a positive impact on restaurants tax receipt, albeit a less pronounced one when compared to TMD. WS allotment brought about 55 percent higher restaurant tax payment compared to those in the control group. The estimates are statistically significant at 5 percent and 10 percent, respectively.

Figure 8 Panel A depicts our main DiD results on restaurant observations. Overall, the observed wedge between treatment and control group for TMD far exceeded those observed in RPOS and WS. TMD’s stand out impact on tax compliance among restaurants is highly visible, as treatment group’s estimated tax payment diverged further away from that of control group upon TMD’s distribution in October 2019 (Figure A.14 Panel A). WS, on the other hand, seems to impose a less significant impact as visible divergence between treatment and control group. Lastly, the wedge between treatment and control group in RPOS’ case is much less sizeable throughout the observation time, both in terms of outcome value (Figure A.14 Panel A) and growth (Figure A.14 Panel B), which manifests in a quite thin gap between its DiD estimates graphs (Figure 8 Panel A).

WS’ moderate impact on tax compliance is also shown among our hotel observations (Tabel 5). WS-receiving hotels paid tax by around 51 percent higher compared to their control counterparts. This result is statistically significant at 10 percent. Such a sizeable impact of WS among hotel samples in West Manggarai is visible, both in terms of trend and change overtime (see Figure A.16).

15
50 GEL and the ones in Treatment 3 received 70 GEL.<sup>8</sup> The survey measured outcomes to assess the effectiveness of the embedded information intervention, such as self-reported health symptoms.

# **2.3 Balance test**

We show in Table A1 that treatments are generally balanced across observables using data from the baseline survey. We report the difference in means between each of the treatment groups and

> 8At the time of the payments, 1 GEL was approximately 0.38USD.

8
more than 80 percent of total revenues. In this case, it is inevitable that oil revenues are partially consumed to run basic functions of the government. For that reason, the user of the LTGM-NR can choose any positive value for ����� . A possibility is to set ����� to 0 ⁄ 00 implying gg that the cannot increase over time (as in section IV.B).tt tt<sup>25</sup> Although this SSBBII SSBBII cc zz configuration does not match perfectly the asset-preservation principle, it prevents tt SSBBII the government from increasing consumption in times of high resource revenues.

> 23 Some countries consider spending on education and health as investment in human capital, so the HR would not constrain this type of expenditure.

> 24 Botswana is the most celebrated country to incorporate the SBI rule in its fiscal framework for diamond revenues (AfDB 2016). 25 Another possible application is to set ����� to match historical SBI values.

> SSBBIItt 17
# **4 Does micro-credit support the subsidy program?**

We present the findings from our analysis, which focus on differential impacts of the sanitation microcredit intervention by subsidy eligibility, for our four main outcomes of interest - sanitation loan uptake, toilet uptake, toilet usage and subsidy uptake. For completeness, we also present the average impacts of the intervention on these outcomes for the sample of interest in this paper – households without a toilet at survey baseline.<sup>19</sup>

**Sanitation loan uptake and toilet uptake** We start by considering whether impacts of the sanitation micro-credit on sanitation loan uptake and toilet uptake vary across subsidy eligible and subsidy ineligible households. These findings, presented in columns 1-2 of Table 4, show that first, the new loan product was taken by 19% of the MF client households without a toilet at baseline. Second, Column 2 shows that subsidy eligible households were as likely as subsidy ineligible households to take up a sanitation loan.<sup>20</sup><sup>_,_21</sup> At first sight, this latter finding might come as a surprise given that subsidy eligible households should, in principle, be able to access government subsidies to cover the costs of toilet construction. However, as already mentioned above, in this context even subsidy eligible MF client households might want to seek additional credit to construct a toilet, either as bridge financing or to supplement funding. We will explore these channels in more detail in Section 5.

Moreover, households could have taken the sanitation loan for reasons other than to invest in sanitation. As discussed in ACGMS 2019, the sanitation loans had a lower interest rate than most other loan products offered by the MFI, and loan use was only weakly monitored and not enforced. Therefore, uptake of the loan product does not necessarily represent the desire to construct a toilet. Indeed, ACGMS 2019 provide evidence that, on average, only about 50% of sanitation loans were converted into
endorsing its predictions, at least qualitatively.<sup>3</sup> The model predicts protection to be a simple function of the existence of lobbying organization, the inverse of the import-to-output and export-to-output ratios and the import demand and export supply elasticities. The tightness of its theoretical predictions make the model amenable to testing using cross-sectional data. We apply the Grossman-Helpman model to the case of US agricultural trade policy, using precise measures of lobbying organization and agricultural protection and subsidies to a cross section of agricultural commodities from the late 1990s. To our knowledge this is the first study to use agriculture data to test the Grossman-Helpman model. It is also the first to seriously consider subsidies within the Grossman-Helpman framework.

We go beyond previous empirical studies of the Grossman-Helpman model as well. We would expect a government that is influenced by contributions to trade off consumer welfare loss for contribution dollars on an about equal basis. However, previous studies indicate that the US government is actually a welfare lover and places almost negligible weight on contributions. This paper suggests a novel resolution for that puzzle. We show that it is the consequence of taking a feature of the theory all too literally. Doing away with the “unitary” government assumption and introducing policy uncertainty, that is, making the model more real-worldly, rids the paradox.

The paper proceeds as follows. Section 2 briefly surveys the literature. It emphasizes the difference between the reduced form methods traditionally used in the agriculture protection literature and the structural method implemented in this paper. In Section 3 the GrossmanHelpman model is introduced and its main predictions stated and described. The data are described in Section 4. The data are new and theoretically appropriate for testing the model. In Section 5 the model’s prediction is tested and the results analyzed. A puzzle posed by this and previous empirical work in this area is resolved. Section 6 concludes.

> 3 Eicher and Osang 2002, Gawande and Bandyopadhyay (2000), Goldberg and Maggi 1999, Mitra, Thomakos, and Ulubasoglu 2002,
In Phase III districts, 49 percent of students enrolled score more than 60 percent marks in grade 5 examinations, whereas in Phase I districts only 38 percent do. Similarly, these percentages for grade 7 examinations are 43 percent in Phase III districts and 36 percent in Phase I districts. Additional summary statistics about the schools and districts in different phases are presented in Appendix Tables S1 and S2 and the details are discussed in the online Supplemental Appendix.

# **4 Empirical Strategy**

We use the timing of roll-out of the MGNREGA program across districts of India for identification. Phase I districts received the program in February 2006, Phase II in April/May 2007, and Phase III in April 2008. We use 2005 as the baseline year and include data from 2005-2008 in our analysis. Later we use data from 2003 for the districts we have it for to provide support to our identifying assumption.

## **4.1 Roll-out and Selection**

The timing of the roll-out of the program was not randomly determined. Thus, a simple comparison of the districts across different phases is not likely to generate causal estimates of the program. In order to circumvent this issue, we compare outcomes within districts that received program in different phases over time. This allows us to control for time invariant differences in unobserved characteristics of districts that received the program in different phases. We also use within-school variation for identification by including school fixed effects to purge any time invariant school level characteristics that may be correlated with the treatment.

We further interact the three variables determining selection into the phase of roll-out with year indicators to control for trends in these variables. In addition, we include a rich set of district specific controls including: 2001 levels of total population, percentage 9
Data**

_Specification_ The model sketched above suggests that, after controlling for idiosyncratic factors in each market, export sales of individual apparel products towards the EU and US 9 This is the route followed by Cherkashin et al (2010). They rely on cross-section data only for estimation which does not allow them to tackle directly the effects of change in RoO as we do here. Their identification relies on assuming (rather than estimating) that costs associated with meeting origin requirements raise unit production costs by 15%, that obtaining a quota license to sell to the US under the MFA costs 7%, that decisions about entry into each market are made separately, that expenditures on Bangladeshi goods come entirely at the expense of expenditures from other exporters. In any case, to fit a structural model to the exports of apparel by several African countries to the EU and US markets would require firm-level data which was collected by the authors of the Bangladesh study but that are not available for any of the AGOA beneficiaries.

9
of the three months. To calculate , we measure missed or partial coupon payments based on our newly collected bond-level dataset on default and restructuring outcomes (see Appendix B2). Appendix ii,jj,mm CC B2 also explains how we account for bond haircuts when calculating , that is, how we deal with exchanges of old into new bonds at a loss. In a nutshell, to compute returns in restructuring months, we ii,jj,mm NN combine the old defaulted bonds and the new instruments that creditors receive in the exchange, with the implicit assumption that creditors keep the newly restructured bond in the portfolio. We then account for the share of debt written off (face value debt reductions, if any) as well as potential cash payments that are made in the wake of a restructuring (including for the settlement of past arrears). In case the restructuring only involves a rescheduling of maturities but no face value debt reduction (sometimes called “debt reprofiling”), the write-off is zero, while the change in maturities should be reflected in the secondary market price of the new bond. This also implies that the exact NPV haircut estimation approach chosen (see Section 3) is not decisive for the computation of the returns series. In fact, the haircut estimate is a snapshot at one point in time, while returns are measured continuously on a monthly level. Ultimately, the key default-related variable that matters for the returns is missed coupons, since much of the returns come from interest payments and if these are not made, total returns drop. We do not account for transaction fees or taxes (See Appendices B2 and B5 for further details).

We use _real_ ex-post returns as the baseline measure, although we show results for nominal returns as well. By definition, all bonds in the sample are denominated in GBP or USD, so we need historical inflation data for the United Kingdom and the United States to compute real returns. For this purpose, we rely on the historical inflation indices provided by
4.4|19|2.2|25|1.7|89|2.6|
|**Memo:**|||||||||
|BRICS 5 countries|12|3.0|36|4.3|92|6.2|458|13.2|
|OPEC 12 members|38|9.1|28|3.3|38|2.6|137|4.0|

Source: Based on World Bank World Development Indicator database.
survey year, based on the subsample of households with heads in the age range of 25–60.

# 3. Results

We discuss the results of our analysis of economic mobility in three parts. First, we focus on absolute mobility at the lower end of the income distribution by investigating poverty dynamics. Then, we take a broader perspective by studying upward and downward absolute mobility between three income groups, which takes into account considerations of vulnerability and economic security. These first two analyses are based on fixed income thresholds. The third part studies relative mobility by analyzing movements between income quintiles. This allows us to analyze what is happening in the richer parts of the income distribution too.

> 15 We are aware that the index is thus based on expenditure patterns at the very low end of the income distribution. However, this is the best we could do in the absence of spatially disaggregated consumer price data.

9