rafmacalaba/data-use-mentions-extended huggingface dataset:
men and 500 young women were randomly selected and invited to attend one of three regional, in-person lotteries to allocate the vacancies

> top 5 countries in the 2019 Africa Gender Equality Index—there are still prominent gaps in some areas. In 2021, labor force participation was 62% for men and 47% for women, and the share of youth not in education, employment or training was 41% for women and 30% for men (Rwandan National Institute of Statistics, 2022).

> 12Notably, the observed soft-skills gender gap is largest among the most educated and returns to these soft skills vary by gender, with intrapersonal skills strongly correlated with women’s earnings.

> 13Groh et al. (2016) have a sample of only educated women and find no effects of soft skills training on labor market outcomes. Among the papers focused on lower-educated samples, Acevedo et al. (2020) find more positive effects of internships combined with soft skills training for women than for men on personal skills, expectations and short-run labor market outcomes; while Barrera-Osorio et al. (2020) find mainly similar effects of vocational training with focus on soft skills for men and women, with some evidence of stronger effects for men. Among papers studying non-labor market outcomes, Campos et al. (2017) find large effects for both men and women of personal initiative training on business outcomes in Togo, and Ashraf et al. (2020) find significant effects on education outcomes of a training focused on negotiation skills for girls in Zambia.

5
their groups’ formation.<sup>7</sup> The data include variations in group and individual repayment rates, where the individual repayment is to be examined against both individual and group repayment data and other characteristics. To study group behavior over time, the paper relates variations in group behavior with variations in individual-level borrowing and repayment behavior after controlling for the sorting behavior of members into groups. The cross-member effect of individual-level behavior is measured as one of the effects of group liability on individual repayment behavior.

The question on the role of joint liability in group-based lending is thus answered by weekly loan disbursement and repayment data of group members from selected groups. For each group, the time series must start with the week of the first loan disbursement to any member of the group and end on the date when the data were collected. Using such data, we attempt to resolve the endogeneity of the basic group-based lending feature of the Grameen Bank.

The five-member group is the central pillar of Grameen Bank. Separate groups are formed for men and women. Group membership is strictly restricted to people of the same villages with similar economic resources, who own less than half an acre of land, and are not members of the same household as another microcredit program member. The group is also responsible for loan delinquency of group members (for details, see Hossain 1988; Khandker 1998).<sup>8</sup>

> 7 Simple group panel data, irrespective of when the groups were formed, are inadequate for resolving the endogeneity of why certain members join a group in the first place.

> 8 However, in recent years some of these conditions are relaxed in Grameen's lending.

9
practices) might have on sibling caregivers. For example, the standard format of the Family Care Indicators, one of the most widely used measures of early childhood stimulation, does not record stimulating activities carried out by household members under 15 years old (Hamadani _et al_ . 2010, Kariger _et al_ . 2012). Researchers also tend to ignore the potential impacts of parenting interventions on sibling caregivers. For example, a recent systematic review of 466 impact evaluations of early childhood development interventions in LMICs found that only four measured indirect effects on older siblings in middle childhood or adolescence (Evans, Knauer, and Jakiela 2020). This tendency to ignore the caretaking role of older siblings is at least partially attributable to the widespread perception that parental stimulation is more beneficial to young children than stimulation by older children (though it may also entail a higher opportunity cost). In a published response to Weisner _et al_ . (1977), Brian Sutton-Smith argued that “Maximal personal and social development of infants is produced by the mother (or caretaker) who interacts with them in a variety of stimulating and playful ways. Unfortunately the intelligence to do this with ever more exciting contingencies is simply not present in child caretakers,” Weisner _et al_ . (1977, p. 184). Thus, sibling caregiving, though widespread, is often considered a second-best alternative to greater maternal investment — particularly since childcare responsibilities might limit older siblings’ ability to invest in their own human capital through formal schooling.

2
### **E. Intellectual property rights**

To promote private equity funds in the country, Nepal should modernize its Patent, Design and Trademark Act 1965 to incorporate WTO trade-related intellectual property rights (IPR). Nepal’s legal framework for IPR is found in the Patent, Design, and Trademark Act 1965 and the Copyright Act 2002. However, the law does not meet modern standards for trade-related intellectual property rights. International trademark holders have complained that that they are unable to protect trademarks which are well-known in international markets, despite Nepal being party to several IPR related conventions: i) Paris Convention for the Protection of Industrial Property (June 22, 2001); ii) Convention Establishing the World Intellectual Property Organization (February 4, 1997); and iii) World Trade Organization (WTO) - Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS Agreement) (1994) (April 23, 2004). Nepal also adopted The Competition Promotion and Market Protection Act in 2007 which governs anti-competitive practices, monopolies, fair competition, and mergers and acquisitions However, this Act has not been effectively implemented or enforced in practice.

## **V.**

# **Business-Enabling Environment**

In addition to the legal and regulatory environment, the task team has analyzed several other factors that affect PE/VC investment in Nepal. These factors impact a fund’s ability to operate during different phases of its lifetime, including fund-raising, investment entry, management of portfolio companies and investment exit. Table 3 provides a summary of the task team’s findings.

In general, there is substantial room for improvement in business environment in Nepal, which currently ranks as number 107 (out of 190) in the World Bank Doing Business ranking. While this is better than the rankings for India (130) and all other south Asian countries except Bhutan, on a broader international basis it is an unsatisfactory result. Nepal’s position also deteriorated from 2016 to 2017. Five components of the
be biased towards locally produced goods (Morey (2016)) or consumers’ identities, which may be expressed by consuming local varieties (Atkin, Colson-Sihra, and Shayo (2021)). A second possibility is that regional and national governments maximize their respective citizens’ welfare and use public procurement to manipulate the terms of trade. In that respect, our paper is related to the vast literature that analyzes optimal trade policy (Limao, 2008). A third possibility is that governments’ home bias is simply driven by “politics”. Applying the Grossman and Helpman (1994)’s theory to the context of public procurement, it could be that the presence of locally based lobbies biases governments’ choices towards local firms, even if that is not in the interest of local consumers. Along these lines, recent work shows that firms with political connections are more likely to win grants and procurement contracts (Choi, Penciakova, and Saffie (2022) in the case US and Baltrunaite (2020) in the case of Lithuania).

**Trade in services.** The richness of our data enables us to observe trade in procurement services, allowing us to contribute to an active literature on the tradability of services (Mu ̃noz (2021), Dingel, Gottlieb, Lozinski, and Mourot (2022)). Procurement services account for one third of procurement contracts in our data (and up to two thirds if we count construction as a service). Using our granular establishment-level data, we find that the gravity equation derived from a standard theoretical framework provides as good a fit for services as for goods, confirming its suitability to study trade in services.

**Government decentralization and economic outcomes.** Finally, and more tangentially, our paper contributes to the debate about the optimal degree of government decentralization (e.g., Oates (1985), Besley and Coate (2003), Treisman (2007), Tricaud (2021)). Our results present a new channel through
(Table 7.A columns (2), (3), (5), (6), (8) and (9)) and on the amount received (Table 7.B column (2), (3), (5), (6), (8) and (9)), whether it is included on its own or together with GDP per capita.

Table 8A and 8B show the second stage estimations where we instrument the likelihood of receiving remittances and the amount of remittances received per capita, respectively. We use the municipality level weighted GDP per capita for US states where migrants reside and the corresponding poverty rates individually and jointly as instruments. The overidentification tests reported in both tables 8A and 8B suggest that economic conditions in US states where

> 11 Following Rodriguez (1996), we expect the length of time spent by migrants abroad to have a non-linear inverted U shape relation to remittances and for remittances to increase with the closeness of the relationship of the migrant to the household.

13
037***|0|-0.034***|-0.062***|0|
|Not employed in the<br>agricultural sector|-0.006***|-0.024***|0|-0.018***|-0.036***|0|
|Access to electricity|-0.022***|-0.080***|0|-0.104***|-0.162***|0|
|Piped drinking water|-0.010***|-0.009*|0.9891|-0.027***|-0.027**|0.9891|
|Improved sanitation|-0.010***|-0.035***|0|-0.043***|-0.068***|0|

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD)</u>

|**Table A12**<br>|**: Identifying**<br>|**the $3.20poo**<br>_Pakistan_<br>|**r based o**<br>_2018_<br>|**n covariates o**<br>|**f poverty**<br>||
|---|---|---|---|---|---|---|
|**Category**|**Per capita**<br>**poor only**|**Square root**<br>**poor only**|**Diff.**<br>**p-value**|**Per capita**<br>**
)|839.743***|(277.622)|
||P-valueforSES|0.008||0.013||
|CountyDummie|s|Yes||Yes||
|Observations||2,190||2,190||
|R-squared||0.079||0.082||

Note: 1) The sample includes those who are no younger than 25 and with at least one parent no younger than 60.

- 2) Parent characteristics are from main respondents.

- 3) Clustered standard errors at family level are in parentheses.

- 4) *** p<0.01, ** p<0.05, * p<0.1

28
1 and 2 show, there are significant fluctuations around this expected relationship. Given levels of development, there are some countries that are over-performing in expected discovery activity, and others that are under-performing.

To further study the pattern of discoveries across countries and regions, we utilize a more flexible functional form. To this point, we have used GDP per capita in order to analyze the relationship between discovery and development, and specifically to determine the income level at which the discovery curve peaks. However, we would expect that country size would affect the number of economic discoveries. That is, a country of 300 million has a larger number of entrepreneurs and businesses that could discover new products for export when compared to a country of 300 thousand. When considering the absolute number of discoveries in an economy, population should be accounted for separately from the effect of the scale of each national economy. Therefore, we add population to the model, which enters as statistically significant and positive using the HS 4- and 6-digit well as SITC 3-digit data (see estimation results in Appendix III). In order to allow for flexibility in the effects of population and wealth, we switch from GDP per capita and GDP per capita squared to total GDP and total GDP squared when population is included in the model. We will refer to (11) as the basic model of discovery: We estimate this equation using a negative binomial regression (see Appendix III for estimation results), calculate the residuals, and then scale them by dividing each by the standard deviation of the residuals. These standardized residuals are measures of over- or under24
for which data is retrieved from the US Census ICT survey. Then in equation (1), the data‐intensities are stated as a ratio over labor, called LAB� , that is employed in each downstream sector _j._ The data for labor is retrieved from the US Bureau of Labor Statistics (BLS). As a result, we apply the following formula: Note that we put the intensity indicators in logs, in line with previous literature on factor intensities. This expression of intensities is close to the literature of comparative advantage such as Chor (2011), Nunn (2007) and Romalis (2004).<sup>2</sup> <mark>Finally, in equation (1), th</mark> e data policy index refers to a country‐ year specific variable measuring restrictive data policy (see Section 3.3), whereas the data on software refers to the US‐specific data on software use by industry for one year (see Section 3.2), which is done to avoid endogeneity issues. This may occur in the event that high data‐intensive sectors with greater digital innovation activities over time push for lower regulatory restrictions regarding data in any particular country. The use of this common sector‐specific data‐intensity for one country therefore makes the variable more exogenous.

> 2 An alternative way of measuring intensities such as the one used in Arnold _et al_ . (2011; 2015) and Bourlès _et al_ . (2013) is to create an indicator of dependency using input‐output matrixes, which we have also done in Ferracane _et al._ (2018a) and Ferracane and van der Marel (2018). We use this information as well in our paper for the country‐specific case studies. In such way, we have two approaches to data‐intensities, i.e. one based on data from surveys and one from accounting data. In our view, however, data‐intensities over labor are a more
# **Summary**

This paper reviews and analyzes the status of public debt management performance in small states, defined as states with a population of less than 2 million. The focus of the paper is on small states classified by the World Bank as low- or middle-income.<sup>1</sup> It draws on the findings of the Debt Management Performance Assessment (DeMPA) reports of 17 small states to assess the current state of debt management performance, identify strengths and weaknesses and contrast debt management performance in small states with that of other low and middle income countries for which DeMPA reports have been produced. Based on the 17 sample countries the broad conclusions are;

- Small states lag well behind other developing countries in most dimensions of debt management performance;

- Small states that issue domestic debt have better capacity to manage domestic debt as compared to their capacity to manage external debt despite the fact that, in most small states reliance on external sources of financing is, on average, much greater than on domestic markets;

- The debt management capacity of small states is striking similar, with relatively little difference observed between the strongest and the weakest performers or between small states in one geographic region and those in another;

- Debt management capacity in small states does not correlate to income levels with small states classified as low income exhibiting, on average, better debt management performance (in terms of the DeMPA scores) than small states classified as upper middle income.

Empirical evidence supports the view that the higher the quality of a country’s policies and institutions, the better is its capacity to carry debt and withstand exogenous shocks.<sup>2</sup> Borrowing for productive purposes can be an important element in boosting GDP growth but, conversely, excessive or indiscriminate borrowing can quickly offset the positive impact, deter new foreign and domestic investment, compromise reform programs, depress GDP growth and exacerbate the challenge of meeting debt service obligations. Poorly structured debt in terms of maturity,

> 1 The
, especially, with regard to spending financed by mineral revenue. With the change in policy emphasis, over time, some rules have been phased out. The rule to limit public spending to 40 percent of GDP is no longer in use. Neither is the Sustainable Budget Index (SBI). Currently, the remaining rules are the statutory ceilings of 20 percent of GDP for government external and domestic debt and guarantees, respectively. Should Botswana adopt additional rules to ensure medium-term fiscal sustainability?

The statutory debt and guarantee ceilings can continue to serve as effective instruments to in restraining the size of overall fiscal deficits. Rather than introducing new rules, Botswana could benefit more from implementing the existing rule effectively. In this regard, focus needs to be given to addressing obstacles that hamper the effective implementation of the rules.<sup>28</sup> Specifically, this requires a more specific definition of “government debt” in the Stock, Bonds and Treasury Act (1995), without which the rule could be interpreted in an inconsistent manner, or could potentially be abused.<sup>29</sup> As important as the definition is the ability to monitor the adherence. Again, this requires the timely availability of reliable fiscal, debt and macroeconomic data, in particular, within the period to which the rule is applied.

Botswana could also consider the use of the non-resource fiscal balance as a benchmark to evaluate the fiscal stance. Recent literature shows that, particularly from a short-term stabilization perspective, the use of non-resource balance, scaled by non-resource GDP, provides a useful guide by delinking the fluctuations associated with the developments in mining. Indeed, in countries such as Norway, budget

> 26 There is a specific tax on transport fuels, but it is very low (currently P 0.21 for petrol, P 0.12 for diesel per liter).

> 27 Botswana’s social outcomes, which fall short of those expected in a middle income country despite generous funding, may be a reflection of
# **_Reconstructing the climate migration chain_**

Recognizing that human mobility happens along a continuum of needs, possibilities, and choices that are shaped by direct or indirect push and pull factors, implies there can be many steps in the migration process possibly culminating in cross-border movements (Malmberg, 1997). However, internal and international migration are almost always seen as separate processes and rarely analyzed jointly (Cirillo et al., 2022), and climate-related migration is no exception. In the context of climate migration, reconstructing a potential chain and assessing the existence of indirect and secondary climate migration effects is a complex analytical task requiring an extensive amount of information that household surveys alone cannot provide. While priorities such as the need to better understand potential climate immobility traps can easily be accommodated in the framework of LSMS-type surveys through the means discussed above, there is little that can be done to improve the traceability of international migrants: first, because the ‘rare event’ issue gets even more severe when it comes to international migration, in light of its higher costs; second, because although household surveys are often well-equipped to track internal movements, tracking cross-border migration is much harder. This hinders a thorough investigation of the full climate migration chain which potentially leads from internal to cross-border mobility. Yet again, some concrete possibilities may be offered by enhancing the integration of LSMS surveys with other traditional and non-traditional data sources. In general, integrating these different sources would enhance the ability of LSMS surveys at all levels but, as depicted in Figure 1, would be especially critical for shedding better light on the intermediate and final steps of the climate migration chain. Towards this end, we propose the following suggestions:

- LSMS-type surveys are crucial for the investigation of internal rural-urban movements triggered by initial climatic shocks and should be used to cover data needs for this type of empirical analysis which pertain to the first steps of the climate migration chain. For subsequent steps, _i.e._ , crossborder migration out of overcrowded urban areas triggered by
, however, conduct their own national detailed surveys with school principals, teachers, and students in addition to administering standardized tests across grades. Latin America is particularly prolific: for example, Brazil’s Prova Brasil, Colombia’s SABER, Chile’s SIMCE, and Peru’s ECE are all available to researchers. These questionnaires provide rich information about

> 6We replicate the primary figure from Bloom et al. [2015a] in Figure B.3 in Appendix B. It plots the school-level student learning outcomes by each quartile of school management score.

> 7We provide details to enable replication in Appendix C

> 8Our main focus is on the 2012 data because that survey wave contains a richer set of questions, particularly relating to people management. See Appendix C for a mapping of the 2015 PISA data.

> 9The set of WMS topics that had matching PISA questions is detailed in Appendix C.

> 10We also built the indices using alternative methods (straightforward standardization, factor analysis, and factor analysis with Bartlett correction) which yielded similar results.

7
TA 2-specific findings) indicates that Models 1, 2 and 8 yield estimates of 21.6–22.5 – all within one standard error of the true poverty rate. Model 9 yields estimates of 23.3–23.4, within the 95 percent CI of the true poverty rate. Notably, Model 3.1’s estimates (28.4 percent and 28.7 percent) using the food expenditures data collected with the lighter questionnaire under TA 2 are outside of the 95 percent CI of the true rate. Likewise, Table 4 (for TA 3-specific findings) indicates that Models 1 and 2 estimate poverty rates at 21.6–22.6 percent, within one standard error of the true poverty rate. The estimates for Models 8 and 9, specifically 23.4-23.8, are still within the 95 percent CI of the true poverty rate. The estimates for Models 3.1 and 4.1, specifically 38.5–48.9 percent, are significantly higher than and outside of the 95 percent CI for the true rate.

However, when we standardize the distribution of the variables associated with Model 3 in TA 2 and Models 3 and 4 in TA 3 by those in NPS 2020/21, more promising results emerge. The means of the standardized variables in TA 2 and TA 3 are not statistically different from those in NPS 2020/21 (Appendix A, Table A.3). When we predict the poverty rate using standardized variables, the estimation results demonstrate statistically significant improvements for Model 3.2 in TA 2 (Table 3), as well as for Models 3.2 and 4.2 in TA 3 (Table 4), with all estimates falling within one standard error of the true poverty rate. The improvement after standardizing variables is consistent with earlier standardization evidence using household surveys and labor force surveys for Jordan (Dang _et al._ , 2017). Figure 2 provides a visualization of the key results from Tables