rafmacalaba/datause-encoder-data huggingface dataset:
<mark>We focus on issuances of corporate bonds in U.S. dollars, which is a prerequisite to being included in the bond indexes we analyze. We study issuances that take place only in international markets, defined as a firm issuing a bond in a market that is different from its country of origin. Additionally, we compare international dollar-denominated bonds issued by emerging market firms with a sample of investment grade bonds issued by firms from developed markets. In this way, we are able to compare yield and issuance outcomes for firms that are inherently riskier (emerging market firms) with a control group of firms that are considered relatively safe (investment grade developed market firms). This comparison is relevant because we hypothesize that investors’ search for yield leads them to increase their exposure to riskier firms around the world.</mark><sup>14</sup> <mark>We include firms from 68 developed and emerging economies (countries or markets) for the period 2000-2016. We use the nationality of the firm that is provided by SDC Platinum to classify firms into developed and emerging markets (as listed in Appendix Table 1).</mark><sup>15</sup> <mark>We include both financial and non-financial firms, because the market structure effects that we document affect issuances by any type of firms. However, our results are robust to excluding financial firms. Our sample includes 19,906 issuances from 4,965 firms.</mark> <mark>We complement these data with additional information, mainly from three different sources. We use injections/redemptions to emerging market debt funds from Emerging Market Portfolio Research (EPFR) Global to gauge changes in investor interest in emerging market debt. We use data from Morningstar Direct on the asset level portfolios of mutual funds to understand the different types of investors holding emerging market</mark>

> 14 In the Appendix, we provide additional results using jointly high-yield developed market firm bonds and emerging market firm bonds.

> 15 SDC Platinum contains a category that classifies the type of bond issued, which sometimes conflicts with our classification using the nationality of the issuer. If this category indicates that an emerging market firm issues the bond, we classify it as such regardless of the nationality of the firm provided by SDC. This affects only 300 observations (1.5% of our sample).

15
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# _Productivity Loss Per Case_

We assume a range of values against the benchmark assumption of _5_ weeks loss per case in order to assess the sensitivity of our results. As mentioned earlier, the average degree of incapacitation caused by Guinea worm infection -- as chronicled several studies -- is about 8 weeks. Thus, our benchmark assumption of 5 weeks of work-time loss is quite conservative in that it is more representative of the lower bound estimate of the dracunculiasis-related duration of disability reported in most studies. Table 5 reports the results of a sensitivity analysis with regard to changes in this parameter. As can be seen, the results are quite sensitive. In any case, even at a relatively low estimate of a 4 week loss of productivity per case of infection, the NPVs are substantial.

**Table 5.** Sensitivity of NPV and ERR: _Productivity Loss Per Case_ Project Horizon: **1987-1998.**

|Productivity Loss||||
|---|---|---|---|
|4 weeks|_NPV_<br>**_ERR_**|@3%<br>@10%|$4,927,370<br>$4,233,999<br>11%|
|**5**weeks|**_NPV_**<br>**_ERR_**|**_(@3%_**<br>**@10%**|**S7.235,744**<br>**$6,257,928**<br>**29%**|
|6 weeks|_NPV_<br>_ERR_|**_@3%_**<br>@10%|$9,544,119<br>$8,281,857<br>44%|

# _Annual Incidence without GDEC_

For our benchmark analysis, we have assumed that in the absence of GDEC the annual incidence of Guinea worm would remain at the level of approximately 2.2 million over the course of the time horizon in our study. We now assess the sensitivity of our results to this assumption. We assume growth rates of -1.5% and 1.5% -- in addition to the benchmark of 0% -- in the annual incidence of dracunculiasis in the absence of GDEC.<sup>2 7</sup> Values of other parameters are the same as in the benchmark. Table 6 reports the results. As can be seen, the ERRs again are quite sensitive to this parameter. Not surprisingly, any projected increases in dracunculiasis infection (due to population growth, for instance) significantly augment the economic returns from the program.

- 7 A negative growth in annual incidence growth is also assumed since, arguably, general socioeconomic development can sometimes lead to incidence declines even in the absence of any intervention.

11
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20
2 There are several examples of water-related projects which combine infrastructure investment, with components of either institutional reforms, or other non-structural interventions (e.g., pricing). We provide two examples of recent water-related projects that have a mix of such components.' The first example is from Morocco. The Government of the Kingdom of Morocco is undertaking a major long-term irrigation improvement project, estimated at $367 million (World Bank, 1993). The share of agricultural consumption of available water is estimated at 83% and 79% in 1990 and 2020, respectively (World Bank 1995). Since irrigated agriculture is the major consumer of available water resources in Morocco, this sector is targeted for technical and institutional reforms aimed at improving water use efficiency. The institutional building and policy reforms component of the project is aimed at improving the management capacity of water suppliers, enhancing their service quality and financial sustainability. At the same time, water pricing policies have been modified, including all necessary legislation, to increase water charges to better cover true operation and maintenance (O&M) costs, and to better reflect the scarcity value of water in Morocco. The share of the institutional building and policy reform component in the original project budget was $76.3 million.

During the first three years, the project in Morocco faced delays and political difficulties, some of which were associated with implementation of the institutional and policy reforms. These delays were mainly due to the attitude of the Government and water suppliers' (ORMVAs) and the Government. The ORMVAs initial negative attitude was motivated by possible loss of power, prestige, and additional financial support. They also opposed having more responsibilities and more complicated work without any additional compensation. The Government's reluctant attitude may be explained by the fact that there was a transitional Government unwilling to undertake major policy changes during the four years of project implementation, but that are now being implemented (Nguyen, 1998).

> Additional World Bank experience with institutional analysis include Brinkerhoff (1994) who analyzed 80 randomly selected projects, and Morss (1984), who focused on African countries. Although these two studies provide insight into the structure of projects with a significant institutional component, the analysis does not specifically address implemetation problems due to opposing interests of interest groups.
relationship between strict social distancing policies and stock market returns (Ashraf 2020b).<sup>6</sup> Further work has documented lower firm survival and growth in countries with more stringent lockdown policies (Liu et al. 2021). We add by showing that firms in countries with stringent COVID-19 policy face more trouble in stabilizing demand, input supply and liquidity, and that countries become more resilient with respect to COVID-19 policy stringency over time.

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> 6 This finding is contrary to Kaczmarek et al. (2020), who find that countries with high stringency policies outperform countries with weak stringency policies in terms of stock market returns.

22
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16
# **2. BACKGROUND, STUDY DESIGN, AND DATA**

# **2.1. Study setting**

Malawi, the setting for this research project, is a small and poor country in southern Africa. 81% of its population of 15.3 million lived in rural areas in 2009, with most people relying on subsistence farming. The country is poor even by African standards: Malawi’s 2008 GNI per capita figure of $760 (PPP, current international $) is less than 40% of the sub-Saharan African average of $1,973 (World Development Indicators Database, 2010). According to the same data source, net secondary school enrollment is very low at 24%.

# **2.2. Sampling**

Zomba District in the Southern region was chosen as the site for this study. Zomba District is divided into 550 enumeration areas (EAs), which are defined by the National Statistical Office of Malawi and contain an average of 250 households spanning several villages. Fifty of these EAs lie in Zomba city, while the rest are in seven traditional authorities. Prior to the start of the experiment, 176 EAs were selected from three different strata: Zomba city (urban, 29 EAs), near rural (within a 16 KM radius of Zomba city, 119 EAs), and far rural (28 EAs). The choice of a 16 KM radius around Zomba city was arbitrary and based mainly on a consideration of transport costs.

In these 176 EAs, each dwelling was visited to obtain a full listing of never-married females, aged 13-22.<sup>14</sup> The target population was then divided into two main groups: those who were out of school at baseline ( _baseline dropouts_ ) and those who were in school at baseline ( _baseline schoolgirls_ ) _. Baseline schoolgirls_ , who form 87% of the target population within our study EAs and among whom

> 14 The target population of 13-22 year-old, never-married females was selected for a variety of reasons. As the study was designed with an eye to examine the possible effect of schooling cash transfer programs on the risk of HIV infection, the study focused on females as the HIV rate among boys and young men of schooling age is negligible. The age range was selected so that the study population was school-aged and had a reasonable chance of being or becoming sexually active during the study period. Finally, a decision was made to not make any offers to girls who were (or had previously been) married, because marriage and schooling are practically mutually exclusive in Malawi – at least for females in our study district.

7
**Figure 3. Services as a share of total exports of goods and services**

<!-- Start of picture text --> Shares of Services in Total Exports<br><!-- End of picture text --> <!-- Start of picture text --> 25<br>20<br>15<br>LMC<br>SSA<br>High<br>10<br>5<br>0<br>1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997<br><!-- End of picture text --> A key challenge is clearly to understand and explain the changes in the structure of output and exports that underly these sharp changes in the structure of exports from developing countries. These changes are so profound and rapid as to call into question much previous discussion of developing country trade policy, which typically postulates developing countries as reliant almost exclusively on exports of agricultural and natural resource products (see, for example, Buffie 2001, p151). Clearly, the policy implications for reducing resource dependence, and for development policy more generally, will differ greatly depending upon the causes of this dramatic change. In the next three sub-sections of the paper, we examine the available evidence on the factors most likely to influence the composition of exports. Changes in factor endowments are considered first, followed by changes in protection policy. Finally, the role of technological advance is examined.

15
increases as increases, ceteris paribus, P increases, so that the number of trades that we identify as ii matched vehicle trades with at least a 95% confidence level decreases (Appendix A.1, Figure A.2) NN In our baseline methodology, to assess the unconditional probability of any trade size occurring, we apply a uniform historical distribution of trade sizes over 2017-2021. One might conjecture that this distribution could be affected by changing dynamics over time (see Appendix A.3) and that the methodology should potentially control for this by embedding a time-series dimension in the assessment of the values of �. To assess whether such a dynamic is likely to significantly affect our point jj QQ estimates, we compare the share of trades that are identified by our core methodology (uniform �pp , ... , pp distribution of trade-sizes) versus vehicle trades for each calendar year. We derive the p-values from a year-specific distribution function. Table 4 presents evidence that the impact of such dynamic is indeed limited, and that the computationally significantly more efficient approach applying a uniform tradesize distribution appears to provide a close enough approximation.

**Table 4: Comparing results using uniform and year-specific trade-size distributions**

||(1)<br>Uniform trade-size<br>distribution|(2)<br>Year-specific trade size<br>distribution|
|---|---|---|
|2017|6.3 %|5.8 %|
|2018|8.1 %|7.6 %|
|2019|7.8 %|7.5 %|
|2020|6.6 %|6.3 %|
|2021|7.4 %|6.9 %|

Source: LocalBitcoins.com API, Authors’ Calculations 20