rafmacalaba/data-use-mentions-extended huggingface dataset:
% to 1%. Without lower inequality in 2008, the developing world as a whole would need to achieve China’s growth rate over the last 20 years or so (but without the rise in inequality that China has seen) to get the poverty rate much below 2% within 10 years.

This suggests that a 2% poverty rate by 2022 would be very difficult and could reasonably be seen as close to impossible. A poverty rate of 4.5%—half what we expect to be the poverty rate under the current trajectory—would require a growth rate in mean household consumption per capita of 4.8%, which is almost exactly the expected rate of growth in GDP per capita of 4.9%. But (again) this would require no fall in the household sector share (based on the surveys used to measure poverty) and no increase in inequality within the developing world as a

> 27 For each target poverty rate, I solved backwards to find the growth rate needed to reach that target, holding constant the 2008 Lorenz curve. The accuracy of poverty estimates at low levels is questionable for those countries that use income (rather than consumption) as the welfare indicator. The data base in _PovcalNet_ prefers consumption when it is available, which is for about two-thirds of the surveys. The only region that still uses income surveys extensively is Latin America.

11
Proponents of new structuralism might view the financial histories of developed economies as promising for their approach – since the evolution of financial intermediaries was endogenous to demand for financial services from the real sector, an optimal financial structure might be a by-product of an optimal industrial structure. And yet, the industrial structures that emerged in many countries were far from optimal, and thus the financial structures that evolved did not function as well as they might have. As described in Calomiris and Haber (2011), in less developed economies financial intermediaries are often created by coalitions that include the government and business interests. Those business interests must be assured that the government will not expropriate the intermediaries that they create, nor will it promote the entry of competing intermediaries, because those intermediaries could finance firms that would compete with those of the members of the coalition. The result is a financial system comprised of a small number of relatively large banks and other intermediaries that service affiliated firms, but that do not promote widespread access to financial services, especially credit. This is at odds with the optimal financial structure during early stages of development from the new structural perspective, where a large number of relatively small banks would service small-scale entrepreneurs.

The foregoing discussion leads to (at least) two questions. First, can it be shown that the countries that come closer to achieving the optimal financial structure for their stage of development also achieve higher rates of growth? More specifically, do countries in early stages of development fare better under a bank-based system that harnesses local information rather than a market-based system that does not? Second, if there is evidence supportive of the notion that optimal financial structure varies with the stage of development, why is it that some countries come closer to achieving those structures than others? This paper provides empirical evidence from firm surveys to address the first question. Addressing the second question is beyond the scope of the paper, though we note that if countries were equally successful in achieving an optimal financial structure, we would see little variation in financial
demic’s impact in real time, including text data from earnings reports (Hassan et al., 2020), unemployment claims, and transaction data from credit or debit card purchases (Chetty et al., 2020). This diverse evidence all points towards a severe and immediate impact of the shock on firm revenue and employment. Existing evidence also suggests that the pandemic affected countries with different degrees of lockdown severity alike. Extrapolating estimates from the Republic of Korea, where no lockdown was implemented, Aum et al. (2020) hint at fear of infections rather than lockdown as the main driver of the drop in local employment, and suggests the pandemic has truck high-contact industries the hardest.

As the pandemic unfolds over time, more direct evidence, particularly using surveys, is emerging about its impact on jobs and business activities at different stages of the pandemic. In the short term, severe impacts have been documented across countries in terms of revenue loss, business closures, mass layoffs, and liquidity (see Dai et al. (2020b) for evidence in China and Bartik et al. (2020); Humphries et al. (2020a); Adams-Prassl et al. (2020); Fairlie (2020a,b) for evidence in the United Kingdom, United States and Germany). Firms are hit by multiple channels, with depressed demand being the most frequently reported concern in recent firm surveys and becoming more prominent over time (Dai et al., 2020a; Balleer et al., 2020). Acharya and Steffen (2020) find a sharp increase in US companies’ cash holdings in March, as COVID-19 fears started to materialize. They attributed this increase in cash to a concern among managers that their firm’s credit rating would be downgraded on account of pandemic-related business disruption, and that it would become more expensive or even impossible to raise cash after that. With falling revenue, most SMEs are now in major financial distress (Bartik et al., 2020; Zhang, 2020).
Our results also highlight the importance of sufficient guidelines and training in programs that are implemented through multiple institutions at scale. Heterogeneity in implementation can arise when multiple entities implement a program, and without effective communication, this heterogeneity can create confusion and frustration for program recipients and increase challenges in oversight. Our findings reveal that KUR borrowers did not always have a clear understanding of loan terms and conditions and their rights and responsibilities as borrowers, raising consumer protection concerns. Standardized templates that harmonize the presentation of loan terms and conditions in simple language could help mitigate these consumer protection risks. These types of templates could also be adopted for other commercial lending products to enhance transparency and ensure borrowers have sufficient information to make decisions about loan product adoption.<sup>36</sup> To facilitate oversight of the program and ensure that the program is reaching the targeted borrowers, improvements to the program’s monitoring and evaluation system can ensure data are reported in a harmonized and systematic way.

Our results also identify several areas of future research to better understand the optimal design of public credit schemes for small businesses. While this study is informed by one of the largest databases of beneficiaries of public credit schemes in the world, the analysis relies on descriptive statistics and inferences and cannot conclusively answer questions about the additionality of access to subsidized credit. Importantly, we cannot detect what would have happened in absence of the program. Experimental studies with a valid counterfactual exploring the impact of subsidized credit schemes are needed to assess the additionality of these types of programs. Moreover, rigorous cost-benefit analyses of public credit schemes such as KUR could help determine optimal budget allocations and the fiscal costs of helping unbanked firms enter the financial system. Additionally, further research is needed to understand how policy changes that limit repeated access can influence graduation from subsidized to unsubsidized commercial credit over time. More evidence is also needed about how partial credit guarantee mechanisms can be optimized to ensure they reach borrowers with limited collateral. Finally, further research on how public credit schemes can effectively target
8|15.4|22.4|15.4|12.5|-7.1|22.5|5.7|6.8|
|32|Radio, television and<br>communication equipment|5.6|22.0|36.5|13.9|12.5|-0.6|14.6|3.4|9.1|
|33|<br>Medical, precision and optical<br>instruments|3.1|8.6|13.8|14.3|13.7|3.0|11.3|5.2|8.6|
|34|Motor vehicles, trailers, semi-<br>trailers|1.1|5.5|8.7|18.6|11.4|1.0|17.6|3.3|8.1|
|35|Other transport equipment|0.8|4.1|7.3|18.7|15.0|1.2|17.5|4.4|10.6|
|36|Furniture; manufacturing n.e.s.|10.4|21.6|35.1|12.2|11.9|4.3|7.9|3.2|8.7|

Note: Data is based on two-year averages of 1991-92, 2001-02, and 2007-08.

Source: Based on UN COMTRADE Statistics (trade data) and UNIDO database (production data).

There are significant differences in market shares among different industries, arising from comparative advantage as well as differences in protection. There are two sets of subsectors that have reached high import penetration rates in 2007/08. First set includes the traditional labor intensive subsectors such as wearing apparel (70.3 percent), leather (71.4 percent), and textiles
<br>60% 60%<br>40% 40%<br>20% 20%<br>0% 0%<br>NP 47 UE 22 PT 16 FT 16 NP 83 UE 10 PT 4 FT 4<br>Kosovo<br>Husbands Wives<br>100% 100% Husbands100%<br>80% 80% 80%<br>60% 60% 60%<br>40% 40% 40%<br>20%<br>20% 20%<br>0%<br>0% 0%<br>NP 57 UE 22 PT 10 FT 10<br>NP 57 UE 22 PT 10 FT 10 NP 94 UE 4 PT 1 FT 1<br>Home production Leisure Work<br><!-- End of picture text --> Source: Author’s calculations based on 2017 UNDP-WB-EC Roma Regional Survey.

Note: Abbreviations: FT= employed full-time; PT= employed part-time; U= unemployed; NP= nonparticipant. The numbers next to the labor market labels are the proportion of husbands and wives in each labor market state for each country. Per panel, the proportion of husbands and wives should sum up to 100. Because of approximation, for some panels, the sum is equal to 101. Each bar represents the average time allocation between home production, leisure, and work of husbands and wives on each labor market state.

12
’s empowerment and bargaining power, we evaluate changes in men’s personal attitudes towards gender equity. To measure these attitudes, we seek answers on respondent’s personal attitude on 9 statements that relate to gender equality and assumed gender roles in the society (Dhar, Jain and Jayachandran 2018).

_Table 2: Composition of Mechanisms Indices_

|**Mechanism**|**Summary Index**|**Outcomes Variables**<br>**(Index components)**|
|---|---|---|
|Enhancement of<br>emotional and<br>mental well-<br>being|Women and men’s<br>well-being|Patient health questionnaire index; respondents’ perception of<br>their own as well as their families’ quality of life in current day, and<br>5 years ahead|
|Improvement in<br>women’s<br>bargaining<br>power|Bargaining Power|Fraction of domains for which respondent makes the decision<br>(independent/joint); Fraction of domains in which respondent's<br>opinion is heard; Fraction of domains in which respondent cares<br>about making the decision; Number of domains for which<br>respondent cares AND makes decision; Abs difference in number of|

> 18 These 13 domains are akin to those included in the Demographic and Health Survey.

11
winter crops as of May 1. As decisions on planting of winter crops were made before the conflict started, we would expect to see effects only through either destruction of a field’s crop cover or a failure to manage crops that had already been planted, expecting us to expect this effect to be relatively modest.

Conflict-related damages to fields or input shortages or price expectations may affect the ability to manage crops properly, thus leading to reductions in expected yield. To assess such effects for fields that, based on our crop classification are planted with winter cereal,<sup>4</sup> we use the NDVI, computed on the GEE platform, as a proxy for crop conditions and expected yield. This is motivated by the fact that recent agronomic studies including Dhillon _et al._ (2020) for the US, Vannoppen and Gobin (2021) for western Europe, and Nagy _et al._ (2021) for Eastern Europe show that, for winter wheat, the NDVI, ideally combined weather data (Aula _et al._ 2021), provides rather accurate yield prediction. An early application to the US and Ukraine is Becker-Reshef _et al._ (2010).<sup>5</sup> We can use the parameters obtained by some of these studies to illustrate the order of magnitude of expected yield effects: For data from the 2012-16 growing seasons in Poland, the Czech Republic, and Eastern Germany, Panek and Gozdowski (2020) find that an increase of 0.1 in early season (until mid-May) NDVI increased grain yields by between 1.1 and 2.6 t/ha or by 25%-50% with R<sup>2</sup> of between 0.5 and 0.85. For Argentina, Lopresti _et al._ (2015) find an increase of yield of 109 kg/ha with each additional point of peak NDVI as measured by MODIS data (R<sup>2</sup> of
The indicator allows us to track the severity of the pandemic across countries and over time in ways that complement previous efforts. Like the crude mortality rate, it provides a relative expression of mortality, which helps dimension severity. Unlike the crude mortality rate, it normalizes for past mortality patterns, which provides a sense of proportion relative to what was common. Unlike the excess death ratio, the indicator is globally comprehensive, as it does not rely on ‘nowcasting’ total deaths in the numerator, which typically is not possible for poorer countries due to lack of data. However, where such data are available, the relative severity ratio can incorporate adjusted COVID-19 mortality data based on excess deaths.

The second part of the paper questions the observed inequality in the mortality toll between rich and poor countries. Since COVID-19 exhibits a strong age gradient in the risk of death, our analysis zooms in on the interaction between epidemiological parameters of infectivity and fatality and demographic patterns of age structure. Specifically, we simulate what the global distribution of the death toll from COVID-19 would look like if plausible assumptions of infection prevalence and age-stratified fatality are applied to age distributions of World Bank income groups.

This approach builds on the literature in a number of ways. The strong age gradient of COVID-19 has been well established in many studies.<sup>4</sup> What has been examined less is the interaction of agestratified fatality ratios with population-level demographic structure. A seminal contribution in the context of COVID-19 was made by Dowd and others (2020), who apply common patterns of infectivity and fatality to the country-level age-distributions. This paper extends the approach to the global level.

Following Verity and others (2020), we impose uniform attack rates across age groups and apply age-stratified infection fatality ratios corrected for censoring and ascertainment biases due to lack of testing and other distortions that affect measurement. The simulations are subjected to robustness checks by utilizing other epidemiological parameters.<sup>5</sup> This procedure allows us to produce a baseline of
_0.084_}_|_{_0.053_}_|
||(0.001)|(0.005)|(0.011)|(0.001)|(0.032)|(0.032)|
|API Original = API Plus|[0.043]|[0.043]|[0.178]|[0.020]|[0.469]|[0.134]|
||_{_0.086_}_|_{_0.115_}_|_{_0.225_}_|_{_0.024_}_|_{_0.570_}_|_{_0.229_}_|
||(0.045)|(0.045)|(0.098)|(0.023)|(0.376)|(0.157)|
|Number of clusters|224|224|224|224|182|76|
|Observations|1044|1044|1045|1045|468|106|

_Notes_ : This table shows OLS estimates and the associated _p_ -values on student outcomes measured after two academic years of exposure to the API program under the second experiment designed and implemented by the authors in collaboration with the government. For detailed descriptions of the test scores used in this table, see Appendix A.2. The dependent variables in the first four columns are standardized with respect to their means and the standard deviations in the control group. The dependent variable in the last two columns is computed from administrative school records (see Appendix A.1). _p_ -values reported in brackets refer to the conventional asymptotic inference. _p_ -values reported in braces are computed using randomization inference (randomization- _t_ ). _p_ -values reported in parentheses are adjusted for testing each null hypothesis (null impact of API Original, API Plus, and the comparison) for the two different families
Policy Research Working Paper 8759

# **Abstract**

- This paper assesses how household context and character istics shape the welfare trajectory and more specifically the accumulation of productive assets among beneficiaries of the Vision 2020 Umurenge Programme in Rwanda, the government’s flagship social assistance program. The analysis is based on a unique data set combining panel household survey data with in-depth qualitative interviews of a subsample of male and female beneficiaries from the survey data collected between 2009 and 2015. By combining quantitative and qualitative information, the paper draws a more nuanced picture of how household characteristics—structural and temporal—contextualize opportunities for poor men and women and their households and shape how well

they can leverage access to the Vision 2020 Umurenge Programme to accumulate productive assets. The mixed method analysis reveals that household composition, gender power dynamics, disability, care responsibilities, marital arrangements, intrahousehold communication, and access to other social programs and institutions play a crucial role in access to the Vision 2020 Umurenge Programme and related asset accumulation. The findings suggest that households would benefit from a broader definition of the eligibility criteria and the availability of flexible and complementary programming, to reap the benefits of the income transfer received from the program.

This paper is a product of the Social Protection and Jobs Unit, Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at lrawlings@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the
57|11.26|35.58|19.32|7.90|2.61|
|Cult. in 2019 (ha)|8.71|4.67|33.81|83.43|40.16|17.42|5.62|
|Cult. in 2020 (ha)|8.80|4.72|34.18|84.44|40.78|17.62|5.67|
|Cult. in 2021 (ha)|8.75|4.69|33.88|83.88|40.31|17.45|5.65|
|Cult. in 2022 (ha)|8.23|4.37|32.38|79.65|37.93|16.52|5.32|
|Cult. in 2023 (ha)|8.45|4.51|32.85|81.42|39.05|16.87|5.45|
|Applied for other programs||||||||
|Numberof farms|23,343|21,368|1,236|739|1,464|1,817|20,062|

_Source:_ Own computation from SAR records and cadastral data as described in the text _Note:_ ‘Cultivated area’ is area for which the farm has ownership or lease rights registered and that was, according to crop maps elaborated by KPI and JRC, covered by agricultural crops in the relevant year. Eligible area excludes area located in conflictaffected areas as described in the text.

21
than a particular policy<br>itself. More frequently, there are<br>indicators about the general<br>satisfaction with government<br>effectiveness or, in some cases,<br>with some sectoral disaggregation,<br>although much more rare.|<br> <br>**Voice**: Share of people who voted in most recent<br>national elections; share of population that thinks<br>they have freedom of speech;<br>**Accountability**: World Governance Indicators;<br>**Citizen engagement**: World Governance<br>Indicators.|
|out are considered|||||
|fair, credible, and<br>|||||
|acceptable by all<br>members and<br>groups of a given<br>community or<br>society.<br>Source: Own e|Legitimacy is<br>anchored on current<br>norms and values so<br>policies are carried<br>out in a concrete<br>context<br>laboration|Indicators of legitimacy capture the<br>social norms and values that<br>defines a particular society in a<br>given time|<br>Indicators avaialble typically<br>capture gendered norms and<br>values such as gender<br>discrimination, and much less<br>norms related to other vulnerable<br>groups such as (integration of)<br>migrants and displaced<br>populations, (discrimination of)<br>ethnic, religious or sexual<br>minorities|**Social norms**: Share of people who agrees is a<br>problem if women have more income than men;<br>when jobs are scare, men should have more rights<br>than women; share of women who are chief wage<br>earner in the household;|

8
Table (D5) Impacts of the Program on Mental Health

|**Outcomes**|**Treatment**|**(se)**|**FDR**<br>**q-values**|**Control**<br>**mean**|**(sd)**|**N**|
|---|---|---|---|---|---|---|
|CESD score (higher is better)|0.13|(0.17)|[0.37]|0.00|(1.00)|1,607|
|Perceived stress scale (higher is better)|0.10|(0.15)|[0.38]|-0.00|(1.00)|1,607|
|Depression (=1 if depressed)|-0.09|(0.07)|[0.29]|0.56|(0.50)|1,607|

**_Notes_** :

- The table shows the regression results on pre-specified mental health outcomes using the phone survey midline data. Each row is its own dependent variable.

- The independent variable of interest is the TOT treatment indicator, which is the predicted value from a first-stage regression of treatment implementation on treatment assignment.

- The outcomes that require definitions are: Center for Epidemiologic Studies Depression Scale (CES-D) is a measure for depressive symptoms and includes 10 items asking about the past week. It is defined as a mean-effect index of 10 items scored 0-3 in the past week (I was bothered by things that usually don’t bother me, I had a problem in concentration on what I was doing, I felt depressed and troubled in my mind, I felt that everything that I did took up all my energy, I felt hopeful
while our focus on one remedy-imposing country does obviously impose a limit on the extent to which one can extrapolate from our results to lessons for other remedy-imposing countries; nevertheless, the United States’ trade remedy laws and procedures in particular serve as a model that many other WTO members emulate as they construct their own provisions. Thus lessons learned from the U.S. experience and interaction with WTO dispute settlement may arguably have implications for other international users of trade remedies as well.

Table 4 uses the U.S. data on trade remedies to illustrate a number of comparative results that our formal econometric investigation formally seeks to confirm. First, and perhaps not surprisingly, a U.S. trade remedy that leads to the loss of a large value of imports in the U.S. market is more likely to result in a measure being challenged than one resulting in the loss of a small value of imports. This is consistent with the first row of table 4, which shows that the mean level of lost imports in WTO-challenged measures is $49.9 million, relative to $3.2 million for nonchallenged U.S. remedies. Second, we also find that the capacity for the foreign country to retaliate over U.S. exports should it “win” the case is also associated with a higher probability of it bringing forward a dispute in the first place. This is illustrated by a simple comparison made in table 4, where the mean share of U.S. exports sent to countries that challenge trade remedies is 16.9%, whereas the U.S. is less reliant on the average country that does not formally challenge a U.S. remedy at the WTO, as that country receives only 6.5% of U.S. exports.

We also investigate and provide evidence of a second avenue through which the capacity for retaliation threats matters, and we find that this avenue could be serving as a _substitute_ to formal WTO dispute settlement proceedings. The data suggests