rafmacalaba/data-use-mentions-extended huggingface dataset:
11 even from improved sources. Among unimproved sources, 86% of samples had coliform levels over 100 per 100mL, ‘very high risk’ according to WHO standards [30]; among improved sources, 60% of samples had coliform levels over 100 per 100mL. Water samples from intervention household water containers showed no difference in thermotolerant coliforms per 100mL compared to control household samples.

The intervention had no effect on the psychological well-being index, the life satisfaction & selfesteem index, or school attendance.

In the prespecified subgroup analysis of primary outcomes by province, we find that the intervention reduced diarrhea in one of four provinces (Kongo Central), reduced length-for-age in one province (Kasai Central), and increased the WASH institutions index in two provinces (Kasai and Kasai Central) (see S3 Table). We also used interaction terms in linear models to test for effect modification on the additive scale (S5 Table). Of the nine coefficients (three provinces, leaving out a reference, and three primary outcomes), two were statistically significant: in Kasai Central province, the results suggest that the intervention increased diarrhea prevalence relative to the treatment effect in the reference province (Kongo Central); in Kasai province, the results suggest that the intervention increased length-for-age. For both diarrhea and length-for-age, the Wald test rejects the null hypothesis that all province-by-intervention coefficients are zero at the 0.05 level. For the WASH institutions index, the Wald test fails to reject the null.

In the prespecified subgroup analysis of diarrhea and length-for-age by child sex, we find no difference in intervention effects by sex (see S4 Table). In linear models with interaction terms for intervention-by-sex, the coefficients are not statistically significant (S7 Table).

# Discussion

We tested the effects of the national community-led rural WASH program in the DRC on child length-for-age, diarrhea, and WASH institutions. The program improved community WASH institutions, with intervention villages more likely to have a WASH committee, and for this institution to actively monitor community health conditions.
# **4 Design and Methods**

## **4.1 Experimental Design and Sample**

The evaluation sample comes from 80 villages in four districts of Balkh province. Households were selected based on a wealth ranking of the entire population of households in these villages, conducted through a Participatory Rural Appraisal (PRA), and resulting in an eligible UP group of slightly under 6% of the population, or the census of ultra-poor households in study villages. A baseline survey was conducted from February to April 2016. A public lottery was then held in May 2016 where 1,219 UP households in the study villages were randomly assigned into one treatment group (491 households) and one control group (728 households).<sup>11</sup> Additionally, approximately 20 households in each of the study villages were randomly drawn at baseline (excluding TUP-eligible households), which allows us to have a representative benchmark for the TUP sample, referred throughout the paper as the non-ultra-poor (non-UP) sample. Further details on the sample are described in Bedoya et al. (2019).

The randomization was stratified by PRA groups, with larger villages split into multiple PRA groups, typically defined by the catchment area of a _masjid_ (mosque). Starting in May 2016, the treatment group received the TUP package, and the control group did not receive any of the components. A midline survey was conducted from July to October 2018, approximately two years after the asset transfer, and an endline survey took place over several short data collection rounds between January and June 2021, approximately 5 years after the asset transfer. Due to the COVID-19 pandemic and the escalating conflict in Afghanistan, the endline survey was conducted by phone, with the implication that only households with a working phone number could be surveyed. This covered 69% (839/1,219) of randomized UP households. Additionally, data were collected from 1,081 non-UP households at endline, which represents 64% (1,081
key driver of globalization since the 1960s as it allows for (i) faster transportation, loading, and unloading, (ii) reduced cargo damage and theft, and (iii) refrigeration or temperature-controlled environments for perishable goods (Hummels, 2007; Levinson, 2008; Bernhofen et al., 2016). Moreover, the of container has the of world trade and availability shipping changed geography global supply chains (Notteboom and Rodrigue, 2007). These days, 95% of trade in manufactured goods is transported by containerships.

To get a better understanding of the trade trends, we leveraged Clarksons Shipping Intelligence Network (SIN)<sup>3</sup> to calculate maritime containerized trade and trade imbalances for years 2002-2020 for eight regions: Africa, Europe, Far East (including South East Asia), Indian Sub-Continent, Latin America, Middle East, North America, and Oceania.<sup>4</sup> The imports and exports trends are presented in Figure 1 with imports and exports for each region normalized to 100 in the year 2002. We summarize it as follows.

**Stylized Fact 1.** _Between 2002 and 2020, Far East, Indian Sub-Continent, and Middle East faced the most pronounced export and import growth—up to 300%. Africa, Indian Sub-Continent, and Oceania faced much faster imports than exports growth._

> 3See section 3.3 for details on Clarksons SIN database.

4Naturally, even within the same region, the country-specific trade and trade imbalances may differ in terms of magnitudes and even directions. Furthermore, the dry-bulk trade imbalances are different from the containerized trade imbalances.

4
. imports than the products for which new TTBs were being imposed.<sup>18</sup> Finally, it is worth noting that according to both modes of measurement, the share of imports subject to U.S. TTBs is substantially lower in 2007 than it was during its peak of the 1997‐2007 period. Using the _value_ approach in particular, 2.33% of imports were covered in 2007 as compared to a 2001 peak of 6.14% of imports. The timing of the decline starting from that 15 In addition to the fourteen TTB‐using G20 economies that I discuss below, I have also constructed TTB measures for more than 20 other policy‐imposing economies with data available in the Temporary Trade Barriers Database. Because of space constraints I do not report on them here but instead focus primarily on the G20 economies as users and (in section 4 below) the exporters that ship products to the G20 import markets.

16 Another item to note for the U.S. is that it had been using these policies (antidumping in particular) prior to 1990, and thus there is an underlying stock of products affected by these policies that I am not capturing, since I am starting with barriers first imposed in 1990. (This is due to consistency of access to import classification under the Harmonized System which took hold across countries only in 1988). This will also apply to Australia, Canada, and the European Union who each had a substantial stock of antidumping barriers in place by the time the HS system started in 1988.

17 This is consistent with other research linking antidumping use, in particular, with business cycle fluctuations such as changes in real GDP and currency movements. See Knetter and Prusa (2003).

18 Again, following the discussion of equation (2), the share of imports for products that had been subject to a previous TTB that was being removed would be based on what the product’s share of the U.S. import market
landscape of the labor market in each country is presented in Tables 2 and 3. Overall, labor force participation across the sample of countries is high and ranges from 49.5 percent in Armenia to 84.3 percent in Ghana. However, the employment rate is fairly low in Armenia (27.8 percent) and Georgia (25 percent), while it is above 50 percent in the other countries.<sup>12</sup> The low employment rate observed in Armenia and Georgia impacts the effective sample size used to estimate the returns to education and the relationship between various skills and hourly earnings.

The average hourly earnings in 2011 purchasing-power-parity-(PPP)-adjusted U.S. dollars range from approximately US$2.15 in Ghana to US$3.60 in Bolivia and Ukraine. There are also

> 10 A value of 0.00001 was imputed to workers who reported no hourly earnings.

> 11 In the case of Ghana, about 800 cases are missing data on the noncognitive skills measures. These items were administered in English and respondents reported inadequate English language skills to complete this battery. As a result, data from Ghana are not included in estimating the effects of noncognitive skills.

> 12 According to UN data, labor force participation among males and females in Armenia in 2012 was about 51 percent and 73 percent, respectively. Corresponding figures for Georgia in 2012 were 56 percent and 75 percent, respectively

> (http://unstats.un.org/).

10
reduction.

To begin, we re-derive the identity that exists between changes in poverty, changes in inequality, and changes in mean incomes. Using World Bank PovcalNet data from 1974-2018 for 135 countries, we then search for an empirical model that allows us to approximate the identity well. We find that the assumption that per-capita income is log-normally distributed leads to a very decent approximation: our model is able to explain 94% of the variation in observed changes in headcount poverty.<sup>3</sup> Using this approximation, we empirically quantify the importance of changes in inequality on poverty vs. changes in mean incomes on poverty. We find that for most countries, a 1% reduction in inequality (as measured by the standard deviation of log-income) leads to a larger

> 1. To be precise, the effect that initial inequality has on the growth elasticity is theoretically ambiguous (Ravallion, 2007). However, under certain assumptions it can be shown that the (absolute) growth elasticity of poverty is unambiguously decreasing with inequality (Bourguignon, 2003; Ferriera, 2012). 2. See, for example, Bourguignon (2003), Kraay (2006), Lopez and Serv ́en (2006), Kalwij and Verschoor (2007), Klasen and Misselhorn (2008), Bluhm et al (2016), and Fosu (2017).

> 3. This _R_<sup>2</sup> is notably higher than that found in Bourguignon (2003) and Klasen and Misselhorn (2008) who also approximate the identity under the assumption that income is log-normally distributed. We believe our improved fit is due to two reasons: first, we use a much larger dataset, and second, we estimate a version of the identity that allows for discrete changes in mean incomes and inequality.

2
nationally representative survey wave of the HFS. The survey covered rural and urban areas across 7 of the 10 former states of South Sudan. The regions covered include Greater Equatoria, Greater Bahr el Ghazal, and Lakes. The 10 former states are used in planning for the HFS instead of the 28 more recent ones because the sample was constructed based on the sampling frame derived from the 5<sup>th</sup> Sudan Population and Housing Census from 2008.<sup>9</sup> The survey was designed to be representative at the state level and employs a stratified two‐stage clustered sample design. Within each state the primary sampling units are enumeration areas (EAs) that were drawn randomly proportional to size. The EAs were drawn by the NBS for the 2008 Census (Southern Sudan Center for Census, Statistics, and Evaluation, 2010).<sup>10</sup> The number of EAs and households was equalized across states in order to balance the fieldwork across teams. Within the EAs, 12 households were drawn randomly as the unit of observation based on a listing exercise.<sup>11</sup> The EAs were allocated across urban and rural areas within each state to minimize the variance of indicators of interest across the strata while explicitly taking into consideration the design effect. The data used for the sample size calculations came from the NBHS 2009, and the indicator used for the sample size calculations was the real total per capita household expenditure.<sup>12</sup> While this variable is one of several that are of interest in the HFSSS, consumption/expenditure is generally strongly positively correlated with other indicators of interest. For the purposes of comparison, the relative standard error (complex standard error / mean) is used. The allocation was

> 8 The data from Wave 3 (2016) of the HFS and the code used to process these data can be downloaded from the World Bank MicroData Library at the following link: http://
# 5.1 Malaria Policies Toward Travelers

The NGO PATH has specically focused on reducing imported cases in the district Richard Toll. They have used volunteers in the community to alert health workers to the arrival of new travelers. Health workers track down these travelers and ask to test them using an RDT, and treat those that test positive. Based on 2015 data provided by the Richard Toll Health District Director, 3,609 people were identied as travelers, of these 3,386 were tested and 10 tested positive for malaria. In 2015, there were a total of 186 imported cases; therefore, this strategy was only able to detect 5% of imported cases.<sup>25</sup> A more systematic policy to target travelers was implemented in one health post in Richard Toll, which is privately run by the Senegalese Sugar Company (CSS) for its workers and their families. CSS hires over 3000 migrant workers every year to help with the sugar harvest. Malaria was a large burden for the company, causing lower productivity, high absenteeism, and high spending on pharmaceuticals to treat it (Djibo and Ndiaye 2013). In late 2011, the CSS implemented a new mandatory policy for all seasonal workers: testing every worker at the beginning of the season using an RDT, treating anyone testing positive, and providing workers and their families with bednets and information. There was a drastic decrease in cases after the implementation of this policy, with case numbers at zero or close to zero after the policy (Figure 5). Data on two types of schistosomiasis among workers at the CSS show no drop in those diseases after late 2011, demonstrating that the drop in malaria cannot be attributed to an overall improvement in the health care facility. This closely mirrors the outcomes measured by Dillon, Friedman, and Serneels (2014), who nd that a policy oering testing and malaria treatment for workers at a sugarcane plantation in Nigeria leads to a 10% increase in earnings due to increased labor
29

# **_Appendix A2. The determinants of agricultural productivity_**

Standard Cobb-Douglas production functions are estimated here based on the data from all three survey rounds. This permits estimation of the marginal productivities of fertilizers and seeds (in value). The specification includes the standard input variables: land, labor, capital, traditional and modern seeds (in value) and organic and inorganic fertilizer (in value). As the effectiveness of fertilizer use is affected by the timely supply of water, they are further interacted with the percent of land irrigated. Household demographics and education level, land quality, crop portfolio and access to credit were further added as controls affecting total factor productivity.

Table A2-1 shows the estimation results. In the first two columns the log of total crop income is used as the dependent variable and in the last two columns the log of maize and rice income. Where necessary a small number is added to the log of asset, fertilizer and seed variables to enable inclusion of observations with zero values. Given the specification in double logs, the estimated coefficients on the input variables can be interpreted as elasticities. The other variables serve as control variables. Their coefficients are the marginal productivities. Two estimates are reported: the OLS estimate and the household fixed effect, which mitigates against omitted variable bias from unobserved household heterogeneity.

Land, agricultural assets, livestock have a significant effect on crop production. The estimates of the elasticity of both traditional and improved seeds are significant, and so are the elasticities of inorganic fertilizer, with irrigation increasing the effect of fertilizer on maize and rice plots. Male headed households and better educated households tend to be more productive. Especially households planting bananas see their crop income systematically increase.

Using the results in Table A2-1, the marginal productivity of improved seeds and inorganic fertilizer are calculated using the marginal productivities for maize/rice production
which nets out baseline differences. Given that Tanzania did not collect baseline information for recall households, the double difference estimator can only be calculated for Ghana.

**Table 12: Listing bias, Ghana and Tanzania**

|||**Tanzania**|**Ghana**|
|---|---|---|---|
|Listing Bias|Workers|-1.6***|-1.5***|
|(Simple Difference)|Plots|-2.1***|-0.6***|
|Listing Bias|Workers|N/A|-1.2***|
|(Double Difference)|Plots|N/A|-0.3***|

Note: *** Denotes significantly different from weekly visit estimate at 1%.

19
In order to ensure that the selected Census Towns were as representative as possible of the demographic and socio-economic characteristics of Census Towns in their respective states, all settlements were situated with respect to other settlements, using their population, as well as a composite development index of 17 variables and population of the settlement. These 17 variables are male and female literacy, non-farm workforce, child gender ratio, possession of at least one asset, household size, share of households with phones, electricity, television, two-wheelers, access to banking, households using LPG for cooking, houses with burnt brick walls, concrete roofs, flush latrines and tap water within the house.

The site selection for Bihar was done more systematically given the state’s specific characteristics, including a low official level of urbanization, and a high proportion of people living in large villages. A first selection was done at the district level using the following measures of development:

- a. The composite development index developed based on census data for 17 variables, as in other states.

b. A second, additional separate index, utilizing 71 variables in six different groups – Economic, Governance, Infrastructure, Social, Finance, Primary — was built using data from the Bihar Economic Survey. This involved building weighted (generated using principal components analysis) sub-indices for each of the groups and then building a weighted index of the sub-indices.

- c. Third, a rank based on the district domestic product

- d. Fourth, the rural poverty headcount ratio for the district

On each of the above four measures, districts were divided into three groups. The intent was to focus on districts which were in the middle third, across multiple measures.

In addition, given our strategy, the district also had to have a large village, a Class III statutory town (between 20,000 and 50,000) and a census town. Applying the criteria above, three districts, Madhubani, Samastipur, and Paschim Champaran, emerged as likely candidates.

In Samastipur, all
abilities versus the transferring of nonperforming loans to residual entities. Presumably, a significant portion of the short-term decline in _Total Loans/Assets_ reflects this removal.

Finally, state-owned banks that were restructured tend to have decreased loans relative to other assets, similar to privatized banks, and also have less of their loan portfolios allocated to manufacturing loans and more to agricultural loans. Again, these dynamic effects may be difficult to interpret because of the stripping off of nonperforming loans at the time of restructuring.

# **6.  Conclusions**

We test the effects of corporate governance on bank performance using data from Argentina in the 1990s, although our principal contribution may be methodological. Our method represents an expansion of the existing research literature that might be usefully applied elsewhere. In particular, we 24
informal, and part-time work in zones out of the city center, nonpeak journeys, and the disproportionate household and care burdens that require “trip chaining”—multipurpose, multistop excursions (Boulin 2006; Kunieda and Gauthier 2007; Levy 2013a, 2013b; Schmink 1986; UN-Habitat 2009).

Therefore, good governance may not only increase infrastructure investment volumes, but more importantly may also improve the effectiveness of infrastructure spending. Rajkumar & Swaroop (2008) use data from 91 countries and three years between 1990 and 2003 to investigate the relationship between governance and public expenditure into education and health. They find that public spending is significantly more effective when an index of corruption within the political system is lower and an index of the quality of a country’s bureaucracy is higher. If this result is to be extended to infrastructure investments, infrastructure spending would be more effective in delivering high service value to users if governance standards are high. Indeed, in an analysis of infrastructure in Latin America and the Caribbean, Cerra et al (2016) reach the diagnosis that fiscal policy and strong institutions are crucial for improving countries’ infrastructure networks.

# 3. What can be done to improve infrastructure reliability?

And more precisely, to what extent better governance can improve the efficiency of infrastructure spending? Analysis of government expenditures in electricity, water and transport assets in conjunction with their quality in a large panel of country suggests that infrastructure quality is strongly linked to indicators of good governance, as well as to the attention to the trade-off between investment and maintenance.

5
vary with the business and/or political cycles<sup>28</sup> ; and whether some suppliers benefit from better payment terms than others (Ricca, 2019). All of these have important implications for competition and value-for-money in the public sector and deserve further investigation, which is possible using the granular data we provide.

Several other research questions related to local public finances can be explored using these novel data, particularly when matched with other administrative data available in Brazil. Ash et al. (2021), for example, combine the well-known audit courts data that reveal corruption at the municipal level (Ferraz and Finan, 2011) with aggregate data from SICONFI to predict out-of-sample corruption. The new dataset we provide could be used to compute additional measures of local budget and procurement decisions – such as payment delays or shares of purchases from local and/or politically connected firms – which could improve machine learning models used to predict mismanagement and corruption. The granular data available can also shed new light on how subnational entities adjust their expenditures throughout the business cycle, the political cycle (Foremny et al., 2018) and in response to fiscal rules that might 28In Figure A.6, we document that the share of late payments was much higher in the 2014-2016 period, when Brazil faced a severe recession, and then improved substantially in more recent years.

16