rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Introduction**

**In Sub-Saharan Africa (SSA), the COVID-19 pandemic caused significant income loss, disrupted value chains, and reallocated resources toward short-term measures to fight the pandemic.** While the health impacts of the illness were moderate, with the exception of South Africa, the economic impacts have been significant (Calderon and Kobata 2021). Mobility restrictions were widely implemented, severely disrupting economic activity, whether by preventing people from going to work and earning a wage or by prohibiting the self-employed from engaging in commerce (Khamis et al. 2021). Associated disruptions in supply chains, trade patterns, and fiscal spending further affected product markets and related jobs. The persistence of the disruptions, as the COVID-19 virus morphed into new strains, led to a long-term suppression of economic activity in SSA. The disruptions created by the pandemic pushed the region into the first recession in 25 years (Aga and Maemir 2021).

**Urban non-farm household enterprises in SSA were particularly vulnerable to the shock.** Recent high-frequency phone surveys in four African countries found that 86 percent of urban and rural non-farm household enterprises (NFHE) lost income due to the pandemic, as compared to 62 percent of farm households and 54 percent of wage-earning households (Contreras-Gonzalez et al. 2021). Khamis et al. (2021) found similar results using a larger sample, reporting that, when taking a simple average across nine countries in SSA, 82 percent of those with NFHE reported an income loss in the early stages of the crisis. This is particularly worrisome as most urban workers in Africa (70 percent, by ILO (2018) estimates) are engaged in mostly informal NFHEs. They are particularly vulnerable to COVID-19-induced shutdowns since they rarely have private risk management strategies due to their small size and low, irregular earnings (Guven et al. 2021). They also tend to not be connected to social protection systems, particularly in urban areas where the prevalence of safety nets and cash transfer programs has been meager
Figure 3.

In Table 7, we repeat the interactions with Intangible Capital/Assets in Table 3 but re-estimate _ROIC_ using different treatments of cash. In columns 1-3, we use the firm’s total cash holdings in computing ROIC, _ROIC_<sup>CASH</sup> , in columns 4-6, we consider excess cash to be any cash over 1% of sales, _ROIC_<sup>1per</sup> , and in columns 7-9 we consider excess cash to be any cash over 10% of sales, _ROIC_<sup>10per</sup> . Across the columns, we obtain similar results wherein large firms with high intangible capital and firms with high markups and high intangible capital are less likely to be in the top 10% of ROIC in a year.

# **5 Conclusion**

We assess the performance of publicly-listed star firms in the United States. When we use financial statement data as conventionally presented, a small percentage of firms seem to be pulling away over time from other firms in the economy in terms of their return on capital. In particular, star firms in highly skilled industries and industries with high levels of intangible capital seem to be pulling away from the others.

However, conventional financial statements do not capitalize R&D expenditures or organizational capital. Once we adjust firms’ returns to capital to address these shortcomings, there is little evidence that the most profitable 10% of firms are pulling away from the rest of the economy, and the differences in firm returns in highly skilled and other industries shrink dramatically. Furthermore, once we adjust markups based on operating expenses for investment in intangible capital, we only find a modest increase in market power especially in high skilled industries. Star firms tend to be larger, younger, and have higher markups. While they may have more pricing power than other firms, at each level of markup star firms tend to produce more than other firms. We also find no evidence that star firms are differentially affected by import competition
(1).

We translate these residuals to emissions using the EDGAR global database of gridded CO2 emissions estimated from local activity measures and standard emissions parameters (Crippa et al. 2020). The current EDGAR database terminates in 2018, so we perform the conversion using data for 2015 – 2018, the period of overlap with our OCO-2 database.

> 18  For detailed assessments of net carbon emissions from land-use change, see Gasser et al. (2020) and Winkler et al. (2021).

29
from the household of origin and this increases the probability of engaging in multiple concurrent sexual partners. Second, miners have traditionally been accommodated in single-sex hostels and this has boosted an active sex industry in the area surrounding the mining sites where miners have disposable income to engage in sexual relationships with commercial sex workers. Finally, women, who wait for their husbands to come back from the mines, might be more likely to engage in sexual relationships with other partners as well.

The data used in the paper come from the Demographic and Health Survey (DHS) conducted in Swaziland (2006-2007) and Lesotho (2004). In this set of African countries, the DHS provides the information required for our empirical analysis: a complete standardized questionnaire on sociodemographic characteristics and HIV test results for a nationally representative sample of the adult population. The latter represents a great advantage compared to previous surveys that collected information on HIV/AIDS based exclusively on self-reported behaviors.

To analyze the relationship between migrant miners and HIV status, we estimate a _probit_ model for the probability of being HIV positive and adopting the safer sexual behaviors promoted

> 3 Botswana is also considered one of the countries with the highest HIV prevalence in the world. The available and most recent data on HIV prevalence in Botswana reveal an HIV prevalence equal to 24.8% (UNAIDS, 2009b). This data comes from Sentinel Surveillance Surveys among pregnant women attending antenatal clinics but are not nationally representative. Because there is no recent DHS survey with HIV testing for Botswana, we cannot include it in the analysis.

2
showing that the relationship is positive up to a point, beyond which the impact of additional use of fertilizer on yields is negative. We also show the relative inefficiency of the subsidy in increasing rice production, since fertilizer subsidies are significantly higher than the value of the increase in production achieved through the fertilizer subsidy. In the fifth section we discuss the rationale for subsidizing agricultural inputs and review some of the international experience to date. Finally, section six concludes and provides some policy recommendations.

For this research we use two different surveys by the National Statistics Office (BPS), the Agricultural Census 2003 and the Rice Household Survey 2008. The first comprises a random sample of over 46,000 rice farmers in 29 provinces, while the latter uses a smaller sample of over 11,000 rice farmers from the 15 largest rice-producing provinces in Indonesia. The results of the benefit incidence analysis is very similar using both surveys; there is little targeting in the subsidy, with the largest farmers capturing a disproportionate share of the subsidy.

**3**
with attendance to growth and development checks of the minors. Additionally, an education incentive is provided, the incentive is conditional on attendance to at least 80 percent of the classes and repeating no more than two years. The value of the transfer differs according to the educational level and the municipality. The health incentive is delivered per household, while the transfer for education is assigned for each child and up to three per household.

All families with children and adolescents under 18 years of age and who are defined as poor and vulnerable according to the targeting tool are eligible, as well as families belonging to the Estrategia Unidos [United Strategy], who are indigenous or victims of armed conflict. In 2017, 2,001 billion pesos were allocated to this program, equivalent to 0.217 percent of GDP, out of which 1.911 billion pesos correspond only to transfers.

# **_<u>Jóvenes en Acción</u>_**

This conditional cash transfer program is targeted at young people in conditions of poverty and vulnerability, who are between 16 and 24 years old. The program seeks to incentivize young people to continue their education process. More precisely, the program incentivizes young people to enroll in a tertiary education institution. The program provides a bi-monthly incentive to young people who meet the poverty requirements or belong to a vulnerable group and are enrolled in a Higher Education Institution (IES) or the National Apprenticeship Service (SENA). In 2017, 191,35 billion pesos were allocated to this program (of which 188.64 billion pesos correspond only to transfers), that is to say, 0.021 percent of GDP.

# **_<u>Transferencias por Victimización</u>_**

Due to the armed conflict that Colombia suffered for more than five decades, the country has 8,785,305 victims registered in the Victims Registry.<sup>21</sup> In 2011 Law 1448 or the "Victims Law" was enacted
0.08|0.08|0.12|0.12|0.12|0.14|0.17|46%|
|<br>finalproducts|0.34|0.33|0.30|0.34|0.33|0.37|0.44|0.45|20%|
|**D. Total 'Buyer-Driven' value chains**||||||||||
|Share of above exports in Bulgaria's total exports (%)|27.4|29.7|33.1|35.8|33.7|38.9|37.6|38.0|-2%|
|<br>Share of EU exports of chains' products and parts (%)|81.1|80.3|81.7|82.4|81|80.2|77.8|77.8|-3%|
|<br>Share in EU external imports of furniture & parts (in %)|0.77|0.87|0.97|0.97|1.06|<br>1.31|<br>1.27|<br>1.39|<br>6%|
|of which: parts|0.15|0.17|0.16|0.14|0.14|0.18|0.23|0.24|33%|
|finalproducts|0.61|0.70|0.80|0.83|0.92|1.13|1.04|1.14|1%|

Source: Computations based on UN COMTRADE Statistics.

As for footwear and furniture, the ratio of imported parts to total exports of footwear chain products fell from 38 percent in 2000-01 to 34 percent in 2003 and in exports of
<!-- Start of picture text --> GNI per capita,<br>current USD<br>13000<br>12000, | ————— :::_hh_<br>11000 High income: 12,056<br>10000<br>9000<br>8000 *<br>7o00 a<br>6000 oe? ” Co *<br>5000 a *<br>Had<br>4000 Upper-middle-income: 3,896 a<br>3000 ee at .<br>2000 . aa _----7"<br>- oo ———=<br>1000 . are ee<br>a Lower-middle-incame: 996<br>a a 2 a@« 44 a4 a a4 a4 8 wm a om m ¢ ¢ ¢ = | &<br>o =I i=} o =I oS o a oS o i=] oS ao i=] a a a a a a a<br>ia | i | tl ia | i | el el el el el el el el el el a a el a a el<br>=etiee Conflict ——Counterfactual (no conflict) = = = Pessimistic Moderate ---«--- Optimistic<br><!-- End of picture text -->

Notes: GNI per capita based on the World Bank Atlas Methodology for Syria is only avaible up to 2007. We impute future values based on actual GDP per capita growth up to 2010 (at 2010 prices), our projections for GDP per capita growth over the conflict and post conflict periods (both also at 2010 prices). We account for inflation up to 2019 based on US inflation rates. On using GDP growth rates to proxy GNI growth rates, our calculations based on pre-conflict data suggest the the growth rates of GDP per capita in current USD and GNI per capita based on the Atlas Methodology on average are quite close (
Tetui M, Ekirapa EK, Bua J et al (2012). Quality of antenatal care services in eastern Uganda: implications for interventions. Pan Afr Med J. 13: 27.

Thandar MM, Rahman MO, Haruyama R, Matsuoka S, Okawa S et al (2022). Effectiveness of infection control teams in reducing healthcare-associated infections: a systematic review and meta-analysis. Int J Environ Res Public Health 19(24): 17075.

Tumlinson K, Pence BW, Curtis SL et al (2015). Quality of care and contraceptive use in Urban Kenya. Int Perspect Sex Reprod Health 41: 69–79.

Umscheid C, Mitchell M, Doshi J, Agarwal R et al (2011). Estimating the Proportion of Healthcare-Associated Infections That Are Reasonably Preventable and the Related Mortality and Costs. Infection Control & Hospital Epidemiology 32(2): 101-114.

United States CDC (2023). Healthcare Associated Infections. Atlanta: Centers for Disease Control and Prevention.

Upadhyay RP, Rai SK, Krishnan A (2013). Using three delays model to understand the social factors responsible for neonatal deaths in rural Haryana, India. J Trop Pediatr. 59(2): 100-5.

Watson J, D'Mello-Guyett L, Flynn E et al (2019). Interventions to improve water supply and quality, sanitation and handwashing facilities in healthcare facilities, and their effect on healthcare- associated infections in low- income and middle-income countries: a systematic review and supplementary scoping review. BMJ Global Health 4: e001632.

White Ribbon Alliance (2019). What Women Want survey: Demands for quality healthcare for women and girls. Washington DC: White Ribbon Alliance.

Woldeyohanes TR, Woldehaimanot TE, Kerie MW et al (2015). Perceived patient satisfaction with in-patient services at Jimma University Specialized Hospital, Southwest Ethiopia. BMC Res Notes 8:285.

WHO (2020a). Global report on the epidemiology and burden of sepsis: Current evidence, identifying gaps and future directions. Geneva: World Health Organization
||**(1)**<br>**Price dispersion (%)**|**(2)**<br>**Price dispersion (%)**|
|---|---|---|
|Tanzania's harvest|0.104<sup>***</sup>|0.105<sup>***</sup>|
||(0.03)|(0.03)|
|Mozambique's harvest|−0.386<sup>***</sup>|−0.384<sup>***</sup>|
||(0.12)|(0.12)|
|Zambia's harvest|−0.030|−0.031|
||(0.07)|(0.07)|
|Observations|46,402|46,402|

_Note:_ The regression results are based on the specification in equation 9. Standard errors are clustered by region pairs. Both regressions include country pair-by-year fixed effects. Controls include harvest quantity in neighboring countries and distance between market pairs. * _p_ < 0.10, ** _p_ < 0.05, *** _p_ < 0.01.

**Figure 8: Comparison of price responsiveness to harvest ‘with’ and ‘without’ export bans**

<!-- Start of picture text --> 0.30<br>0.20<br>0.10<br>0.07<br>0.00<br>-0.09<br>-0.10 -0.16 -0.15 -0.16<br>-0.18<br>-0.20 -0.25 -0.24<br>-0.30<br>-0.40
respectively called _Mehta I_ and _Mehta II_ in legislative digests, and are together known as the “Ganga Pollution Cases” **–** the foundational water pollution litigation in the Indian court system. In October 1987, the Court invoked the Water Act and Environment (Protection) Act as well as Article 21 of the Indian Constitution to rule in Mr. Mehta’s favor and order the tanneries of Jajmau to clean their wastewater within six months or shut down entirely. This was followed by a January 1988 judgment that required the Kanpur local municipal bodies to take several immediate measures to control water pollution: the relocation of 80,000 cattle housed in dairies or the safe removal of animal waste from these locations; the cleaning of the city’s sewers; the building of larger sewer systems; the construction of public latrines; and an immediate ban on the disposal of corpses into the river. The court also required all schools to devote one hour each week to environmental education and awareness.

Of the 87 tanneries named in Mr. Mehta’s petition, approximately 20 were shut down and at least 60 established primary treatment plants (PTPs). Moreover, several initiatives were undertaken in 1987 and 1988 to clean drains, expand the number of handpumps, and build latrines to improve sanitation systems in Jajmau (Alley 2002). Subsequent litigation in the Supreme Court over the past 25 years, and indeed many academic researchers of pollution in Kanpur, have argued that these projects were a failure and that newly established technologies were not appropriately maintained or used (Alley 2002; Singh 2007).

# **2 Data**

To assess the relationship between policy, water quality, and health in Kanpur, we collect and combine three types of data: infant mortality, river pollution, and other variables to be used as controls. In most of our analysis, we restrict our sample geographic area to the Ganga Basin, which is defined as the area that drains the Ganga and all of its tributaries. We make this
11 decompose total income growth and the total number of people exiting poverty into that realized by people making the transition to (or staying in) the rural areas (further split into its agricultural and non‐ agricultural sectors), to secondary towns, or to cities (Table 2).

This decomposition analysis is interesting from an analytical and a policy perspective. It shows that moves to the city led to large income increases and poverty decreases on average more for those who made the move. However, even in the presence of larger migration premiums from moving to the more distant cities, most people engage in the surrounding nonfarm economy or move to secondary towns. Thus moves to secondary towns make up a much larger share of total growth and poverty reduction than moves to cities.

Other emerging country and cross‐country evidence confirms the relevance of the composition of urbanization for poverty reduction. Recent econometric findings from India, for example, using satellite observations of night lights as measure of urban growth, show that growth of secondary towns has a larger direct and indirect effect on rural poverty than does big city growth (Datt, Gibson, Murgai and Ravallion, 2016). Similarly, and directly accounting for growth linkages and foreign trade as well as internal migration and agglomeration effects in an economywide dynamic recursive computable general equilibrium model for Ethiopia, Dorosh and Thurlow show that a 10 percentage point reallocation of public investment from cities (and rural areas) to towns leads to faster poverty reduction than when reallocating public investment from towns (and rural areas) to cities<sup>5</sup> , though the latter yields faster overall growth. Applied to Uganda, on the other hand, where rural areas are already more diversified, public investment shifts towards cities also yielded faster poverty reduction, underscoring the importance of context specificity. Importantly though, in these simulations, public investment is taken away from both towns and the rural area and reallocated to the cities, while it is in fact the reallocation

> 5 Cities are defined here as
Intra-Household Dynamics and Attitudes towards Vaccines: Experimental and Survey Evidence from Zambia<sup>1</sup> Christopher Hoy<sup>*</sup> , Rajee Kanagavel<sup>**</sup> and Corey Cameron<sup>**</sup>

# **JEL-Classification:** I12, I15, I18, D90, D91.

# **Keywords:** COVID-19, Vaccines, Survey experiment, Low and Middle-income countries.

> 1 The survey experiment was pre-registered on the AEA RCT registry (AEARCTR-0009027). We wish to thank the Zambia Statistics Agency (ZAMSTATS) for access to data and acknowledge that the ZAMSTATS survey was supported by Arden Finn and Domingos Mazivila. We also wish to thank UNICEF and UNDP for access to the U-Report database and acknowledge that this work was supported by Edgar Lungu, Domingos Mazivila and Andre Lesa. We appreciate contributions made to the online survey experiment by Renos Vakis, Zeina Afif, Ellen Moscoe, Laura Zoratto, Mohamad Chatila, Daniel Pinzon Hernandez, Jungkyu Rhys Lim, Haena Kim and Michelle Dugas. We are grateful for comments by Leonardo Lucchetti and Rob Swinkels on an earlier version of the paper. This study was partially funded by the Alliance for Advancing Health Online. The findings, conclusions, and recommendations expressed in this article are those of the authors and do not necessarily reflect the views of the collaborators or the organizations they work for.<sup>*</sup> World Bank and corresponding author (choy@worldbank.org),<sup>**</sup> World Bank.
26 The third information variable that is used is a dummy variable capturing whether countries reported data on the prices of exports and imports in the International Financial Statistics25 Countries that are lax or reluctant about reporting such price data are more likely to be the same countries in which voters are less well-informed. In fact, in countries that do not report these data, newspaper independence is rated 2.9 on average, relative to 3.4 for countries that do report.

Institutional variables The Database of Political Institutions (DPI), version 2 (Beck, et al., 2000) provides useful measures of the political institutions that the foregoing discussion has identified as important. All of the political variables used below are three year averages, starting with the first year of crisis (reported by Honohan and Klingebiel), and extending back to two years prior to crisis (or the average of values in years ¢, t-1, t-2, t=first year of crisis).

The number of veto players is captured by the variable Checks2a from that data set (hereinafter, checks). This variable is built up from several other variables collected in the data set. Two of these are che legislative and executive indices of electoral competitiveness (EJEC and LIEC in DP]), scaled one to seven, that indicate the competitiveness of elections. EJEC is also used to establish the validity of the underlying assumptions in the information literature, that the effect of imperfectly informed voters depends on the existence of competitive elections.26 25 Educational attainment was also tried, but was insignificant in all specifications.

26 Where there are no elections, countries receive a one; the scores rise to seven when there are multiple candidates and multiple parties, and no single party or candidate receives more than 75 percent of the vote. If the legislative index of electoral competitiveness is less than five (where five indicates that multiple parties can legally be established, but where only one party wins any seats in the legislature), checks is one. This reflects the
# **2. Methods and data**

The RAI is calculated by overlaying a road network with a population layer, removing the urban areas, drawing a buffer around the roads, and counting the number of people in the buffer (World Bank, 2016). The code and documentation are available at <u>http://rai-wb.readthedocs.io/index.html.</u> We construct a GIS composed of a model of the distribution of rural population, and a geospatial model of rural roads (including their location and type). This first section describes the data that were used and the assumptions that were made to estimate rural accessibility with open source data.

## **2.1. Population and urban data**

We calculate rural accessibility for a subset of countries using different data sets for population distribution (WorldPop, LandScan, GHS-Pop) and urban area boundaries (Grump, EC LandScan and EC GHS). The results, displayed in Table 1, show a wide spread of values (RAI ranges from 29% to 51% for the Lao People’s Democratic Republic, depending on the choice of data sets, for example), and different rankings of countries depending on the data sets used. Overall, the RAI is lower when using WorldPop, and higher when using GHS-Pop. This is because WorldPop is the population data set with the least concentrated population in rural areas, while GHS-Pop is the most concentrated of those tested. The impact of the urban/rural area definition is very dependent on the country, though overall EC LandScan tends to lead to higher RAIs.

_Table 1 RAI calculated using the same roads but different population distribution datasets for a selection of countries with diverse geographies and population distribution_

|Population||WorldPop|||LandScan|||GHS-Pop||
|---|---|---|---|---|---|---|---|