rafmacalaba/data-use-mentions-extended huggingface dataset:
particular-year rainfall from the long-term median.<sup>5</sup> The choice of IV is consistent with existing literature (Fisher & Kandiwa, 2014; Harou, 2018; Karamba, 2013; Ragasa & Mazunda, 2018; Sibande et al., 2017). The variables also reflect the social and political capital that influences access to subsidized inputs.

Provision of irrigation infrastructure is a less endogenous in the sense that household behavior may not be integrated in the policy choice to make the investment. However, it becomes an issue if the government targets locations with specific characteristics; for instance, locations that are dry or with the poorest communities. This may dampen the impact estimates. Therefore, our modeling accounts for geospatial indicators reflecting agroecological zones. We additionally include a variable for whether the village chief receives payment for land use or sale as one of the IVs. The actions of the chief may facilitate access to irrigatable land for some individuals but may not alter household food consumption. The instrumental variables validity is checked using the IV-2SLS for panel-data models since the IV Poisson method does not produce post estimation tests. The combined effects of the interventions are assessed by including interaction terms in the models.

Models estimating program effects on food consumption scores are estimated using singleequation instrumental-variables regression via the GMM to take advantage of pooled data. Additional analysis considers food and nutrition security as measured by coping strategies using Tobit estimation. A comparison of the panel Tobit model with the pooled Tobit model using the likelihood-ratio test shows that there were panel-level effects in the relationship; hence, results based on panel data analysis are reported.

# **4 Results**

# **4.1 Descriptive statistics**

Table 2 presents the descriptive statistics for the pooled sample. On average, households consumed food from 8 food groups and the maximum was 12 food groups. The consumption of 8 food groups compares well with other national-level studies (Jones et al., 2014; Matita et al., 2022). The mean FCS was 49
period, a conservative estimate suggests that, focusing solely on trade in services through commercial presence in 70 developing countries where surveyed investors had operations, irregular government conduct lies behind decisions to withdraw or cancel expansion plans in up to 900 FDI projects a year valued at approximately US$65 billion.<sup>41</sup> The most common source of irregular governmental conduct generating investor-state conflicts relates to administrative actions, including by sub-national and specialized regulatory agencies. Up to the 1970s, expropriations used to be the most prominent and impactful source of irregular government conduct generating investor-state conflicts (MIGA 2009). Although perceived unlawful expropriations still rank among the leading types of irregular governmental conduct prompting FDI project discontinuation, the most common source of conflict today relates to a lack of transparency and predictability in dealing with public agencies and sudden/arbitrary changes in the enactment or interpretation of laws and regulations with negative effects on companies. This is followed by issues relating to payments and transfers and restrictions on currency convertibility. Although less frequent, contract breaches and expropriations perceived as unlawful constitute the most impactful types of irregular governmental conduct, leading between a third and half of investors to totally withdraw and/or cancel project expansions in 2017 (MIGA, 2009/13, GIC 2017; see Figure 12).

WBG research has revealed growing tensions between the single state paradigm at the core of domestic and international legal systems on the one hand, and the multilayered agency composition of

> 41 Authors’ calculations based on data from WBG (2019).

40 See WBG (2019).

26
||
|GDP per capita, PPP 1995 US dollars|PPPGDP|World Bank, World Development Indicators|
|Trade in goods (% of GDP)|OPEN|World Bank, World Development Indicators|
|Net official transfers, US dollars|OT|World Bank, Global Development Finance|
|Concessional debt (% of external debt)|CONCD|World Bank, Global Development Finance|
|Dummy equal to 1 if private credit over<br>GDP is above median|FD|World Bank, Global Development Finance|
|Dummy for exchange rate arrangements|ARR|Rogoff and Reinhart (2002)|
|Dummy equal to 1 if population<500.000|SMALL|World Bank, World Development Indicators|
|Dummy equal to 1 if the literacy rate is<br>above the median|LIT|World Bank, World Development Indicators|
|Dummy equal to 1 if country-year with a<br>civil conflict with more than 1000 battle<br>deaths|WAR|Armed Conflict Database, International Peace<br>Research Institute of Oslo (Norway) and the<br>University of Uppsala|
|Dummy equal to 1 if country is located in<br>Africa|AFRICA||
|Dummy equal to 1 if country is located in<br>Asia|ASIA||
Measuring True Sales and Underreporting with Matched Firm-Level Survey and Tax office Data The direct approach is a micro approach that uses surveys to reveal the extent of underreporting directly. Respondents are either randomly sampled or selected as part of tax auditing or other compliance methods. For instance, the (P)ICS and BEEPS surveys include a question which is typically formulated along the following lines: “Recognizing the difficulties many firms face in fully complying with taxes and regulations, what percentage of total annual sales would you estimate the typical firm in your area of business reports for tax purposes?” Together with other information collected on respondents’ behavior and environment, these sample surveys provide rich information about underreporting and its correlates but are sensitive to the formulation of questionnaires and largely depend on the respondents’ willingness to cooperate. Tax auditing methods may be better able to extract truthful information from the auditees, but tax authorities may not be able to fully discover the true incomes of the audited group. Moreover, the audited group is typically a biased sample of the population (Schneider and Enste 2000).

The second type of approach is the indirect approach or indicator approach and has been primarily used in macroeconomic settings. The approach consists of constructing indicators that reflect the development of a shadow economy over time, such as the discrepancy between national expenditure and income statistics and/or between the official and actual labor force. The quality of the approach depends therefore on the accuracy of these indicators and may be seriously affected by measurement errors and systematic underreporting in the indicators (Giles 1999, Schneider and Enste 2000).

The third approach to estimating the extent of underreporting is the model approach. This approach was introduced into economics by Frey and Weck-Hannemann (1984) in their 4
<br>μμ̂ ± 2RRD<br>-5% -4% -3% -2% -1% 0% 1% 2% 3% 4% 5% 6% 7% 8%<br>Leader contribution to per capita GDP growth (value added)<br>Notes: is leader i's effect on growth, and is its estimate. represent the average growth rate residuals under leader i and other<br>leaders in the same country (respectively). See Section 2 for further details.<br>μμii μμ̂ii gg̅ii , gg̅−ii<br>Probability Density<br><!-- End of picture text -->

In contrast, we estimate the growth contribution of every individual national leader since 1950.

Even when we grant the presumption that leaders matter for growth in general, a hugely important question remains: which leaders matter? This is a signal extraction problem, where the true leader contribution is the signal we seek to extract from growth data, but it is obscured by annual countryspecific noise (e.g., measurement error, good or bad luck), country-level growth trends, and global/regional shocks. Our optimal (least-squares) signal extraction method utilizes the overall 3
irrespective of gender, the amount of time it takes for the acquisition of the necessary knowledge and experiences for one to become an educated scholar did not accommodate the social status of females, who were expected to be married off by the end of their teenage years. Hence the itinerant Muslim scholar, the cleric, and the long-distance trader extensively documented in the history of pre-colonial Africa were all male (Goody (1968), Sanneh (1976), Owusu-Ansah (1991)). It is not surprising then that the African Muslim assessors attached to colonial courts in French West Africa and those assisting in the codification of customary law were all men (Ginio (2006), Jézéquel (2006)).

Education is a key area, instrumental in colonial understandings of the role of the colonial subject, and for independent governments the role of empowered citizenry in developing nations. Lord Hailey‘s magisterial African Survey, revised in 1956, provides an 25
**Figure 5** : Availability of emergency transport* by facility type (hospital vs. non-hospital) in Kenya, Sierra Leone, and Tanzania

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Hospital Non-hospital Hospital Non-hospital Hospital Non-hospital<br>Kenya Sierra Leone Tanzania<br>Has ambulance Other emergency transport No emergency transport<br><!-- End of picture text -->

* “Other emergency transport” arrangements include other official vehicles, private vehicles or another mode of emergency transport.

# Availability of WaSH and acceptable waste disposal

Availability of appropriate water, sanitation, and hygiene (WaSH) facilities is critical for infection prevention and control, especially in health care settings. Hand hygiene (hand washing) and appropriate waste segregation and disposal, for instance, are critical for both patient and health care worker safety. By the end of the 2018 Ebola epidemic, the rate of infection among health care workers was several folds higher than that of general population, partly owing to lack of robust infection prevention and control measures in place in the health facilities.<sup>15</sup> How do health facilities in Kenya, Sierra Leone and Tanzania fare in terms of WaSH arrangements?

The evidence from the SDI surveys is worrisome. Disquietingly, less than half of the facilities across these three countries have appropriate handwashing facilities for patients, with fewer rural facilities compared to their urban counterparts equipped with this basic amenity (Figure 6). Disaggregating by facility ownership, these data indicate that 65% of public facilities and 42% of private facilities in Kenya lack appropriate handwashing arrangements for patients (Appendix Table 5). Over 70% of facilities in all three countries have access to an improved water source
overall lending since then. In the case of the World bank Group, for example, trade finance operations and supply chain finance helped SMEs whose needs would not have been met<sup>30</sup> (IEG, 2012).

**Equity:** Venture capital plays a key role in financing innovative startups, which will particularly be needed during the recovery phase. A small proportion of SMEs have sufficiently strong growth prospects that they can afford to take on external equity. These SMEs have a disproportionate impact on job creation. DFIs provide venture capital with new fund managers in frontier markets lacking in fund management capacity. Providing equity is strongly complementary to government financial policies which expand the availability of debt capital. Without equity, SMEs will not be creditworthy borrowers, and excessive leverage can jeopardize their survival and resilience to future shocks.

A major concern is that DFIs might not necessarily be in the countries where severe disruptions are underway. According to IFC data, for example, SME projects in 2018 were in place in 71 countries and COVID related commitments in the financial sector were only reported in 16 countries. Moreover in countries where DFIs are present, their commitments might not be commensurate to the impact facing the countries.

To expand their reach and presence DFIs would need to invest in relationships with key market players and financial institutions in countries where the pandemic is having the greatest impact. Leveraging existing relationship to launch COVID-related support is one way to do this rapidly. IFC for example has non-COVID related commitments in 19 additional countries in FY2020, indicating potential. Although outstanding SME related portfolios are present in about 70

> 30 IEG (Independent Evaluation Group). 2012. The World Bank Group’s Response to the Global Economic Crisis—Phase II. Washington, DC: Independent Evaluation Group, the World Bank Group.

22
Mwamba et al took a broader look using a cholera risk in Zambia between 2008 and 2017, identifying 16 districts at higher risk using a discreate Poisson-based space-time scan statistic to allow for variation over the ten-year study period (Mwaba et al. 2020). However, this study provides a unique contribution in terms of the combination of data sources that it brings together, considering multiple known environmental risk factors, and its granular focus on Lusaka.

# **A2 MATERIALS AND METHODS**

Studies of previous epidemics in Lusaka and elsewhere have implicated a range of different factors as contributing to cholera risk, including inadequate provision of safe water and sanitation (Mengel et al. 2014; Adedire et al. 2015), climatic conditions (Luque Fernández et al. 2009), inadequate drainage (Sasaki et al. 2009) and food-borne contamination (Dubois et al. 2006; Sinkala et al. 2004). We also see associated environmental factors where cholera outbreaks could be more prevalent such as areas with higher levels of poverty (Cowman et al. 2017) and densely populated peri-urban settlements (Chimusoro et al. 2018). In this work multiple data sources that reflect these covariates as far as possible were brought together to identify where the strongest correlates with risk were in the case of the 2017-18 Lusaka outbreak. Table 1 provides an overview of all the data sources that went into the analysis. They include the cholera case data itself, data on poverty rates by ward, population estimates, household size information, groundwater vulnerability data, distance from water and sewer networks and burial grounds and household reported access to treated water, improved sanitation, and handwashing facilities. Finally, data on the risk of flooding; frequency of complaints regarding sewer network; water supply/quality and prevalence of E. coli in drinking water sources were all input into the model.

**<u>Table 1- Data sources used to generate the covariates in the analysis</u>**

|Data Description|Data Type, Resolution,
# 6. Policy discussion

The adoption of tax and pricing policies to discourage the consumption of ultra-processed foods and beverages (making resources available to strengthen health promotion) has been proposed in the context of Brazil’s ongoing efforts to introduce broader tax reforms. The NUPENS NOVA food classification system, developed at the University of São Paulo, can be used as the basis for these policies, and is increasingly being applied globally through both research and to reduce NCDs, overweight, and obesity, and resulting morbidity and mortality. While the NOVA classification requires regulatory specifications to enable its application through fiscal policy, it can offer several advantages in providing a more holistic approach to nutrition promotion. Evidence also indicates that this approach may more accurately and effectively target the association between foods and health outcomes when compared to single-nutrient-based taxes (e.g., targeting sugar, sodium, or fat content) (Leonie et al., 2022).

This paper contributes to this policy discussion through results demonstrating that processed and ultraprocessed food taxes in Brazil can have a progressive effect in terms of: (i) changes in product expenditure, (ii) changes in medical expenditure; and (iii) changes in years of life lost. Our findings suggest that processed and ultra-processed food taxation would benefit households at the lower end of the consumption distribution, contributing to reduce the prevalence of diet-related health issues and decreasing medical expenditure, particularly among households reliant on the public health system.

Consumption data indicates that there may be a narrowing window of opportunity to harness the potential progressiveness of processed and ultra-processed food taxes in Brazil. While currently, urban, wealthier, and more educated population groups in Brazil consume a higher proportion of ultra-processed food groups relative to minimally processed/in natura food groups, increasing consumption of ultraprocessed foods by rural, poorer, and less educated population groups over the last decade is reducing this gap in consumption profiles (Louzada et al., 2022). This indicates that Brazil could follow the path of more industrialized countries, increasing inequities in
sup>_∗∗∗_</sup>||
||||(0_._009)||||(0_._009)||
|Country FE|_Y es_|_Y es_|_Y es_|_Y es_|_Y es_|_Y es_|_Y es_|_Y es_|
|Adjusted R2<br>|0_._521<br>|0_._659<br>|0_._673<br>|0_._741<br>|0_._540<br>|0_._667<br>|0_._678<br>|0_._748<br>|
|Nr. of countries|16|16|16|16|16|16|16|16|
|Nr. of hh<br>1|34630<br>1|34630<br>|134630|73365<br>1|34630<br>1|34630<br>1|34630|73365|

Source: International Urban Poverty Database.

Note: *** p < 0.01, ** p < 0.05, *** p < 0.1. Robust standard errors in parentheses. GHSPOP 1km is used for the DOU method. The dependent variable is the log of per capita consumption expenditures, expressed in PPP and not spatially deflated. Only urban households (“Urban center” or “Urban cluster”) are included in specifications (4) and (8). The baseline category is “Rural” in all specifications, except (4) and (8), where it is “Urban cluster”. Baseline categories for the control variables are the following: “Married” for marital status of household head, “No education” for education of household head, and “Employed in Agriculture” for employment of household head. Robust standard errors are in parentheses. Observations are weighted using population weights.

67
Brown _et al.,_ (2017) that a high proportion of undernourished children in Africa reside in nonpoor households.

Poor developmental outcomes for children have a lifelong impact on their well-being. Childhood undernutrition increases the risk of death from common childhood illnesses such as diarrhea, measles, pneumonia and malaria. Black _et al._ (2013) argue that undernutrition is a cause of approximately 3.1 million child deaths annually, most of which occur in developing countries. Child undrnutrition increases the costs of health care and social safety nets and lowers the efficacy of investments in education. In addition, childhood undernutrition is associated with delays in cognitive development which can lead to poorer learning outcomes in school and decreased productivity and earning potential in adulthood.<sup>3</sup> As a result, the costs of child undernutrition in Africa and Asia have been estimated to range from 4 percent to 11 percent of GDP.

This paper uses detailed data on crop prices and nutritional outcomes for children under 5 from the Living Standards Measurement Survey (LSMS) to examine how food prices and accessibility to markets affect child undernutrition in Ethiopia. Approximately 80 percent of the population live in rural areas in Ethiopia and agriculture generates 40 percent of gross domestic product (World Bank 2016).<sup>4</sup> The empirical analysis seeks to contribute to the debate on the role the government can play in preventing childhood deaths in a context where child undernutrition may contribute to 51 percent of childhood deaths annually (Government of Ethiopia (2013)).<sup>5</sup> Despite substantial progress in reducing child undernutrition over the last decade, a large proportion of children under 5 years are still stunted and underweight (Cintron, Seff, & Baird, 2016).

The remainder of the paper is structured as follows: in section 2 we briefly summarize the main features of the literature on the links between food prices and household welfare in the context of a simple conceptual framework by which to trace through the linkages between
# _Outputs: Relative Trends Inside and Outside Millennium Villages_

_Chronic malnutrition_ . Figure 7 shows that in all three countries, stunting rates for small children fell in the rural areas of the regions in which the Millennium Villages are located. In Ghana, stunting declined at the MV intervention site at the same rate as in the surrounding region. In Kenya and Nigeria, stunting fell much more at the MV intervention site than in the surrounding region.

_Gross primary school attendance_ . Figure 8 shows different trends in gross primary school attendance across the three countries. In the regions where the MVs are located, attendance increased greatly in Kenya and Ghana, while dropping in Nigeria. At the MV intervention site in Kenya, attendance rose somewhat more rapidly than in the surrounding region. At Ghana’s MV site, attendance rose less than in the surrounding area. Finally, at Nigeria’s MV site, attendance rose while attendance in the surrounding area was nearly flat.<sup>10</sup> _Mobile phone ownership_ . Mobile phone ownership has skyrocketed across all three countries, as Figure 9 shows. In Kenya and Ghana, mobile phone ownership rose about as much at the MV intervention sites as in the surrounding areas. In Nigeria, it rose somewhat more at the MV site than in the surrounding area.

_Malaria prevalence_ . MVP (2010c) documents declines in malaria prevalence among all ages, from 50 to 8 percent in Sauri, 28 to 13 percent in Pampaida, and 15 to 6 percent in Bonsaaso. Malaria tests are not administered as part of the DHS, so definitive figures on malaria trends are not available. Thus we cannot construct a figure for malaria like the other figures. However, there is suggestive evidence that malaria rates have dropped in Kenya overall, where the MVP site decline is greatest. First, in the DHS data, the percentage of children under 5 reporting fever during the last two weeks—a very rough indicator of malaria prevalence<sup>11</sup> —dropped from 40
(Exhibit 1). The plants operate a continuous production process that runs constantly (Exhibit 2). The factories’ floors were rather disorganized (Exhibits 3 and 4), and their yarn and spare-parts inventory stores lacked any formalized storage systems (Exhibits 5 and 6).

# **III. THE MANAGEMENT INTERVENTION**

## **_III.A. Why use management consulting as an intervention_**

The field experiment aimed to improve management practices in the treatment plants. To achieve this we hired a management consultancy firm to work with the plants as the easiest way to

> 7 The main reasons we were given for refusing free consulting were that the firms did not believe they needed management assistance or that it required too much time from their senior management (1 day a week). But it is also possible these firms were suspicious of the offer, given many firms in India have tax and regulatory irregularities. 8 For example, the program firms had slightly less assets ($12.8m) compared to the non-program firms ($13.9m), but this difference was not statistically significant (p-value 0.841). We also compared the groups on management practices using the BVR scores, and found they were almost identical (difference of 0.031, p-value 0.859). 9 Dunn & Bradstreet (August 2009) lists 778,000 manufacturing firms in the US with only 17,300 of these (2.2%) with 270 or more employees and only 28,900 (3.7%) with $7.5m or more sales.

> 10 Note that most international agencies define large firms as those with more than 250+ employees.

6