rafmacalaba/data-use-mentions-extended huggingface dataset:
report results based on a few extra countries outside Sub-Saharan

> 15GHSPOP data is available on the following European Commission website: https://ghsl.jrc.ec.europa.eu/ghs_ pop2019.php. WorldPop data is available here: https://www.worldpop.org/project/categories?id=3.

> 16See Combes et al. (2023) for details.

> 17The results based on 1km data are available upon request.

8
BURKINA FASO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE then exceeded, the peak volumes of the early 2000s (figure 10). As of 2010 SITARAIL was on track to achieve its highest-ever traffic volume of more than 900 million tonne-km.

**Table 6. Railway indicators for SITARAIL and selected other countries, 2005–09 average**

||**SITARAIL**|TRANSRAIL|CAMRAIL|MADARAIL|
|---|---|---|---|---|
||**Côte d’Ivoire**<br>**Burkina Faso**|Senegal<br>Mali|Cameroon|(Madagascar)|
|Concessioned (1)/ state run (0)|**1**|1|1|1|
|Freight traffic volume (million tonne-km)|**794**|393|1,061|113|
|Passenger traffic volume (million passenger-km)|**210**|91|377|3|
|Total traffic volume (million TU)*|**878**|429|1,212|114|
|**Efficiency**|||||
|Staff: 1,000 TU per staff|**558**|247|547|118|
|Derailments/million TU|**0.01**|0.45|0.15|2.31|
|Mainline locomotive breakdowns per 100,000 km|**6**|15|9|6|
|**Tariffs**|||||
|Average unit tariff, freight, cents/tonne-km|**6.3**|7.0|8.1|6.0|

_Source:_ World Bank 2010; Bullock 2009. Derived from AICD rail operator database (www.infrastructureafrica.org/aicd/tools/data). — = Not available.

_Note:_
), based on whether households report any time spent during the last week on activities like weeding, harvesting, caring for animals, collecting firewood or water, food collection and preparation, etc. As with REE, values outside of the 1% of the tails of the distribution are replaced with the cutoff values prior to estimation. For individuals younger than 6, activity levels are set equal to the sample mean for 6 year olds, which is 1.46.

# **C Estimation of caloric intake**

The alternative, independent caloric intake estimates reported here, the NSS consumption survey (schedule 1) is used for the same years as the NSS-E. For 2010and 2012 the main results use a comparable 30-day recall period. Household respondents are asked to recall the total quantity and expenditure for each food item from a detailed list over the previous 30-days. The standard 22
# **3.2 The Size Distribution of Firms**

Besides affecting the sectoral allocation of production and the aggregate gaps in productivity, frictions that misallocate resources will manifest also in the shape of the firm size distribution. Thus, it is informative to confront the measurement of misallocation that we perform below with some information about the shape of the firm size distribution in the countries that we cover in this study.

Table 1 presents some descriptive statistics. The table illustrates that, Kenyan firms, on average, are much larger (in terms of the number of workers) than firms in the other countries. While the average number of workers is approximately 145 in Kenya and 67 in Cˆote d’Ivoire, it is only 30 in Ethiopia and 29 in Ghana. The distribution of firm size in all countries is skewed to the left with the median firm in Kenya employing 34 workers while the corresponding figures in Cˆote d’Ivoire, Ethiopia and Ghana are only 9, 8 and 12 workers, respectively. Figure 2 clearly shows that the size distribution of firms in Kenya looks different from the distribution in the other countries.<sup>3</sup> Table 1: Size Distribution of Firms

||**Cote d’Ivoire**|**Ethi**|**opia**|**Gh**|**ana**|**Kenya**|
|---|---|---|---|---|---|---|
||Census (2012)|Census|(2011)|Censu|s (2003)|Census (2010)|
|**Size**|**N**|**N**|**N(wt)**|**N**|**N (wt)**|**N**|
|_<_ 5|469|1,618
Three important, recent papers examine fishing cooperatives empirically. Deacon et al. (2008) and Deacon et al. (2013) develop a model incorporating concerns specific to cooperatives and then empirically test this model. They examine the intraseasonal allocation of fishing effort across space, time, and fishers in a salmon fishery with one cooperative and independent fishers. Our work provides less detail on the location of fishing effort and instead focuses on the allocation of effort across time in the face of species-specific differences in mobility and large-scale environmental oscillations that cycle over several years. Ovando et al. (2013) use a survey of 67 cooperatives from around the world to examine what management tools cooperatives use and how this is shaped by differing economic, political, and ecological contexts. We focus on a particular management instrument—the choice of what price to pay cooperative members—and we complement our empirical analysis with a detailed theoretical model that delivers clear predictions for how this instrument should respond to a variety of circumstances.

The paper proceeds as follows. Section 1 describes the setting of Mexico’s fisheries in more detail. Section 2 describes the data used in the empirical analysis. Section 3 develops the theoretical model and derives three testable implications. Section 4 uses the model to develop empirical tests of these implications and presents empirical results from these tests. Section 5 concludes.

# I MEXICO’S GULF OF CALIFORNIA FISHERIES

Bordering five Mexican states, the Gulf of California is one of the most biologically productive areas of the world’s oceans.<sup>4</sup> While the region’s remarkable biodiversity has considerable conservation value, it also is of substantial social and economic importance. The states surrounding the Gulf contribute 71% of Mexico’s total fisheries volume and 57% of total value (OECD 2006). As in many parts of the world, the Gulf’s fleet is characterized by small-scale subsistence or commercial fishing on small two or three-person boats. Small-scale fisheries are a major source of employment and income,
the period of 2016-2030. The granular cost breakdowns of the LSMS-ISA database suggest that there is considerable variation in terms of aggregate costs for specific budget categories, inclusion/exclusion of line items in a given category, and unit costs for comparable line items. The factors underlying the cross-country variations include:

- 1) whether a given cost category/line item is absorbed through technical assistance outside the implementation budget (such as data analysis and dissemination, training and capacity building activities, and specialized equipment procurement outside the country),

- 2) survey duration, sample size and spatial distribution,

- 3) interview length and approach to respondent selection,

- 4) survey mode (computer-assisted personal interviewing (CAPI) versus pen-and-paper interviewing (PAPI) with field-based data entry and processing versus PAPI with centralized data entry and processing),

- 5) required field staff and their organization (mobile teams versus resident enumerators, need for specialized staff),

- 6) in-country salary and per diem structure for field-based and supervisory staff, and

- 7) costs for vehicle purchase versus rental, maintenance and repairs.

The second step in our analysis was to expand the country coverage and the regional representation of the survey implementation unit cost estimates, including as much as possible surveys that are used for estimating official poverty and inequality statistics. While the advantage of this database is that it includes actual implementation costs of household surveys that have been used for estimating official poverty statistics, its disadvantage is that the design and quality of surveys differ quite significantly across countries. To conduct a comparative review of the survey implementation budgets tied to the LSMS-ISA activities, we first created templates for aggregate and detailed implementation cost reporting for multi-topic household surveys to ensure to the extent possible cross-country consistency in accounting, both within and across budget categories. The templates, which are provided in the Appendix Tables A1 and A2, were in turn circulated among World Bank staff to facilitate their intelligence gathering from their NSO counterparts. The information that was received from the regional poverty economists was based
continue to play a central role for most developing countries, for least-developed countries (LDCs) especially, the role of private—notably foreign—investment will be indispensable to advance developmental objectives.

But how do we measure the contributions of foreign multinationals to development? While there has been a proliferation of cross-country indicators related to investment policies and business environment conditions, a lot fewer indicators exist on measurements of investment outcomes across countries and sectors—a compass to tell how different multinational firms fare in one country versus another, and where each country stands relative to its own development objectives. An ample amount of literature has demonstrated the direction and magnitude of effects of foreign multinationals on job creation, wages, productivity, and innovation in specific contexts, periods, or sectors. Yet, what is lacking to date are consistent measurement and benchmarking of these development outcomes of international business across countries.

This study aims to fill this information gap. It takes advantage of rich sources of data collected by the World Bank Enterprise Surveys over the past decade to measure differences between foreign multinational and domestic firms consistently across 63 countries over 10 dimensions that matter for development. These dimensions have been selected based on their importance
is associated with a 1.7% increase in income. Each percentage-point increase in the labor force share with no secondary schooling

> 14 Although structural modeling of these interrelationships is beyond the scope of this paper, these results are consistent with the interpretation that geography and institutions as measured by RST affect income in part through their effects on schooling, morality and on the correlation between them. Alternatively, “institutions” (i.e. the rule of law index) may be measuring, in part, morality and its correlation with ability.

20
Policy Research Working Paper 8626

# **Abstract**

Despite the well-known positive effects of tobacco taxes on health outcomes, policy makers avoid relying on such taxes because of their possible regressive impact. Using an extended cost-benefit analysis to estimate the distributional effect of cigarettes in the Russian Federation, this paper finds that the long-run impact may in fact be progressive. The methodology applied incorporates the negative price effect caused by an increase in tobacco taxes, combined with a presumed future reduction in medical expenditures and a rise in working years caused by a reduction in the rate of smoking among the population. The analysis includes estimates of the distributional impacts of price rises on cigarettes under various scenarios, based on information taken from the Russia Longitudinal Monitoring Survey—Higher School of Economics for 2010–16. One contribution is the quantification of impacts by allowing price elasticities to vary across consumption deciles. Overall, cigarette taxes exert a positive long-term effect on household incomes, although the magnitude depends on the structure of the conditional price elasticity. If the population is more responsive to tobacco price changes, then it would experience greater gains from the health and extended work-life benefits.

This paper is a product of the Poverty and Equity Global Practice. 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 afuchs@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 authors. They do not necessarily represent the views of the International Bank for
the rest of the distribution, finding that the estimated income Gini coefficient is unchanged by this replacement of top incomes.

# _Using survey data and administrative data_

As noted above, Hlasny and Verme (2017, 2022) described solutions to under-capturing of the top tail of the income distribution in household surveys as methods that used either replacement or reweighting. Lustig (2020) used this distinction to also classify many of the methods that use both household survey and administrative data as methods using either replacing and/or reweighting. In the context of combining administrative and household survey data, replacement involves replacing some part of the top of the income distribution in the household surveys with data from another source. Reweighting involves increasing the weights of those at the top of the distribution, to match the distribution of income in the administrative data source. We now discuss each of these methods and provide examples.

# Reweighting

Survey calibration means the adjustment of survey weights, usually after unit non-response adjustment of the weights, so that the weighted population estimates for basic demographics (such as age, sex or regions) from the household surveys matches information from demographic models or other external sources of population information. These “reweighting” methods have also since been used to improve estimates of the top tail of the income distribution when administrative data on earnings has been available. For example, Campos-Vazquez and Lustig (2019), obtained Mexican Social security administrative earnings data between 2000 and 2017. The admin data they had access to is the proportion of employed individuals in 25 earnings categories representing multiples of the minimum wage. The authors’ comparison of the survey and admin data clearly shows the undercapturing of the top tail of the earnings distribution. In 2017 the admin data showed that 9% of formal sector workers earned more than 10 times the minimum wage, whereas the proportion was only 2% in the labor force survey. The authors then adjust the survey weights so that the proportion of formal sector workers in each earnings category in the surveys matches the proportion in the administrative
International Energy Agency (2003). Energy Balances of Non-OECD Countries 2000-2001. IEA, OECD, Paris.

International Energy Agency (2013). World Energy Outlook 2013. IEA, OECD, Paris.

International Energy Agency (2015). Energy Balances of Non-OECD Countries 2012-2013. IEA, OECD, Paris.

Jorgensen, E.A. and M. Shkaratan (2016), Romania Green Growth Country Assessment: Addressing Changing Climate and Moving to Low Carbon, World Bank, Washington, D.C.

Malla, S. and G.R. Timilsina (2016). Long term sectoral final energy demand analysis by end-uses for Romania. World Bank Policy Research Working Paper. Forthcoming.

Naăulea, C. (2014). The Effects of Fiscal Policy Instability on Wind Energy Resource Development in Romania. Economics & Sociology, Vol. 7, No. 3 Prisecaru, I., D. Dupleac, T. Chirica and A. Havris (2007). Nuclear Energy Development in Romania. World Review of Science, Technology and Sustainable Development, Vol. 4, No. 2-3, pp. 268-83.

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Growth constraints: some frequently-mentioned constraints, e.g. lack of demand, could be much less of a problem for firms active in GVCs.

- Conflict resolution: weak domestic institutions are often an impediment to business development. For firms active in GVCs, relational contracts (i.e. incentives coming from the continuation value of a relationship) could be effective substitutes for formal institutions (as in Van Biesebroeck, 2014)

- Employment/Capital: the surveys contain very detailed information on the structure of employment and types of capital which could be used to investigate necessary conditions for effective integration in GVCs.

More specifically, Taglioni and Winkler (2014) describe how some of the measures that have been calculated in the GVC literature using a variety of data sources in a variety of countries, can also be calculated using the WBES information. An important advantage of this data source is that the necessary questions have been posed identically and in the exact same situation in all the different countries. An additional advantage is that the data represent a 8
stools; blood in the stool; sunken eyes; and unusually hot (cold) skin. We constructed four more symptoms from the “other” category: skin conditions (e.g. spots, rash, itch), cold symptoms, ear pain, and injuries. We observe on average 60

> 11Since the survey took place towards the end of the rainy season, illness rates were declining over the course of the panel, so we cannot assess if a time trend is due to a fading Hawthorne effect. However, in the baseline pilot of the health diaries, initially elevated symptom reporting rates stabilized at a lower level after the first week of surveying and then declined at a slower and more continuous rate (data available on request). This is consistent with data on doctors, who rapidly return to unobserved length and quality of doctor-patient interactions even when observed by other doctors (Das et al. (2008)).

13
population that is categorized as moderately or severely food insecure using the FIES depending on the dependent variable; and the estimate of _β_ 1 represents how much higher the share is for the poor population. Based on the simple model above, if there was a sufficiently large penalty to undernourishment, we would expect both the prevalence and the severity of undernourishment and undernutrition to be worse for poor Nigerians than for non-poor, which would correspond to _β_ 1 _>_ 0.

Given that the severity of undernourishment and undernutrition are exacerbated along the welfare distribution and not just discontinuously at the poverty line, and given that the income at which households begin to tackle macro- and micro-nutrient deficiencies is an empirical issue, we also estimate how the FIES varies across all expenditure deciles. Specifically, we estimate the following specification: where _Expend Decile_<sup>_j_</sup> _i_<sup>denotes the expenditure decile of household</sup><sup>_i_based on the expen-</sup> diture used to identify the household’s poverty status, with higher deciles corresponding to higher levels of expenditure; and all other variables are the same as above. The es— timate of _β_ 0 represents the share of the population in the first expenditure decile — the poorest that is categorized as either at least moderately food insecure or severely food insecure according to the FIES depending on the dependent variable; and each _βj_ represents how much larger the share is in decile _j_ than in the poorest decile.

Based on the simple model above, if there was a sufficiently large penalty to undernourishment, we would expect both the prevalence and the severity of undernourishment and undernutrition to be worst for the Nigerians in the poorest expenditure decile and for food access to improve for higher deciles, which corresponds to _βj <_ 0 for all _j_ and for _βj_ to decrease in _j_ . Furthermore, the model also predicts this improvement in certain

> poor food access and monetary poverty. Rather, we simply want to characterize how households divide consumption among food
.

> 26 A range of other single-country microsimulation studies find similar results either for reduced VAT rates as a whole or for selected reduced rates such as on food (e.g., Davis and Kay, 1985, for the United Kingdom; Leahy et al., 2011, for Ireland; Caspersen and Metcalf, 1994, for the United States; Creedy, 2001, for Australia; Ball et al., 2016, for New Zealand; Cseres-Gergely et al., 2017, for Hungary; and Gaarder, 2018, for Norway). See Thomas (2020) for a detailed review of this literature.

> 27 Warwick et al. (2022) assume a constant degree of informality across households in their base scenario. In sensitivity analysis for Senegal, they vary the degree of informality across households and products, based on place of purchase data (as used by Bachas et al., 2023). This has only a small impact on their results, and their conclusions

12
may have a simple causal interpretation.

To use counterfactual outcomes to represent impacts on subsequent children, we must define the universe as being the _potential children_ : all those who might exist under either the treated or control state.<sup>38</sup> Outcome measured for potential child _i_ at time _t_ is<sup>_Y_</sup> _it_<sup>. This outcome is</sup> only observed if the child is born and survives, a binary outcome denoted by _Sit_  1. The probability that woman _i_ has a surviving child at time _t_ , as a function of her baseline characteristics, can be written as _Sit_  _S_ ( _X i_ ) , where _X i_ is a set of pre-treatment maternal characteristics. For children who are born, we observe _Yit_  _Yit_ ( _xit_ , _ait_ ; _X i_ , _Ait_ , _Zit_ | _Sit_  1) where _Ait_ is the age of the mother at the time at which child data is collected,<sup>_x_</sup> _it_<sup>are child-level</sup> determinants of the outcome,<sup>_a_</sup> _it_<sup>is child age, and</sup> _Zit_ gives attributes of the father.

Critically, in a study tracking potential mothers, _X i_ is observed for all respondents (regardless of whether or not they had children) and hence can be used to predict fertility within the universe of these potential mothers. Controlling for selection into motherhood thus resembles standard attrition adjustment, and requires a (weaker) selection on observables assumption. Controlling for factors that are observed only among extant children, however, must be done on the intensive margin, and so resembles mediation analysis as in Baron and Kenny (1986), which

> 38 This study tracks female respondents and their descendants, so it is concerned with the potential children of a fixed set of women. We do not attempt to capture all potential children born to the fathers in this study, and to
. This approach assumes that products with the same HS 6-digit code are substitutes.

An advantage of this approach—matching upstream and downstream firms based on product codes—is that it eliminates the need for firm-level matching by names or reliance on industry IO tables, which often pose significant data limitations. This method also captures heterogeneity by allowing each firm to have its own set of relevant domestic suppliers, even within narrowly defined industries. Furthermore, since we focus on potential rather than actual suppliers, we avoid issues of endogeneity or selection bias, making the analysis more robust and flexible across various data environments. However, several caveats are worth noting. First, this strategy may overlook domestic 15In Section 6, we establish the theoretical foundation for the Connectivity Index, which captures the role of HSR in linking exporting firms to domestic suppliers.

> 16Alternatively, industry IO tables could be employed to link downstream firms with upstream suppliers, as in Javorcik (2004). However, this approach presents several challenges. First, IO tables, typically available at the regional or industry level, obscure firm-level heterogeneity, potentially leading to aggregation bias. Second, since IO tables are often based on data from larger firms, they may introduce selection bias and underestimate the production linkages among small and medium-sized enterprises. Finally, the infrequent updates of these tables limit their ability to capture annual variation.

17