rafmacalaba/data-use-mentions-extended huggingface dataset:
(2013b) also demonstrate that unit values are unsuited to measure differences in food prices between income and spatial groups. In the poorer metropolitan areas of Vietnam, food prices are 4.5% lower than in upper income neighborhoods, but unit values for the richest households are 20% higher than for poorest households. Gibson and Kim (2019) compares the UM with the Standard Price Method (SPM) and Deaton’s method using the VHLSS 2012. In this instance, Gibson and Kim demonstrate that the own-price elasticity of quantity demand is overstated by a factor of four when the quality response is disregarded.

Looking at the expenditure and price elasticities in tables 1 and 2 we note a strong heterogeneity across studies. For example, the expenditure elasticity of “Other meats and seafood” ranges from 0.25 (Le, 2008) to 1.75 (Vu, 2020), passing from normal to luxury goods; the uncompensated own price elasticiy of rice ranges from -0.06 (Le, 2008) to -1.01 (Niimi, 2005), covering many degreases of inelastic responsiveness. This heterogeneity is mainly explained by publication bias, demand system (AIDS, LAIDS, QUAIDS), sample size and data (Colen et al. 2018). Time differences are also very important as income growth can increase food consumption and inequality growth can increase the diversity of diets and preferences.

As discussed so far, the demand analysis literature of Vietnam focuses mainly on foods and beverages, sometimes including other goods in the demand system, while studies specifically addressing other goods and services are rare. One noticeble exception is the study of Phu (2020) that estimates the elasticity of residential electricity demand for various income groups in Vietnam, using 2015 microdata. Instead of a demand system, Phu (2020) adopts a double-log function with a two-stage instrumental variable (2SLS) estimator and finds an own price elasticity from − 1.48 to − 0.94; a very low income elasticity between 0.
# **3 TUP Program**

Originally designed by BRAC in Bangladesh, the TUP program targets UP women and provides them with a productive asset (typically livestock), training, mentoring, a cash stipend, and other services for a set period until the household “graduates” into a more sustainable, self-sufficient livelihood.

The TUP program was implemented in eight Afghan provinces under the World Bank-supported Access to Finance program between 2015 and 2021, reaching 12,698 households. The impact evaluation focuses on Balkh province, where approximately 1,500 households were reached. The intervention was implemented by the Microfinance Investment Support Facility for Afghanistan (MISFA), an independent apex organization. Various local nongovernmental organizations (NGOs) delivered the frontline program activities in collaboration with MISFA.

Program villages were selected among the poorest villages in the province. A participatory rural appraisal (PRA), through which the community ranked the population of each village based on their socioeconomic status, was conducted to identify UP households. This was followed by a verification survey to ensure that households met program eligibility criteria.

While similar in approach to other TUP programs, the exact design for the Afghanistan TUP was developed by MISFA to address local constraints, and its duration was shorter than other TUP programs implemented before (12 months vs. 18-24 months). The following components were delivered to the eligible households:<sup>10</sup>

1. Productive asset transfer (although households could choose from several types of livestock, TUP households mainly selected a cow with a calf or a pregnant cow; livestock was replaced if livestock became sick or died during the program duration).

2. A monthly cash transfer (USD 15 nominal per month for 12 months).

3. Biweekly training sessions on livestock rearing and entrepreneurship for 12 months.

4. A health subsidy with a hygiene kit worth about USD 42 nominal.

5. Fortnightly mentoring visits by social organizers to discuss topics related to health, education, women’s empowerment, financial inclusion, and
variables then include information on past late payments and defaults while positive variables include a borrower’s (non-default) rating, aggregate exposure, the number of open credit lines, and availability of collateral. To ensure consistency across countries, we restrict the dataset to private sector non-financial enterprises. The Mexican public registry only includes loans above US $20,000, this is adopted as the baseline for empirical testing in Argentina and Mexico. For the case of Brazil a higher cutoff, of US $300,000, was adopted.

8
Policy Research Working Paper 10882

# **Abstract**

While development literature has come a long way in conceptualizing and measuring poverty multidimensionally, policy interventions to address it remain trapped in fragmented, sector-specific approaches. One of the main challenges in implementing integrated policy responses to multidimensional poverty reduction is understanding how the different dimensions are interlinked and how they jointly evolve over time. For example, this could require disentangling how a person’s health, education, and standards of living all interact in a dynamic sense. Motivated by economic complexity methods and applications, this paper uses network science to propose two new measures to understand the interconnected structure of multidimensional poverty: the Poverty Space (a network that visualizes the interactions among different indicators of poverty) and Poverty Centrality (a measure of the relative importance of each indicator within this network). Applying these measures to 67 developing countries using data from the Oxford Poverty and Human Development Initiative–United Nations Development Programme Global Multidimensional Poverty Index, the paper finds that the structure of multidimensional poverty networks is similar across countries and stable over time. The findings also show that indicators that are more central in the Poverty Space witness a more significant reduction in the censored headcount ratio over time, compared to peripheral indicators. These results are used to demonstrate how the Poverty Space can be applied in policy: using the forward-looking Policy Priority Inference framework to help guide policy choices. Overall, the paper points to the relevance of using network science methods to help to identify key “nodes” in the structure of multidimensional poverty where applied pressure (targeted interventions) could lead to a greater effect on the system as a whole.

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/prwp. The authors may be contacted at lflopezcalva
deal _d_ increase by 0.9 percent more following the introduction of a new establishment in the market. Column 2 of Table 2 replaces the Deal-UPC and Deal-Chain fixed effects with an even stronger Deal-UPC-Chain fixed effect. Column 3 adds various time-varying controls, such as lagged unemployment rate and lagged house price growth rate in these markets. In Column 4, we additionally consider a deal-market FE to account for any unobserved changes in the market during the particular deal. As an example, in this most preferred specification we compare the price of Diet Coke in Whole Foods in the same quarter between the winner and loser counties, after accounting for various time-varying market level factors. In this preferred specification, we find that prices of common products which were sold in both winning and losing counties around a particular deal _d_ increase by 0.7 percent more following the introduction of a new establishment in the market. Across all the very demanding specifications, our estimates remain robust.

In the next few paragraphs, we show the results of various specifications to address different concerns to our identification. The first concern is that of pre-trends: prices might already be rising in markets that attracted the MDP. In Figure 2, we show the results of specification (S2) but with the post dummy replaced by a dynamic event study design, four years before and after the MDP introduction. The figure shows that while there was no noticeable trend in prices across the winner and loser markets, prices started rising a year after the introduction of an MDP in winning compared to losing markets.

Second, if the MDP disproportionately benefits skilled workers, then it is possible that only the prices of products consumed by skilled workers increase. In that case, the rising UPC-level inflation would not matter much for worker welfare. We now divide the UPCs into two groups: products consumed primarily by skilled and primarily by unskilled workers. Using the demographics data and household transactions data from the IRI, we determine which products are primarily purchased by skilled (household head has a bachelor’
and an ownership module, which can be matched through a BvD firm identifier (ID).<sup>7</sup> The raw database comprises firm-level information from 24 European countries during the period 2010-2016.<sup>8</sup> We work with unconsolidated financial balance sheets, which better capture the activity of a firm in the country where it operates, to create the financial module. Building on the work of Kalemli-Ozcan et al. (2015) and Cusolito and Didier (2020), we clean the financial raw files. Appendix A describes the procedure implemented to do so. Appendix B presents the representativeness analysis for the final sample.

We build on the work by Cusolito and Vranic (2020) to create the ownership module.<sup>9</sup> Our main explanatory variable, SOE, measures the percentage of direct shares owned by all the public authorities (PAs) that govern the country where a firm operates. To create this variable, we work with ORBIS Links historical files (often known as Vintage disks) and the Entities file. A Links file is a matrix. Each row of the matrix contains information about the ownership structure of a firm at the first layer of the firm’s ownership tree. This includes the firm identifier, shareholders’ names, each shareholder’s ownership stake, source of information, and date the ownership information was validated. The Entities file contains additional information for each firm, such as the firm identifier, entity type, entity name, and whether the firm is a subsidiary or a parent company.

To build the SOE variable, we first clean the raw Links files and eliminate branches (See Appendix C for details). Then, we merge the Links and Entities files to identify the shareholders that are public authorities. We do so using the entity category S, which corresponds to governmental agencies, public departments, and local authorities. We exclude sovereign wealth funds and institutional investors from this set, as

> 7Each BvD firm
Underlying this measure is an assumption that political autonomy is more meaningful if regions can finance their expenditures out of their own revenues, hence establishing a degree of independence from the center. Regions that spend a lot out of central government transfers are less independent in that they are uncertain if they can fund their future spending deficits, since from year to year the center might adjust the transfer amount.

Although we propose an economic measure of sovereignty, our analysis is likely to apply also to different concepts of decentralization or autonomy.<sup>33</sup> While economic decentralization (income and asset redistribution and regional fiscal decentralization) and political decentralization are conceptually distinct, they are highly correlated.<sup>34</sup> This is confirmed by simple bivariate correlations. Using data from the Polity III database, we coded three binary variables denoting the level of centralization (federal, semi-federal, and centralized) and correlated each indicator with an economic decentralization index – the size of regional expenditures as a share of total government expenditures (this index is compiled by Fisman and Gatti (2000), using data from the International Monetary Fund’s _Government Finance Statistics_ ). Economic decentralization data are averaged for each country for the period 1980-95, and political decentralization is taken for the year 1994. The Pearson correlation between a binary variable identifying all federal systems<sup>35</sup> and a variable denoting economically decentralized countries (identified as those countries with a decentralization level above the sample mean) is positive and highly significant (Pearson <sup>2</sup> (1) = 33 On decentralization measures and concepts, see Fisman and Gatti (2002) and Treisman (1997). The results we present are substantively the same for federal systems and for unitary systems with some degree of regional fiscal independence. Our analysis could be extended to different political measures of sovereignty, such as a region’s freedom to choose its own secondary school curriculum, its freedom to use local languages in teaching,
happening on the outer edges of the region, such as those in Algeria (expulsion of Al-Qaeda in the Islamic Maghreb) and the indirect consequences of the broader Arab Spring and its indirect impact on trafficking related dynamics (arms, drugs – especially since 2005, contraband, and migrants). Additional factors such as a high population growth rate have wielded an influence on other factors including a large youth bulge heightening poverty and low employment issues to which climate vulnerability and now the COVID-19 crisis further contributes to form a dangerous “fragility trap”.

It is not the purpose of this paper to review all these factors, and many other researchers have aptly done so before.<sup>3</sup> Rather, it draws the focus on the issue of water for three reasons. First, water has been overlooked as a separate factor. It tends to be lumped with other environmental factors as a resource, and with the issue of access to basic services when talking of water supply. Climate shocks are taken as a given vulnerability of the region and, except for the case of extreme weather shocks, given a coarse “background assumption status”. Figure 1 above characterizes the climate on the African continent using data from Willmott and Matsuura (2001).<sup>4</sup> Specifically, the top panel plots a map of the means of annual evaporation, temperature, and precipitation over the 1990 – 2017 period in each location, while the bottom panel shows the inter-annual variability of these climate variables within a given location over the same period. The sharp spatial gradient in climate variables experienced in the Sahel region stands out, indicative of the fundamental nature of agroclimatic conditions in characterizing the region.

Second, most of the research available on fragility in the Sahel has been qualitative. This reflects data challenges found in Sub-Saharan Africa in general (limited granularity in available variables, sample size and time coverage etc.), and others more specific to the region – including important variation in population densities. Water data
level GCR port efficiency scores, indexed from one to seven (7 is the highest score).<sup>39</sup> To illustrate how their analysis may be applied to predict trade flows, the authors developed a country-specific maritime cost index (driven to a large degree by WEF GCR port efficiency) and used it in the standard bilateral gravity model of trade. They concluded that increasing maritime transport costs from the 25th to the 75th percentile would decrease bilateral trade by 22 percent.

# **4.4.3 Transport Costs as Measures of Port Efficiency**

Blonigen and Wilson (2008) expanded upon Clark et al. (2004) by estimating port-specific efficiency rates. Whereas Clark et al. (2004) utilizes _country_ level measures of port efficiency, which are based on survey results that are unsuitable for longitudinal analysis, Blonigen and Wilson (2008) used timevarying port fixed effects to capture an _individual_ port’s contribution to maritime shipping costs. Using US import data sourced from the National Data Center of the Army Corps of Engineers, they produced estimates for the top fifty (50) American ports and top one hundred (100) foreign ports (by import value from the US perspective). Foreign port efficiency measurements are next aggregated into a country-level estimate and then are used in a standard gravity trade model. Without controlling for country-level fixed effects, the elasticity of bilateral trade with respect to port efficiency is 1.27. Since port efficiency estimates are time-varying, country fixed effects can be included. Thus, controlling for unobserved country-specific confounds reduces the bilateral trade port efficiency elasticity to 0.32. Based on these, moving a country from the 25th to 75th percentile of port efficiency increases bilateral trade by a more modest 5 percent.

Feenstra and Ma (2014) used this approach to measure trade facilitation. They used port infrastructure to analyze the extensive margin—or product variety—on both import and export trade. A 10 percent increase in port efficiency increase export variety by 1.
by many factors (altruism, political ideology and values), while demand for redistribution to the middle class appears to be driven by self-interest and knowledge of the tax system.

Although the middle class is not at the core of their analysis, a recent study by Cruces, Pérez Truglia and Tetaz (2011) provides relevant evidence on how individuals form perceptions with strong biases to the evaluation of their own relative position in the distribution. Using data for the Greater Buenos Aires, they assess the relevance of such biases by examining their impact on attitudes towards redistributive policies. An important characteristic of the survey lies in its experimental design, as the interviewer informs a randomly selected group of respondents whether their subjective income position coincides with the objective figures. They find that respondents who were relatively poorer than they had thought became more supportive of redistribution to the poor when informed of their true income rank, while for those with negative biases (i.e. who were relatively wealthier than they had thought), there are no significant results.

# 3. Data and methodology

Our analysis draws on the 2007 _Ecosocial_ values surveys. These values surveys were implemented by CIEPLAN, a Latin American Think Tank, in seven Latin American countries – Argentina, Brazil, Chile, Colombia, Guatemala, Mexico and Peru. The surveys are representative of the adult population (18 years or more) living in larger urban centers in each country. The sampling design is probabilistic and multistage, and the questionnaire was applied through face-to-face interviews in the respondents’ household. We choose to use the _Ecosocial_ surveys because of their rigorous sampling methodology, the information they collect on a variety of values, and because they collect information about households’ assets, which will allow us to construct a measure of households’ permanent income (see below). We exclude from the analysis Argentina because we were not able to match assets with other surveys and build an income measure. The analysis is therefore based on a pooled dataset combining six countries that was created from the national datasets
Policy Research Working Paper 6424

# **Abstract**

Using newly collected survey data on direct suppliermultinational linkages in Chile, Ghana, Kenya, Lesotho, Mozambique, Swaziland, and Vietnam, this paper first evaluates whether foreign investors differ from domestic producers in terms of their potential to generate positive spillovers for local suppliers. It finds that foreign firms outperform domestic producers on several indicators, but have fewer linkages with the local economy and offer less supplier assistance, resulting in offsetting effects on the spillover potential. The paper also studies the relationship between foreign investor characteristics and linkages with the local economy as well as assistance extended to local suppliers. It finds that foreign investor characteristics matter for both.

The paper also examines the role of suppliers’ absorptive capacities in determining the intensity of their linkages with multinationals. The results indicate that several supplier characteristics matter, but these effects also depend on the length of the supplier relationship. Finally, the paper assesses whether assistance or requirements from a multinational influence spillovers on suppliers. The results confirm the existence of positive effects of assistance (including technical audits, joint product development, and technology licensing) on foreign direct investment spillovers, while the analysis finds no evidence of demand effects.

This paper is a product of the International Trade Department, , Poverty Reduction and Economic Management Network. 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://econ.worldbank. org. The author may be contacted at dwinkler2@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
historical political capital. The only exception is Douala, Cameroon, which is larger than Yaounde. The historical political capitals that are not current formal political capitals are Dar es Salaam, Tanzania, which was replaced by Dodoma, Abidjan, Cˆote d’Ivoire, replaced by Yamoussoukro, and Lagos, Nigeria, replaced by Abuja.

# **A.3 Roads**

The African Infrastructure Country Diagnostic database contains comparable roads data for all countries of the African mainland with no Mediterranean coastline, plus Madagascar, except for Djibouti, Equatorial Guinea, Guinea-Bissau, and Somalia.<sup>51</sup> Two datasets were produced for Sudan: one for the north and one for the south. The datasets generally have comparable metadata on road surface, quality, and hierarchy (primary/secondary/tertiary), as well as estimates of traffic. Still before they could be used for the current analysis, several changes had to be made. All steps below were carried out using ArcGIS 9.3 software, except for some tabular cleaning done in Excel and Stata. Nearly all steps in ArcGIS, and all steps in Stata, were automated in Python, Arc Macro Language (AML), or Stata scripts.

The roads data were cleaned tabularly, to ensure that the relevant fields were coded consistently, and projected to a sinusoidal projection, which is more conducive to distance calculations than their native plate carr ́ee (latitude and longitude).<sup>52</sup> Next, roads from all countries were combined into one large dataset, and a topology was built with the rule “no dangles”. This means that every dead end was flagged. In most cases, dead ends are legitimate features of the road network. In other cases, however, they are artifacts of a data generation process in which some segments that are connected in the real world are not connected in the dataset. This is critical in the network analysis to follow.

Problematic dangles were fixed in several ways. First