rafmacalaba/data-use-mentions-extended huggingface dataset:
||_εi,d_ (model 1), where _Dratioi,d_ is excess<br>xcess demand by all investors in the same<br>or each group of Active Share. Models 2,<br> (calculated from the S&P500 index).|Number<br>Number of<br><br>Lag U.S. returns<br>of Funds<br>Observations<br>-.02<br>190<br>20,863<br>(0.9)<br>0.01<br>185<br>18,108<br>(0.43)<br>-0.01<br>198<br>15,105<br>(0.65)<br>0.03<br>185<br>11,768<br>(1.58)<br>-0.01<br>247<br>107,748<br>(1.23)<br>-0.01<br>813<br>135,573<br>(1.51)<br>-0.01<br>591<br>62,024<br>(1.22)<br>-.01<br>357<br>29,780<br>(1.15)|
|---|---|---|
|**style**|_iDratio−i_<br>_d_ +<br>_o−i_<br>_d_<br>is the e<br>egressions f<br>ock returns|(4)<br>Lag Returns<br>0.04**<br>(2.05)<br>0.03<br>(1.52)<br>0.04**<br>(2
economic evaluation to other contexts. In practice, the clear definition of the decision maker, with explanation of how this perspective affects methodological decisions and parameterization will facilitate transferability of economic evaluation to other contexts as users will be able to assess which aspects of an analysis require modification to apply it to another context.

# STEP 2: Determine the intervention type

Specification of the intervention, including its use-case and intended population is an essential element of the evaluation. This framework requires not only a general description of the intervention, but also alignment to established classification systems. In the first instance, the intervention should be assigned to a WHO DHI type, with specification as to whether there is a predictive analytics component. This creates three major categories of DHI, with further granularity for DHIs under the WHO classification system.

1. DHIs defined under the WHO DHI classification system without AI enabling functionality – e.g., simple text message to patients as a reminder for regular followup.

2. DHIs defined under the WHO DHI classification system with AI enabling functionality – e.g., machine learning radiology diagnostics.

3. Non-digital health interventions enabled by the used of AI technology – e.g., use of demographic and clinical information for identification of at-risk patients for health professional consultation.
firms appear constrained, but only 10 percent of the large firms. About two-thirds of the large firms choose not to participate in the credit market, compared to only a third of the micro firms. The notion that the smallest firms are credit constrained is supported by regression results indicating that, controlling for other important factors such as expected profitability and indebtedness, the likelihood of a successful loan application varies with firm size. While this suggests banks are biased against small firms, the authors note that this result may reflect transaction costs on the part of banks. In any case, the size effect is substantial: for a micro firm to have an equal chance of getting a loan as a large firm, the micro firm needs to have an average return on fixed capital more than 200 percentage points higher than the large firm.

**Table 2. Credit Constraints by Firm Size**

||Micro|Small|Medium|Large|All|
|---|---|---|---|---|---|
|No credit demand|33|50|67|66|55|
|Demand, but rejected*|64|42|21|10|33|
|Received loan|3|8|12|23|12|

*Includes firms that suggested that a loan application would be rejected by banks _Source_ : Bigsten et al. (2003)

Another novel approach for analyzing the links between formal credit and company investment is adopted by Habyarimana (2004) who uses matched bank-firm data from Uganda, collected as part of the 2002 RPED, to estimate the effect of losing a banking relationship on firm performance. The background to this study is that four Ugandan banks were closed between September 1998 and 1999 because of imprudent banking practices. As a result, 30 percent of the firms in the sample lost one or several banking relationships. Habyarimana estimates that over the three years following the banking crisis, the average annual growth rate of employment
The methodology consists in a survey-based dataset and is drawn from the experience of the World Bank’s Doing Business<sup>18</sup> and Women, Business, and the Law<sup>19</sup> reports, which have produced global objective quantitative indicators of business regulation and gender discrimination.<sup>20</sup> The dataset presents laws and policies in three main areas:

1. Laws that establish discriminatory treatment based on ethnicity, religion, gender identity, and sexual orientation

2. Laws that prohibit discrimination against ethnic, religious, and sexual minorities 3. Policies and other enforcement mechanisms to implement existing antidiscrimination laws The six indicators identified to measure the equal treatment of people belonging to ethnic, religious, and sexual minorities are:

1. Access to institutions

2. Access to education 3. Access to the labor market 4. Access to property 5. Access to public services and social protection 6. Protection from hate crimes or hate speech Data were collected from two sources, namely, surveys to experts of the pilot countries and desktop research. In particular, three surveys were developed: (a) one for ethnic minorities and indigenous peoples, (b) one for religious minorities, and (c) one for sexual minorities. Each survey was then divided into six sections, one for each indicator. The surveys were distributed to legal experts in the six pilot countries, including lawyers, ombudsman institutions, judges, academics, and civil society organizations (see appendix B).

The survey on ethnic minorities and indigenous peoples includes questions on laws, policies, and regulations related to obtaining citizenship and how they differ, if at all, from those that apply to the country’s ethnic majority. The survey also includes questions on the rights of indigenous peoples to obtain collective land titles under access to property and questions on whether legal provisions on bilingual education exist, among others.

Under the indicator for access to institutions, the survey on religious minorities includes questions that address constitutional
Kenya<sup>85</sup> considers a behavioral economics model in which some farmers are patient and others procrastinate, to rationalize low investment in fertilizer in the face of high returns to its use. A study of microfinance in Thailand<sup>86</sup> uses a structural model of entrepreneurship to explain why credit provision can have highly heterogenous impacts on subsequent investment. A later RCT of a microfinance program in India confirms precisely this pattern of response across beneficiaries.<sup>87</sup>

- **Structural modeling also enables researchers to predict the impact of hypothetical policies that have yet to be implemented.** A study<sup>88</sup> of Mexico’s celebrated PROGRESA conditional cash transfer program uses a dynamic decision‐making model to show that an alternative subsidy scheme could generate greater schooling impact at similar cost to the existing program.  In the Thailand microfinance study, a counterfactual policy of providing credit explicitly tied to investment is found to substantially outperform the actual policy of unconditional credit provision.

- **Finally, a tight link to theory can allow researchers to go beyond cost‐benefit analysis and make quantitative welfare statements, identifying winners and losers from a policy (or a range of alternative policies) and the magnitudes of these gains or losses.** In the Kenya study, the authors use the structural model to show that small and time‐limited fertilizer subsidies can lead to higher social welfare compared to heavy subsidies or no subsidies at all.  The Thai study shows that large‐ scale credit provision is not a cost‐effective policy on average; although some households value the added liquidity substantially more than it costs to provide, many more value it very little.

The World Bank, as well as many governments and large NGOs, now insist on evaluations – often in the form of RCTs – wherever feasible. Many World Bank researchers regularly use these methods and a specialized unit named DIME was set up to facilitate impact evaluations within World Bank projects
# **4. Data**

Our dependent variable is (logged) disbursements of trust funds to recipient countries based on the financial accounting tables of the World Bank.<sup>13</sup> In the disbursements data, recipient countries are almost always indicated but donors are unknown, so that we cannot discriminate between the sources of funds.<sup>14</sup> The unit of analysis thus is the recipient country‐year, rather than donor‐ recipient‐year. For purposes of comparing the overall aid selectivity of World Bank trust funds to IDA and to other bilateral aid, this is the appropriate unit of analysis, because it implicitly weights larger trust fund donors more heavily in the analysis. With donor‐recipient‐year observations, each donor‐recipient pair would receive an equal weight. If selectivity of trust fund aid differed systematically for large and small donors, selectivity coefficients could be a misleading indicator of overall selectivity.<sup>15</sup> The structure of the disbursements data is depicted in Figure 2. The figure indicates that trust funds may differ along three dimensions: by the number of contributors (Figure 3), the country‐ specific, regional or global mandate, and by their execution type (Figure 4). We analyze a subset of the universe of World Bank funds, IBRD/IDA trust funds, which are most numerous. The Bank makes a technical distinction among three types of trust funds. The Bank only provides financial services to Financial Intermediary Funds (FIFs).<sup>16</sup> The number of trust funds of the International Finance Corporation (IFC) is relatively minor and has different objectives than IBRD/IDA trust funds. IBRD/IDA trust funds, the focus of this analysis, are classified further by recipient‐executed trust funds (RETFs), which are implemented by a third party but supervised by the Bank, and Bank‐ executed trust funds (BETFs), which support the Bank’s
group remain stable for both categories of treatment households (except for stove durability for the second sample). The ASC estimates (Table 2) for the control households are not significant, indicating that the overall preferences for the stoves have not changed for control households. This conclusion is robust across both definitions of treatment households.

The standard deviation estimates from the MMNL indicate that there is significant heterogeneity for most attributes in both the treatment group and the control group for both definitions of treatment households. Given the complexity of the model that we are estimating, we are not able to include interaction terms to better analyze heterogeneity in the estimates.

The average WTP results for the base/2013 and the 2016 treatment households are provided in Table 4. We find that households in the treatment group are WTP on average 54 birr (about 2 USD) for a stove that lasts a year longer (17 birr in 2013), 86 birr (about 3 USD) for a stove that reduces cooking time by 10% (47 birr in 2013), 142 birr (about 5 USD) for a stove that reduces fuelwood use by 10% (62 birr in 2013), and 112 birr (about 4 USD) for a stove that reduces smoke by 10% (45 birr in 2013).

9
the weights. The main data source for Eslava et al. (2004) is the Colombian Annual Manufacturers Survey (AMS) by DANE (National Statistical Institute). I discuss further details about the productivity estimates below in Section 5.2.1.

The import demand elasticity measure is based on the structural estimates in Kee, Nicita and Olarreaga (2004), which I combine with the GDP data from the World Development Indicators (WDI), and import data from COMTRADE. The import demand elasticities are available only at the 3-digit ISIC level.<sup>42</sup> In order to obtain the TFP measure of the upstream industries, I employ the input-output tables provided at the 3-digit ISIC level by Nicita and Olarreaga (2001), which were compiled from version 4 of the Global Trade Analysis Project (GTAP) database. Excluding the inputs being used from the own sector, the upstream measure is based on a combination of TFPs of the remaining input sectors as weighted by their share of usage.

The variable definitions and sources are presented more in detail in the appendix (Section A.3). In Table 4, I provide the summary statistics for all the variables I use in the estimations.

# 5.2.1 Productivity Estimates

The productivity estimates come from Eslava, Haltiwanger, Kugler and Kugler (2004). They estimate total factor productivity (TFP) as the residual from the following production function for each firm i = 1, ..., N and period t = 1982, ..., 1998 where Kit, Lit, Eit, and Iit denote capital, labor (total employment hours), energy consumption, and materials, respectively. An important concern in such an estimation is the simultaneity bias; that is, productivity shocks may be correlated with the inputs. They correct for this bias by considering a measure of downstream demand as an instrument for inputs along with regional government expenditures and input prices. A great advantage of this dataset is that it involves plant
calculated as the sum of the actual intersectoral productivity gap and the contribution of structural transformation to the predicted change in the intersectoral productivity gap in each region. At the national level, the contribution of structural transformation to the change in the intersectoral productivity gap is positive, increasing the gap from 111.8 thousand cedis per worker to 158.3 thousand cedis per worker. For most regions, the contribution of structural transformation to the change in the regional intersectoral productivity gap is positive. As a result, accounting for the contribution of structural transformation, the disparity in regional intersectoral productivity gaps increases. Measured in standard deviation terms, the disparity in regional intersectoral productivity gaps increases from 55.0 thousand cedis per worker to 75.6 thousand cedis per worker.

The disparity in regional intersectoral productivity gaps could be smaller if the negative own-sector effect were to outweigh the positive contribution of structural transformation. For this reason, our results here should only be taken as suggestive evidence that the disparity in regional intersectoral productivity gaps is not lowered by the differing pace of structural transformation across regions.

22
Table 18: Medium-term impacts of _Mais Educação_ on 2007 dropout rates

|High dropout rates<br>in 2007|Medium dropout<br>rates in 2007|Low dropout<br>rates in 2007|
|---|---|---|
|Dependent variable|||
|(1)|(2)|(3)|
|Panel A: Impacts on fifth-grade v|ariables||
|Dummy_Mais Educação_* year 2011<br>0.614*<br>(0.312)|1.446<br>(0.969)|1.517<br>(2.033)|
|Rsquared<br>0.625|0.686|0.769|
|Observations<br>780|730|642|
|Dummy year 2011<br>YES|YES|YES|
|School fixed effects<br>YES|YES|YES|
|Panel B: Impacts on ninth-grade v|ariables||
|Dummy_Mais Educação_* year 2011<br>–0.448|2.059|1.472|
|(0.838)|(1.519)|(3.417)|
|Rsquared<br>0.601|0.746|0.765|
|Observations<br>672|654|552|
|Dummy year 2011<br>YES|YES|YES|
|School fixed effects<br>YES|YES|YES|

_Sources:_ Authors’ calculations using MEC/FNDE administrative data, _Prova Brasil,_ School Census, and INEP.

_Note:_ Treatment group are schools that joined _Mais Educação_ in 2008 and remained in the field until 2011, divided into groups according to their dropout rates level in 2007. This table reports coefficients from the ordinary least squares estimation of equation (1) in the paper, where the dependent variable in all columns is the dropout rates. In column (1) we
methodologies for the consistent measurement of urbanization across countries.<sup>17</sup> In this paper, we focus on two of these methodologies – the Agglomeration Index (AI) that was originally developed by Uchida and Nelson (2010), and Dijkstra and Poelman’s (2014) cluster method. The AI methodology has already featured in several Flagship World Bank publications (World Bank, 2008; World Bank and IMF, 2013; and Ellis and Roberts, 2015). By contrast, the cluster method, which was originally developed for application to the European Union, is more recent and its use at the global level is just beginning to be explored. The two methodologies differ both in terms of their underlying conceptual approaches to defining urban areas and their data requirements. The decision to focus on these two methodologies is based on the fact that they can be feasibly implemented to produce estimates of urban population shares for a large global cross-section of countries. This is a basic requirement for the (re-)examination of the basic stylized facts of urbanization, development and structural transformation, and the comparison of LAC with non-LAC countries. Below we provide a more detailed overview of the two methodologies.

# 2.2.1. The Agglomeration Index

From a labor market perspective, individual urban areas should ideally be conceptualized and defined as functional urban areas (FUAs), where a functional urban area captures the spatial extent over which a city or town’s labor market may be considered integrated. Approaches to delineating FUAs typically begin with an urban core, defined by size and/or density criteria, and then identify a ‘commuting shed’, i.e. a surrounding area from which workers commute to the core.<sup>18</sup> A good example of an algorithm to identify FUAs is that used in the construction of the OECD’s Metropolitan Areas database.<sup>19</sup> Hence, this algorithm defines FUAs by first identifying densely inhabited urban cores using
Policy Research Working Paper 6452

# **Abstract**

This paper examines the microeconomics behind the dramatic export boom experienced by Turkey during the 2000s. Using disaggregated customs data covering the universe of export transactions for Turkey during the period 2002–2011, it characterizes firm-level dynamics in the export sector and decomposes export growth at the aggregate, sector, and destination market levels to identify the role of firm turnover, destination turnover, and product turnover. The paper shows that in the shortrun, aggregate export growth is dominated by growth in continuous exporters, and for these, growth is dominated by exports to their continued destinations and of their continued products. However, the observed high degree of churning across firms, destinations, and products accounts in the long run for a substantial part of Turkey’s export growth. The patterns of micro-dynamics of export growth are verified across sectors and across groups of destination markets with some exceptions regarding exports to new emerging markets where net entry by Turkish-based exporters plays a more critical role for long-run growth.

This paper is a product of the Trade and Integration Team, Development Research Group. 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 authors may be contacted at afernandes@worldbank.org and tcebeci@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 Reconstruction and Development/World Bank and its affiliated organizations, or those of the
# **Understanding the Links between Diet Quality, Malnutrition, and Economic Costs: An Evidence Review for LMICs**

Kendra Siekmans<sup>1</sup> , Patrizia Fracassi<sup>2</sup> , Tomoko Kato<sup>3</sup> , Ti Kian Seow<sup>4</sup> , Diana Carter<sup>5</sup> , Susan Horton<sup>6</sup> , Felipe Dizon<sup>7</sup> , and Kyoko Shibata Okamura<sup>8</sup>

**Keywords:** diet, undernutrition, overweight, obesity, non-communicable disease

**JEL classification:** I12 (Health Behavior), I18 (includes public health), Q18 (food policy)

1 Consultant, Food and Nutrition Division, FAO. Corresponding author: ksiekmans@nurturedev.com 2 Food and Nutrition Division, FAO. Patrizia.Fracassi@fao.org 3 Food and Nutrition Division, FAO. Tomoko.Kato@fao.org 4 Food and Nutrition Division, FAO. Tikian.Seow@fao.org 5 Food and Nutrition Division, FAO. Diana.Carter@fao.org 6 University of Waterloo. sehorton@uwaterloo.ca 7 Agriculture and Food, World Bank. fdizon@worldbank.org 8 Health and Nutrition, World Bank. kokamura@worldbank.org

## **Acknowledgments**

The authors would like to thank our colleagues from the ‘Estimating the economic costs of unhealthy diets project’ (under the World Bank ‘Repurposing Agrifood Public Policies and Support for Healthy Diets’ Programmatic Advisory Services and Analytics (PASA)), for their support of related work, including Erkan Ozcelik and Giles HanleyCook from FAO, and Hina Khan Sherwani from the World Bank. We thank Lora Iannotti, Mireya Vilar and Theresa McMenomy for their reviews of the paper. We thank Lynnette Neufeld, Bridget Holmes, Ramani WijesinhaBettoni and participants in a joint World Bank-FAO seminar on September 14
specific policy interventions and increasing their impact since health and nutrition budget allocations are sent to the commune level basic health centers.

# 4. Application of the Small Area Estimation Approach

Data description The 2018 census marked the third census undertaken in Madagascar, following the most recent one in 1993. The census enumeration process spanned from May 18 to June 25, 2018, encompassing the entirety of the country. During the 2018 census, Madagascar's population was estimated to be approximately 25,674,196 individuals residing in a total of 6,079,876 households. While most households live in rural areas (80.7%), the gender composition is slighted tilted toward women as they represent 50.7% of the population.

In parallel, the Enquête Permanente Aupres des Menages 2021-2022 (EPM21-22) represented a living standards measurement survey (LSMS) conducted between 2021 and 2022, encompassing all nine provinces and 22 regions. With the exception of one district (Mitsanjo), the survey covered all districts. The EPM is representative at both the national and regional levels. This means that we can use the data to draw inferences at the sub-regional level. A key objective of the EPM21-22 survey was to generate monetary poverty estimates, defined based on per capita consumption, using a national poverty line set at approximately 1.57 million ariary per year per person. Based on those estimates, 75.2 percent of the national population was poor (79.9 percent for rural and 55.5 percent for urban areas). This is a slight (non-statistically significant) increase from the 72.9 percent estimated for 2012. Rural poverty decreased from 80.6 percent in 2012 to 79.9 percent in 2022 (a statistically insignificant change), while average consumption among the rural poor increased by 1-percent per year.<sup>2</sup> At the same time, poverty significantly increased in urban areas, from 42.2 percent in 2012 to 55.5 percent in 2022.

The Demographic