rafmacalaba/data-use-mentions-extended huggingface dataset:
example, despite being given an adequate diet, children may still face an impaired growth or not gain weight because of underlying inflammations linked to infection. Conversely, individuals with healthy weight may have unhealthy diets and be at high risk of NCDs. Hence, estimating the hidden costs of unhealthy diets differs from estimating the cost of malnutrition in all its forms.

The new suites of healthy diet metrics have promise for policy analysis. They have potential for tracking the impact of nutrition-sensitive agrifood system policies, especially if they go beyond a focus on supply chain and also consider food environments and consumer behaviors. Using a more holistic indicator of healthy diets can avoid the double counting problems inherent in trying to add up the costs of failing to reach a dozen or more dietary guidelines. Work is underway to further validate these new measures against health outcomes.

Regressions undertaken for the current study using cross-country data suggest that increased calorie availability and greater dietary diversity, as expected, tend to be associated with less child stunting and wasting, and less underweight in women. Calorie availability and dietary diversity also are associated with increased overweight and obesity in both children and women, although these results are less significant than for undernutrition. The new GDR score’s submetrics, NCD-Protect and NCD-Risk, have the expected effects on obesity in women, although not significantly so. Finally, cross-country data on NCD risks (hypertension and diabetes) have measurement issues but have the expected negative relation (although significant only for hypertension) with the NCD-Protect score.

In this note we have suggested that a way to proceed further would be to partner with national researchers to obtain similar regression results using national (or state-level) health and nutrition survey data to elucidate the relationship between overweight and/or cardiovascular risk or diabetes, and the new diet quality variables from individual-level data. This could then inform analysis for other countries in the same region of policy interest, which have individual quantitative dietary recall data but not associated with data on NCD risk factors.

The new data quality variables could help
by region—provided by the Famine Early Warning System Network (FEWS NET) of the United States Agency for Agricultural Development (USAID).<sup>11</sup> Ideally, one could argue that we should attribute to each migrantsending household only the average SPEI during the growing season months between the last wave and the month in which the individual migrated. However, this would imply that migration is an

> anonymized survey datasets with publicly available remote-sensing weather products, since the current spatial resolution of geospatial data is not fine enough for spatial anonymization to result in mismeasurement of weather events that are experienced by sampled households.

> 10 The SPEI data are based on gridded data produced by the Climatic Research Unit of the University of East Anglia and can be downloaded here. The level of resolution is 0.5 x 0.5 degrees.

> 11 The Nigeria FEWS NET crop calendar is available <u>here.</u>

14
By shedding light on underlying beliefs and expectations that uphold norms, including for specific sub- groups such as migrant households, findings from this paper can help inform the design of interventions to improve FLFP, as well as reduce the disparity in job quality between men and women. Better information on the type, strength, and specific circumstances under which norms bind women’s economic participation can inform policies and services to increase their access to the labor market.

The paper is structured as follows. Following a summary of the <u>country context, Section 1</u> describes the methodology used for measuring social norms. Section 2 discusses the sampling approach for data collection and its limitations. Section 3 highlights the demographic and socio-economic characteristics of the sample. <u>Section 4 details the empirical strategy employed to assess the role of social norms in determining women’s paid</u> work outcomes outside the home. <u>Section 5 presents the findings, starting with descriptive statistics on labor</u> market outcomes among sampled respondents, behaviors, personal beliefs, and social expectations towards thematic areas associated with women’s work, followed by a systematic exploration of whether and to what extent norms and personal beliefs do predict women’s paid work outcomes. This section also discusses the role of social norms when considering ‘rational’ factors, observables that may explain liberality (or conservativeness) of beliefs and social norms, and whether the influence of social norms on women’s paid work outcomes varies for certain groups or under specific circumstances. <u>Section 6 concludes.</u>

# NEPAL CONTEXT

Existing data on personal attitudes and beliefs around gender roles and women’s economic participation in Nepal shows a contrast between views and practices when it comes to women’s employment. On the surface, most Nepalis have a favorable outlook towards women’s jobs. Per a Survey of the Nepali People – a series of public opinion surveys of a nationally representative sample of Nepalis 18
de Gorter and Swinnen survey with an analytic survey of recent models about pressure groups that has begun to receive attention in the literature. Of specific interest is the debate over two schools of thought about how those pressure groups operate. One set of models emphasizes that pressure groups lobby by paying for services, while the other set of models maintains that pressure groups seek to informationally lobby policymakers, and money primarily buys access but not the policy itself. It is hoped that the analysis in this paper informs that debate in the context of agricultural policy. We begin with 2
imports are more differentiated by the measure of Rauch (1999) have fewer net separations as they maintain relationships even in the presence of a supply shock. In contrast, firms with more suppliers, and those with larger overall import volumes experience greater net separations, potentially because they have a greater pool of relationships on which to draw. Khanna, Morales and Pandalai-Nayar (2022) find similar patterns of heterogeneity when studying the response of firms to supply-side shocks generated by lockdowns in Indian districts. Using firm-to-firm transactions data, similar to those in Panjiva but covering also domestic transactions, they exploit the differential exposure of firms in that state to supplier risk during the pandemic. Because the severity of lockdowns differed greatly across Indian states, suppliers located in states with more strict lockdowns experienced a larger disruption. In an event-study, they show that firms with suppliers exposed to more severe lockdowns experienced an increase in net separation rates, a similar result to Column 1 of Table 1. The intensive margin of trade as measured by the value of inputs and firm output also suffered. They also document that these disruptions were less severe 25
ported sharing a mobile phone)<sup>21</sup> ; the geographic coverage of the survey reaches the vast majority of the country<sup>22</sup> ; and the WFP survey itself demonstrates that the number of mobile phones owned by households has not changed much at the national and governorate levels.<sup>23</sup> Fourth, it is important to investigate the degree to which sample selection might limit the generalizability of the results. To the degree that we are able to assess in such a dataand evidence-scarce environment, we validate the WFP survey’s representativeness of the broader mobile phone-using population by demonstrating that the survey is capturing trends that are independently corroborated by other sources. The timing of displacement is identical to estimates by the Task Force for Population Movement, where there was an initial surge of displacement in March of 2015 followed by a relatively constant rate of displacement as the location and intensity of violence changed across the country (e.g., TFPM 2019; OCHA 2018; etc.).<sup>24</sup> Additionally, we demonstrate that the mobile phone survey does in fact detect large declines in many welfare outcomes and access to basic

> 21Registration for the World Bank’s cash transfers program (implemented by UNICEF), which covers approximately one-quarter of the total population and is aimed at relatively poorer households, demonstrates the vast majority of households can be reached via phone (e.g., World Bank 2018c). Evidence from different WFP surveys of food aid beneficiaries suggests that the share of food aid recipients that can be reached via mobile phone has remained very high (e.g., WFP 2018b). And evidence from the WFP mobile phone survey itself suggests that particularly struggling households are well-represented in the sample with approximately one-third of the sample being displaced and over one-third receiving food assistance.

> 22See Appendix 6 for a map presenting the number of respondents by district over the entire course of the WFP survey, where there
||East Asia &<br>Pacific<br>(N=35)|South<br>Asia<br>(N=19)|Sub-Saharan<br>Africa<br>(N=52)|
|---|---|---|---|
|Annual change in $1.25/day poverty rate<br>(% points per year)|-2.2|-1.2|-1.5|
|Annual change in $2/day poverty rate<br>(% points per year)|-2.3|-1.2|-0.9|
|Annual percentage change in domestic food prices<br>(% per year)|0.6|0.7|0.6|
|Annual percentage change in food production<br>(% per year)|4.4|3.3|4.0|
||Europe &<br>Central Asia<br>(N=41)|Latin America<br>& Caribbean<br>(N=157)|Middle East &<br>North Africa<br>(N=4)|
|Annual change in $1.25/day poverty rate<br>(% points per year)|-1.6|-0.5|-0.4|
|Annual change in $2/day poverty rate<br>(% points per year)|-1.7|-0.7|0.0|
|Annual percentage change in domestic food prices<br>(% per year)|0.2|0.4|0.0|
|Annual percentage change in food production<br>(% per year)|0.9|3.5|4.2|

_Source_ : Author’s estimates from World Bank (2013) poverty
GTAP Database Version 6 (Dimaranan, 2006) and use this with a modified version of the standard GTAP model (Hertel, 1997). Maize is included in the GTAP database and model parameters as a component of the ‘Coarse Grains’ composite commodity group. In Tanzania, maize accounts for more than 78% of the total output of ‘Coarse Grains’ and more than 98% of ‘Coarse Grains’ trade. As such, we consider the market and production structure of the Tanzanian ‘Coarse Grains’ sector to be a reasonable approximation of the Tanzanian maize sector. The global database has the additional advantage of reconciling the global input-output and trade data from a range of sources, and benchmarking them to a single representative year; 2001 in our particular case. The reconciled data may 10
.F., Dobermann, A., Dawe, D., 2010. Rice yields in tropical/subtropical Asia exhibit large but opposing sensitivities to minimum and maximum temperatures. Proc. Natl. Acad. Sci. 107, 14562–14567.

- World Bank, 2010. Vietnam ‐ Economics of adaptation to climate change. World Bank, Washington, DC.

- World Bank, 2012. Well Begun, Not Yet Done: Vietnam’s Remarkable Progress on Poverty Reduction and the Emerging Challenges, 2012 Vietnam Poverty Assessment. World Bank, Hanoi.

- Yang, D., Choi, H., 2007. Are remittances insurance? Evidence from rainfall shocks in the Philippines. World Bank Econ. Rev. 21, 219–248.

- Yu, B., Zhu, T., Breisinger, C., Hai, N.M., 2010. Impacts of climate change on agriculture and policy options for adaptation. International Food Policy Research Institute (IFPRI).

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50

# **Appendix: List of 82 Countries in the Updated Agricultural Distortions Database**<sup>**a**</sup>

|**Sub-Saharan African developing**|**European transition and Mediterranean**|
|---|---|
|Benin|Bulgaria|
|Burkina Faso|Czech Republic|
|Cameroon|Egypt, Arab Rep. of|
|Chad|Estonia|
|Côte d’Ivoire|Hungary|
|Ethiopia<br>|Israel<br>|
|Ghana|Kazakhstan|
|Kenya|Latvia|
|Madagascar|Lithuania|
|Mali|Morocco|
|Mozambique|Poland|
|Nigeria|Romania|
|Senegal|Russian Federation|
|<br>South Africa|Slovak Republic|
|Sudan|Slovenia|
|Tanzania|Turkey<br>|
|Togo|Ukraine|
|Uganda||
|Zambia|**Other high-income countries**|
|Zimbabwe|Australia|
||Austria|
|**Asian developing**|Belgium|
|Bangladesh|Canada|
|China|Cyprus|
|India|Denmark|
|Indonesia|Finland|
|Korea, Rep. of|France|
|Malaysia|Germany|
|Pakistan|Greece|
|Philippines|Iceland|
|Sri Lanka|Ireland|
|Taiwan, China|Italy|
|Thailand|Japan|
|Vietnam|Luxembourg|
||Malta|
|**Latin American developing**|Netherlands|
|Argentina|New Zealand|
|<br>Brazil|Norway|
|Chile|Portugal|
|Colombia|<br>Spain|
|Dominican Republic|Sweden|
|Ecuador|Switzerland|
|Mexico|United Kingdom|
|Nicaragua|United States|

a NRA updates are computed in part from OECD (2011) PSEs for all high-income and European transition countries, plus Brazil, Chile, China, Israel, Korea, Mexico, South Africa, and Turkey.

Source: Anderson and Nelgen 2012b.
The rest of this paper proceeds as follows. The next section describes the data and the methodology. The third section presents basic results on extreme poverty rates by age, and how these are divided by region and country. The following section considers how the estimated poverty rates for adults and children change when using different household equivalence scales, while the fifth and final section concludes.

# **2. Data and methodology**

The analysis on child poverty presented below is derived from a combined sample of 104 surveys containing records on 7.7 million individuals from 89 developing countries, taken from the September 2016 vintage of the Global Micro Database (GMD). The GMD is a collection of globally harmonized household survey data recently developed by the Data for Goals group of the World Bank’s Poverty and Equity Global Practice. Full details on the background of the GMD are given in Castañeda et al (2016). A crucial feature of the GMD is that the welfare aggregates are the same as those used to compute the poverty estimates published by PovcalNet and the World Development Indicators.<sup>7</sup> These aggregates are based on household per capita income or consumption, depending on which concept is used to measure national poverty in a particular country. The sample of GMD used in this analysis consists of surveys collected in 2009 or later. The year 2009 was selected to balance the competing goals of increasing coverage while minimizing error due to extrapolating poverty figures forward when lining up to 2013.

Table 1 shows the sample’s coverage in number of countries, of population and of child population, based on figures from the UNDESA Population Division. The bottom panel indicates that countries in the sample have a total population of 5.25 billion persons and 1.69 billion children. This represents around 82 percent of the developing world’s child population in 2013, which was 2.05 billion. 85.4 percent of the 767 million extreme poor reported in PovcalNet are contained in the GMD sample.

*
effects of moving to free trade by developing countries versus by high-income countries, and in agriculture as compared with non-agricultural sectors. We also make use of the latest GTAP database (Version 6.05, see www.gtap.org) which has the virtue of including not only reciprocal but also non-reciprocal preferential tariffs, the latter providing low-income exporters duty-free access to protected high-income country markets. This allows us to take into account the fact that reform may cause a decline in the international terms of trade for those developing countries that are enjoying preferential access to agricultural markets of high-income countries (in addition to those that are net food importers because their comparative advantage is in other sectors such as labor-intensive manufacturing).

The paper begins with an examination of current distortions, the emphasis being mainly on import tariffs since they are later shown to be far more important than agricultural subsidies. This is followed by a description of the LINKAGE model of the global economy to be used to analyze the consequences of removing those distortions. The key results of the simulations are then presented. After discussing some qualifications, the paper concludes by highlighting the key messages and drawing out implications for developing countries in particular.

# **Key distortions in global markets**

Border measures traditionally have been the main means by which governments distort prices in their domestic markets for products, with the price of tradables relative to nontradables affected by interventions in the market for foreign exchange, and the relative prices of the various tradables affected by trade taxes-cum-subsidies or quantitative trade restrictions. Multiple exchange rates have also altered relative prices among tradable products. Product4
Discussion

Published evidence on the nutrition and health economic costs associated with dietary risk factors in LMICs is limited. Most of the studies found were based on one specific modeling approach, the GBD, that focuses its estimates on adults and a group of dietary risk factors that are associated with NCD outcomes. While useful for describing the high costs of diet-related NCDs to human life and the health care system, this body of literature has limited relevance for countries facing the DBM, where the burden of undernutrition is still a public health concern, especially in young children. The one study that captured both ends of the spectrum was the Cost of Not Breastfeeding model which described the global costs of child deaths due to infection, child obesity and women’s cancer and T2DM (Walters, Phan, and Mathisen 2019). The estimations nevertheless omit the unpaid health care costs of not breastfeeding, such as lost paid work hours or reduced leisure associated with caring for a sick child, opportunity costs which are largely borne by mothers and contribute to gender inequalities (Smith 2019). NonGBD studies commonly estimated the direct and indirect health care costs for the burden of disease associated with dietary risk factors, evidence that is valuable for increasing awareness of decision makers in LMICs of the burden of disease attributable to diets (Alcaraz et al. 2021).

Similarly, our review of the literature available on the risk of malnutrition associated with diets has revealed the challenges in summarizing a very broad body of research that is not yet well-standardized in terms of common metrics for diet quality. Diet metrics were either limited to the dietary diversity score or they were concentrated on specific dietary factors with known risks for nutrition and health outcomes. Definitions of ‘healthy’ or ‘unhealthy’ dietary patterns varied widely across studies and were often data-driven (derived from analytic approaches such as factor analysis) and context-dependent. The lack of consensus on the use of standardized metrics and the heterogenous dietary measures and outcomes used in quantitative studies are an important impediment for a more precise understanding of the
In this paper we take advantage of the 2016 HCES/WMS data which is representative for the droughtprone lowlands unlike earlier rounds, and provide estimates of the vulnerability to poverty for the drought-prone lowlands relative to the other ecological zones of Ethiopia using a two-level hierarchical regression model first proposed by Guenther and Harttgen (2009). As in Demissie and Kassie (2017), the Gunther and Harttgen model uses cross sectional data to get _ex ante_ estimates of the mean and variance of consumption, but it also allows the distinction between idiosyncratic (household level) and covariate (community level) shocks. In addition, the model also allows the decomposition of vulnerability into poverty-induced and risk-induced vulnerability.

To the best of our knowledge, this constitutes the first study on vulnerability focusing on the drought-prone lowlands. We find that the incidence of vulnerably in the drought-prone lowlands is two times the incidence of poverty– 56 percent versus 27 percent. Although the poverty rate is relatively higher in the drought-prone lowlands than any of the other ecological zones, the prevalence of vulnerability is remarkably higher. The incidence of poverty and vulnerability for the nation as a whole is 23 percent and 38 percent, respectively. While both poverty induced (vulnerability due to low average consumption) and risk induced (vulnerability due to high consumption variability) vulnerabilities are higher in the drought-prone lowlands, the relative importance of poverty induced vulnerability is higher in the drought-prone lowlands than in other ecological zones – the ratio of poverty induced vulnerability to risk induced vulnerability is 1.5 in the drought prone low lands as opposed to 0.9 for the whole country. Similarly, although vulnerability rates due to both covariate and idiosyncratic shocks are higher in the drought-prone lowlands, the relative importance of vulnerability associated with covariate shocks is higher in the drought-prone lowlands than in the rest of the country.

The remainder of the paper is organized as follows. In section 2, we present an intuitive explanation of the distinction between poverty and vulnerability and in section 3, we discuss the estimation technique applied. Section 4
# 4 Econometric Modeling

We will adopt a reduced form approach encompassing a variety of saving determinants identi...ed in the saving literature instead of deriving a narrow model of consumption/saving decisions from ...rst principles. This approach is followed by several papers in the cross country saving literature such as Edwards (1996), Masson, Bayoumi and Samiei (1998), and Loayza, Schmidt Hebbel and Serven (2000). However, our main focus will not be on all saving determinants but on pension saving.

The dependent variable will be the national saving rate, nsr. The determinant of our interest is the pension saving rate, psr.

An important remark has to do with the choice of our dependent variable, the national saving rate. We could have chosen the private saving rate instead and also include the government saving rate as one of the private saving determinants. Nevertheless, following that path would have been associated with several measurement problems. For example, private saving is usually overestimated in the national accounts because it is not adjusted for the in‡ation tax su¤ered by money and nominal bond holders; the opposite happens with government saving measures. Another important measurement problem is that available government statistics do not correspond to the same level of government for each of the countries. Depending on the country, The Government Financial Statistics publishes data corresponding to the general government, the consolidated central government, or the budgetary central government. These problems do not appear when we work with the national saving rate.

As clearly explained by Loayza, Schmidt Hebbel and Serven (2000), when 22