rafmacalaba/data-use-mentions-extended huggingface dataset:
land and livelihood lowered the standard of living for everyone”.<sup>5</sup> The refugees’ distinct identity was further cemented by clear allegiances to opposing political parties during the interwar period, with refugees actively supporting the liberals and natives standing behind the conservatives.

Has the refugees’ initial economic hardship upon arrival turned into persistent social marginalization? Has the historical strife between refugees and natives cemented into deep-seated mistrust and oppositional identities? Has social cohesion – understood as “a sense of shared purpose and trust [...] and willingness to engage and cooperate” among members of a same society (Kim, Sheely and Schmidt, 2020) – been eroded by major inter-group cleavages? Or, to the contrary, have the League of Nations’ resettlement efforts proved sufficient to prevent refugee’s exclusion and foster their integration into the Greek society ?

To address these questions, I use Murard and Sakalli (2018)’s geocoded dataset of refugee settlements in 1928, which I combine with several indicators of social cohesion at the local level in the 2000s and 2010s. I also use various Greek population censuses from 1928 to 2001 to examine refugees’ social integration, and the European Social Survey to look at the second-generation refugees’ political values, trust, and civic engagement.

The first important result is that refugees experienced a successful social integration in the host society. By 2001 refugees display a high rate of intermarriage, with close to 50 percent of refugee women being married with a Greek native. Second generation refugees also report levels of trust in others and in institutions similar to natives, do not perceive to be discriminated against, and are more likely than natives to engage in political activism and to participate in voluntary associations. I also find no persistent inter-group inequalities between refugees and natives in terms of educational attainment or occupation. Furthermore, the civic engagement of refugees appears to have contributed to higher involvement in voluntary associations at the community level. I find that, among small communities (bellow 1,100 inhabitants
error). We find no consistent evidence of the self-reporting error being associated with recall length: only in the Malawi IHS4 data set, the overstatement of plot area increases in recall length at a rate significantly different from zero. All other specifications show no significant correlation (Table A.5). Overall, this suggests that respondents’ difficulties in accurately estimating land area are not associated with memory decay over time. It is plausible that farmers at the time of the interview still operate many of the same plots as at the beginning of the season and therefore have a belief or estimate of plot area based on relatively current information.<sup>8</sup>

# Crop production

The maize harvest quantity as reported by farmers increases with the length of the recall period. The results are consistent across all three data sets, with point estimates ranging from 4.9 kg (2 percent) in Tanzania NPS 2012/13, to 11.97 kg (4 percent) in Malawi IHS4, to 15.85 kg (8 percent) for each additional month of recall length in the Malawi IHS4 panel (Figure 5, Table A.6, Table A.7). Based on BIC’, a quadratic specification yields a better fit of the relationship in Malawi IHS4, suggesting that here the recall length effect is strongest in the first 3 to 8 months, leveling off after that (Table A.8, Figure 6).

The results support the hypothesis that there is non-random measurement error in the farmer-reported quantity of maize harvested related to the length of the recall period. However, this result is valid only if no relevant confounding factor is omitted in the regression. There is one potential confounding factor that merits discussion: the weight of maize produced can be reported in different states of the crop. For example, at the time of harvest, maize is still on the cob. Before being sold, stored, or consumed, maize grains may be removed from the maize cob and dried.
future by observing marriages that have recently taken place in their reference group, i.e., their marriage market.

We assume that the relevant marriage markets are based on caste (and religion) and state. This is a reasonable assumption given the highly endogamous nature of the Indian marriage market. In the 2005 IHDS, only 4.4 percent of women were married to a spouse from a different caste.<sup>16</sup> In fact, genetic evidence suggests that the practice of endogamy in India is several thousand years old (Moorjani P. (2013)). In addition to this “horizontal” preference for same-caste marriages, inter- _jati_ marriages are governed by strict rules of hierarchy. Although caste is primarily a Hindu phenomenon, the notion of caste-based hierarchy remains well-preserved among many other religious groups in India.<sup>17</sup> Inter-religious marriages are even less common than inter-caste marriages. While patrilocal exogamy is widely practiced, most people marry within their state. To illustrate, less than 4 percent of the population had moved across states in the last ten years according to the 2001 Census data.<sup>18</sup> Consequently, we define expected dowry as the average net dowry paid by brides or received by grooms from the same caste and state as the child and who married during the year of the child’s birth or the prior four years.<sup>19</sup> This definition essentially assumes that parents have static or adaptive expectations, i.e., they expect the value of dowry next period to be equal to the weighted average of dowry in the past few periods. If dowry follows a random walk, then the static expectation is also the rational expectation. We explicitly test if the dowry data follows a random walk by performing the modified Dickey-Fuller _t_ test for a unit root in which the series has been transformed by a generalized least-squares regression. The results (in Table A.2) show that the
educational attainment, such as years of schooling, may emerge more gradually as the benefits of increased income permeate societal structures.

To explore these dynamics, we associate the change in multidimensional poverty with current and lagged GDP changes. For instance, the change in multidimensional poverty between 2012 and 2015 is linked to the change in GDP per capita during the same period, referred to as the current change in GDP. Additionally, we test for lagged impacts, where the change in GDP is defined as the change in the five years before the first survey or before the spell starts. In our example above, this would mean that the lagged effect is defined between 2007 and 2011. The lagged changes in GDP are consistently measured over the five years preceding the first survey. In contrast, the period for the current change in GDP varies depending on the interval between the surveys. Finally, we combine both current and lagged changes to assess the total (current + lagged) impact.

By testing different specifications, we aim to discern whether the effects of economic growth on multidimensional poverty are immediate or if they occur with a delay. This will enhance our understanding of the intricate ways economic changes influence various aspects of poverty.

# _Sample_

Our sample consists of 84 countries distributed across six world regions following OPHI’s region classification. Table 2 lists the six regions, details the number of countries and spells available for each region, and the first and last years for which we have MPI data.<sup>2</sup> Sub-Saharan Africa has the most representation, with 36 countries and 60 spells, whereas South Asia has the least representation, with five countries and seven spells. The number of observations per country ranges from two to four, and the length of each spell varies from one to twelve years, with an average spell length of five years. For further insights, Table A 2 in the appendix provides descriptive statistics for both the dependent and independent variables used in our analysis.

Table 2: Regions of the
_5_

# **III. Data and Variable Definitions**

The surveys used are listed in Table 1. Surveys are nationally representative except for Bangladesh, Brazil, China, Guatemala, Indonesia and the Philippines whose coverage is regional. Survey years range from 1987 to 1997. In selecting countries and surveys, we wanted to achieve a degree of geographic heterogeneity. In addition, however, the surveys included also needed to include (i) data on consumption or income, (ii) anthropometric data to measure malnutrition (height, weight, age and gender of children under 5), and (iii) an acceptably large sample size of children. There are 20 countries and surveys in total, of which 11 are Living Standards Measurement Study (LSMS) surveys, 9 are similar multi-topic surveys, which satisfy the above criteria. However, we need to keep in mind that quality standard, sampling method and variable definitions may not be uniform across surveys, especially when conducting agencies are different.

## **MEASURING LIVING STANDARDS**

As the main focus of this survey is to see the poor-nonpoor inequalities in malnutrition, choosing a variable according to which households are ranked is of paramount importance. Consumption is usually considered a better indicator of living standards than income, as ranking by the latter is generally more unstable than the former over the years. This reflects the fact that households can smooth out their consumption by saving and dissaving, while income fluctuates yearly depending on factors such as the household's employment situation and agriculture yield [22]. Therefore, whenever possible, consumption was used to rank households. Consumption variables were available for all countries except for China and the Philippines.

In addition to the choice between consumption and income, different methods by which consumption/income is aggregated reduce data transparency and comparability. It is therefore ideal to use consumption/income aggregates constructed using the same method. Due to lack of such data in some cases, however, we aggregated various consumption components, following the methodology proposed by
between the FSP and FISP programs is that the former has been able to graduate significant numbers of beneficiaries. Since the 2005/2006 agricultural season the FSP pack has been largely provided to new beneficiaries only.

# **4. Data Sources and Allocation of Benefits**

# **4.1. Data Sources**

The empirical analysis presented below is based on the Living Conditions Monitoring Survey of 2010 (LCMS VI), which was designed to monitor the impact of the Fifth National Development Plan (20062010) and to constitute a baseline for the Sixth National Development Plan (2011-2015).<sup>14</sup> The survey includes modules covering health, education, economic activities, household expenditures and household agricultural production. The LCMS VI is a nationwide survey covering both rural and urban areas in all nine provinces. The survey includes representative samples for each of Zambia‘s 72 districts. The total

> 14 Both plans are part of a series of medium-term strategies with the objective of ―making Zambia a prosperous middle-income country by 2030‖ (Government of the Republic of Zambia, 2011).

16
/sup> Among the age group of the 15 to 21 year-old, one in ten attended university in 2002, compared to one in 25 in 1988, and close to one in five upper secondary education, compared to one in 16 in 1988 (Figure 21.C).

How does Thailand compare internationally in terms of access to education? At the primary level, there is almost universal access, similar to other East Asian countries (Figure 21.D). By contrast, gross secondary enrolment lacked behind until the late 1980s. It increased strongly during the 1990s, and reached levels similar to Malaysia by 2001 (Figure 21.E). Tertiary enrolment began increasing from the mid-1990s onwards, reflecting partially the impact of higher secondary enrolment (Figure 21.F). By 2001, Thailand was on track to closing the gap to the Philippines.

> 16 Universal primary education was introduced in 1932, in the aftermath of the coup d’etat, in order to promote nationalism and support for the new regime.  The government also wanted to equip ethnic Thais to compete with the Chinese (Christensen et at 1993).  Mandatory schooling was extended to six years (upper primary) in 1978 and to nine year (lower secondary) in 1999.

> 17 Vocational education covers lower, upper, and higher education. University education includes teacher training.  The education questions in the SES questionnaire changed in 2002, making a comparison of secondary, vocational and university enrolment between 2000 and 2002 problematic.

51
Postolovska et al. (2017) in Armenia finds an elasticity of demand for tobacco of -0.54. Estimated at the decile-level, all results continue to be inelastic, and they decrease with income level (in absolute value). The price elasticity for the poorest quintile is relatively high at -0.74, while the elasticity for the richest quintile in Armenia drops to -0.28. In Ukraine, Denisova and Kuznetsova (2014) estimate the national price elasticity of demand at -0.28. However, decile-specific estimations suggest that the poorest 10 percent of households would reduce their cigarette consumption by 44 percent when faced by a 100 percent price increase. Meanwhile, the wealthiest decile would only react to an equivalent price shock by reducing the quantity of cigarettes consumed by 11 percent. Fuchs and Meneses use the Ukrainian household budget survey 2010-2013 to 9
away from Home ( _Encuesta para Medir la Composición Nutricional de los Principales Alimentos Consumidos Fuera del Hogar_ , ENCONUT); (3) ENAHO; and (4) the National Survey of Family Budgets (ENAPREF).

The continuous demographic and health survey is part of the traditional demographic and health survey (DHS), which provides nationally representative information on various aspects of maternal and child health. While the standard demographic and health survey had been carried out in Peru at five-year intervals since 1986, the continuous design replaced it in 2004 and is being conducted annually. This allows health information to be monitored on women of reproductive age (15–49) and their children under age 5. The traditional questionnaire covers fertility, mortality, nutrition, and anthropometric information measured by health professionals, as well as individual and household characteristics. For 2013, the data set contains information on 2,106 non-pregnant women of reproductive age living in 40 districts and neighborhoods in the Lima Metropolitan Area (Lima and Callao).

In 2013, the Peruvian National Institute of Statistics and Informatics ( _Instituto Nacional de Estadística e Informática_ ), with technical support from the National Food and Nutrition Center ( _Centro Nacional de Alimentacion y Nutricion_ in the Ministry of Health) and the support of the World Bank, conducted a unique, representative 5
among the largest databases of microdata concerning development and forced displacement (Thompson 2010; EGRISS 2023).

For the purpose of this study, microdata are defined as primary data collected from household surveys. Our focus lies in identifying micro-level datasets that are publicly available and designed to have a representative sample<sup>6</sup> of FDP so that collected data can be disaggregated for refugees and/or IDPs specifically. By studying the geographical and thematic coverage of existing FDP microdata, we seek to also shed light on critical data gaps that remain to be filled with further data collection efforts.

The paper identifies critical gaps in geographical and thematic coverage as well as compliance with international recommendations for proper identification of displacement status. The paper highlights that microdata is comparatively rich in Sub-Saharan Africa in contrast to other regions. However, data scarcity is notably pronounced in countries facing fragility and conflict and also among IDPs. There are also certain topics that are relatively lacking in the FDP microdata. These include topics like labor, finance (e.g., credit, debt, banking), agriculture/livestock/fishery, and education whereas FDP datasets are relatively rich in health, food insecurity, and water and sanitation in addition to coping strategies and protection. Furthermore, a large majority of questionnaires for these existing micro-level datasets do not include a core set of questions needed for proper identification of displacement status. This calls for further efforts

> 2 See https://www.unhcr.org/news/stories/unhcr-s-grandi-110-million-displaced-indictment-our-world.

> 3 The UNHCR MDL is available at https://microdata.unhcr.org/index.php/about.

> 4 The WB MDL is available at https://microdata.worldbank.org/index.php/about.

> 5 We focus on low-income and middle-income countries because they account for roughly 95 percent of FDP (refugees + IDPs) in the world based on data from UNHCR Refugee Finder and IDMC.

> 6 A representative sample means that when analyzed,
# **Methods**

This paper leveraged previously published food price datasets, available in different public repositories (Andrée, 2021a; Solano-Hermosilla, et al., 2021; Arbia, et al., 2023; Andrée & Pape, 2023), which were coupled with unpublished metadata (mainly ID of data submitter – volunteer crowd or trained enumerator) and wrangled to establish linkages across data sources and generate new evidence regarding the validity of the high frequency data outputs from the newer methods (i.e. crowdsourcing and AI-based imputation).

Briefly, the FPCA crowdsourced price data were submitted by ~1,200 volunteer citizens (so called “crowd”) who were onboarded and encouraged to submit prices of six (6) major commodities whenever they leisurely or purposefully visit nearest market within or beyond their locality (Adewopo, et al., 2022). The crowdsourced data were spatially rich because volunteers submitted georeferenced datapoints from many (>100) market locations and across nine (9) market types which all fall within three major market segment classification – Farmgate (nearest to production locations), Retail, and wholesale (Arbia, et al., 2023; SolanoHermosilla, et al., 2022). We accessed the monthly AI-estimated prices with the version date of 09/26/2023 from the World Bank microdata library. The AI-estimated data were generated by imputation of sparse, incomplete, or infrequent price data gathered from multiple sources and provided by Humanitarian Data Exchange (HDX) in partnership with the World Food Programme (WFP), through hybrid machine learning models that integrate local relationships within specific regions of the feature space, similar to the concept of local receptive fields in CNN, to support investigation of local price dynamics in markets where prices are sensitive to localized shocks and traditional data are not available (Andrée, 2021a).

We computed basic statistical metrics and implemented dual-layered correlative analyses. The first layer focused on the relationship between daily crowdsourced prices and ground truth prices, and this cascaded to second layer assessment
_<mark>Journal of International Economics</mark>_ <mark>92(1), 51-62.</mark>

- <mark>Barrios, S. and E. Strobl. 2002. “Foreign direct investment and productivity spillovers: Evidence from the Spanish experience,”</mark> _<mark>Review of World Economics</mark>_ <mark>138(3), 459-81.</mark>

- <mark>Barro, R., and J.-W. Lee. 2010. “A New Data Set of Educational Attainment in the World, 1950-2010,”</mark> _<mark>Journal of Development Economics</mark>_ <mark>104, 184-198.</mark>

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- <mark>Bolwig, S. and P. Gibbon. 2009. “The Economics of Smallholder Organic Contract Farming in Tropical Africa,”</mark> _<mark>World Development,</mark>_ <mark>37(6), 1094-1104.</mark>

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33
Policy Research Working Paper 9904

# **Abstract**

This paper evaluates the impact of an employment subsidy scheme covering employers’ social contribution costs on registered employment in small firms in Turkey. It utilizes a rich, firm-level administrative data set with monthly frequency, which allows for closely following the dynamics of registered employment in firms before and after the implementation of the subsidy. The empirical approach utilizes the geographically targeted implementation of the subsidy to estimate its effects using a difference-in-difference specification. The paper finds that the subsidy scheme had a sizable and positive impact on registered employment in small firms. The results are robust across specifications and to the choice of the control group. Positive effects on formal employment are also fairly constant and sustained over time. Corroborative evidence suggests that the positive effects on registered employment are mainly driven by the formalization of existing workers as opposed to new job creation. Therefore, the results indicate that social security contribution subsidies in small firms can be effective in reducing informality in contexts where informal employment remains common.

This paper is a product of the Social Protection and Jobs Global Practice and 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 gasik@etu.edu.tr, lbossavie@worldbank.org, Jochen.Kluve@kfw.de, snasozen@worldbank.org, mnebiler@worldbank.org, and aoviedo@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
# **Estimation of Climate Change Damage Functions for 140 Regions in the GTAP9 Database**

## Roberto Roson

Ca' Foscari University, Venice and IEFE, Bocconi University, Milan

## Martina Sartori

University of Trento and IEFE, Bocconi University, Milan

### **Keywords** :

Climate change, integrated assessment, computable general equilibrium, damage function, climate impacts.

**JEL Codes** : C68, C82, D58, Q51, Q54.

**Acknowledgement** : The research for this paper was supported by the Knowledge for Change multidonor trust fund. The views and opinions expressed in this paper are solely those of the authors and do not represent the views of the World Bank Group.