rafmacalaba/data-use-mentions-extended huggingface dataset:
region-specific data and further highlights the potential of new data sources in shedding light on the intricate relationships between climate change, poverty, and inequality.

# _2.2.2. Empirical Models_

Previous studies often rely on three primary types of econometric models to examine the economic effects of climate change and natural disasters: cross-sectional models, panel models, and long differences models. The first two models have been used to evaluate the effects on poverty and inequality (Arouri _et al._ , 2015; Azzarri and Signorelli, 2020; Paglialunga _et al._ , 2022), while the last model has been used recently on other outcomes such as agriculture, health, and labor reallocation (Burke and Emerick, 2016; Obradovich _et al._ , 2018; Liu _et al._ , 2023).

Cross-sectional models offer the starting point where the relationship between climate change and poverty (inequality) is commonly formulated as C C C (1) ii ii ii ii In such models, the dependent variable (YY = αα + ββ TT) typically represents a socio-economic indicator, + γγ PP + εε ii such as poverty (inequality), in a specific location (YY _i_ ) during a given period. These models factor in variables like annual temperature ( ) and precipitation ( ) (Azzarri and Signorelli, ii ii 2020).<sup>10</sup> TT PP A potential omitted variable bias is an inherent challenge in Equation (1), which refers to a situation where unobserved correlations between temperature and other factors like technological changes or labor productivity, could influence the outcome variable. To

> 10 While rainfall is not the central focus of our review, it is worth noting that a substantial body of literature has applied similar models to explore the impacts of (various metrics of) rainfall on different outcomes. For example, previous studies have used total annual precipitation (Damania _et al._ , 2020), rainfall deviations from the historical means (Corno and Voena, 2023), and
Table 2: Diagnostic of merging process between survey and tax records

|20|+ population (survey)|Tax data observations|Share obs from tax data (%)|Merging point (L)|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|2003|3,165,874|3,169|0.10|595,000|
|2004|3,347,641|6,708|0.20|445,000|
|2005|3,489,479|6,992|0.20|505,000|
|2006|3,583,144|7,180|0.20|580,000|
|2007|3,715,919|7,446|0.20|670,000|
|2008|3,917,356|1,959|0.05|1,440,000|
|2009|4,037,917|4,041|0.10|755,000|
|2010|4,209,107|3,369|0.08|1,110,000|
|2011|4,377,335|13,171|0.30|520,000|
|2012|4,464,598|17,931|0.40|480,000|
|2013|4,588,901|41,667|0.90|320,000|
|2014|4,494,971|22,587|0.50|475,000|
|2015|4,671,707|28,198|0.60|475,000|
|2016|4,814,880|38,834|0.80|425,000|
|2017|4,942,
## **A.3.2 Nigeria: Measuring Management Practices**

In the Nigerian context, RR also followed BSVR’s approach to measuring management practices, adapting their survey tool to the Nigerian public sector setting. Management practices were elicited from senior management in each organization. While each manager filled in their own questionnaire, enumerators looked for a consensus and recorded that in their own questionnaire. This is the information used to construct management practice indices for each organization. Enumerators held double blind interviews in the 63 organizations with project completion data. The management survey covered nine topics: roles, flexibility, incentives, monitoring, culture, targeting, facilities, skills and staffing. RR combined answers to the roles and flexibility questions to construct an index of management practices capturing bureaucrats’ autonomy (CS-autonomy). RR combined answers on the incentives and monitoring topic areas to construct an index capturing the incentives/monitoring management practices bureaucrats operate under (CS-incentives/monitoring). RR combined answers on all remaining topics to construct an ‘other’ management practices index (CS-other). The responses to each practice were converted into normalized z-scores by taking unweighted means of the underlying z-scores (so are continuous variables with mean zero and variance one by construction), where, as in Ghana, both are increasing in the commonly understood notion of ‘better management’.

# **References**

- [1] acemoglu.d and j.a.robinson (2012) _Why Nations Fail_ , Crown Business, New York.

- [2] alesina.a and e.la ferrara (2005) “Ethnic Diversity and Economic Performance,” _Journal of Economic Literature_ 43: 762-800.

- [3] ashraf.n, o.bandiera and k.jack (2014) “No Margin, No Mission? A Field Experiment on Incentives for Pro-social Tasks,” _Journal of Public Economics_ 120: 1-17.

- [4] ashraf.n, o.bandiera and s.s.lee (2018) Losing
definition, about 30 percent of South Asia are in urban areas. The travel distance is defined as the travel time from a given location to the nearest settlement and is calculated using a least-cost path algorithm on a friction surface where each pixel has a cost and travel time associated with it (Nelson et al. 2019). The friction surface incorporates a variety of factors such as connectivities, elevation, road network, land cover and slope, etc. The travel time obtained is validated against actual travel times from Google Maps.

# **2.2 Estimation**

We estimate the relationship between temperature or flooding and relative wealth, as measured by the RWI, or between temperature or flooding and firm size using Ordinary Least Squares (OLS) regressions.

We use the following specifications for households: R s = T T 28s + T T 29s + T T 30s + T T 31s + T T 33s + T T 34s + T T 35s + T T 36s s (1) II ss R s = A f s + vv + uus (2) ss Where R s is the average RWI at grid cell II AA f dd + vv in state or province + uu . The basic specification includes country fixed effects to take into account the country specific information used in the II gg ss construction of the RWI, but the preferred specification includes state fixed effects, , to take into account state specific characteristics within a country. All standard errors are clustered at the ss νν district level (third administrative unit). For temperature (eq. 1), in the main specification, the variable T e s includes indicators for the average maximum temperature between 29 degrees Celcius or lower and 36 degrees Celsius, relative to 32 degrees Celsius (the omitted ttT ttuueeTT dummy). This specification takes into account potential non-linearities in the relationship between RWI and temperature. For flooding (eq. 2), the first specification uses the variable A f s , an indicator that takes the value one if a
Notice that according to these estimates, the total effect on exports of benefiting from the Special Rule four years is 242%, a figure larger than the average effect of 168% estimated in column 5 of table 2, as only three exporters in our sample make it to the fourth year.

Columns 2a and 2b show the differential effect of the single-transformation rule across the 7 exporters. The effect for all countries is positive. The effect of the Special Rule on exports from Kenya, Madagascar, and Swaziland are found to be the largest. The different performance among receivers of the Special Rule begs the question: Why were some African countries so much more successful at taking up preferences and at experiencing higher export growth in apparel? Among others, a possible explanation lies in the business environment of a country that may be more conducive to attract foreign investment in apparel plants and to diminish trading and other fixed costs which can be proxied by a country’s rank in the World Bank “Doing Business” indicator.<sup>17</sup> Figure 2 confronts the Ease of Doing Business (DB)<sup>18</sup> ranking of African countries benefiting from the Special Rule against their apparel export growth during AGOA (measured by the difference of exports (in logs) at 2004 and at the beginning of AGOA). Indeed, on average countries best ranked along the DB indicator experienced higher growth in apparel exports during AGOA, and the correlation coefficient, ( ρ =-0.55) is highly significant.

Figure 2 here: Export Growth and the Business Environment Recall that the SR dummy is set equal to one if a country benefits from the rule for at least 9 months. As Botswana and Malawi were eligible to the Special Rule from August 2001, whereas Swaziland was eligible from July 2001, the effect may already be reflected in the exports data from 2001 onwards. Column 3 reports the export elasticity estimate at an average preferential margin when the dummy, _RA_<sup>_j,
approach to the existing data on refugee self-reliance using 11 microdata surveys from low- and middleincome countries. Section 4 discusses the results and the potential implications of the findings. Section 5 draws key conclusions.

# 2. Review of literature on measuring self-reliance

The most widely used definition of refugee self-reliance comes from the United Nations High Commissioner for Refugees (UNHCR, 2014) which defines self-reliance as “the ability of an individual, household or community to meet essential needs and to enjoy social and economic rights in a sustainable manner and with dignity. By becoming self-reliant, refugees and displaced persons lead active and productive lives and are able to build strong social, economic and cultural ties with their host communities. Self-reliance can assist in ensuring that persons of concern are better protected by strengthening their capacity to claim their civil, cultural, economic, political and social rights” (UNHCR, 2014).

Self-Reliance has emerged as a priority outcome of refugee support in recent years. Driven by landmark agreements of the New York Declaration for Refugees and Migrants (2016), its accompanying Comprehensive Refugee Response Framework (CRRF), and the UN Global Compact on Refugees (2018), there is more emphasis than ever on the promotion of refugee self-reliance (Betts, 2021; Easton-Calabria, 2021; UNHCR 2024b). However, the centrality of self-reliance to international refugee discourse marks more of a resurgence than an innovation, as self-reliance has been a repeated goal of assistance actors for over a century (Easton-Calabria, 2021). Since its emergence in 1920s Greece, when the League of Nations provided livelihood assistance to over 1.5 million refugees, self-reliance has been through many different incarnations, all of which have repackaged similar practices under different monikers, such as ‘selfsufficiency’, ‘self-supporting’, ‘rehabilitation’, ‘self-help’, ‘rural animation’ and ‘sustainable programming’ (Betts, 2021; Easton-Calabria, 2022; Easton-Calabria and Omata, 2018; UNHCR 2024b). Despite refugee self-reliance, in its various forms, existing for over
important concern by the highest share of the respondents (30 percent, Figure 5). “Unpredictable and extreme weather that harms agricultural production” was third on both lists.<sup>14</sup>

**Figure 5: Tajikistan: Potential climate change impacts of greatest concern (% respondents)**

- (a) Cited as potential impact of concern (percentage of total respondents)

<!-- Start of picture text --> Increase in natural disasters (e.g. floods, fires, mudslides, etc.) 78<br>Extreme hot/cold seasons that are harmful for health 69<br>Unpredictable and extreme weather that harms agricultural<br>59<br>production<br>Decline in fresh water availability for drinking 53<br>Decline in fresh water availability for irrigation 44<br>0 20 40 60 80<br>Percent of respondents<br><!-- End of picture text -->

- (b) Cited as potential impact of greatest concern (totals sum to 100 percent)

<!-- Start of picture text --> 7<br>30 Extreme hot/cold seasons that are harmful for health<br>18<br>Increase in natural disasters (e.g. floods, fires,<br>mudslides, etc.)<br>Unpredictable and extreme weather that harms<br>agricultural production<br>Decline in fresh water availability for drinking<br>Decline in fresh water availability for irrigation<br>20<br>25<br><!-- End of picture text -->

**Source:** Authors calculations based on P-LITS 2 data.

> 14 Respondents reporting themselves as being fairly/very well informed about the main consequences of CC were more likely to select natural disasters and increased damage to agriculture (and less likely to select less water for drinking and irrigation) compared to those reporting themselves as not at all/
# Introduction

The most immediate consequences of floods are the fatalities and casualties and the first priority of disaster risk management is to save lives. But natural disasters also have economic consequences, which affect wellbeing and need to be accounted for and managed (Cavallo and Noy, 2011; Rose, 2009; Skoufias, 2003). These economic consequences and wellbeing losses depend on the value of what is lost or damaged, and on many other factors, including how long it takes to rebuild, how asset losses translate into income losses, and how coping mechanisms and ex‐post support (from insurance to social protection) protect the victims and help smooth consumption losses (Carter et al., 2007; Le De et al., 2013). In addition, wellbeing losses are larger when losses are concentrated, especially when concentrated on poor people – as is often the case (Hallegatte et al., 2016a). Disasters can also have irreversible and long‐term health consequences, particularly on children (Dercon, 2004; Maccini and Yang, 2009).

Many policies can minimize wellbeing losses and protect the population: from building dikes and restoring mangroves to better land‐use planning and early warning, to evacuation, insurance and social safety nets. Risk management policies are best designed as holistic strategies that combine many of these levers (World Bank, 2013).

Designing such a consistent policy package is challenging; here we propose an approach and a model to support this process. In this work‐in‐progress, we combine data on flood hazard, population and asset location, asset vulnerability, and socioeconomic characteristics and combine insights from natural and social sciences to assess how floods affect wellbeing, measured using a _social welfare function_ ( _welfare_ is the metric used by economists to measure wellbeing). We present the model using a case study – the 2005 floods in Mumbai, India, for which macro‐ and micro‐economic data are available. We then use the model to define and assess the socioeconomic resilience of 90
social security systems are based on those individuals receiving a benefit (retirement or pension). However, such coverage comprises two stages. The first stage relates to the term in which a worker contributes to the system and becomes entitled to a benefit, including protection against other contingent losses. This stage refers to the coverage of economically active population. The second stage relates to the receipt of a monetary benefit when an individual retires from the labor market. This stage refers to the coverage of passive population.

Figure 1 presents the percentage of active population contributing to the social security system between 2003 and 2016. Two different target populations were taken into account when creating this indicator: working population and wage-earner working population, that is to say, "employees".

The Ecuadorian contributory system is featured by providing low level coverage, both considering the total number of working people as well as considering only the wage-earner working population. In this sense, between 2003 and 2007, the percentage of working population contributing to social security was on average only 18.6%, while if wage-earner working population were considered, this percentage climbed on average up to 34.8%.

Figure 1. Percentage of People Contributing to Social Security <!-- Start of picture text --> 70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br>Wage-earners Working people<br><!-- End of picture text -->

# Source: author elaboration based on ENEMDU

The result achieved by the social security system directly links to the performance obtained in the labor market, defined by the formality level of the workforce. In effect, in the contributory pension schemes, the coverage of a working person is mainly determined by the manner in which that person is involved in the labor market.

8
1 = 0 _._ 05 _}, {r_ 2 = 0 _._ 935 _, δ_ 2 = 0 _._ 035 _}_ and _R_ = 0 _._ 90. In other words, as soon as there are even-length sub-vectors with uneven correlations, which should be expected to occur purely from random draws, then for every sub-vector pair with correlation _δ_ 2 above the total vector correlation, there must be a sub-vector with _δ_ 1 _> δ_ 2 below the total vector correlation. This dis-proportionality increases with a power 2 for squared correlations. Since this argument can be repeated iteratively on the sub-vector pairs, it holds that always a larger divergence _∥rN_ 2 _− R∥ > ∥rN_ 1 _− R∥∀ N_ 2 _< N_ 1 can be found. Note that in all cases, the sub-vector correlations _rN_ 1 _, rN_ 2 _, ..., rNX_ are generated by the same process that has a correlation _R_ . Strictly, this also makes the sample estimates of the R<sup>2</sup> not directly comparable across unequal samples sizes, unless _N →∞_ so that we deal with the deterministic limit _RN → R∞_ .

> 19The 2020 prevalence data were not yet available when this work was done. At the time of reading, the official 2020 figures are likely published and the reader is encouraged to compare, bearing in mind that the model projects unmitigated impacts not reflective of supportive policies. Future updates of the projections will include the new data. Generally, there are three interesting moments in the year to update the results. In April when the WEO is published, after July when the historical annual food insecurity data are published, and in October when the WEO is revised. In January and June, some country projections may also be updated based on the World Bank’s Global Prospects report.

> 20As the WEO identifies only a global average risk, the downside scenario is generated under the
The year of the last state election ranges from 2010 through 2016 for respondents in the NFHS data. We merge the NFHS data with electoral data for statewide elections obtained from the ECI. Since the election data are at the level of the constituency—a smaller geographical unit than a district—we follow Bhalotra and Clots-Figueras (2014) and aggregate election data to the district level before merging it with the NFHS data using the election year and respondent’s district of residence. Districts in our data have between 1 and 40 electoral constituencies, and the median district has 6 constituencies.

The electoral data comprise candidate-level information within each state assembly constituency that allows us to identify the gender of winners and runners-up as well as the victory margins. We construct a district-level aggregated variable that measures the fraction of constituencies in district _d_ in state _s_ that were won by female politicians— both overall ( _Fds_ ) and in close elections against a male runner-up ( _FCds_ )—in any given election year. We define an election to be close if the margin of victory in terms of vote share was less than 3 percent.<sup>9</sup> To analyze effects on fertility and birth spacing, we reshape the cross-sectional NFHS dataset to construct a retrospective woman–year panel spanning the time period between the last state election and the year of the survey. Moreover, we use the 2012–13 DLHS to examine whether female leaders influence village-level health infrastructure. The DLHS is merged with the election data using the last round of state elections and district information. Finally, we utilize data from the Socioeconomic High-Resolution Rural–Urban Geographic Dataset on India (SHRUG) (Asher et al. 2021) to obtain information on district characteristics, as per the 2011 census of India.

Table A.2 describes the characteristics of the 628 districts covered by our NFHS estimation sample using election and census data. On average, 9 percent of constituencies in a district had a female winner; 3 percent of constituencies had a close election between a
picture text -->

# 4.3.2. Productivity trends in agriculture, forestry, and fishing

In contrast to trends in other segments of the economy, formal firms in agriculture, forestry, and fishing have actually experienced steady increases in labor productivity over the 2014-2021 period, driven by within-firm growth (see Figure 23). The divergence between the formal firms in the CIT dataset and broader agricultural trends in Zambia reflects how the firms in the CIT dataset are often large-scale commercialized farms. Such farms have a relatively greater ability to leverage Zambia’s strong resource 13
, instead of using cash. The share of adults making or receiving digital payments in developing economies grew to 57 percent in 2021, from 35 percent in 2014.

When account owners _use_ their accounts for digital payments, savings, and appropriate credit, they experience a range of positive benefits, including greater security and greater privacy for their transactions. For example, a field experiment in Bangladesh found that factory workers

> 3 Digital payments are defined as using a mobile money account, a debit or credit card, or a mobile phone or the internet to make a payment from an account, or the use of a mobile phone or the internet to send money to relatives or friends or to pay bills. Digital payments also include in-store or online merchant payments; paying utility bills; sending or receiving domestic remittances; receiving payments for agricultural products; or receiving wages, government transfers, or a public pension directly from or into an account. Global Findex 2021 survey respondents were prompted to answer whether they used digital payments based on their experience during the 12 months prior to the survey.

7
2003). On the supply side, the factors that potentially undermine the use of commercial fertilizer by farmers include unfavorable business climate, excessive regulations, an abundance of taxes and fees, and high levels of rent seeking (Morris et al., 2007).

With these considerations in mind, the objective of the present study is to use nationally representative household level data from Malawi to analyze the profitability of fertilizer use in maize production. Specifically, the study seeks to answer the following questions: 1) what is the level of nitrogen use efficiency (NUE)<sup>1</sup> and how does it vary across Malawi? 2) to what extent is the use of fertilizer in maize production profitable in Malawi? Given the NUE estimates from our study, we seek to then answer two key policy questions. First, would the majority of smallholders in Malawi be better-off with subsidized fertilizer or with an equivalent cash transfer? Second, could the majority of smallholders increase their profits by applying fertilizer at the rates recommended by the government?

The study focuses on Malawi for two reasons. First, productivity growth in Malawi’s agriculture is typical of countries in SSA. For the past two decades, the productivity of most agricultural crops in the country has increased only modestly. Even now, the already modest increase in productivity is further undermined by an annual population growth of 2.7% (MoAFS, 2010). In addition, the Ministry of Agriculture and Food Security (2010) estimates that Malawi’s yield gap – the difference between potential yield and the actual yield of the average farmer (1,536 kg/ha for maize in 2013) – ranges between 38% and 53% for cereals, and between 40% and 75% for legumes (Lobell et al., 2009). This implies substantial room for productivity improvements. Yield improvements likely will be essential for reducing poverty and improving food security in Malawi because given Malawi’s already high average population density of 178 people