rafmacalaba/data-use-mentions-extended huggingface dataset:
**Figure 2.2: Left: Seasonal frequency of severe convective local storms (SLCS) from 1990 to 2005. Right: Average monthly number of lightning flashes from 1998 to 2014**

<!-- Start of picture text --> 900 200<br>800 180<br>700 160<br>140<br>600<br>120<br>500<br>100<br>400<br>80<br>300<br>60<br>200 40<br>100 20<br>0 0<br>Number of SLCS lightning flashes<br>Average number of<br>January February March April May June July August September October November December January February March April May June July August September October November December<br><!-- End of picture text --> _Source_ **_:_** _Left : Yamane et al. (2010) ; Right: NASA Tropical Rainfall Measuring Mission Lightning Imaging Sensor (LIS) and Dewan et al. (2017)_ In order to better capture SLCS, we use lightning data as a proxy. Lightning data are constructed by Dewan et al. (2017), who use data NASA’s Tropical Rainfall Measuring Mission Lightning Imaging Sensor (LIS) from 1998 to 2014. This dataset provides estimated counts of lightning strikes. The LIS is a measurement device launched in 1997 to record lightning flashes between 35° N and 35° S latitude. With a spatial resolution of 4–7 km, LIS captures total flash counts, including both cloud-to-cloud and cloud-to-ground lightning flashes. The LIS is also well suited for identifying the spatial distribution of lightning, recording the time of events and measuring the radiant energy of lightning flashes (Dewan et al., 2017). They analyze lightning flash counts to examine annual, monthly and diurnal patterns of flash activity in Bangladesh over the study period (1998–2014). Overall, a total of 83,641
from factory to ship reduces relative exports of time sensitive goods by 6 percent. The _—_ coefficient estimate on distance is not significant indicating that domestic restrictions are a bigger constraint to trade in time-sensitive agricultural goods than distance.

Poor trade facilitation affects the composition of trade, preventing countries from exporting time-sensitive goods. Time-sensitive goods also tend to have higher value, implying that some of the effect of time delays on _aggregate_ exports results from countries with poor trade facilitation concentrating on low-value time-insensitive goods. Taken together, our results suggest that time delays depress exports, at least part of which is due to compositional effects.

# **VI. Conclusions**

We use a new World Bank dataset on the time it takes to move containerized products from the factory gate to the ship in 126 countries. A difference gravity equation is first estimated, by regressing relative exports of similar countries—by location, endowment, and facing the same trade barriers abroad—on relative time delays, remoteness and other standard variables. Our estimates imply that on average each additional day that a product is delayed prior to being shipped reduces trade by at least 1 percent. We find a larger effect on time-sensitive agricultural goods: a day’s delay reduces a country’s relative exports of such products by 6 percent on average.

The size of the effect suggests that a one-day reduction in delays before a cargo sails to its export destination is equivalent to reducing the distance to trading partners by about 70 km. This may explain why Mauritius has enjoyed success as an exporter. At 16 days to process cargo, the efficiency of its trade infrastructure is identical to that of the United Kingdom and better than France’s.

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*classification**|**PM2.5 concentration**|**Mortality**|
|---|---|---|---|
|**Safe**|**No/minor risk**|≤ 5μg/m<sup>3</sup>|_baseline_|
||**Low**|5 – 10μg/m<sup>3</sup>|4% increased mortality rate|
||**Moderate**|10 – 15 μg/m<sup>3</sup>|8% increased mortality rate|
|**Unf**|**High**|15 – 25 μg/m<sup>3</sup>|16% increased mortality rate|
|**sae**|**Very high **|25 - 35μg/m<sup>3</sup>|24% increased mortality rate|
||**Hazardous**|over 35μg/m<sup>3</sup>|> 24% increased mortality rate|

_Assign air pollution risk categories to population headcounts at the pixel level and aggregate to the administrative unit:_ As the air pollution and population density maps are converted into the same spatial resolution, each population map cell can be assigned a unique air pollution risk classification – these cells can then be aggregated to the administrative unit level (e.g. province or district level). This allows the calculation of population headcounts for each risk category and for each (sub-)national administrative unit. This process yields an estimate of the number and share of people exposed to no-, low, moderate, high, very high and hazardous air pollution concentrations throughout the year. We offer these exposure headcount maps as global gridded layers with a resolution of 90 meters. In addition, we aggregate them to administrative units, including for each country and subnational unit.
# Conflict, Household Victimization, and Welfare:

# Does the Perpetrator Matter?<sup>∗</sup>

Heidi Kaila<sup>†</sup> and M Abul Kalam Azad<sup>‡</sup>

**JEL Codes:** I32, O12

**Keywords:** Conflict, victimization, food insecurity, consumption, mental health, Nigeria

>  We thank Chris Barrett, John Hoddinott, Carlos Rodríguez-Castelán, Vasco Molini, Nadia F. Piffaretti, Christopher Boone, Saurabh Singhal, Smriti Sharma, Clement Joubert, and participants at the 2019 Centre for the Study of African Economies conference in Oxford and seminar participants at Cornell University and the University of Newcastle for their valuable comments. We thank Kevin Robert McGee and Michael Wild for their work on the sampling and Aibek Baibagysh Uulu for the work on the consumption aggregate. Nigeria Poverty Team gratefully acknowledges the financial support from Korea Trust Fund (KTF) for Economic and Peace Building Transitions for a round of phone survey on food security, and Kaila acknowledges financial support from Danmarks Frie Forskningsfond (DFF). The authors are responsible for any remaining errors.

> † Cornell University, Ithaca, New York Kaila.heidik@gmail.com

> ‡ World Bank, Abuja, Nigeria mazad@worldbank.org
<!-- Start of picture text --> 90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>All urban areas Potential slum areas Urban expansion areas<br>RP10 RP25 RP50 RP100 RP1000<br>RP10_SLR RP25_SLR RP50_SLR RP100_SLR RP1000_SLR<br>% of area exposed<br><!-- End of picture text --> _Figure 1. Slum areas tend to be more exposed than the average, across all flood scenarios. SLR means the scenario includes a 30cm sea level rise due to climate change. RP denotes the turn period event._

# **6. Limitations**

The findings presented should be interpreted considering a number of caveats.

While we use current and future flood data, we only use current population and poverty data, as reliably projecting these socioeconomic trends spatially into the future is almost impossible. Changes in these trends – among many other factors – can lower socioeconomic vulnerability even as the climate change hazard increases (Hallegatte et al. 2016). Along these lines, while we examine which regions within Vietnam have the highest flood exposure, we do not examine the determinants of vulnerability (other than poverty). Recent analyses suggest that the Northwest, Central Highlands, and Mekong River Delta have the greatest socioeconomic vulnerability (World Bank 2010).

In the flood hazard maps developed for this paper, we assume no protection due to a lack of data and as a result the hazard maps present an upper bound of flood risk. Work is currently ongoing to develop a global database of flood protection, and this information can be mobilized for future work (Scussolini et al. 2015). For the national‐level analysis, flooded areas are defined as any area with inundation higher than 0. We have not yet explored the depth dimension, although the flood hazard maps developed for this study allow for this potential in
the Afghan refugee crisis significantly lengthens the average duration of displacement situations, while in 2016 the Syrian refugee crisis will become protracted according to UNHCR’s definition and consequently it will substantially reduce the estimate of average duration. Fifth, estimates of average duration exclude IDPs due to conceptual problems of defining an end to internal displacement as well as data constraints. And finally, there are some discrepancies in the available displacement data that further distort estimates of average duration, for example South Sudanese

> 54 Excludes Palestinian refugees under UNRWA’s mandate and refugees in high-income OECD countries.

> 55 Calculations are not based on individual data, which would provide a truer estimate of how long an average a person is in displacement, but on ‘situations’ (i.e. refugee populations in host countries grouped by country of origin, e.g. Ethiopia currently hosts refugees from Eritrea, Somalia, South Sudan and Sudan and so has four refugee situations). Calculations do not take into account fluctuations in refugee numbers if the size of the situation remains over 25,000 people. Moreover, since population estimates are annual snapshots, they cannot take into account increases and decreases within years, which can also be substantial.

> 56 Arguing that few refugee crises are resolved quickly, a 2015 ODI report proposes broadening the definition of protracted displacement to situations where refugees and/or IDPs have been in exile for three years or more, which increases the number of refugees in protracted displacement situations in 2014 from UNHCR’s estimate of 6.4 million people to 12.9 million people (two-thirds of all refugees) (Crawford, et al. 2015).
and Shinyanga and two districts in the Kagera Region.<sup>4</sup> The districts were purposively selected to capture variations in socioeconomic characteristics. In each district, 24 communities were randomly chosen from the 2002 census based on probability-proportional-to-size criteria. Within communities, a random subvillage (enumeration area) was chosen, and all households therein were listed. Per subvillage, 24 households were randomly selected to participate, and three households were randomly assigned to each of the eight modules. Among the original households selected, there were 13 replacements because of refusals. Three households that started a diary were dropped because they did not complete their final interview. Another five households were dropped because of missing data on some of the key household characteristics, yielding a final sample size of 4,029 households.<sup>5</sup> The basic characteristics of the sampled households generally match those from the nationally representative national Household Budget Survey 2007. The randomized assignment of households to the eight different questionnaire variants was successful in terms of balance across various characteristics relevant for consumption and consumption measurement.<sup>6</sup> In regard to reporting error, there are several points to note about the survey experiment. The recall modules 1–5 ask the respondent about consumption, but not food acquisition. The questionnaires record details on meals consumed outside the home by household members as well as meals within the household that were shared with non–household members. The diaries are acquisition diaries that account for food given to animals (for example, scraps or leftovers), food used for seed, food taken from stocks, and food brought into the household by children (individual diary only). At the end of each week, there is a review of the main meals the household ate each day, and additional information is recorded if any components of these meals were not captured in the diaries. This is important because the 2012 State of Food Insecurity report incorporated, for the first time, tentative estimates of food losses
# Women’s Access to Labor Market Opportunities, Control of Household Resources, and Domestic Violence

## Rachel Heath<sup>_∗_</sup>

July 25, 2012

**Abstract**

While there are many positive societal implications of increased female labor force opportunities, some theoretical models and empirical evidence suggest that working can increase a woman’s risk of suffering domestic violence. Using a dataset collected in peri-urban Dhaka, this analysis documents a positive correlation between work and domestic violence. This correlation is only present among women with less education or who were younger at first marriage. These results are consistent with a theoretical model in which a woman with low bargaining power can face increased risk of domestic violence upon entering the labor force as a husband seeks to counteract her increased bargaining power. By contrast, husbands of women who have higher baseline bargaining power cannot resort to domestic violence since their wives have the ability to leave violent marriages. These findings are inconsistent with the models of assortative matching in the marriage market, expressive violence, work in response to economic shocks, or underreporting of domestic violence. The results on age at marriage are also inconsistent with the implications of a reverse causality model in which women enter the labor force to escape violent situations at home, although the results on education are consistent with that story.

JEL Classifications: O12, D13, J16 Keywords: domestic violence, female labor force participation, intra-household bargaining, Bangladesh

> _∗_ University of Washington Department of Economics and the World Bank (GEN); rmheath@uw.edu. I thank Elena Bardasi, John Giles, Gil Shapira, and Adam Wagstaff for helpful comments. The findings, interpretations and conclusions expressed in this paper are entirely those of the author, and do not necessarily represent the views of the World Bank, its Executive Directors, or the governments of the countries they represent.

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Policy Research Working Paper 9057

# **Abstract**

This paper collects meta data on transport corridor projects financed by the Asian Development Bank, Japan International Cooperation Agency, and World Bank and links them to one important wider economic benefit—local economic activity. The meta data cover 47 projects in 16 countries, with appraisal dates between 1991 and 2007. First, the paper reviews the variation in project design and implementation—including the local initial conditions, complementary non-transport interventions, and private sector involvement. Second, using the difference-in-differences methodology, the paper links this variation to a measure of local economic activity—the geocoded intensity of nighttime lights. The effect of the supported corridor projects on local economic activity could be very heterogenous and significantly depend on certain initial conditions and project characteristics. The latter could include locations with access to the sea, as well as projects with a strong theory of change and better engagement of the private sector.

This paper is a product of the Office of the Chief Economist, South Asia Region, the Transport Global Practice, and the Finance, Competitiveness and Innovation 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://econ.worldbank.org. The authors may be contacted at malam5@worldbank.org, mdappe@worldbank.org, and mmelecky@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
i.e. the cassava belt) lead cassava production and gear much of their cassava production towards home consumption. These districts predominantly grow bitter cultivars that constitute an essential component of diets and that can be harvested, depending on the variety, within a 12- to 18-month period. The preference for bitter varieties is anchored in the desire to protect the crop against thieves and animals, and in the tradition to produce _kondowole –_ fermented cassava – from these cultivars (Kambewa and Nyembe, 2008). Elsewhere in Central and Southern Malawi, while the caloric contribution of cassava consumption is not nearly as high, sweeter cassava varieties are grown, over harvest periods of 6 to 12 months, predominantly for commercial purposes (sold either in raw form or as processed snack food) and/or as a last resort food security measure during the lean season or in the face of unforeseen shocks (Moyo et al., 2004). Typically, when non-cassava belt districts in southern Malawi rely on cassava as a staple food, they produce _makaka,_ a nonfermented cassava flour formed from dried cassava chips. The spatial differences in cultivated cassava varieties are also at the heart of spatial differences in production and yield (Kabambe, 2011).

> 6 Based on the authors’ calculations using the Integrated Household Panel Survey (IHPS) 2016 data.

5
20 Figure 8: Rural Africa derives a much larger share of income from agriculture, a similar share from non‐farm self‐employment, but less from wages (in and outside agriculture) <!-- Start of picture text --> 100% 1 3<br>7<br>90% 15<br>15<br>80%<br>14 Others<br>8<br>70%<br>5<br>Transfers<br>60%<br>21<br>Non‐farm self empl<br>50%<br>40% 13 Non‐farm wage income<br>30% 64 Ag wage income<br>20% On farm income<br>34<br>10%<br>0%<br>SSA ROW<br><!-- End of picture text --> Note: ROW=Rest of the World Source: Davis, Di Giuseppe, and Zezza, 2017.

Of course, there are differences across African countries, and within countries, partly driven by agro‐ ecological potential and market access, which are discussed in more detail by the authors. Yet the overall employment and income patterns discerned here correspond to the picture emerging from the macro‐ data. There has been structural transformation away from agriculture in SSA over the past two decades— the agricultural share in GDP fell from 23 percent in 1995 to 17 percent in 2013, and the share of agricultural employment likewise fell by an estimated 10 percentage points (Barrett et al., 2017). Yet, it has been towards self‐employment in (non‐tradable) services, not wage employment in tradable manufacturing (Rodrik, 2016). This is partly because of Africa’s commodity boom, which fueled the emergence of consumption cities, characterized by higher shares of imports and employment in non‐tradable services (Gollin, Jedwab, and Vollrath, 2016). Given their prevalence, this
true _R_<sup>2</sup> to a constrained version of the true _R_<sup>2</sup> that fixes the intercept and coefficient to zero and one, respectively. By constraining the _R_<sup>2</sup> in this manner, we remove its ability to adjust for systematic differences between the poverty estimates and the true poverty rates. Panel (b) of Figure 1 presents the results and we see that, as expected, the unconstrained _R_<sup>2</sup> overstates the model’s performance, albeit only to a small degree. We nevertheless find that even these small differences can lead to model selection issues. In particular, the constrained _R_<sup>2</sup> identifies the PSU level as the appropriate level of estimation for 355 of the 500 samples whereas the unconstrained version, as mentioned, selects the PSU level for 437 out of the 500 samples. The commonly used unconstrained _R_<sup>2</sup> then selects the incorrect model for 82 of the 500 samples.

Given the shortcomings of the _R_<sup>2</sup> , we have argued that the empirical MSE of the poverty estimate at the area level represents a better metric for validating the performance of a particular set of estimates. Panel (a) of Figure 2 thus presents our empirical MSE results for several alternative models. The first three models apply gradient boosting at the municipality level using three alternative covariate sets: geo-reference covariates (“GIS-MUN”), census-based covariates (“CEN-MUN”), and then a model with all available covariates (“ALL-MUN”). The fourth model applies gradient boosting at the PSU level and only uses census-based covariates (“CEN-PSU”).<sup>29</sup> The final three models are all traditional methods that we use to benchmark the performance of gradient boosting. We present unit-level (CensusEB), unit-context, and area level models. We consider three alternative implementations
terms “trust” and “confidence” and respondents’ definition of government (OECD, 2013). Cultural factors and societal norms influence how individuals define public institutions, making valid crossnational comparisons of trust difficult (OECD, 2013). These issues affect all trust-related, international surveys. However, this study takes advantage of the detailed information on individual-level food insecurity provided by the FIES. This information, combined with the globally standardized trust-related questions in the GWP, make it possible for the first time to examine the direct impact of food insecurity on both dimensions of trust around the world.

# **_2.4. Measuring Food Insecurity_**

FAO created the FIES to gather consistent and comprehensive information on the prevalence and severity of global food insecurity. The GWP/FAO FIES survey module contains eight questions designed to assess the adequacy of individuals’ access to food, adapted from the long-established United States Household Food Security Survey Module (US HFSSM) and the Latin American and Caribbean Food Security Scale (ELCSA). The FIES survey questions focus on respondents’ behaviors and experiences when they have encountered difficulties in meeting their basic food needs over the past 12 months. Similar to other food insecurity surveys, the FIES questions are ranked by severity. Each question specifies that food insecurity results from a lack of money or other resources to obtain adequate food (see Appendix, Table A1).

In this analysis, we use a binary measure of individuals’ severity of food insecurity. The measure, _food insecurity_ , is coded as one if an individual experienced moderate or severe food insecurity in the past 12 months; zero otherwise. This measure captures experiences ranging in severity from a reduction in food quality and variety to the reporting of physiological hunger (Nord et al., 2016; Cafiero et al., 2018).

# **_2.5. Control Variables_**

In line with the literature, we control for other determinants of trust that include individual and household characteristics. These include gender, age, and lifecycle
past and current union membership); we also look at perceived discrimination. This analysis sheds some light on the worker mobility between formal and informal sector, thus complementing country-specific studies (Packard, 2000; Bosch et al., 2007; Bosch and Maloney, 2010; Nikolovova et al., 2010; see also Le (1990, Section 4.1) for a survey of earlier studies). Section 3.3 compares household income of informal and formal employees.

# **3.1 Key personal characteristics and job profile**

**_Gender._** Figure 4 shows that in nine European countries (Latvia, Bulgaria, Poland, Estonia, Ukraine and Hungary in the East; Finland and Norway in the North; and UK in the West) proportion of males is much higher among the shadow sector employees than in the formal economy. The opposite situation is found in Sweden, Italy, Spain, and Switzerland. In the rest of the countries, shadow and formal employees do not differ substantially in terms of

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