rafmacalaba/data-use-mentions-extended huggingface dataset:
The method to estimate exposure proceeds as follows. First, we generate categorical hazard maps for several return periods indicating the areas exposed to events exceeding each intensity threshold. Second, since the hazard and population data come from different sources with varying resolutions, we resample the hazard maps to align grid cells with the GHS population grid using nearest-neighbor matching. For flood, we resample the 3-arcsecond flood map to align with the GHS population grid of the same 3- arcsecond resolution.<sup>8</sup> We count the population exposed to different levels of flooding at this high resolution and then aggregate to a 30-arcsecond grid (approximately 1 km) for overlaying with other hazards.<sup>9</sup> Hazard data for drought, heatwaves and cyclones uses a coarser spatial resolution than the flood maps (1 km, 10 km, and 11 km, respectively), also reflecting that these events are less localized. We resample each of these three hazard maps to the 30-arcsecond GHS population grid. Finally, we calculate the number of people exposed at grid level by overlaying the population grid. Figure 5 illustrates the approach to estimating the number of people exposed to each type of extreme event and to any.

In the following subsections we describe the hazard data used in more detail, and how the four hazards are overlaid.

# 3.1 FLOOD

We use flood data from Rentschler, Salhab, and Jafino (2022) which indicates the maximum inundation depth for a 100 year return period considering the three most common flood types: (1) Fluvial flooding, occurring when intense precipitation or snow melt causes rivers to overflow; (2) Pluvial flooding, occurring when rainwater builds up beyond the absorptive capacity of soil; and (3) Coastal flooding, caused by storm surges and high tides in coastal areas. Country-level pluvial and fluvial flood data are from the 2019 version of the Fathom Global 2.0 flood hazard dataset (Sampson et al., 2015). These were combined with a separate coastal
Policy Research Working Paper 9735

# **Abstract**

This study documents the labor market outcomes and timeuse patterns of women in urban Bangladesh. Using survey data collected in 2018 in low-income neighborhoods of Dhaka, the paper finds that women with children aged 0–5 years have lower likelihood of labor market participation, lower likelihood of working, and lower likelihood of being an earner, compared to women with no children and women with children aged 6 years or older. While this motherhood penalty affects all mothers, those who have young children but have no access to childcare support face the largest penalty. Time-use patterns confirm these findings, indicating that mothers of young children with no access to childcare spend less time on market work, more time on unpaid work, and less time on leisure or other activities. In addition, they are more likely to perform childcare as a secondary activity along with other paid and unpaid work, which may have implications for their productivity and the quality of care provided to children. The paper proposes entry points to ease the double burden of paid and unpaid care work on mothers in urban areas, where the availability and affordability of formal childcare services is low, and community-based or other informal care arrangements are not common.

This paper is a product of the Social Sustainability and Inclusion 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 etas@worldbank.org and tahmed9@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
**Regression results are presented in the Appendix and are centered on identifying correlates of downward mobility between 1989 and 2006.** The dependent variable takes a value of one if the individual reported moving up or staying in the same position, and zero if it reported downward mobility. We estimated probit regressions to facilitate the interpretation of the results; however, results were qualitatively equivalent under a multivariate regression on three types of movements. A movement is defined (as in section 3) as a movement in any direction of at least two steps, regardless of the initial point. Movements of only one step are considered as staying in the same step. We excluded households in the two lowest and uppermost steps in 2006 from the sample to avoid biases introduced by the mechanical effect of being unable to move in all possible directions (move down for the upper steps in 2006, and move up for lowest steps in 2006). All regressions and a thorough discussion of the methodology used are presented in the Appendices.

# **Perceptions of inequality, fairness and trust matter**

**Increases in country income inequality are associated with more negative perceptions of economic mobility.** Table A2.2 shows that household in countries in which inequality increased between 1989 and 2006 – measured by the ratio of the 80<sup>th</sup> /20<sup>th</sup> percentiles– had a higher probability of reporting downward movements in the income distribution (Column (3)). Using an alternative measure of Gini, obtained from the World Income Inequality Database shows also a negative effect of the increases in inequality with respect to the perception of movements up<sup>9</sup> (Column (4)). This alternative measure, though, is less consistent than the obtained from the World Development Indicators and is incomplete for most former Yugoslavia countries, so we preferred to use the original from the WDI for the rest of the report. As reported earlier when citing the
|125|162|110|
|Tajikistan|147|70|168|
|Ukraine|152|112|181|
|Uzbekistan|166|96|157|

Source: World Bank, 2012a Note: Economies are ranked on their ease of doing business, from 1 – 183. A high ranking on the ease of doing business index means the regulatory environment is more conducive to the starting and operation of a local firm. This table only shows the ECA countries within the international ranking. The rankings for all economies are benchmarked to June 2011.

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<!-- Start of picture text --> CZ EE UA SK RU LV BG HU PL RO SI DK FI NO SE CY IL GR PT ES IT BE IE DE UK AT FR NL CH<br>East North South West<br><!-- End of picture text --> <!-- Start of picture text --> CZ EE UA SK RU LV BG HU PL RO SI DK FI NO SE CY IL GR PT ES IT BE IE DE UK AT FR NL<br>East North South West<br>Employees with contracts current Employees with contracts past<br>Employees without contracts total Employees without contracts current<br>Informal self-employed total<br><!-- End of picture text -->

**Figure 16 Trade-union experience of formal and informal workers, by country. 2004-2009**

_Note:_ The sample size of employees without contracts is less than 30 for IT, SE and FI; between 50 and 90 for CZ, RO, SK, HU, EE, BE, FR, DE and CH; between 100 and 745 for other countries. _Source:_ Calculation with ESS data By contrast, in Hungary, Romania, Slovenia, Spain, Italy, Netherlands, and Switzerland proportions of informal employees with union experience are low both in absolute terms and in comparison with formal workers, suggesting substantial segmentation. It is more difficult to interpret situation in remaining Eastern European countries (the Czech R., Slovakia, Poland, Estonia, Latvia, Bulgaria, Ukraine, and Russia), where the proportions of informal workers (both employees and self-employed) with union experience are substantial but it is not clear whether this experience refers to the socialist era or to the market economy period.

# **3.3 Are informal workers poorer than the formal ones?**

Figure 17 compares perception of household financial situation by formal and informal employees. In both the Eastern and Southern Europe, the proportion of those seeing their
that a silver lining story in terms of reduction in congestion can be drawn after the relaxation of lockdown measures?

The focus of these studies has been only on a selected number of countries, mostly the United States and developed countries with the exception of few emerging countries. Using a global sample of 186 cities in 39 countries, this paper assesses how mobility and congestion were affected by the pandemic and lockdown measures, including public transport restrictions.

This paper proceeds as follows. Section 2 describes the data that we will subsequently use to provide a real-time estimation of mobility and congestion. Section 3 provides estimates of the impact of the pandemic and lockdown evolution on mobility and congestion. Section 4 concludes with a discussion on policy implications.

4
magnitude to the findings of Choe et al. (2005) for the Republic of Korea between 1996 and 1998. The comprehensiveness of our data set allows us to go beyond just the documentation of aggregate trading losses and make several contributions relative to prior research. We begin by documenting that the disadvantage of foreign investors in our sample is attributed to passivelymanaged foreign funds, that is, those whose strategy consist of replicating the return on an index by buying and holding all (or almost all) index stocks in the official index proportions. In addition to paying higher prices for stock purchases and receiving less when they sell, these funds display inferior risk-adjusted returns than foreign active funds. Our evidence suggests that managers of foreign passive funds are paying a price for a trading style that differs from the trading style of other investors. In particular, worst trades are on days when they are trying to accommodate large flows in similar proportions to their benchmark index by (i) trading multiple stocks in the same direction, (ii) buying (selling) the same stock multiple times, and (iii) making large trades at the end of the trading session.

In addition to comparing performance across foreign investors with different styles of management, we also compare the performance of foreign versus local investors by level of active management. We find that the yearly returns of passive foreign funds are between 1.19 to 1.90 percentage points smaller than domestic funds with similar passive strategies. This suggests that index investing by itself cannot explain the under-performance of foreign investors.

In order to understand outstanding characteristics which may be driving performance, we analyze the timing of flows across funds. The key finding is that daily net flows of passive funds are strongly correlated in the cross-section. Specifically, we show that managers of passive funds trade more intensively on days when other passive funds are trading in the same direction. Furthermore, the large imbalance of daily flows by foreign passive funds appears to be generating a significant price pressure on the stocks that they trade.
sources_ : Poverty rates are based on Tendulkar poverty lines updated from 2004/05 to 2009/10 using statespecific consumer price indices for agricultural laborers (CPIAL) and per capita consumption expenditures in Schedule 1.0. Poverty rates for J&K and Uttarakhand not reported because data not available on state-level CPIAL. Female share of person days from MGNREGS administrative data (http:\\nrega.nic.in). Remaining columns from authors‘ calculations from unit record data of 2009/10 National Sample Survey Schedule 1 (for headcount rate) and Schedule 10.

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**1 Introduction**

During the 2007/08 global food crisis, the world average price of staples more than doubled; perhaps one of the most serious consequences of the crisis was its disproportionate effect on the world’s most vulnerable populations. For those who spend the majority of their budgets on food, the crisis led to a severe erosion of purchasing power, pushing households into poverty and crippling the already poor. Poor households cope with negative shocks in many ways, for example, by relying on family assistance, selling off assets, borrowing money, or – in the most desperate cases – reducing intake of food and nutrients. Such coping mechanisms can have both short and long-run consequences for a family’s wellbeing and livelihood.

This paper investigates the impact of the global spike in food prices on household welfare in Afghanistan – one of the world’s most food-insecure countries.<sup>1</sup> The analysis in this paper provides one of the very few empirical assessments of the effects of the global food crisis based on data collected prior to and during the crisis and using direct measures of household consumption; further, it is the first analysis on this topic from a conflict country, Afghanistan. It is rare to have high-quality household data from conflict countries, where many of the world’s most vulnerable live, and even rarer to have such data collected during a largely unanticipated shock. In countries like Afghanistan, shocks like the food crisis can compound the deleterious effects of years of conflict, drought, and economic hardships.

The estimates provided in this paper are based on direct measures of food security and food-based coping strategies, e.g., moving to cheaper foods, based on observed household behavior prior to and after the spike in food prices. Most studies on the global food crisis use pre-crisis national household survey data to simulate the short-run effects of price increases on poverty. See for example, Robles and Torero (forthcoming) who examine simulated results from four Latin American countries,
/sup>

Given estimates _σ_ ˆ _Ln_<sup>2ofthedispersionparameters,wecanthenestimatethescaleparameters</sup> _μn_ so as to match the average landholding size in each district. Specifically, the properties of the log-normal distribution imply that the average landholding size in district _n_ is exp( _μn_ + _σLn_<sup>2</sup><sup>_/_2), so</sup> we set the empirical analogue of this expression equal to the average landholding size observed in the data and rearrange terms to obtain our estimator of _μn_ : where _Ln_ is total arable land (in ha) and _Nn_<sup>_F_is the number of farmers in district</sup><sup>_n_.</sup>

# **C.D Evolution of Rice and Tea Agriculture over Time**

We combine cutting-edge remote sensing data (described in Appendix A.C.2) with detailed weather data (described in Appendix A.J) to show the evolution of yields of rice and tea (and, for rice, of cultivated area) over time.
of these three indexes and the remaining eighty percent of the IEF index is calculated. The table in this box represents how the index values were assigned for an economy's degree of openness. To summarize, the countries with a value between the mean and the median for each of the three indicators is assigned an index value of 3. Those countries with indicator values between zero and half the distance to the median (which happens to be the smallest between the latter and the mean) are assigned a value of 5. From there to the median a value of 4 is given to the complying countries. The value of 2 is assigned to countries that are in between the mean value and the upper bound reported in the table below. The index value of one is reserved to those countries which register the worst openness indicators.

## **Intervals Used to Assign Index Values to Openness Indicators.**

|Value<br>Assigned|Average Share<br>of Tariffs|Average Share of Industrial<br>Output Subject to QRs|Premiums in the<br>Exchange Rate Market|
|---|---|---|---|
|5|x < 0.065|x < 0.04|x S 0.05|
|4|0.065•x<0.13|0.04•x<0.1|0.05sx<0.1|
|3|0.135x<0.17|0.1Sx<0.21|0.1Sx<0.25|
|2|0.17Sx<0.7|0.21 sx<0.5|0.25sx<0.5|
|1|0.7Sx|0.5Sx|0.5S•x|
|Minimum Value|0.000|0.000|0.000|
|Median|0.133|0.103|0.115|
|
for boys, cash given to mothers decreases illness episodes and outperforms CTFs. For girls, while CTMs and CTFs are not statistically different from each other, only CTFs lead to a significant reduction. For boys, both CCTs and UCTs lead to significant reductions in morbidity, but for girls only CCTs achieve such impact and the difference between both types of transfers is significant. At round 3, for boys, CTMs and CTFs have a similar impact in reducing illnesses, while for girls only transfers given to fathers lead to a reduction. For males, only CCTs reduce illnesses episodes and they outperform UCTs, while for girls only UCTs lead to a significant reduction (but the difference with CCTs is not statistically

> 24 For pre-school age children in Sub-Saharan Africa, the empirical evidence indicates that while malnutrition is widespread, girls are on average better off than boys. See Svedberg (1990) for an earlier analysis and Wamani et al. (2007) for a more recent analysis using DHS data. A similar pattern was found in Burkina Faso in an impact evaluation of school feeding programs (Kazianga, de Walque and Alderman 2014).

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weighted poverty lines, which is evidence of the existing heterogeneity in terms of the size of the subnational population and the number of poverty lines by country.

The last two rows show the mean and median values of the currently-used $2.5 and $4 per person per day in 2005 PPP poverty lines –reported in Gasparini, Cicowiez, and Escudero (2013) – updated to 2011 PPPs based on CPI data of each country. The extreme poverty lines are approximately $3.3 per person per day on average, while the moderate poverty lines are approximately $6.4 per person per day on average. These numbers are different in some cases from those reported in the first four rows of the table. However, there are several issues regarding these currently-used poverty lines converted to 2011 PPPs. First, Jolliffe and Prydz (2015) show that poverty lines updated using CPI information are sensitive to the inflation data used to update the lines. For instance, the authors find that the global extreme poverty line is $1.70 per person per day when updated to 2011 values using official CPIs from the World Bank’s WDI database, whereas it increases to $1.82 when updated using inflation data from PovcalNet. Second, the $2.5 and $4 per person per day poverty lines are based on official poverty lines from main cities and, therefore, are estimated from a smaller sample of official poverty lines which can significantly affect poverty line estimates (Jolliffe and Prydz 2015 and Ferreira et al. 2015). In addition, since these poverty lines are based on information from major cities, they are likely to be higher than the poverty lines based on a larger sample of subnational poverty lines that include smaller cities. Third, Brazil, Haiti, and the Dominican Republic are excluded from the estimates, which is also likely to affect the estimates as Brazil is the largest economy in LAC and Haiti is the poorest country. Finally, the limited available information on the collection of the underlying
Mozambique, and Comoros have a similar discrepancy. Despite these differences, of the 27 countries which have data from both sources, all but three show the same sign for the difference between the enrollment of girls and of boys (the exceptions are Bangladesh, Indonesia, and Zimbabwe where the difference is close to zero in any case).

Another comparison one can make on the basis of these data is that to the stock of education as reported by Barro and Lee (1993) which has been used in numerous papers to investigate the determinants of growth. Table 4 reports the average years of schooling of the female population over 15 from the DHS data as well as the average years of schooling of the population over 15 based on the Barro-Lee (BL) data. Here the DHS imply a stock of schooling that is slightly higher than that in the alternative data source: the mean of the average years of schooling among women 15 and older across all the Countries is 3.7 in the DHS data and 2.6 in the BL data. A possible explanation for this is that the DHS are from a period spanning 1990 to 1998 whereas the BL data are an estimate for 1990. T he discrepancy for some countries is substantial ranging from a high of 3.2 years in (Zimbabwe where the DHS imply an average of _5.9_ years and the BL where the average is 2.7 years) **to** -1. 1 (Pakistan where the DHS imply 1.8 years and BL estimate 2.8).

Focusing on the male/female ratio in the stock of education, the DHS tend to imply a lower degree of male advantage. The cross-country average male/female ratio from the DHS is 1.58 whereas that in BL is 1.73. Again, this would be true if male advantage were declining over time and the DHS were capturing a later period. In some countries the discrepancy is especially large, for example in Ghana BL imply that men have
abroad. Many policymakers also believe that potential migrants, knowingly or unknowingly, are trading off risks at unreasonably low prices to the extent that their experience is often termed exploitative.<sup>2</sup> Put together, these notions suggest that the observed rate of migration is higher than is optimal and that accurate information would lower the level.

Academic studies, on the other hand, find migration to be profitable and hugely beneficial for the marginal migrant and his or her family (see Bryan, Chowdhury, and Mobarak, 2014; McKenzie, Stillman, and Gibson, 2010, for a few examples). These studies suggest that the level of migration is suboptimal and that increased migration would be welfare improving. If anything, potential migrants’ beliefs about earnings and risks are pessimistic, which suppresses migration (as briefly suggested in Bryan, Chowdhury, and Mobarak, 2014). Alternatively, many academic studies assume that individuals are fully informed and have rational expectations about the conditions at their destinations, and attribute low levels of migration to high costs, monetary and otherwise (see Kennan and Walker, 2011; Morten and Oliveira, 2014; Morten, 2013; Shenoy, 2015, for example). This literature argues that the costs, most of which are fixed, keep migration sub-optimally low and give rise to a large spatial disparity in earnings.

In this paper, I investigate whether misinformation causes suboptimal levels of migration in the context of the migration of Nepali workers to Malaysia and the Persian Gulf countries. Given the concerns on the part of policymakers, I focus on how potential migrants’ beliefs on earnings and the mortality rate abroad, and the tradeoff between these two factors – the value of a statistical life (VSL) – affect migration.

Using data collected for this study, I find that potential migrants are indeed misinformed about potential earnings and mortality risk, but not always in a way that policymakers expect. Consistent with the widely held notions described above, inexperienced potential migrants, meaning those who have never
that liquidity constraints matter significantly for purchase decisions, and also inconsistent with a view that households simply have little interest in insurance against rainfall risk.

Finally, in the Andhra Pradesh sample, a common response to the 2004 survey is “do not understand the product.” The fraction of households citing this reason falls from 21% in 2004 to 2% in 2006, suggesting that households have learned about the policy over time.

# **VI. Improving household risk management: tentative lessons and conclusions**

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