rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1. Introduction**

In parts of Asia, South Caucasus, and the Balkans, son preference is strong enough to trigger substantial levels of prenatal and postnatal sex selection, result in the excess mortality of girls, and skew child sex ratios in favor of boys (see Figure 1). Every year, an estimated 1.8 million girls under the age of five go “missing” because of sex selective practices widely adopted in these regions (The World Bank 2012).<sup>3</sup> This is almost as if the entire female population of Los Angeles, California, vanishes annually.

Figure 1: Child sex ratios for select regions and countries, 2010-15 <!-- Start of picture text --> SUB-SAHARAN AFRICA<br>NORTHERN AMERICA<br>EUROPE<br>Pakistan<br>Georgia<br>India<br>Vietnam<br>Armenia<br>Azerbaijan<br>China<br>1.00 1.02 1.04 1.06 1.08 1.10 1.12 1.14 1.16<br><!-- End of picture text --> Notes: Child sex ratio: boys/girls, < 5 years of age Figure 1 displays child sex ratios for countries where the ratio is greater than 1.06. 1.06 is used as a cutoff to restrict this figure to countries where the ratio of boys to girls is strikingly skewed. Normal child sex ratios, i.e. when there is no prenatal and postnatal sex selection, would be 1.00 or close to 1.00. Normal sex ratios at birth (SRB), i.e. when there is no prenatal sex selection, would be 1.06.

South Korea is not included here because it has seen a turnaround in its' sex ratio since the mid-1990s Source: United Nations (2017) While excess girl mortality has been documented in countries like China; Taiwan
# **Figure 9: Number of births per woman and per year, by year and birth cohort. From birth histories in the Cambodia Demographic and Health Survey 2000**

<!-- Start of picture text --> 0.35<br>0.3<br>0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>19701971197219731975197619771978197919801981198219831984198519861987198819891990199119921993199419951996199719981999<br>Average number of births per woman and per year<br><!-- End of picture text -->

**Year**

Women born 50-54 Women born 55-59 29
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relatively low.<sup>7</sup> There must be significant economic potential to explore business opportunities in the regional and global markets. To support such potential, port accessibility is critical.

33. One unexpected result may be that the coefficient of labor costs turned out positive, meaning that firms prefer to be located where labor costs are high, not low. There seems to be high correlation between wages and city population. Local wages tend to be higher in more urban areas where more people live, partly because they may be more educated and have higher skills. To avoid this problem, the secondary school enrollment rate is used to control for possible heterogeneity in labor quality. It is found that firms prefer to have access to the quality labor market, regardless of potentially high wages. The coefficient is estimated at 0.012. When both factors are incorporated, the last column model shows that higher school enrollment has a positive impact to attract firms, while the coefficient of local wages is still positive. It can be interpreted to mean that labor quality is more important than wages. Firms may prefer to pay high wages if the quality of labor is also high.

**<u>Table 7. Conditional logit estimation: Firm location</u>**

||Coef.|Std.Err.|Coef.|Std.Err.|Coef.|Std.Err.|
|---|---|---|---|---|---|---|
|ln No. of existing firms|0.451|(0.060) ***|0.137|(0.052) ***|0.091|(0.051) *|
|Road density|0.045|(0.003) ***|0.063|(0.004) ***|0.063|(0
municipal variables, performs the best by between 1.5 and 23 points. The second-most accurate model is the one that include state dummies and municipal variables, suggesting that the AGEBlevel variables, conditional on the municipal level variables, is adding only a modest amount of accuracy in this case. In this context, including dummies at the level at which the sample is representative, as well as area and sub-area level variables notably improves out-of-sample predictive performance.

## _6.6 Design-based simulation_

Finally, we also present results from a design-based simulation in Appendix E. Since we use a single sample from 2014, one possible concern is that the results presented here may not hold in other samples. While the results when using the 2016 sample suggest that the main results are robust, we nonetheless present additional results using repeated sampling from the intercensus. For this exercise, because AGEB level identifiers are not available in the intercensus, we use auxiliary data aggregated to the municipal level. The overall results presented in Appendix E are consistent with the findings and conclusions from the 2014 and 2016 surveys. In particular, they indicate that the household level model provides equally accurate estimates in-sample, and significantly more accurate estimates out of sample. In particular, for out-of-sample municipalities, the average correlation with truth in the simulations was 0.80 out-of-sample for the unit-context model, as opposed to 0.75 for the area-level model.

# 7. Conclusion

This paper examined the ability of methods combining survey and satellite data to generate more accurate estimates of monetary poverty in Mexican municipalities. Mexico is a valuable case study to evaluate small area estimation methodology because official estimates, generated using a bestpractice unit-level model with household-level auxiliary data from the 2015 intercensus, provide a solid benchmark for comparison. Unfortunately, these types of comprehensive intercensal surveys are rarely available in developing countries, which often face challenges fielding censuses every ten years. In addition, CONEVAL produces and makes publicly available estimates generated using the 2010 census, which provides a useful point of comparison.

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000)|(0.127)|(0.141)|(0.152)||
|||[1.000]|[1.000]|[1.000]||

_Notes:_ Treatment effects are estimated using ANCOVA, controlling for the community-level average of the outcome family index at baseline and matching-triplet fixed effects. Robust standard errors are shown in parentheses. Multiple-inference corrected q- values that adjust for the false discovery rate within treatment arm are shown in square brackets. The Clinic service provision index is composed of measures on facility maintenance (mainly cleanliness, orderly medicine storage and signposting) and whether required services like pre- and post-natal care, immunization, reproductive health and other forms of consultation are provided. The employee satisfaction index consists of employees’ satisfaction with their job, with the communities’ participation in the clinic and the extend to which they feel supported by the communities. Significance: * is significant at the 10% level, ** is significant at the 5% level and *** is significant at the 1% level.

A18
12 reported above? To address this question, we ran another partial liberalization scenario in which we:

- remove all cotton export subsidies globally,

- remove tariffs on imports by all high-income countries (HICs) of cotton from pertinent UN-defined least-developed countries (LDCs, comprising South Asia excluding India, Pakistan and Sri Lanka plus Sub-Saharan Africa excluding Nigeria and the Southern African Customs Union in terms of our regions),<sup>7</sup> and

- reduce by one-third all applied cotton production subsidies in all high-income countries (not just in the US as in the previous partial reform scenario).<sup>8</sup>

# **_Comparison of the two partial-reform scenarios with the full-liberalization results_**

Impacts of this Doha partial reform simulation and the US-only partial reform simulation on regional welfare and on cotton net farm income are reported in Table 4 and in Figures 1 and 2, and the effects on cotton output and exports are shown in Table 5, from which several points are worth stressing.

First, the US-only reform would provide virtually all of the net benefits to the US economy that are generated by the Doha scenario, but only around three-fifths of the estimated net welfare and net cotton income effects, and two-fifths of the export effects,

> 7 For the list of LDCs, see <u>http://www.un.org/special-rep/ohrlls/ldc/list.htm. Due to regional</u> aggregations in the GTAP dataset we use, our Sub-Saharan African group has some non-LDCs (including Zimbabwe, although it – like the three poorest Central Asian nations – probably now qualifies as an LDC) while LDCs in other regions are not so classified because they are too small a part of ‘rest of region x’ categories.

> 8 There may be also some reduction in bound cotton tariffs as a result of the non-agricultural market access negotiations, but we ignore that by assuming applied tariffs are sufficiently below bound rates (‘binding overhang’) for the
qualifications more than twice those for lower qualifications), Thailand (largest premiums for university qualifications), and China (highest premiums observed for university and upper secondary vocational qualifications). These findings are of interest given similar recent findings, even from African countries. Existing literature based on empirical evidence from a large number of studies in both developed and developing countries implies an empirical tendency for private internal rates of return to education to decrease at higher levels of education. This was first observed by Becker (1964) for the United States (for other countries, see Psacharopoulos and Woodhall 1985; Psacharopoulos and Patrinos 2004). However, along with the frequently discussed evidence of an increase in the returns to college educated workers in the United States (see, for example, Katz and Autor 1999; Acemoglu 2002; Lemieux 2006c) and evidence of the same for a host of countries, such as Brazil; Chile; Colombia; Mexico; Korea; Taiwan, China; Indonesia; Thailand; and China (Topel 1999; Schultz 2004). Schultz (2004) finds that evidence from recent representative surveys in Africa confirms the same regularity. It was found that in six African countries (Ghana, Cote d’Ivoire, Kenya, South Africa, Nigeria and Burkina Faso) the wage gains associated with each year of higher education are relatively attractive by world standards at between 10 and 15 percent. Schultz (2004) finds it ironic that world leaders of higher education are reluctant to consider wage structures for policy purposes because they think that wage returns to higher education are low and that large returns to education are probably evidence of distorted labor markets.

# **4.3 Interpreting the quality of schooling link – PISA and dispersion**

One possible contributing factor to the observed pattern of returns is the interaction between ability and schooling, which results in an amplification of the impact of ability upon earnings. Another possible explanation has to do with school quality differences. In particular, it may be 28
# **1. Introduction**

This paper presents and discusses a new deposit insurance database that updates an earlier one constructed in 1999 by Demirgüç-Kunt and Sobaci (2001) and extends it in several important dimensions.

This new comprehensive database includes 14 new countries that have adopted deposit insurance schemes since 1999<sup>1</sup> and identifies 12 other countries<sup>2</sup> that had adopted deposit insurance as of 1999 but do not appear in Demirgüç-Kunt and Sobaci (2001) due to lack of data. Apart from the use of various country sources, we have carried out surveys directed to officials of deposit insurance institutions, central banks, and related government officials around the world. The other important contribution of this dataset is the addition of historical time series (rather than data for year-end 1999 only) for several key variables, including deposit insurance coverage, coverage ratios, and co-insurance. The variables are also expanded to include the level of co-insurance requirements, percentage of the value of deposit covered, and whether the payments are per depositor or per depositor per account. Finally, the dataset incorporates part of the survey data relevant for deposit insurance provided by Barth, Caprio and Levine (2004).

Deposit insurance has become an increasingly used tool by governments in an effort to ensure the stability of banking systems and protect bank depositors from incurring large losses due to bank failures. Almost all countries actually have financial safety nets in place which include explicit and implicit deposit insurance, bank regulation and supervision, central bank lender of last resort facilities, and bank insolvency resolution procedures. Although deposit insurance is gaining in popularity among policymakers, its desirability is debated by many economists who point to the moral hazard problems involved and the accompanying excessive risk taking by banks (see, for example, Demirgüç-Kunt and Kane 2002).

This paper aims to support the recently growing empirical literature that deals with the effects of deposit insurance design on different banking outcomes (for example,

> 1 The new adopters are Albania (
.017)|(0.017)|(0.068)|
|Participattion in civic organisation|0.107|0.154|0.047|0.060*|0.193*|
||(0.012)|(0.036)|(0.034)|(0.034)|(0.111)|
|N|1,652|264|1,916|1,916|1,916|

**Notes** This table shows the average attitudes and behaviors of second-generation refugees and Greek natives. In the fourth column, the table reports regression estimates of the refugee-native gap after controlling for age and gender dummies. The fifth column reports the same coefficient using the standardized outcome (Z-score) instead. Standard errors in parentheses. *** _p <_ 0.01, ** _p <_ 0.05, * _p <_ 0.1; Source: Author’s elaboration on the European Social Survey (ESS).

30
food (Figure 5b). In Ethiopia, with the lowest access to adequate food security, only 12 percent of the children met the criteria for adequate food security. As figure 5a above clearly indicates, this is primarily due to the very low prevalence of adequate dietary diversity (13 percent). Bolivia and Indonesia were the only countries where more than half of the children had access to adequate food security, at 51 and 68 percent, respectively. However, in Indonesia the dietary diversity was not measured for each child, and the measure is based exclusively on the dietary diversity at the household level so it is not comparable with the other studies. Depending on how different the child’s diet is from the general diet of the family, the true prevalence of adequate food security may be much smaller.

In most countries, the improvements in access to adequate food security (as defined above) have been modest (Cambodia, Peru and Zimbabwe) to none (Ethiopia, Nepal) in the 5 to 10 years between surveys. The decrease in Bolivia in access to adequate food security is due to the change in the definition. In 2003 no information was collected on meal frequencies and thus the minimum acceptable diet variable could not be constructed. In 2008 the information was available and it was included as part of the food adequacy measure, thus making the 2008 definition a more comprehensive one than the 2003 measure. Furthermore, based on the Helen Keller data, in Bangladesh the access to adequate food halved between 2010 and 2011. Comparing the second 23
1997, indigenous-monolingual children have higher child labor incidence in all age groups except 16. Bilingual children have the lowest child labor incidence in all groups in 1997. The indigenous-monolingual group has a higher incidence for children between 8 and 12 years. An increasing child labor with age pattern is still noticed when we separate children in the treatment and control groups (see Table 3).

> 18 An “indigenous-monolingual population” is a location where more than 30 percent of households report a household head that is indigenous-monolingual.

19
Policy Research Working Paper 6373

# **Abstract**

The Living Standards Measurement Study—Integrated Surveys on Agriculture project collects agricultural and livelihood data in seven countries in Sub-Saharan Africa. In order to maintain representativeness as much as possible over multiple rounds of data collection, a subsample of households are selected to have members that have left the household tracked and interviewed in their new location with their new household members. Since the sub-sampling occurs at the level of the household but tracking occurs at the level of the individual, a number of issues arise with the correct calculation for the sub-sampling and attrition corrections. This paper is based on the panel weight calculations for the initial rounds of the Integrated Surveys on Agriculture surveys in Uganda and Tanzania, and describes the methodology used for calculating the weight components related to sub-sampling, tracking, and attrition, as well as the criteria used for trimming and post-stratification. It also addresses complications resulting from members previously classified as having attrited from the sample returning in later rounds.

This paper is a product of the Poverty and Inequality Team, Development Research Group. 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 author may be contacted at khimelein@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 organizations, or those of the Executive Directors of the World Bank or the governments they represent.
official records were filled through desk research. Finally, a team of enumerators were deployed to map the actual routes (and capture other attributes like frequency, fares, etc.) using a mobile based application. The building blocks of the field work were public transit stations. Enumerators then conducted station surveys to identify all routes departing from a specific

> 10 In the MNA region social and cultural norms might lead to different results than what had been observed in developed countries

> 11 Note that metropolitan areas include both the city municipality and its surrounding important areas.

> 12 Route-based public transport modes are defined as modes whose routes have a fixed origin and destination, as well as travel through key defined landmarks along the route.

6
Low responsiveness of poverty reduction to growth in India is a result of two persistent factors: low elasticity of household consumption (from NSS) growth to GDP growth, and growth in household consumption that is insufficiently pro‐poor. The first one is a recurring feature in India, where growth in GDP (and private consumption) in national accounts (NAS) has consistently surpassed that in household consumption from surveys so that the gap between the two series has widened over time. The second factor, that growth in household consumption is not pro‐poor, is reflected by what is seen for the shared prosperity indicator earlier.

Both these factors have shown significant improvements from the past, which resulted in the elasticity of poverty reduction to GDP growth more than tripling from the period 1994‐2005 to 2005‐ 12.<sup>33</sup> Even _within_ the second period, there was an improving trend, with the last two years (2010‐12) showing a much higher elasticity due to a reversal in both the factors: the gap between NAS consumption growth and household consumption growth narrowed and household consumption growth itself became more pro‐poor. If these changes represent a trend and not an aberration, one would expect poverty reduction to become more responsive to GDP growth in the coming years, with encouraging prospects for the achievement of the global goal of “ending” extreme poverty by 2030. The rise in the growth elasticity of poverty in 2005‐12 compared to the previous decade was also accompanied by fundamental shifts in how the composition of growth – between urban and rural and different sectors – affected rural and urban poverty.<sup>34</sup>

# 7. Conclusion

In concluding, it is useful to recap the highlights of the findings. Compared to the 1994 to 2005 period, poverty reduction accelerated during 2005‐2012, falling from 37 percent nationally in 2005 to 22 percent in 2012. The annual rate of reduction was 2.2 percentage points during 2005‐2012, compared to
context, CDR data have been proven to allow planners, academic and government stakeholders to get a deep insight in mobility patterns and even extract information on demographic dynamics such as gender differentiated mobility [15]. Despite the clear benefits of using CDR to understand urban mobility, urban planning agencies are behind academics and proof of context, and there is almost no example of government agencies taking CDR in their routine planning activities. Just recently, some European agencies, such as the Transport Metropolitan Agency in Barcelona [16], have started to incorporate mobile phone data analytics in their efforts to track effectiveness of mobility restriction policy measures in the fight against COVID-19.

Other examples of the use of mobile phone data in Africa for transport related projects include its use to collect and digitalize data on urban transport routes, such as mapping matatus in Kenya or Jitney Network in Accra [17-18]. In Freetown, we collected urban transport routing information using a similar methodology as in Kenya and Accra, compiling the first transit data set ever collected in that city [19].

# **BACKGROUND**

This study is part of an ongoing WBG lending operation to the Government of Sierra Leone, the Sierra Leone Integrated and Resilient Urban Mobility Project (IRUMP) that aims to improve, in Freetown, the quality of public transport, road safety, and the resilience of transport infrastructure to climate and weather hazards. As part of the project conceptualization and design, our team used mobile data to generate, in a low-cost way, a set of indicators required for various planning aspects of the project. These transport indicators include OD matrices and miscellaneous, individual- based mobility characteristics. Although the goal of this study was not to support the Government of Sierra Leone in COVID-19 mitigation efforts, the lessons learned are directly applied to such efforts.

The overall goal of this study is to generate information about mobility patterns in Freetown and assess the impact that weather-related events have on the city’s mobility. In particular, the project set out to achieve the