rafmacalaba/data-use-mentions-extended huggingface dataset:
, and sovereign debt crisis (Leaven and Valencia 2018). Sample is restricted to the observations where labor productivity growth data exist for the period 19602018. For each country-year pair, the episode dummy of a specific type of event is 1 if the event occurs at least once ( _≥_ 1), 0 otherwise. The total number of episodes (in bold) for each group of events (all financials, all disasters, all wars) may include events that occur simultaneously. The events are defined in **Annex A** . AEs = advanced economies, EMDEs = emerging markets and developing economies (including low income countries), LICs = low income countries.

# **2.1.2 Stylized facts for natural disasters**

**Increase in frequency of climate and other natural disasters.** Climate disasters accounted for around 70 percent of natural disasters during 1960-2018, occurring twice as often as biological and geophysical disasters combined ( **Figure 2** ). From 1960-79 to 2000-18, there was a large increase in the number of natural disaster episodes.<sup>10</sup> Increases occurred in all three categories, but most

> 9The overall impact of a natural disaster depends partly on initial economic conditions. A disaster may be more economically damaging in periods of high employment and capacity utilization because the increase in output needed for reconstruction may not be feasible, and the increase in demand generated may induce inflation. By contrast, a disaster that occurs when the economy is depressed may cause less economic damage as the stimulus effect of reconstruction will activate unused resources (Cuaresma et al., 2008; Hallegatte and Vogt-Schilb, 2019; Skidmore and Toya, 2002).

> 10To some degree, the increase in the number of recorded events may reflect improved measurement of natural disasters, particularly for small events.

7
evidenced in Table 2 Model 1 (β = 0.38, p < 0.001). Additionally, these data showed no significant interactions between ECE enrollment and child gender (see Figure 3).

# **Discussion**

The presence and patterns of gender disparities across domains in the early years of life have not been well explored in Pakistan. This study contributes to the knowledge base not only by exploring differences in distributions of risk and protective factors and outcomes by child gender, but also by investigating how a child’s gender may moderate differential outcomes in the early years. Our findings indicate that distributions of ECD outcomes and risk or protective factors were similar for both boys and girls. Equally low levels of psychosocial stimulation for boys and girls support findings from prior work that overall levels of minimum adequate stimulation in South Asian populations are low (Cuartas et al., 2020). Levels may be particularly low in the present sample, given it comprises about 80% father caregivers, who on average provide lower levels of stimulation for children relative to other caregivers in the region (Cuartas et al., 2020). Average levels of parental distress were also similar by child gender, though under-reporting of potentially sensitive topics should be considered. Overall ECE enrollment rates in our sample were higher compared to the 2019-2020 PSLM survey (35% vs. 19%, respectively). Average enrollment rates were also slightly higher in our sample for girls than boys (two percentage point gap), though this was not statistically significant. The differences in the two survey results may be attributed to requirements for phone access for participation in the current survey. This approach systematically excludes the population in Pakistan that does not have a mobile phone (6%), typically those in the lowest wealth brackets (Pakistan Bureau of Statistics, 2021). Additionally, PSLM data were collected in 2019-2020 while the present data were gathered in late 2021/early 2022.

With respect to the second research question, we found some variables had
I

# **I. Introduction**

In its latest World Health Report (WHR) _Health Systems: Improving Performance,_ the World Health Organization [1] breaks new ground not only by focusing attention on the performnance of health systems, but also by arguing that a key dimension of a health system's performance is the fairness of its financing system. In addition to discussing the ways policymakers can improve fairness in health financing, the report proposes an index of fairness, discusses how it should be operationalized, and goes on to present a league table of the world's 191 countries ranked by the fairness with which their health services are financed. Apparently, the report has been the subject of a good deal of comment-favorable and unfavorable, it seems-but for the most part this appears to have been amongst journalists and policymakers. Critical assessments by researchers of the report's methods and results have been limited to date. Such assessments are, however, clearly important, given the potential impact the report and its league tables may have on policymakers and the international development community.

The aim of this paper is to provide an assessment of the report's quantitative work on the fairness of health care financing. Regrettably, the focus of the paper has been confined to the properties of WHO's fairness index, since no details are available about the empirical work on financing presented in the report.' The only thing that can be said with any certainty is that although values of the WHO fairness index are presented for 191 countries, _in only 19 of these was the index computed from household survey data._ Indeed, in all but one of the countries ranked in the top 40 (Colombia) the FFC index was "estimated". Whilst it is not unheard of to interpolate "missing" data in this way, it is surprising that no documentation is available setting out the methods used. Perhaps even more surprising is that WHO should attach so much importance to the values of the index for the 172
show that 39.1 percent of Nigerians lived below the international poverty line of US$1.90 per person per day, corresponding to 78.5 million people (Castaneda et al., 2020).<sup>2</sup> While the 2018/19 estimates provide a crucial “snapshot” of the state of poverty just before the COVID-19 crisis, they do not provide information on poverty _trends_ in Nigeria. Prior to the 2018/19 NLSS, the last official poverty estimates for Nigeria came from the 2009/10 Harmonized Nigerian Living Standards Survey (HNLSS), but given crucial differences in the methodology used to collect

> 2 Poverty measured at the US$1.90 poverty line using World Development Indicators for population data (available at <u>https://data.worldbank.org/indicator/SP.POP.TOTL?locations=NG). This estimate differs from the population</u> estimate used by the Nigerian NBS (see Data section and Table 16 for a comparison). Poverty headcount rates are expressed at the international poverty line of US$1.90 per person per day unless otherwise stated.

2
The remainder of this paper is organized as follows. Section 2 provides an overview of the spread of COVID-19 and the government measures in Kenya. Next Section 3 describes the dataset used in this paper and discusses the empirical approach. Section 4 presents empirical results on the gender differences in household coping strategies, including the main results, potential reasons for the difference, as well as sensitivity analyses. Finally, Section 5 concludes.

# **2 Background**

In Kenya, it is estimated that about one-third of the population (20 million people) live under the poverty line (The World Bank, 2021 _b_ ). The COVID-19 pandemic reached Kenya in early 2020, with the first case reported on March 13, 2020. Since then, there have been more than 210,000 confirmed cases, with 3,595 deaths as of June 29, 2021.

The Kenyan government adopted several containment measures following the outbreak of COVID-19. These included promotion of social distancing practices, restrictions on curfews and limits on public gatherings, night public transport passenger capacities. Schools were closed from March 15, 2020, and the re-opening was done in phases, with schools fully re-opening on January 4, 2021. Another lockdown and renewed school closures were put in place in late March 2021. Based on the Oxford Stringency Index (Hale et al., 2021), a composite measure of the severity of policy response in nine areas, Kenya has consistently had more stringent policies in place than other countries in SubSaharan Africa.

# **3 Data and Empirical Strategy**

We use the first five waves of the data from a rapid response phone survey conducted with Kenyan households by the World Bank from May 2020 to June 2021 (The World Bank, 2021 _a_ ). It includes samples of Kenyan nationals as well as refugees. The Kenyan national sample was drawn from two sources: (i) all households that were part of the 2015/16 Kenya Integrated Household Budget Survey Computer Assisted Personal Interview pilot and provided a phone number,
https://www.mahkamahagung.go.id/id/berita/2905/ketua-ma-hakim-perempuan-indonesia-memiliki-peran-penting- <u>dalam-pembangunan-hukum-nasional).</u>

> 25 Susenas, 2019.

> 26 Repetition rate is computed from student repeaters divided by total student last academic year. Source: Primary School Statistics 2017/18 and 2018/19.

> 27 Susenas 2018 MSBP.

13
|densely-populated Indonesia. In ourview, the more likely explanation isthatthe areas populated by many<br>3.4 The Global Distribution of Ants<br>In 2002, Clarkand May identified a severetaxonomic bias in conservation research. Vertebrates accounted<br>and techniques (including alphahull, the algorithm we employ) to estimate range maps.<br>In our GBIF<br>occurrence maps database, we identify 5,445 ant species that are also mapped by Kass. We rasterize both<br>sets of maps using a global grid with 0.05 degree (5 km) resolution and compute species densities by<br>cellwise addition across 5,445 rasters for each set.<br>are notable. Both maps identify a large high-ranking region in the Western Hemisphere, but it is further<br>north for GBIF than for Kass. Both maps identify a band of relatively high ranks across West and northern<br>drawn from the Global Ant Biodiversity Informatics (GABI) database in July 2020.<br>In contrast, our records<br>estimated map regions are significantly more conservative.<br>For example, our maps exclude bounded<br>in methodology.<br>Kass joins us in making plentiful use of alphahull estimation, but our approach is more<br>conservative. We exclude species with fewer than 3 unique occurrences, but Kass includes 5,168 ant<br>species with fewer than 3 unique occurrences.<br>Since alphahulls cannot be estimated for these species,<br>Kass estimates their ranges by drawing 30 km buffer zones around the occurrence locations.<br>Given this<br>difference,comparingfulldatabaseresultsforGBIFandKasswouldeffectivelybecomparingapplesand|
|---|
is to use mobile phones to create virtual buyer groups, agglomerate daily orders from store owners, bulk buy from farmers, and then deliver the produce directly to stores. The aims were to reduce the amount of time and cost store owners spend traveling to the central market, improving their incomes and work-life balance as a result, lowering the cost of fresh produce purchased by stores, and, in turn, reducing the prices paid by consumers.

We conducted a randomized experiment to test the extent to which this innovation was able to achieve these aims. We began with a baseline survey of 1,620 firms in 63 market blocks, in which firm owners were asked about their potential interest in using the new service, with 70 percent expressing interest. These blocks were then grouped into matched pairs, with one block in each pair randomized to treatment and the other to control. Agruppa then phased in their service at the 2
Similar evidence on endogeneity of caseloads is seen in several other studies. Spaić and Đorđević (2022) investigate the relationship between the relative number of judges within a court and the efficiency of the judiciary. To determine how the number of judges influences effectiveness, they compare data on the judiciary from six countries: Serbia, Croatia, Slovenia, France, Austria and Norway. The authors conclude that judicial effectiveness is static with the number of judges in a jurisdiction. Dimitrova-Grajzl et al (2012), Alencar and Ponticelli (2016), Grajzl and Shikha (2020) and Dimitrova-Grajzl et al (2016) study courts in Europe and Asia and find that the number of serving judges, a key court resource, matters only in a subsample of courts, a result suggesting that judges adjust their productivity based on the number of judges serving at a court.

Also, there is some evidence that restructuring the judiciary with a view towards increasing the quality of judgments pays off. Garoupa and Ginsburg (2009) provide a theory of the formation of judicial councils and their effect on judicial quality and from there to economic growth. They test the extent to which different designs of judicial council affect judicial quality and find a modest relationship between councils and quality. This result echoes Voigt and El Bialy (2013) in their findings on reforms in European courts. Further research is sorely needed to provide a detailed mapping of the path from quality of judgments to judicial effectiveness.

There is robust evidence on who benefits from judicial effectiveness. Improvements to judicial effectiveness disproportionately benefit the least privileged groups of society. Chemin (2021) studies data on 36 African jurisdictions and documents that the perception among the poorest decile of the population that the head of state will never ignore the courts and laws is lower at baseline by 11 percentage points compared to other more well-off groups in society, and that this perception differentially increases by 10 percentage points after a judicial reform, nearly closing the gap between groups.

# **4. Conclusions**

Courts around the world
%<br>0%<br>Non-Poor Poor Sudan 0%<br>Poorest Poor Middle Rich Richest Sudan<br>2009 2014/15<br>2009 2014/15<br><!-- End of picture text --> _Source_ : Authors’ calculation using 2009 NBHS and 2014/15 NHBPS data.

Majority of the households affected by shocks live in the Central Darfur and Kordofan regions of Sudan. At state level, Southern Kordofan, Blue Nile, Western Darfur, and Northern Darfur have the highest incidence of shocks—more than 80 percent of households in these states reported at least one shock 2009 and more than 50 percent in 2014/15. On the other hand, low prevalence of shocks is observed in North16
consumption is remarkably stable over time, allowing us to use the third moment estimated to observed NSS consumption from the NSS-2011. For the second moment, we consider two options, namely estimate it using (a) observed NSS consumption data from the NSS-2011, and (b) imputed NSS-type consumption in the CPHS (which we established does reasonably well in matching the second moment from the observed NSS log consumption data). The first moment (mean log consumption), which is the least stable moment over time, is obtained from the imputed NSS-type consumption data. The resulting estimates of the three parameters _a_ , _b_ , and _σ_<sup>2</sup> for the different years are shown in Figure 14.

The next step is to estimate the parameters of the unconditional distribution of NSS log consumption, which is assumed to follow a Normal Mixture distribution. Normal mixtures (NM) are very flexible. Two or three components are generally sufficient to closely fit any empirical distribution function underlying household consumption data.<sup>23</sup> In our case, it offers two practical advantages. First, it follows that the distribution of NSS log consumption conditional on CPHS log consumption too follows a NM distribution. Second, the parameters of the NMs associated with both the unconditional and conditional distribution of NSS log consumption can readily be derived from the parameters of the NM estimated to CPHS log consumption combined with the parameters governing the relationship between CPHS and NSS consumption (i.e. _a_ , _b_ , and _σ_<sup>2</sup> ).

We start by fitting a NM with three components for the unconditional NSS log consumption distribution. When the estimated variance of one or more of the components is negative, the number of components is reduced by one, until all components are estimated to have positive variance. See assumption 2 for details on the positive variance constraint (and why positive variance is not necessarily guaranteed). Negative variance estimates are only obtained for urban samples during 2018.

Once we
Environmental sustainability often involves public goods, which are non-exclusive (i.e., they cannot be owned by any group or individual) and non-rival (i.e., their benefits can be shared by many without reducing their value) – such as clean air (Daly and Cobb 1994, Tirole 2017). It can also involve common goods, which are typically non-exclusive but often rival (i.e., their use by some decreases their availability or value for others) – such as fishing or groundwater resources. In both cases, there is often not enough investment to ensure environmental sustainability – given that their benefits are not exclusive, while in the case of common goods there are often efforts to capture the benefits for a select few. The science to manage environmental sustainability may be clear, but the policies needed to achieve it are more challenging and disputed. Likewise, given the nature of public/common goods, collective action to pursue environmental sustainability (whether at the local, national, or supranational level) is often unlikely unless compelled by regulations. As has often been seen, well-intended political agreements without legal binding often lead to underwhelming outcomes in terms of progress on environmental goals, reform commitments, or the mobilization of financial resources. Environmental and climatic impacts are typically expected to materialize in the long term, though the failure to adopt sufficiently bold measures may risk more severe impacts sooner. Consensus building around environmental sustainability works at two different levels: while experts have strong and compelling scientific evidence on the severity of the risks and the effectiveness of policy options, the public is more divided. Perception surveys from around the world show large variations in the extent to which individuals are aware of, concerned about, or ready to act upon environmental risks. The key metrics for environmental sustainability include objectively measurable factors like carbon emissions, the fulfillment of legal or policy obligations and commitments, and natural resource usage and replenishment levels.

15
# How Accurate Is a Poverty Map Based on Remote Sensing Data? An Application to Malawi<sup>_∗_</sup>

Roy van der Weide<sup>_†_</sup> , Brian Blankespoor,<sup>_‡_</sup> Chris Elbers<sup>_§_</sup> , and Peter Lanjouw<sup>_¶_</sup>

**Keywords:** Poverty, Small Area Estimation, Remote Sensing Data **JEL Classification:** C13, D31, I32, O15, R13, Y91

> _∗_ The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. The authors gratefully acknowledge financial support from the World Bank Knowledge for Change Program.

> _†_ World Bank: rvanderweide@worldbank.org

> _‡_ World Bank: bblankespoor@worldbank.org

> _§_ Vrije Universiteit Amsterdam: c.t.m.elbers@vu.nl

> _¶_ Vrije Universiteit Amsterdam: p.f.lanjouw@vu.nl
share with poor food access between the first expenditure decile and all other expenditure deciles. Column (1) estimates a specification using an indicator for the household being severely food insecure according to the FIES as the dependent variable, and illustrates that there is little difference between the share that is severely food insecure in the lowest expenditure decile and the share in nearly every other expenditure decile.

The estimate of the constant in Column (1) of Table 4 illustrates that 26.4 percent of — — households in the first expenditure decile the very poorest Nigerians are severely food insecure according to the FIES, which is nearly identical to the average share reported for the country in Table 1. Additionally, aside from the indicator for the tenth — — decile the very richest Nigerian households the estimates are small in magnitude, imprecisely estimated, vary in sign, and are statistically indistinguishable from zero at standard significance levels. Only for the richest decile is the share that is severely food insecure lower by approximately 8.1 percentage points, with the poorest Nigerians having only 1.4 times the share of individuals that are severely food insecure than the richest Nigerians.

The FIES is similarly poorly aligned with expenditure using different thresholds to denote poor food access. Column (2) re-estimates Specification (2) using an indicator for the household being moderately food insecure or worse according to the FIES. Similar to Column (1), the share of the population that is moderately food insecure or worse is similar for nearly all expenditure deciles. Until the ninth expenditure decile, the estimates are again small in magnitude, imprecisely estimated, and are statistically indistinguishable from zero at standard significance levels. Only for the ninth and the tenth deciles is the share with poor access significantly lower than in the first decile, with the share declining by 8.7 and 20.5 percentage points respectively. However, even with the decline, the share of the very poorest Nigerians that were moderately food insecure or worse was only 1.5 times more than the share
12 by the Mexican Government in the latter half of the 1980s. They are especially suitable, therefore, for making 'before and after' comparisons.

The ENIGH surveys identify the educational attainment, income received and number of hours worked per week by each family member.'<sup>0</sup> Income is differentiated into about 25 items, which were aggregated into three broad categories: (i) wage and salary income, (ii) current monetary income, which includes wages and salaries, income from self-employment, property income and rents, monetary transfers and income from financial assets, and (iii) total current income, which includes all of the above items, plus non-monetized income such as imputed rent, in-kind transfers and stock dividends. In contrast to this disaggregation of income sources, the ENIGH surveys do not differentiate the number of hours worked per week by activity. The rates of return calculated below are based on two measures of earnings per hour: the wage rate, which refers to wage and salary earnings per week, divided by the total number of hours worked per week, and the monetary earnings rate, which refers to current monetary income divided by the same number of hours worked per week.

Tables 5 and 6 present the private and the public rates of return on investments in education in Mexico. The method used to estimate these rates of return is explained in detail in the Annex. The distinction between public and private rates of return in this context centers around who bears the cost of financing the investment in education: in both cases, the benefits of an additional year of schooling are reflected in the stream of additional earnings that accrue to an individual as a result of that additional education. The rate of return to education is then the discount rate that equates the present value of this stream of additional earnings to the present value of the costs incurred. When only private costs are considered in this calculation, it is tenned the private rate of return, and when all public and private