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

Tracking poverty at the global, regional, and national levels helps international organizations and the development community identify lagging regions and allocate their resources accordingly (see e.g. Ravallion (2018), Ferreira et al. (2015), Bourguignon (2017)). Poverty is commonly measured by the percentage of the population whose income or consumption expenditure falls below a poverty line. The household income and expenditure data used to estimate poverty come from household surveys that are conducted every 1-5 years by the national statistical agencies. Individual countries use these data to monitor poverty at the national and subnational levels.

Elbers et al. (2007) demonstrates the value of estimating poverty at a more disaggregated level. Specifically, they estimate by how much governments could lower the costs of providing resources to the poor if they had access to a poverty map that provides estimates of poverty down to the district or municipality level. Their simulation study using data from Madagascar, Cambodia, and Ecuador suggests that the gains can be substantial. In all three countries, the reduction in poverty that is achieved with a uniform transfer to all households can often be realized with less than half the budget if the transfers could be targeted on the basis of municipality level estimates of poverty. Unfortunately, collecting data on household income and expenditure is costly and time consuming (Kilic et al. 2017; Fujii and van der Weide, 2020). As a result, survey samples tend to be relatively small, permitting only a limited degree of disaggregation, often not beyond large sub-regions.

Estimating poverty at the small area level requires alternative sources of data, ideally a population census, that are able to provide complete coverage of the population. Elbers, Lanjouw, and Lanjouw (2003), hereafter referred to as ELL, pioneered the small area estimation of poverty by combining household survey with population census data. The proposed approach imputes household income or consumption expenditure (multiple times) for each household into the population census, and then aggregates
# **VI CONCLUDING REMARKS**

This study investigates the impact of hosting the displaced Rohingya population on the local communities and areas surrounding the refugee camps in Cox’s Bazar, Bangladesh. It utilizes a difference-in-differences approach, contrasting regions and individuals closer to and farther from the most populous camps, both before and after the Rohingya arrived. The research incorporates remote sensing data to analyze night light density, deforestation, and land use patterns, alongside longitudinal data representative of the local communities near the camps.

The indicate that the effects. us- findings hosting Rohingya produces complex Analysis ing remote sensing data reveals that regions near the most populated camps experienced an increase in night light density and greater deforestation following the arrival of the Rohingya in 2017. These effects align with the results from land use data, which shows a decrease in dense-open forests and an increase in areas used for crops and grass. However, the magnitude of these effects varies significantly; the change in night light density is larger, while the impact on deforestation is smaller.

The individual-level data in this study presents a consistent narrative: namely, that the has local in Cox’s Bazar. include Rohingya presence influenced dynamics Key findings higher formal employment, enhanced service provision (particularly in aid), more food, and a greater variety of food. This positive shift is largely due to the presence and efforts of humanitarian organizations in the area.

Nevertheless, the data also indicates some adverse effects associated with the Rohingya presence. Notably, safety concerns are higher within the host communities, as is the prevalence of viral diseases such as diarrhea, fever, and cough. Taken together, our findings underscore the multifaceted effects of refugee camps on host communities, highlighting both the beneficial and challenging aspects of hosting large displaced populations.

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employees, while a median firm has only 2 employees. Less than 10 percent of the firms have more than 20 employees.

Table 2 – Descriptive statistics of firm-level data for Tunisia (1996-2015)

|**Variable**|**N**<br>|**mean**|**p50**|**p10**|**p90**|
|---|---|---|---|---|---|
|Age|1,167,050|13.79|11|3|28|
|Age square|1,167,050|294.90|121|9|784|
|Employment|1,167,050|15.01|2|1|19.75|
|Export status|1,167,050|0.06|0|0|0|
|Import status|1,167,050|0.10|0|0|1|
|Exports (3-years)|539,550|0.21|0|0|0|
|Imports (3-years)|539,550|0.40|0|0|2|
|Foreign|1,167,050|0.03|0|0|0|
|Offshore|1,167,050|0.06|0|0|0|

Note: Exports (3-years) and Imports (3-years) refer to the sum of export or import statuses over 3 years. The calculation is performed only for those firms with 3 or more years of existence for each year, which explains the differences in the number of observations.

> 10 The National Institute of Statistics found that 8% of the registered self‐employed firms (reporting no employees) are “falsely active.”

5
Gini in 2017 from 62 to 67.<sup>34</sup> Panel B of Figure 6 shows trends that are broadly similar for the adjusted and unadjusted surveys, showing a robust decline in global inequality since about year 2000. The levels and patterns are in line with those of Lakner and Milanovic (2016) who use a different form of adjustment and aggregation, but only provide estimates until 2008. For 2008 they estimate a global Gini of 67.0 using 2011 PPPs, while our estimates are 66.7.<sup>35</sup> Their topincome adjusted estimates are 2.9 to 6.3 percentage points higher for 2008, within the same range as our top-adjusted estimate, which is 4.3 percentage points higher for 2008. Using the unadjusted distributions with national accounts means instead of survey means, global inequality is lower and declining faster. This is expected from the pattern of national accounts measures, which are larger and growing faster than survey measures, especially in middle-income countries. Notably, global inequality has continued falling rapidly using all measures. Our survey-based estimates have fallen 6 to 7 percentage points since 2000.

Generally, the larger HHS - NAS gap among middle-income countries leads to relatively larger adjustments to inequality measures in these countries. These systematic differences along the income gradient of countries also lead to insights into the relationship between economic development and levels of inequality. Evidence of the cross-sectional Kuznets curve – the hypothesis that income inequality first increases and then declines with development – has recently been questioned. Palma (2011, p.87) suggests that the “the ‘upwards’ side of the ‘Inverted-U’ between inequality and income per capita has evaporated.” This is indeed true for the unadjusted Gini coefficients in our sample. However, Gini coefficients from the top-income adjusted data, suggest that there is an upwards sloping segment and firmly re-establish a Kuznets-like relationship between economic development and observed inequality, as seen in Figure 7. The clearly inverted U-shaped curve results from the lowess regression
category, respectively, it is not possible to explicitly separate total income earned by being a cadre from other incomes.<sup>26</sup> Nonetheless, it is important to examine whether the income gap between cadre and non-cadre households is simply driven by an employment contract. To do this, we examine the difference in local wage income between cadre and non-cadre households by source.<sup>27</sup> Specifically, given the design of the 1986-1991 waves of the RCRE survey, we are able to disaggregate local wage income into three components: (a) wage income from businesses and economic activities managed by villages, (b) subsidies, aid and fund from villages, and (c) wage income from the private sector. Component (b) includes compensation and subsidies for being a cadre along with aid and funds such as comfort funds to families of revolutionary martyrs, financial aid to families living in extreme 26 While we cannot explicitly calculate the wage income earned by cadres (i.e., the compensation and subsidies for being a cadre from both village coffers and government treasury), we are able to infer it under reasonable assumptions. Specifically, when comparing village subsidies, aid and funds between cadre and non-cadre households, we find that, on a per capita basis, cadre households earn an average of 60 Yuan per capita from this income component (i.e., 54.33+5.559 = 59.889) while non-cadre households earn about about six Yuan, which are aid and fund from villages and do not contain cadre compensation and subsidies (Appendix Table 7). Since there is no reason to believe that the village aid and fund are distributed systematically in favor of cadre households, we consider the difference of 54 yuan in village subsidies, aid and fund between cadre and non-cadre households to be the average compensation and subsidies for being a cadre from village coffers. Moreover, and again as we discussed above, the other part of compensation and subsidies for
SENEGAL’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 1. Infrastructure has contributed much to economic growth—but could contribute much more**

<!-- Start of picture text --> a. Infrastructure’s contribution to annual per capita economic b. Potential contribution of infrastructure to annual per capita<br>growth in West African countries, in percentage points, 2001–05 economic growth in West African countries, in percentage points<br>5<br>5<br>4<br>4<br>3 3<br>2 2<br>1 1<br>0 0<br>-1 -1<br>-2 -2<br>Roads Electricity Telecom Roads Electricity Telecom<br>Percentage Points Percentage Points<br>n a go r e i us<br>Beni Cote d'Ivoire Mauritius Guinea Burkina Faso Senegal Ghan Mauritania T o Nigeria South Africa Gambia Nig Sierra Leone Guinea-Bissau Niger Togo Nigeria Burkina Faso Guinea-Bissau Sierra Leone Benin Mauritania Gambia Senegal Ghana Guinea Cote d'Ivoire South Africa Maurit<br><!-- End of picture text --> a. Infrastructure’s contribution to annual per capita economic growth in West African countries, in percentage points, 2001–05 _Source_ : Calderón 2009.

It is in this context that Senegal’s sustained growth has been driven not only by groundnut and fish exports and tourism and trade, but also by transport and primarily the development of telecommunication services (World Bank 2007a). Despite its weaker performance in 2006, between 1996 and 2006 Senegal’s economy grew at an average annual rate of 4.7 percent, above the average of Sub-Saharan Africa (table 1), but still short of the sustained 7 percent mark per year needed to make important progress in reducing poverty.

**Table 1. Economic indicators in Senegal and Sub-Saharan Africa**

||GDP growth<br>(1995–2005)|Inflation rate<br>
<sup></sup>test|<br>0.0081|0.0081|0.0081|0.1670|0.1819|0.0118|
|<br>Instrument validity (p-value)|||||||
|<br>Anderson-Rubin Wald Ftest|0.2749|0.6677|0.2411|0.3559|0.2265|0.2870|
|Stock-Wright LM S stat., P><sup></sup>|0.2315|0.6375|0.2002|0.3497|0.1594|0.1747|
|<br># clusters/obs/regressors|27/67/12|27/67/12|27/67/12|25/61/13|25/61/13|25/61/13|

_Notes:_ All statistics are country-cluster robust and het-robust. All models are exactly identified, except for Table 5: [1a], where overidentification test‘s p-value P(Hansen J > <sup></sup> (1)  0.6050  In all models, F-statistics of Kleibergen-Paap (2006) Wald rank test for weak identification equals that from F-test of excluded instruments. # regressors excludes constant and (for Table 5) partialled out variables. _Source:_ Calculation with ESS data.

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receive in-school safe spaces, and a group to receive in-school safe spaces and support classes in mathematics and French.<sup>5</sup> The randomization was stratified by the number of localities around the schools and the number of girls eligible to attend the selected schools.

In the second stage, we randomize the 280 localities selected around the schools into treatment and control groups. The 56 localities around the 20 control group schools (figure 1) were automatically considered as a pure control, i.e., with no intervention taking place either in selected schools or in the localities. The remaining 224 localities around treatment schools were randomly assigned to four groups: a group of 56 localities receiving community

> 4Using data from the 2014 General Population Census (Recensement Général de la Population et de l’Habitat), we first selected localities with an estimate of at least 50 eligible girls, i.e., out-of-school girls aged 8 to 24. We then restricted to localities that were close enough to secondary schools that girls residing in these localities could reasonably commute to one of the 60 schools.

> 5Due to delays and other implementation issues, the support classes were not implemented. Both treatment arms were therefore merged into one group of 40 schools receiving in-school safe spaces.

8
br>Export Share|
|---|---|---|---|
|Trend break (2008)|2.73|10.09***|0.007**|
||(1.70)|(1.92)|(0.003)|
|Time|11.62***|8.34***|0.007***|
||(0.25)|(0.24)|(0.001)|
|Constant|100.34***|32.55***|0.445***|
||(5.18)|(4.85)|(0.025)|
|Observations|39|39|39|
|SE|HAC(4)|HAC(4)|HAC(4)|

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sector in particular from growing. We look at two possible means of growth. One is the restructuring and growth of enterprises in the formal sector (section 3) and the second is the transition from self-employment to small businesses which usually implies a transition from the informal to the formal sector (section 4). Performances in these two areas are evidently the cornerstones for stimulating growth _cum_ jobs.

# **3. GROWTH CONTRAINTS IN THE FORMAL SECTOR**

This section turns to enterprises in the formal sector and attempts to review all those factors that would potentially constitute barriers to growth and job creation. We will try to establish what are the most critical bottlenecks currently existing in the CIS-7. In the previous sections, we observed that the economy and the labor markets in these countries are segmented into a rural/agricultural sector and an urban/industrial one. By turning to enterprises and given the quasi non-existence of large enterprises in the rural sector, this section evidently focuses on the urban/industrial sector. Moreover, the rise of self-employment in rural and urban areas alike determined a certain segmentation of employment also in urban areas with two rather distinct pools of workers, the employees in enterprises and the self-employed outside enterprises. While the former pool belongs essentially to the formal sector and will be covered in this section, the latter pool is often found in the informal sector to which the next section will turn. Thus, we should keep in mind that this section covers only a share of existing workers.

We mostly draw on data collected by the World Bank Business Environment and Enterprise Performance Survey (BEEPS)<sup>14</sup> and integrate these data with other available databases and data pub-

> 13 See the official websites of the national statistical agencies for a complete set of data on wages.

> 14 For details on this survey see http://info.worldbank.org/governance/beeps/

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</mark> <mark>The paper then details the methodological approach of the GSPS and current state of play of the GSPS dataset (</mark> accessible at: <u>https://www.globalsurveyofpublicservants.org/indicators/</u> <u><mark>). We provide illustrations of how</mark></u> <mark>scholars can use the GSPS data to study core topics in comparative public administration</mark> 3
||information from official sources, which shows that<br>the annual number of health center beneficiaries is 9<br>times the number of hospital beneficiaries.|method 1 we keep expenditure–per-beneficiary<br>constant according to the official data, where each<br>health-facility visit is recorded as a separate<br>beneficiary. In method 2, we hold official annual<br>expenditure constant and divide it by the number of<br>beneficiaries(not visits)reported in the survey.|
|---|---|---|
|5|Some individuals reported being injured during the<br>survey period, but did not report any further<br>information about their condition.<br>(1.71% of surveyed individuals)|Since respondents did not report any additional<br>information about their condition or any treatment they<br>may have received, we consider these cases as<br>misreported and treat them as if no injuryoccurred.|
|6|A small number of respondents received medicines<br>from public facilities but did not report an official<br>patient visit.<br>(1% of those who received medicines from public<br>facilities)|The proportion of respondents reporting treatment<br>without a visit is very small and we disregard the<br>discrepancy. We consider only the expenditure of those<br>who received treatment as a patient.|
|7|The survey does not differentiate between facility<br>levels, and we cannot determine the number of patient<br>visits to third-tier hospitals.|We add public expenditures on third-tier hospitals to the<br>other two tiers and divide by the total number of patients.<br>We then assign the resultingexpenditure to eachpatient.|
|8|The LCMS records information about health posts and<br>clinics separately, but the official public spending data<br>does
nonbeneficiary households was added to the evaluation sample. Among other information in the ENCELURB survey, all households were asked a series of questions relating to their knowledge about, application to, and enrollment in _Oportunidades_ .

The screening survey for the ENCELURB included a module with questions to construct the household poverty index and the program eligibility of households in the evaluation sample.<sup>4</sup> Table 1 presents information on participation levels by eligible and ineligible households. Leakage (that is, participation by ineligible households as a fraction of total participation) was 28.2 percent, and undercoverage (that is, nonparticipation by eligible households as a fraction of the eligible population) was 49.6 percent. Table 1 also illustrates that a lack of knowledge about the program was a major barrier to participation, especially for ineligible applicants.<sup>56</sup>

# **<<Place table 2 about here>>**

Cash benefits for participants in _Oportunidades_ include an unconditional grant (called a “nutrition grant”) as well as grants that are conditional on the school attendance of the children in the household, as described in table 2. The program also includes free medical consultations and nutrition supplements. Because we can calculate the potential (maximum) cash benefits a household can receive under the program using the household’s demographic composition and years of completed schooling of each pre-program child, we can estimate the monetary incentive to participate for each potential applicant.

# **<<Place table 3 about here>>**

Table 3 shows the average household characteristics for eligible and ineligible households. Ineligible households are better off than eligible households in terms of both total and per capita expenditure. Because ineligible households have smaller families, in addition to being less likely to enroll in the 12
In the case of internal migration, Kochar (2004) provides a test using microeconomic data. The method consists of estimating how the differences in returns to schooling between a rural area and the nearby urban area affects the probability of completing middle school for rural boys aged 15-20. The results show that rural schooling decisions in India are undertaken in response to the possibility of employment in urban areas.

# _3.2.3) Consequences of migration for the urban area of destination_

To our knowledge, work on the effects of internal migration on the urban area of destination is scarce. This contrasts with the more numerous studies of the impact of international migration on natives in the host country of destination (for surveys, see Borjas, 1999, or Greenwood and McDowell, 1999). But the effects of internal migration could differ from those of international migration. In the case of internal migration, the absence of an exchange rate makes migration a central equilibriating mechanism for regional imbalances in economic activity so that partial equilibrium analysis seems inappropriate. Furthermore, because of differences in migrant selectivity (which should probably be attributed to differences in migration costs and migration restrictions) and because of differences in local economic contexts (the more regulated labor market in developed countries _versus_ the less regulated urban labor markets in developing countries), the expected effects on the area of destination and their magnitudes should also differ. In particular, internal migrants in developing countries are more likely to be less skilled than natives in the urban area of destination, whereas international migrants might be more or less skilled than the average native in the country of destination. To better characterize internal migration flows and stocks, it would be very useful to build databases on internal migration by education in a selection of developing countries, complementing the work of Docquier and Marfouk (2005) who recently built such a database for international migration. But, to our knowledge, this work remains to be done.

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Different timing of the impact of the onset of the disease could also potentially influence the pattern infection of age. In countries that were struck in an early phase of the pandemic, it came largely as a surprise. In contrast, for countries where the disease emerged at a later stage, there was greater opportunity to take preventative measures, including those to protect older people. Figure 11 shows the dates of the 100<sup>th</sup> death due to the virus in each country plotted against rates of increase by age of the COVID-19 mortality. By this metric, COVID-19 arrived later on average to the countries with flatter age-mortality curves <!-- Start of picture text --> Figure 11: Rates of Increase per Year of Age in COVID-19 Mortality<br>vs. Date of 100 th COVID-19 Death in the Country g<br>CHE<br>CAN<br>NLD<br>DNK<br>AUS<br>ESP SWE<br>ITA KORDEU<br>PRT NORHUN<br>FRA<br>JPN<br>CHL<br>ARG<br>PHL<br>BRA<br>COL MYS<br>ZAF<br>PER AFG<br>PAK<br>MEX<br>Mar1 Apr1 May1 Jun1<br>Date of 100th death<br>.15<br>.1<br>Rate of increase in COVID-19 mortality rate<br>.05<br><!-- End of picture text --> Note: See Appendix for data sources. LMICs are shown in red and HMICs in blue.

Age-specific mortality rates among those infected may also vary by age due to a wide variety of factors. Non-communicable diseases (NCDs) are more widespread in HICs. NCDs like chronic cardiac disease, non-asthmatic chronic pulmonary disease, chronic kidney disease, liver disease and obesity are key risk factors for mortality among COVID-19 patients (Docherty, Harrison et al. 2020). Goldberg and