rafmacalaba/data-use-mentions-extended huggingface dataset:
displaced children and adolescents residing in host communities, rather than in refugee camps.

Particularly, VenRePs-Kids collects data on 2,556 households including 1,338 Colombian and 918 Venezuelan households, respectively. The study collects rich and comprehensive data on children’s and adolescent’s development including anthropometric measures, vocabulary ability tests, and socio-emotional and mental health assessments. It also collects data on risk behaviors, time use, social integration measures, prosocial behaviors, and parents and caregivers sociodemographics, among other dimensions.<sup>2</sup> The study includes Colombian children and adolescents as the comparison group to highlight the developmental differences of Venezuelan forcibly displaced children. This choice stems from the fact that comparing Venezuelan children in Colombia with their counterparts remaining in Venezuela is impractical due to the latter’s exposure to a severe economic and humanitarian crisis, marked by limited access to services and food. This environment severely hampers their potential for normal human development. Additionally, many Venezuelan children and adolescents have spent more of their lives in Colombia than in Venezuela. Therefore, Colombian children and adolescents serve as the most appropriate benchmark for assessing the developmental gaps of their Venezuelan peers.

This paper analyzes data from the initial wave of VenRePS-Kids, conducted from October to December 2022, to outline key demographics and stylized facts about forcibly displaced children and adolescents. Initially, we examine the human development disparities of forcibly displaced Venezuelan children and adolescents in comparison to their Colombian counterparts. Our approach to human development is broad, covering physical, cognitive, socio-emotional, and mental health aspects. Additionally, we complement our analysis by exploring differences in food security, social cohesion, and the economic status of parents. Although our analysis is descriptive, it represents a crucial initial step

> 2Venezuelan households are defined as those where both parents and their children have a Venezuelan nationality. Colombian households are composed of Colombian citizens only.

3
Bank 2018). In some areas of the coast, aquifers are also subject to increasing salinity intrusion (Ayers et al., 2016; Khan et al., 2014). Additionally, shallow groundwater levels in the northwestern, north central, and southwestern areas of the country are declining over time, particularly affecting public water supplies during the dry season when there is notable groundwater abstraction for irrigation (Shamsudduha et al., 2009; Shamsudduha et al., 2012).

Nevertheless, Bangladesh’s progress in providing nearly universal access to “improved” drinking water supply can mostly be credited to its abundance of groundwater sources. According to the 2011 Bangladesh Census, there are 31 million households that have access to primarily groundwater supplies via tubewells, tapwater and other water wells such as dugwells (BBS, 2015). The current number of privately-owned tubewells is approximately 17 million, compared to a previous estimate of 6 million to 11 million in the early 1990s (BGS and DPHE, 2001). In addition to these private wells, an additional 1.6 million public tubewells have been installed by the Department of Public Health Engineering of Bangladesh in order to provide As-safe drinking water supply in rural areas of which about 20% are considered deep (>150 mbgl) wells (DPHE, 2016). Consequently, sourcing groundwater from deep aquifers, has become an effective and popular mitigation strategy for drinking-water supply over the last couple of decades for both As and even salinity water quality issues (Burgess et al., 2010; van Geen et al., 2016). However, there is concern of potential contamination of deep groundwater due to ingress of As and salinity from shallow depths (Shamsudduha et al., 2018). Contrary to these hydrological concerns, from ethical consideration (i.e., positive impacts outweigh the negative consequences of long-term exposure to arsenic), Ravenscroft and others (Ravenscroft et al., 2013) advocate that the development of deep groundwater in Bangladesh is needed. Although it may lead
</sup> Since 2012, mobile cellular basket prices have decreased by 61 percent, well below any of the aforementioned income groups as its subscriptions per 100 population increased by 6 percent (figure 2). However, the market remains highly concentrated. Mexico has the second most highly concentrated market in the world measured through the Herfindahl-Hirschman index (HHI), after the Philippines (Ayala et al. 2017), resulting in the highest HHI among OECD countries (Figure 3). Moreover, per the _OECD Review of Telecommunication Policy and Regulation in Mexico_ (2012), the average loss in consumer surplus due to overpricing and unrealized subscriptions in this sector was 1.8 percent of Mexico’s GDP (2005–09), highlighting the important implications of market concentration for consumer welfare.

> 20 For the purpose of this paper, the definition of poverty is based on household per capita income (instead of adult equivalent). It is thus not exactly the same as the official poverty headcount published by the government of Mexico. The indicator used is the percentage of the population with an income that is lower than the well-being line (linea de bienestar) as defined by the National Council for the Evaluation of Social Development Policy.

> 21 See http://dasp.ecn.ulaval.ca/webwel/welcom.html for more information.

> 22 OECD Prices and Termination rates June 2017. Includes 200 gigabyte/month and 25.0 megabytes per second and above.

11
# **Summary**

This paper estimates an urban CO2 emissions model using satellite-measured CO2 concentrations from 2014 to 2020, for 1,236 cities in 138 countries. The model incorporates the global trend in CO2 concentration, seasonal fluctuations by hemisphere, and a large set of georeferenced variables that incorporate CO2-intensive industry structure, emissions from agricultural and forest fires in neighboring areas, demography, the component of income that is uncorrelated with industry structure, and relevant geographic conditions. The income results provide the first test of an Environmental Kuznets Curve relationship for CO2 based on actual observations. We find evidence for an EKC that reaches a peak near or above $40,000 per capita, which is at the 90th percentile internationally. We also find that economic development has a significant effect on the direction of the relationship between population density and CO2 emissions. The relationship is positive at very low incomes, but becomes negative at higher incomes.

We explore other implications of our estimates in a series of exercises. Using cities’ mean regression residuals to index their CO2 emissions performance, we find wide variation within and across regions. Overall performance exceeds model-based expectations in India, Western Europe and the former Comecon countries, while it falls short in China, the rest of East Asia & Pacific, Middle East & North Africa and Sub-Saharan Africa. We also decompose model CO2 predictions into five broad source categories for each city. We find particularly important roles for CO2-intensive industry structure in India, China and other East Asia & Pacific countries; agricultural and forestclearing fires in Sub-Saharan Africa; the component of income per capita that is uncorrelated with industry structure in Latin America & Caribbean, North America and Western Europe; population and population density in Sub-Saharan Africa, India and other South Asian countries; and climate in the former Comecon countries.

Our results can also inform the discussion of policy instruments for CO2 emissions reduction. Many policy analysts who support Pigouvian pricing also argue for a non-Pigouvian supplement: coordinated public investment in low-carbon land development, energy and transport that will accelerate
**Table 2: Discoveries by Country**

||**Discovery Count**<br>**HS 4-Digit**|**Percent**<br>|**Discovery Count**<br>**HS 6-Digit**|**Percent**<br>|
|---|---|---|---|---|
|**Argentina**|<br>5|2%|<br>32|2%|
|**Australia**|5|2%|22|1%|
|**Burundi**|0|0%|0|0%|
|**Bolivia**|7|2%|15|1%|
|**Brazil**|10|3%|44|3%|
|**Central African Rep.**|0|0%|1|0%|
|**Canada**<br>|2<br>|1%<br>|19<br>|1%<br>|
|**Switzerland**|2|1%|19|1%|
|**Chile**|8|2%|31|2%|
|**China**|8|2%|39|2%|
|**Colombia**<br>|17<br>|5%<br>|43<br>|3%<br>|
|**Cyprus**|4|1%|5|0%|
|**Czech Republic**|17|5%|58|3%|
|**Germany**|0|0%|17|1%|
|**Denmark**|1|0%|10|1%|
|**Ecuador**
comparing this to European data, the probability could be lowered to 3.4 per million. Applying a DALY of US$ 1,000 and 5,000 respectively, this would lead to an annual benefit of between US$ 700 million and 3.5 billion per year.

- Weather sensitive economic sectors create about 25 percent of world GDP. It is assumed that improved forecasting could lead to added economic benefits between 0.1 and 1 percent in these sectors. This would lead to an increase in GDP of between 0.025 and 0.25 percent if forecasting quality is increased to western/central European standards.

We recalculated the estimations from Hallegatte (2012) of potential benefits of European-standard forecasting for LICs only, using the same assumptions but more current data on population and GDP (World Bank 2014). Unsurprisingly, the results are very similar, listed in Table 3.2.

**_Table 3.2_** _: Potential annual benefits of European-standard forecasting in LICs, measured in 2014 values._

|**Benefit type**|**Potential value of improved**<br>**forecasting in LICs (US$)**|
|---|---|
|Reduced asset losses|12-68 million|
|Reduced human losses|65-327 million|
|Additional economicproductivity|99-993 million|
|TOTAL|176-1,388 million|

Hallegatte (2012) assumes a direct relationship between improved availability of weather and climate information and improved decision-making and action, thereby attributing all potential benefits of preparedness to early warning. While forecasting and resultant early warning are critical to early action, the resultant benefits would in fact not be realized without substantially improving the ability of people and institutions to use forecasts. Therefore this is an optimistic assumption particularly in developing countries, where most, if not all, components of the disaster and climate risk management value chain lack capacity to varying degrees. WMO et al (2015) highlights that assumptions of perfect decisionmaking based on all
to Deposit Ratios, 2007–10<br>28 27.9 13 200<br>181<br>175<br>171<br>162<br>26 152<br>25.0 12 150 147<br>138 139<br>24<br>11<br>100<br>22 21.1<br>20.9<br>20.2<br>19.8 19.7 10<br>20 19.0 19.1 18.7 50<br>18 9<br>2007 2008 2009 2010 2011<br>0<br>Total All Banks Large Banks<br>2007 2008 2009 2010<br>Assets/Equity: All Banks Large Banks All Banks Large Banks<br>Percent<br>Total assets/Equity<br>Risk-weighted Assets/Tier 1 Captal (%)<br><!-- End of picture text --> Source: ECB Consolidated Banking Data.

2 The limited decline in loan to deposit ratios may also be due to declining deposits as a result of flight to quality, as experienced by Greece, Ireland, Italy, and Spain.
Thus, a new poverty line was estimated based on the poverty estimation methodology outlined in Section II.

As mentioned earlier, the new poverty line was set at RWF 159,375 per adult equivalent per year in January 2014 prices, and poor households in 2013/14 were identified using the following inequality: ����/�� where e����/�� denotes nominal consumption expenditure per adult equivalent in EICV4, and π����� is the price index (COLI) used to deflate nominal consumption expenditure within EICV4 to January 2014 prices. This yielded a poverty rate of 39.1 percent in January 2014 (see NISR 2015).

III.2. Comparability of poverty rates over time Welfare comparisons based on absolute poverty lines are only valid if the poverty lines represent the ‘same purchasing power’ over time. This can be accomplished by adjusting a ‘given’ poverty line and welfare aggregate for changes in prices/purchasing power, and then comparing them against each other to arrive at poverty headcount rates.

# III.2.1 Theoretical framework

Theoretically then, poverty rates estimated from household surveys conducted at two different points in time are comparable when both the welfare aggregate and the poverty threshold from these time periods are appropriately adjusted for price variations between the two survey rounds. The following is an illustration of how the expenditure data and poverty lines need to be updated for robust poverty comparisons over time.

 Estimation of poverty rates in round t�: Suppose we start with a poverty line (z����) that is estimated using a household survey conducted in year t�, and is adjusted to prices in month m� of the same year (t�). Similarly, the consumption aggregate from year t� is expressed in terms of month m� of year t� prices. Then the poor in t� are simply identified by: Where e����is household i’s nominal consumption expenditure per adult equivalent in month
of political reservations in India connects to heightened entry of women-owned establishments but not to increased employment generally of women in manufacturing.<sup>15</sup> From here on, we report exclusively the

> 15 We confirm this limited employment response for women in manufacturing using the NSSO Employment-Unemployment survey stretching from 1983 to 2009. The estimates in the formats of Panels A and B of Table 2 would be 0.088 (0.353) and 0.089 (0.352), respectively, quite similar to Column 6.

19
38.1481 ***|48.5760|***|45.7466|***|
|R sq. Within|0.8573|0.7718||||||||||
|R sq. Between|0.2783|0.2774||||||||||
|R sq.|0.2823|0.2791|0.8181|<br>0.8847||0.9609|0.8103|0.8896||0.8558||
|N|152|120|56|<br>32||24|49|31||18||
|groups|19|15||||||||||
|Method|RE|RE|POOLED<br>2SLS|IV<br> <br>POOLE<br>2SLS|D IV<br>|POOLED IV<br>2SLS|POOLED IV<br>2SLS|POOLE<br>2SLS|D IV<br>|POOLE<br>2SLS|D IV<br>|
|Wald test Chi<sup>2</sup><br>statisticsp-value|0.0000|0.0000|0.0000|<br>0.0000||0.0000|0.0000|0.0000||0.0000||
|Chow test F<br>statisticsp-value|0.76|52||0.234|8|||0.32|44|||

_Note: *** significant at 1% level, ** significant at 5% level, * significant at 10% level. RE - random effects estimation procedure, POOLED IV 2SLS - pooled two stage least squares procedure with instrumental variables on panel data; robust standard errors utilized. The detailed description of all the models applied in our analyses is given in Annex 4._ _Source
9 case, the sample is limited to stayee households in the 2011 round of the main survey that we used for the empirical analysis.<sup>3</sup> A third of respondents agree that emigration makes life harder for those who stay behind, 62% do not agree that Burundians abroad still contribute to the country of origin and a quarter agreed that they have abandoned Burundi. Panel C of Table 1 provide responses to questions that are specific about returnees. Close to a quarter of respondents agree that returnees do not fit in, and close to 67% agree that they get preferential treatment.

As explained below, in the analysis we also explore how the results change depending on the ethnic composition of the communities. The initial expectation (Hypothesis 3) is that in less diverse communities refugee return can have a more adverse effect on social cohesion.

Regarding ethnic composition in Burundi, the Hutu group accounts for close to 85% of the country’s population, with Tutsis and the Twa accounting for 14% and 1%, respectively. We consider a community less ethnically diverse if the majority group accounts for 90% or more of the population. The information on ethnic diversity comes from the community survey. Close to 45% of the respondents in our sample live in less ethnically diverse communities.

Table 1 suggests that some attitudes towards emigration, remittances and return are different for less and more ethnically diverse communities, although the direction of the difference changes across questions. For instance, the share agreeing that returnees help the country is lower in less ethnically diverse communities (54% versus 61%). Individuals in these communities are also more likely to indicate that returnees do not fit in (25% versus 22%). However, those in more ethnically homogeneous communities are less likely to say that returnees receive preferential treatment (64% versus 69%).

Table 1 – Attitude towards emigration and return (share agree %, 2011, stayees only)

|||Ethnic d<br>
# **_Unit of Analysis Issues_**

Most studies use data at firm level with the great majority coming from administrative data, such as census data about formal firms or large samples of firms. The authors clustered SE accordingly in a study where the intervention took place at municipal level.

# **_Dealing with Missing Data_**

We contacted study authors to ask for missing information, such as descriptive statistics at the baseline (mean, standard deviation, and sample size and intra-cluster correlation when applicable), and received quick feedback in most cases. Unfortunately, the quality of data presented varies considerably across studies. In many cases, we were forced to make assumptions to compute SMD, RR, and the SE, for instance:

- When sample size is not provided for the treatment and control groups separately, we arbitrarily split the sample equally.

- When pooled standard deviation was not reported, we used the standard deviation of the control group to compute SE (SMD) and the t-statistic of the treatment effect coefficient to compute the SMD.

- When a study used a cluster of firms at municipality level but did not report the number of firms, we used the number of clusters (municipalities) to compute the standardized effects and SE.

- If there was no information on sample size, means, and standard deviation, the study was excluded.

- In cases where the baseline data was reported for the pooled sample of firms but estimates were provided for sub-groups of firms according to firm size, we split the sample equally among the subgroups and used the same means for subgroups as for the pooled sample.

- Some studies reported the p-values rather than the SE or t-statistics. To convert p- values into t-statistics, we used a conservative approach and used the lower value of t for cases where the coefficient was statistically significant. For instance, for cases where the p-value was between 0.051 and 0.10 we used a t-statistic of 1.65.

39
UNCTAD value is missing; otherwise, we use the UNCTAD data.<sup>29</sup> This approach of rescaling the UNCTAD data to the CDIS data results in aggregate values that are consistent over time and similar to the country-level aggregates reported in Lane and Milesi-Ferretti (2018).

The treatment of zeros and missing values in the combined CDIS-UNCTAD data set deserves some attention because countries are more likely to report zeros in the CDIS data than in the UNCTAD data.<sup>30</sup> Hence, it is likely that many missing observations in the UNCTAD data are actually true zero-valued observations, rather than missing values. In addition, the procedure of combining the two data sets yields some missing observations, even when data are actually available. Specifically, if for any country pair the CDIS and UNCTAD data do not overlap, we cannot calculate the ratio of the two series and thus cannot rescale the UNCTAD series to CDIS levels. Hence, in these instances, even if the UNCTAD data are available for 2001–08, our data set will have missing values for those years. These two features of the data result in a combined data set containing relatively more zeros from 2012 onward, as some true zeros appear as missing observations during 2001–08. To minimize this underreporting of zero-valued observations, we assume that if the first non-missing observation for a country pair is a zero, then all previous observations are also zero.<sup>31</sup>

# **A.4. International Reserves**

Unlike for the other investment types, holdings of international reserves are unavailable at the countryto-country level. Instead, we construct a country-to-region data set on international reserves by

> 29 We also considered an alternative methodology: start with the CDIS data and fill in backward the values for 2001–08 using the growth rates implied by the UNCTAD data. This approach yielded quantitatively similar results to the ones reported in the paper.

> 30 In the CDIS data, observations with zero value represent
of domestic law. Overall, in the sectors and modes of supply covered by the index, the restrictions on foreign investment are far less stringent than the restrictions on the supply of services by foreign professionals (Gootiiz and Mattoo 2009).

**_Figure 12. Restrictiveness of Services Trade Policies, by Region and Sector, 2007/08_**

<!-- Start of picture text --> GCC SAR EAP MENA AFR LAC ECA OECD<br> Financial Telecom Retailing<br> Transportation Professional<br>Note: The number of countries 102<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br><!-- End of picture text --> Source: World Bank and WTO QUASAR database.

# **In services, Arab countries have in general poor regulatory and implementation capacities, weak negotiations infrastructure, and a shortage of resources to administer trade**

**agreements.** The process may be further complicated by the fact that countries have in the past taken very different approaches to international services liberalization, as illustrated by the very diverse extent of the General Agreement on Trade in Services (GATS) liberalization commitments of the region’s WTO members (Ghoneim 2011). In many instances, the extent of commitments reflects the status quo, or even less, especially for old GATT members. They have been assessed to be relatively modest and to include several restrictions on the participation of foreigners. While regional service liberalization has started in some countries (e.g., Morocco, Jordan, and to some extent new WTO acceding countries), the process is lagging behind in the region as a whole.

# **The Arab world has much to gain from regional services liberalization.** Comprehensive

services reforms that involve increased competition and regulatory streamlining would yield benefits that are two to three times the magnitude of those achieved through tariff removal alone (Konan