rafmacalaba/data-use-mentions-extended huggingface dataset:
main cohort of treated parents we consider (“main cohort” in Figure A2).</mark> <mark>Results are reported in Table 7. We find that mothers who spent their first five years of life under the presence of refugees are less likely to complete more than secondary education as adults. This reinforces the findings of previous studies on the long-term impact of early life exposure to shocks and schooling performance (Shah and Steinberg, 2013 and Cunha et al., 2006). They are also less likely to own land and a house and more likely to participate to the labor market later in their life. In particular, increased labor market participation can potentially translate into reduced child care at home and poorer anthropometrics of their less than 5 years old offspring, as shown in previous literature (</mark> see e.g. <mark>Saabneh, 2016; H</mark> ill et al. 2005; Brooks-Gunn et al., 2002; <mark>Belsky and Eggebeen, 1991)</mark> . <mark>In the same vein, we find that fathers who spent their first five years of life under the presence of refugees are less likely to own key assets such as land and house. However, early life exposure to refugees has no longterm impacts on fathers’ labor market participation and their attainment of more than secondary education.</mark> <mark>Finally, we use the 1991/1992 and 1996 DHS data to assess whether under five years old children’s anthropometric health outcomes in 1996 (during the crisis) were worse off as compared to 1991/1992 (prior to the crisis). We find in table 8 that children in refugee receiving areas in 1996 had lower Weight for Age, Weight for Height and a lower Biomass Index scores as compared to their peers. They were also more likely to be wasting, underweight, have diarrhea or fever. There is no evidence instead of an increased level of under five years
head under age 5, 0.36 children under age 15 that are not the biological children of the head, and 1.18 other members that include grandparents, aunts, uncles, and other extended family members. Only 12 percent of the sampled children have a parent that ever attended school.

We use the Raven‟s Colored Progressive Matrices (CPM) to measure a child‟s cognitive ability. The Raven‟s CPM is a measure of fluid intelligence or problem solving ability, and it does not require formal schooling to be able to answer the questions (Raven, Raven, and Court 1998). The test does not depend heavily on verbal skills, making it relatively “culture free” (Borghans, Duckworth, Heckman, and Weel 2008). In the Raven‟s test, the child respondent is asked to select the image that is missing in order to complete a picture. This type of question is novel to the children in Nahouri province, thus providing a truer measure of problem solving skills. We ask 18 questions from the Raven‟s CPM and on average, children in our sample answer 4.7 questions correctly. Younger children answer fewer questions correctly than older children (the average number correct for children age 5 is 2.7 and for children age 15 is 7.3).<sup>6</sup> To control for this relationship between age and raw test scores, we calculate a z-score for each child measured as the child‟s raw test score minus the average score for the same age children divided by the standard deviation of test scores for children of that age.<sup>7</sup> Therefore, the mean of the Raven‟s z-score is zero and the standard deviation is one for each age and across all ages.

In Table 1, we present summary statistics about children‟s schooling. Few households in rural Burkina Faso ever enroll all of their children. For households with at least two primary school-aged children,
|94|98|109|73<br>137|
|Insurance and financial services|55|46|36|78|76|93<br>60|
|Other services|123|139|183|107|128|35<br>45|

Source: IMF Balance of Payments statistics.

And a cursory examination of trade developments in various categories of services does not alter the picture. In fact, it raises a lot of new questions but offers few, if any, answers, with developments in some instances defying common wisdom. For instance, considering revealed comparative advantage of Turkish construction companies, supported among others by press information about construction bids won abroad (e.g., airports in Cairo and Moscow), a 34 percent lower level of their foreign activities in terms of value in 2004 as compared to 1999 is difficult to explain. Similarly, the decline in both exports and imports of insurance and financial services would be tough to explain in view of the restructuring of the financial sector and burgeoning trade in goods in 2001-2004. In a similar vein, consider the developments in transport services juxtaposed against trade in goods and the expansion in ‘producer-driven’ network trade (See Section 5). The value of total trade turnover in goods in 2004 more was more than twice (2.16) as high as its value in 2001, while trade in transport services rose merely 56 percent or slightly more than half of the increase in goods trade. Simultaneously, ‘producer-driven’ network trade, critically dependent on smooth functioning of logistic services blocs, has been the driver of this expansion with its share in total trade turnover increasing from 15 percent in 2000 to 17 percent in 2003. It is hard to imagine that this two-way trade has not involved provision of transport and other services paid or provided by non-resident providers.

While the relationship between growth in goods trade and transport services purchased abroad or sold to foreign contractors is rarely linear, the gap between respective increases is seldom that
Coronavirus Pandemic (COVID-19): https://ourworldindata.org/coronavirus

- Dong, E., Du, H., & Gardner, L. (533-534.). An interactive web-based dashboard to track COVID-19 in real time. _The Lancet infectious diseases_ , 2020.

- UNHCR & World Bank. (2020). _Understanding the Socioeconomic Conditions of Refugees in Kenya. Volume B: Kakuma Camp. Results from the 2019 Kakuma Socioeconomic Survey._

- UNHCR & World Bank. (2020). _Understanding the Socioeconomic Conditions of Refugees in Kenya. Volume A: Kalobeyei Settlement. Results from the 2018 Kalobeyei Socioeconomic Profiling Survey._

- UNHCR & World Bank. (2020). _Understanding the Socioeconomic Conditions of the Stateless Shona Community in Kenya. Results from the 2019 Socioeconomic Survey._

- WFP. (2020, December). _WFP Warns of Critical Food Shortages for Refugees in Kenya_ . Retrieved from https://www.wfp.org/news/wfp-warns-critical-food-shortagesrefugees-kenya

- Himelein, K. (2014). Weight Calculations for Panel Surveys with Subsampling and Split-off Tracking. _Statistics and Public Policy_ , 40-45.

- Alix-Garcia, J., Walker, S., Bartlett, A., Onder, H., & Sanghi, A. (2018). Do refugee camps help or hurt hosts? The case of Kakuma, Kenya. _Journal of Development Economics, 130_ , 66-83.

- MacPherson, C., & Sterck, O. (2021). Empowering refugees through cash and agriculture: A regression discontinuity design. _Journal of Development Economics, 149_ .

21
# _B. R&D investment_

The main sources of data on R&D investment based on comparable definitions are UNESCO, OECD, and RICYT for Latin American countries.<sup>2</sup> These sources use surveys of government agencies to compile information based on a common definition of R&D, which “includes fundamental and applied research, as well as experimental development.” However, R&D does not include investments in mining exploration or soil analysis, and thus might imply a bias against natural resource activities and agriculture.<sup>3</sup> To check the consistency of the data, we compared country data across sources whenever possible. In addition, we examined the over-time variation within countries of the resulting series and discarded data that seemed not to follow reasonable patterns. For example, we discarded observations that were substantially (more than 50% in US dollar values at constant US prices) above or below preceding or subsequent observations. The resulting total number of observations for total R&D investment is 1,453.

In addition, future research could focus on the composition or structure of R&D funding and performance across countries. R&D funded and/or performed by productive firms might be more efficient than investments undertaken by government entities that are not tied to productive activities. Thus the database also covers the distribution of national R&D effort across sources of funding and the agents that perform the R&D activities. The available information covers the shares of total R&D funded and performed by the “productive” sector, as well as higher education and the “public” sector. The productive sector, however, includes private and public

> 2 RICYT is the Spanish acronym for Ibero American Science and Technology Indicators Network (Red Iberoamericana de Indicadores  de Ciencia y Tecnología). This source provides two alternative definitions of R&D, but these are actually two sub-components of R&D – investments in research and investments in development for a handful of
Figure 6: Means 50 and 95 Percent Confidence Intervals of the Sample Distributions of the Labor Payment Changes by Sector from Armenian-CIS FTA—30,000 simulations.

<!-- Start of picture text --> 5<br> 4<br> 3<br> 2<br> 1<br> 0<br>-1<br>-2<br>iness activitiesus in es ctivities a g n ivitiesct rt<br>al estate and be nsport via pipelucation da iliary transport x ing and quarryi n st and courier aoer land transpo h nkinga urance s<br>Construction ManufacturingHealth and social work R Transport via railwaysTrE Au Mi POt Telecommunications Hotels and restaurants Air transport B In<br><!-- End of picture text --> Note: The boxes are limited vertically by the 25% and 75% quartiles. The bars in the box are the means. The vertical lines extend to the 2.5% and 97.5% percentiles.

70
and collated by the World Bank.<sup>2</sup> These data include various measures of inequality and poverty, as well as a number of other useful distributional indicators, such as decile or (in some cases) percentile shares of the income or consumption distribution. We restrict our analysis to observations after 1980 and for which GDP and other basic socioeconomic variables are available. This reduces the sample to about 165 countries between 1980 and 2018 and includes all economic ranges, from low to high income.<sup>3</sup> The information from PovcalNet is combined with the World Development Indicators (WDI). This is the primary World Bank collection of development indicators compiled from officially recognized international sources.

There are more than 1,900 Gini and other inequality measures observations in our data set, most of which are calculated from direct access to household surveys. In the analysis, the Gini index—an indicator typically not sensitive to the tails of the income distribution—is accompanied by a measure that is more tail-sensitive, the D9/D5 ratio. A bigger gap between income and consumption inequality detected by this ratio is interpreted as a validation of our hypothesis that consumption-based measures tend to capture less accurately the information from the upper tails of the distributions.

In section 4, household-level data on income and consumption are taken from several sources. For a first group of SSA countries—Ghana, Kenya, Nigeria, and Uganda—we use household income data obtained from the Rural Income Generating Activities (RIGA) project, a collaborative effort of the FAO, the World Bank, and American University.<sup>4</sup> The RIGA database is composed of a series of constructed variables about rural and urban income-generating activities created from the original consumption data sources. In particular, we focus on the household-level income aggregate dataset (RIGA-H), which provides data on different income sources, such as crop and livestock production, household enterprises, wage employment, transfers, and nonlabor earnings .

For
Access to adequate water across emerging economies is highest across the seven adequacy dimensions followed by access to electricity. Yet, while substantial progress has been made in increasing access to clean drinking water within the last decade, many households, particularly in Sub-Saharan Africa and in rural areas still lack access. Access to water is lowest in Madagascar, where only 43 percent of HHs have access to adequate water.

# **_<mark>3.4 Aggregating to the Adequate Housing Index</mark>_**

The _Adequate Housing Index_ presents one unidimensional measure of housing adequacy. By harmonizing HCES variables and relying on one uniform definition of adequacy, the _AHI_ offers a unique metric to compare housing adequacy within and across countries. Further, the _AHI_ offers a summary picture of the multiple facets of housing adequacy, reduces complexity, and facilitates evaluation of housing adequacy. _AHI_ also allows to rank countries according to their housing adequacy, monitor progress, and incentivize institutions and governments to revisit their standards in terms of housing adequacy. Rankings have become drivers of behavioral change and are consistently applied in a variety of areas (e.g., Kelley and Simmons 2015).

The aggregation of the underlying variables is the most important step within indicator creation. We construct the _Adequate Housing Index_ for every household _i_ in country _c_ to estimate if that household lives in an adequate house AHIi,c = w1*a1i + w2*a2i + ... + w7·a7i (1) where _AHI_ i,c is the adequate housing index for household _i_ in country _c_ , where (a1i, a2i, ... , a7i) are the seven housing adequacy dimensions of household _i_ coded as dummy variables as discussed above, and where (w1, w2, ... , w7) are the respective weights assigned to each adequacy dimension and sum to one (∑7 = 1 ). The _AHI_ ranges between 0 (highly inadequate housing unit) and 1 (highly adequate housing unit). ii=1
With major hydropower projects coming on stream in the next few years, Bhutan’s macroeconomic environment is expected to change radically in a short period of time. This will potentially exacerbate the already existing imbalances between public and private sector. The three hydropower projects currently under construction in Bhutan together have a cost of 150 percent of the country’s GDP. Already today, hydropower contributes 30 percent of the Royal Government of Bhutan’s domestic revenues and this figure is expected to reach 56 percent by 2024/25. While Bhutan’s hydropower developments are a huge opportunity for the country’s development, they also carry risks. In particular, they are expected to lead to an appreciation of real exchange rates through higher wages and land and real estate prices. This “Dutch disease” phenomenon will make it ever harder for the private sector to compete in the tradable sector. Furthermore, because the hydropower revenues will enter Bhutan through the government budget, the incentives to expand the size of the public sector and increase public sector pay and benefits are likely to rise. This might limit opportunities for the private sector even in the non-tradable part of the economy.

# **3. Data, Analytical Strategy and Econometric Model**

# **3.1. Data**

The analysis relies on data from the 2014 edition of the Bhutan Labour Force Survey (BLFS), described in detail in MoLHR (2015). The BLFS is a household survey that is conducted annually by the Department of Employment of Bhutan’s Ministry of Labour and Human Resources (MoLHR). The BLFS covers a nationally representative sample of around 6,000 households in all 20 _dzongkhags_ of Bhutan. As Bhutan’s urban residents are more heterogeneous than its rural population and economic activities in urban centers are more diverse, urban households are oversampled. In 2014, the BLFS covered 1,479 rural and 4,440 urban households for a total sample of 5,919 households. On average, sampled households contained about four household
describes its key steps and data requirements in more detail.

# **4 Imputation-based Methods**

Calculating unit costs for any economic activity is challenging when there are fixed and joint costs. Hence, even if surveys collect direct information on carriers’ self-reported costs, one can still be skeptical about the degree to which they capture underlying true costs. It is therefore common practice in economics to estimate unit costs assuming that producers minimize costs. As long as there is data on key cost components such as driver wages—which can be proxied by other low-skilled occupational wages—fuel, and vehicle depreciation, a cost minimization routine can be used to impute optimal itineraries and associated costs.

Over the recent past, increased availability and diffusion of Geographic Information System (GIS) data and software enabled researchers to calculate least-cost routing decisions and associated transit times using standard computers. The starting point is a digitized transportation network. Ideally, this should encompass all relevant infrastructure modes (rail vs road) and road types (highway, two-lane, paved, gravel) linking the nodes that represent key centers of economic activity in the economy under study. Information about travel speed along each link helps calculate fastest routes and associated travel times between nodes. A related choice for the determination of the nodes is one of geographic aggregation. Researchers typically use centroids of metropolitan or administrative areas of various size. Another possibility is to impose a high-resolution grid. While doing so at a high geographic detail increases precision, it comes at the cost of steep computational times. Well established algorithms and numerical methods help to resolve the trade-off. Dijkstra’s algorithm and fast-marching method are widely used to find shortest or least-cost paths between nodes in a road transport network (Allen and Arkolakis 2014, Donaldson 2018).

Beyond the technical aspects of route optimization, formulating the relevant cost minimization problem requires making assumptions on how carriers operate: do they chose fastest or shortest routes? What is their willingness to pay tolls if that option is available to reduce travel distance and duration?
<mark>of 0.15kg/USD. Similarly, Colombia's cement export emissions are 0.37kg/USD, below the EU's average of 0.5kg/USD.</mark>

**Figure 5 Top 5 and bottom 5 countries in Aggregate Relative CBAM Exposure Index**

<!-- Start of picture text --> 0.10<br>0.08<br>0.06<br>0.04<br>0.02<br>0.00<br>-0.02<br>-0.04<br><!-- End of picture text --> _Source:_ Maliszewska et al. (2023). _Notes:_ Underlying data for relative CBAM exposure index includes carbon emissions intensity data in 2017 and trade data in 2019. The formula of the Aggregate Relative CBAM Exposure index can be found at <u>https://www.worldbank.org/en/topic/trade/brief/technical-note-for-the-cbam-exposure-index.</u>

**Regarding developing countries’ economic exposure, the economies of Mozambique and Ukraine are the most susceptible to CBAM.**<sup>20</sup> In 2022, affected product exports to the EU accounted for 9.3 percent of Mozambique's GDP to the world and 2.8 percent of Ukraine’s.

**The CBAM sets a precedent that is likely to motivate other countries to implement similar schemes, potentially exacerbating effects on developing countries.** Canada, the UK, the US, and Japan are all contemplating the creation of their own carbon border adjustment mechanisms (Law 2023).  Additionally, the range of products and emission scopes included under the EU CBAM is expected to broaden over time.

**The EU CS3D regulation is set to impact GVC metal exports to the EU, both directly and indirectly.** Figure 6 highlights Armenia’s metal GVC sector as the most affected
In order to explore action at the extensive margin, we also consider the simple number of exporters of good _k_ to OECD countries.

Our data are COMTRADE import data for OECD countries (either taken as a bloc or disaggregated by importer) at the product level. Our preferred product classifications are SITC4. The alternative, HS6, is more disaggregated (with 4,990 to 5,016 lines depending on the year against 1,158 to 1,300 for SITC4), but the sample period is longer with SITC4, which also underwent fewer revisions. In terms of country coverage, SITC4 data covers 210 countries between 1962 and 2006 (44 years); HS6 coverage is nominally available starting 1988, but with only 12 countries (9 of which are OECD members) expanding gradually to 116 countries in 1995 and 140 in 2006. Descriptive statistics for our sample are shown for our indices in Table 1.

||<br>Descrip|Table 1<br>tive sta|<br>tistics|||
|---|---|---|---|---|---|
|Variable|Obs|Mean|Std. dev.|Min|Max|
|_k_<br>_n_|54030|144|53|12|253|
|All exporters<br>_OECD_<br>_k_<br>_n_|54030|57|35|1|223|
|Herfindahl|54030|0.19|0.13|0.03|1|
|Theil|54030|2.68|0.48|0.86|4.87|
|Extra-OECD expo<br>_OECD_<br>_k_<br>_n_|rts only<br>53769|35|31|1|194|
|Herfindahl|53769|0.35|0.22|0.03|1|
|Theil|53769|3.05|0.60|0.
page 23 However, for Ghanaian assumptions on the health impacts of vaccinations and encounters, the degree of complementarity plays an important role. If e and v can be produced as perfect joint products so that the isocost curve looks like Figure ld, then B is very large _(6_ approaches zero) and Strategy F is preferable for a > .9. If, on the other hand, the opportunity cost of superuviung VH Ws from a frxed center is substantial in terms of foregone vaccinations so that the Strategy F iscoat curve resembles Figure la, then B approaches one (d approaches infinity) and Strategy M is preferable for s < 1.0.

Based on these illustrative parameter esatmates, the choice of primary health care integration strategy seems to be quite sensitive to the particularities of the epidemiologLcal situation and the costs of production in a specific region. Where the relative impacts of vaccination and basic health services and the **relative** costs of the two strategies resemble the West African data and assumptions used to generate rows (A) and (B) of column (4), the degree of complementarity of the joint production of vaccinatdons and encounters in fixed centers is an imporcant input to the strategy choice.

# V. Concluding Remarks

With only two parameters for the objective function and three from each cost function, the model presented here is extremely parsimonious. The advantages of this parsimony are that the coefficients of the model are relatdvely easy to estimate and that the model can be relatLvely easily understood by decision-mnakers. Of course, the parsimony is purchased at the expense of several strong assumptions. Most important among these is the assumption that the choice between the fixed and mobile integration strategLes is the important policy decision and is separable from other government policies and programs within and without the health sector. A second critical assumption is that the units of analysis can be the "average encounter" and the "average vaccination" and that the chosen integration strategy is independent of the mix or impacts of these average events. A