rafmacalaba/data-use-mentions-extended huggingface dataset:
on socio-emotional skills, we consider a collection of thirty-two behavioral issues as reported by a caregiver, which resembles the questionnaire in the Children section of the National Longitudinal Study of Youth (CNLSY-79), such as antisocial behavior, anxiety/depression, headstrongness, hyperactivity and peer conflicts (for details, see Appendix A.2). The resulting behavioral problem index is re-scaled in such a way that higher values are associated with fewer behavioral issues (socio-emotional score). The survey also contains a module on instructors’ characteristics as well as pedagogical practices collected through an adapted version of the Stallings Classroom Snapshot (Bruns and Luque, 2015), a module on parental attitudes and investment toward children’s education, as well as information about the mentors’ activities in the communities, among others. To better interpret our results, we standardize most of the survey-based outcome variables using the mean and the standard

> 4Only 5 percent of the children in our sample score at the maximum of the scale in two or more subdomains of the reading score (out of eight subdomains) and in three or more subdomains of the math score (out of a total of seven subdomains). Unlike the first experiment, we cannot leverage the national standardized test scores for the second experiment since the test ceased to be universal during the period of interest (after 2014).

9
A for more details on the estimation of and the energy efficiency scenarios.

# **3. Data and Assumptions**

A wide range of data is required to calculate MACs for buildings sector. In this section we discuss the data and their sources. The data that is needed to calculate MACC are following:

- Demographic and economic characteristics and drivers (i.e. population and household numbers, area of commercial space, etc.)

- End-use penetration and technology characteristics in base year and their future projections

- Fuel net calorific values and emission factors

- Fuel and technology costs

# **3.1 Data Sources for Armenia**

Major statistics such as historical growth of population, households, household size were taken from National Statistical Service Yearbook 2014 (NSSRA, 2014). Prices for electricity for different users are based on information available in various notifications of the Public Services Regulatory Commission of Republic of Armenia (PSRCRA, 2014). Data related to technological and pricing of inefficient and efficient energy utilizing technologies are obtained from various sources including GEF (2014a), GEF (2014b), EBRD (2014). The database of MARKAL-Armenia model used for Armenia’s low carbon study (USAID, 2014) was also used for data and assumptions related to penetration rates in the baseline and climate change mitigation scenarios.

# **3.2 Data Sources for Georgia**

A large number of secondary sources have been used to collect the required data for energy efficiency MAC analysis for Georgia. Some of the key sources include recent household energy survey carried out by the Winrock International EC-LEDS project (Winrock International, 2014), which provided data on average household area, average household size (number of persons per household), average percentage of heated area in dwellings, share of households using gas for heating, penetration of end-use technologies, both efficient and inefficient (refrigeration, washing machine, lighting bulbs) in households. Energy Audits carried out by EC-LEDS project (Sustainable Development Centre Remissia, 2014) was used
areas: Czech Republic, Estonia, Hungary and Slovenia. South Caucasus countries (Armenia, Azerbaijan, Georgia) and Albania, however, invest significantly less in R&D.

**Figure 14 – There is Significant Variation Within ECA in Terms of Innovation; Yet The Region is Well Behind the Top Performer, Switzerland**

ECA innovation index, 2011 <!-- Start of picture text --> 60<br>SUI<br>55<br>50<br>45 HUN<br>EST<br>CZE<br>40<br>MDA SVN<br>35 SRB<br>RUS<br>UKR BGR LVA<br>TUR ROM HRV<br>30 SVK<br>ARM<br>POL<br>MKD LTU<br>25 KYR GEO<br>ALB<br>KAZ<br>20 AZE BIH<br>15<br>10<br>30 40 50 60 70<br>Innovation Input Sub-Index<br>Innovation Output Sub-Index<br><!-- End of picture text --> Source: INSEAD, the Global Innovation Index 2011 Note: The chart compares ECA countries with each other in terms of Innovation Input Sub-Index (covers the Institutions, Human Capital and Research, Infrastructure, Market Sophistication, and Business Sophistication in a given country) and Innovation Output Sub-Index (covers Science Outputs and Creative Outputs in a given country). Switzerland (SUI) is also included in the scatter plot for benchmarking as the highest performer in the Global Innovation Index There are several ways in which governments can anticipate the need to shift away from energy-intensive industries by enacting forward-looking skills, training and innovation policies. In particular, countries may: (i) delay the transition to vocational specialization by extending general education, as Poland did; (ii) align vocational and job training programs to future demand from clean industries; (iii) assess and invest in the development of relevant non-cognitive skills; and
**Figure 5. Labor supply evolutions by gender**

<!-- Start of picture text --> 0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>15 16 17 18 19 20 21 22 23 24 25 26 27 28 29<br>Labor supply of women Labor supply of men Gender gap<br><!-- End of picture text --> Source: own elaboration on the ILO School-to-work transition survey of Azerbaijan.

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# **Figure 2: Awareness of Microfinance, by Country (average 2009-10)**

<!-- Start of picture text --> Are You Aware of Microfinancing in Your Community?<br>Botswana<br>Burkina Faso<br>Burundi<br>Cameroon<br>Yes<br>Central African Republic<br>Chad<br>Congo, Dem. Rep. No, not available in my<br>community<br>Côte d'Ivoire<br>Ghana<br>No, never heard<br>Kenya<br>Liberia<br>Malawi<br>Mali<br>Niger<br>Nigeria<br>Rwanda<br>Senegal<br>Sierra Leone<br>South Africa<br>Tanzania<br>Uganda<br>Zambia<br>Zimbabwe<br>0% 20% 40% 60% 80%<br><!-- End of picture text --> <u>Source: Gallup World Poll 2009 and 2010.</u> The Gallup data illuminates potential reasons for the low take-up of microfinance (Figure 2). Asked if they are aware of any institutions in their community that help people obtain small business loans, 16.2 percent of the respondents stated that they had never heard of such institutions. An additional 46.2 percent stated that such institutions are not available in their 12
# **3.1 Internet Coverage**

Hjort and Poulsen (2019) were among the first to exploit the arrival of submarine internet cables between 2000 and 2010 in Africa by using a difference-in-differences estimation to show the impact of fast internet on employment. They find that the probability that an individual is employed increases between 3.1 and 13.2 percent when fast internet becomes available. In all nine countries covered by relevant household surveys (Benin, Ghana, Kenya, Madagascar, Mozambique, Nigeria, Senegal, Tanzania, and South Africa), the probability of being employed in a skilled occupation increases, but the probability of holding an unskilled job is statistically unaffected when fast internet becomes available. While the impact on the overall process of structural change is likely modest, fast internet appears to shift employment shares towards higher-productivity occupations. The average incomes rise in the areas that see changes in employment when fast internet arrives. Overall, the results imply that expanded access to lower cost ICT resulting from the availability of fast internet increases employment rates in Africa, and that in at least some countries, this happens in part due to the technology’s impact on increasing firm entry, raising firm productivity, and facilitating participation in export markets.

More recently, Masaki et al (2020) identify the relationship between access to broadband internet and household welfare. This research is based on household surveys conducted in Senegal in 2011 and 2018, which provide data on household consumption and poverty level as a proxy for welfare. Addressing the concern of endogeneity by employing two-stage least squares (2SLS) regression analysis (using as an instrument the distance to 3G coverage in neighboring areas outside the immediate vicinity of a given area), the results show that mobile broadband coverage is associated with greater household consumption and reduced poverty incidence, both in rural and urban areas, though to a greater extent in the latter. The total consumption of households with 3G coverage is about 14 percent greater than the consumption of households without 3G coverage. Similarly, 3G
deterministic fashion unlike the EM algorithm. Thus, coefficients are drawn from the parameter posterior distribution rather than chosen by likelihood maximization. Hence, the iterative process is a Monte‐Carlo Markov Chain (MCMC) in the parameter space with convergence to the stationary distribution that averages over the missing data. The distribution for the missing data stabilizes at the exact distribution to be drawn from to retrieve model estimates averaging over the missing value distribution. The DA algorithm usually converges considerably faster than using standard EM algorithms: The performance of the estimation technique was assessed based on an _ex post_ simulation using the NBHS 2009 data and mimicking the Rapid Consumption methodology by masking consumption of items that were not administered to households. The results of the simulation were compared with the estimates using the full consumption from NBHS 2009 as reference. The simulation results distinguish between different levels of aggregation to estimate consumption.<sup>26</sup> The methodology generally does not perform well at the household level (HH) but improves considerably already at the enumeration area level (EA) where the average of 12 households is estimated. At the national aggregation level, the Rapid Consumption methodology slightly over‐estimates poverty by 1.6 percent. Assessing the standard poverty measures including poverty headcount (FGT0), poverty depth (FGT1) and poverty severity (FGT2), the simulation results show that the Rapid Consumption methodology retrieves almost unbiased estimates. Generally, the estimates are robust as suggested by the low standard errors.<sup>27</sup> The assumption that the imputed components of consumption follow a joint normal distribution might provide an explanation as to why poverty is slightly overestimated. This would be due to the imputed means of consumption of the imputed items being slightly lower than the actual means since their true distributions are generally skewed to the right. This possibility was explored by assuming a non‐parametric error term in the imputation procedure through the use of chained equations, which performed almost indistinguishably as well as the multivariate‐normal approximation.

> 26
The indicators developed in the previous section can all be associated with extinction risk. However, their relative importance remains a subject for debate because rigorous empirical analysis is difficult. In practice, the conservation community has relied on risk assessments by experts who draw on unpublished species-level information as well as publicly-available data. These assessments typically group species into categories based on their estimated likelihood of future extinction, given current conditions. The resource intensity of species assessments has created several problems for conservation planning, including relatively long intervals between updates; an often-sizable gap between currently-available information on species sightings and The latter gap has motivated the development of methods that can translate generally-observable risk factors into extinction risk estimates for non-assessed species. These methods frequently use other organizations. Such models attempt to capture the collective judgment of thousands of independent experts. They are fitted to data on estimated extinction risks, not observed extinctions, and they unavoidably incorporate assessors’ assumptions about risk determinants section to estimating and applying a predictive model for non-assessed GBIF species that is based on IUCN assessments. In later sections we will also develop and illustrate some alternative approaches to risk index construction.

3.1 Estimating Extinction Risks from IUCN Data Numerous publications cite IUCN as the authoritative source of species risk assessments, while noting the large gap between species documented by organizations like the GBIF and species that have been assessed using IUCN’s time- and resource-intensive evaluation processes. Researchers have responded to this gap with machine-based learning algorithms that predict extinction risks from readily-observable characteristics of species and their environments. These exercises have 2022 [reptiles], Wieringa 2022 [bats], Cazalis et al. 2023 [mammals, reptiles, amphibians, fish], plants (Brazil)], Levin et al. 2022 (North American plants); and species across multiple taxa classed scant attention to other animals (most notably invertebrates) and other major phyla. Second, these pilot modeling exercises are idiosyncratic, with no convergence to a standard approach as difficult to explain their predictions to non-technical people who are stakeholders in conservation
from a non-linear logistic diffusion model, it is under the suspicion of relying on the nonlinearity instead of homogeneity to isolate the impact of faster internet.

A large body of literature that has started since Kraft and Kraft (1978) examined the causal relationship between energy consumption and output. However, the results are mixed. Review studies by Oztruck (2010) and meta-analysis by Bruns et al. (2014) conclude that economic growth drives energy consumption instead of the other way around. There are some exceptions though, which suggest that energy consumption drives economic growth.<sup>5</sup>

# **4. Infrastructure and Income Inequality**

Is there any empirical evidence to demonstrate that increased infrastructure investment or service provisions increase the incomes of poor households (i.e., reduces poverty) and thereby reduces income inequality? A few studies examine this issue (Démurger 2001; <mark>Calderón</mark> and Chong 2004; <mark>Calderón & Servén</mark> 2004, 2010; Chatterjee and Turnovsky 2012; Chotia and Rao 2017a, 2017b; Hooper et al. 2018). Table 3 summarizes the methodologies used and the findings of these studies.

<mark>Calderón</mark> and Chong (2004) investigate the relationship between physical infrastructure stocks and income inequality using data from more than 100 economies around the world for the period 1960-1997. Relationships of different infrastructure stocks – telecommunications, energy, roads, and railways – with income distribution were investigated individually and in aggregation. Using alternative empirical techniques (e.g., cross-country and GMM on dynamic panel data), they found that infrastructure stock is negatively linked with inequality (i.e., higher the infrastructure stock, the lower is the income inequality); the relationship is more prominent in low-income countries. Calderon and Serven (2004) expanded the study in terms of the number of economies (121 instead 101 before) and time horizon (1960-2000 instead of 1960-1997 before) using the same methodology
> is the output response to temperature shocks already estimated. Using this formula, a 1<sup>_◦_</sup> C increase in temperature, for example, would reduce agricultural wages by about 10 percent in the absence of adaptation.

Finally, a word about precipitation. Except in the land value regressions, where its direct effect is insignificant, we find that higher rainfall is associated with greater agricultural productivity. Putting aside the enormous uncertainty involved in forecasting precipitation patterns over the Indian subcontinent mentioned earlier, whatever consensus has emerged points to a very modest rise in rainfall over the next three decades. Guiteras (2009), for one, uses the most recent IPCC projection of a 4 percent increase. Meanwhile, temperatures across India over this period are expected to warm by around 1 _._ 25<sup>_◦_</sup> C on average (see below). Based on our estimates in Table 4, we predict that a 4% increase in rainfall (at the district area-weighted means of the data) would raise agricultural output per hectare by at most 1 _._ 0%, a figure dwarfed by the 37% decline in yields implied by the concomitant rise in<sup><u>)</u></sup> temperature (i.e., weighted average of<sup>_d_lo</sup> _dT_<sup><u>g(</u></sup><sup>_Y_</sup> ∆ _T_ ). Results in Guiteras (2009) and Sanghi, Mendelsohn, and Dinar (1998) also support the view that climate change will overwhelmingly affect agricultural productivity in India through higher termperatures.

# **4.6 Cereal prices and climate**

Since the main cereals produced and consumed in India, rice and wheat, are traded on both domestic and world markets, it would be nonsensical to use the cross-district correlation between current prices and climate (let alone weather) to
between time inputs by exploiting the variation in parental time inputs and child care choices conditional on educational attainment and marital status.

# **4.2.3 Parental wage offers**

Parameters in equations (22) and (23) include the gender- and education-specific parameters of the lognormal wage offers distributions, _{μwf_ ( _ef_ ) _, σwf_ ( _ef_ ) _}_ for fathers and _{μmf_ ( _ef_ ) _, σmf_ ( _ef_ ) _}_ for mothers, and the _{ρ_ 0 _, ρ_ 1 _}_ parameters of assortative mating in married parents’ wage offers. Since fathers always work in the model, their wage offer distribution is equal to the observed (accepted) wage offer distribution, allowing us to directly estimate the former from the empirical wage distribution. Mothers, instead, decide on labor supply, thereby making the observed distribution of accepted wages a truncation of the unobserved distribution of wage offers. Since the lognormal distribution is recoverable and we characterize mother’s optimal labor supply decision, we can identify wage offer parameters from observed hourly wages. We identify gender- and education-specific parameters from the observed wage distribution conditional on these observables and identify the assortative mating parameters from correlations in spousal wages conditional on education.

# **4.2.4 Child care providers**

The identification of their productivity for child development is discussed in Section 4.2.2; here we focus on the remaining parameters. For child care centers, these are the fixed costs _{F_<sup>_L_</sup> _, F_<sup>_H_</sup> _}_ and the lognormal distributional parameters for heterogeneous variable costs, _{μ_<sup>_L_</sup> _c_<sup>_, σ_</sup> _c_<sup>_L}_and</sup><sup>_{μH_</sup> _c_<sup>_, σ_</sup> _c_<sup
# **c. Percentage change in real earnings, by informality status and sector (public vs. private)**

<!-- Start of picture text --> 0.74<br>0.43<br>0.12<br>0.05 0.04 0.00 0.06 0.02<br>Private Public Informal Formal<br>Short-run Long-run<br><!-- End of picture text --> _Source: Own estimates using the 2018 Kosovo LFS._

**Table 4. Simulated change in the wage bill by sector and firm size**

|Firm size|Sector|Percentage<br>change in wage<br>bill, short-run|Percentage<br>change in wage<br>bill, long-run|
|---|---|---|---|
|Between 1 and 10 persons|Private|1.34|0.34|
|11 to 19 persons|Private|0.67|0.18|
|20 to 49 persons|Private|0.43|0.16|
|50 or more persons|Private|0.01|-0.08|
|Unknown but less than 11 persons|Private|0.55|-0.04|
|Unknown but more than 10 persons|Private|0.14|0.00|
|Total|Private|0.48|0.06|
|Between 1 and 10 persons|Public|0.34|0.06|
|11 to 19 persons|Public|0.14|0.06|
|20 to 49 persons|Public|0.01|-0.02|
|50 or more persons|Public|0.01|0.00|
|Unknown but less than 11 persons|Public|0.10|0.01|
|Unknown but more than 10 persons|Public|
As done in Figure 3 for the cyclicality parameter, we first establish some stylized facts for our measures of volatility by comparing them across the two country groups (Figure 4). A few stylized facts emerge. _First_ , as shown in Figure 4A, the volatility of primary expenditures, government consumption, and revenues is much higher for EMDEs than for advanced economies. The difference between the means of the two groups of countries is statistically significant at the 1 percent level. It is interesting that the gap in the volatility of government consumption is larger than that for primary expenditures, highlighting the role of government consumption in the volatility of fiscal policy.

_Second_ , the primary balance volatility is smaller and closer between the two groups compared with the other fiscal policy indicators, but one must be careful in doing a direct comparison to the other two variables. A 1 percent exogenous change in government expenditures for an average EMDE only leads to a change in the primary balance (as a percent of GDP) of about 0.3 percent because government size is about 30 percent on average.<sup>13</sup> If we adjust for this factor the differences between the two indicators (expenditures and the primary balance) are not that large.

_Third_ , as shown in Figure 4B, the group of commodity exporters displays a much larger volatility of fiscal policy compared to non-commodity exporters or to either of the other two groups in Figure 4A. The difference between the means for commodity exporters and non-commodity exporters is significant at the 1 percent level. As in the case of EMDEs, government consumption is more volatile than the other indicators, although by a small margin.

Now we look at how this volatility has changed over the years. We make use of the results from equation (1) and calculate averages of the standard deviation of the residuals over five-year rolling windows. Figure 5 displays the results for the four fiscal policy indicators for the group of EMDEs. While the patterns across the four charts are not identical, there is
# **4) HOW DIVERSIFIED IS SADC TRADE?**

High barriers between countries reduce not only the volume of a particular product being exported to a particular destination, but also the number of different products being exported there (Melitz, 2003). Therefore, we would expect greater integration to manifest itself in the form of larger varieties of products being traded. To enhance our understanding of SADC integration, this section now assesses the diversity of products being traded; it first reveals the share of exports accounted for by the top 10 export products before presenting measures of the „thickness‟ of trade.

Figure 4 presents the share of total exports to SADC and the Rest of the World (ROW) made up by the top 10 export products by value to each region in 2008. What is striking is the high level of export concentration. The top 10 export products account for over 60 percent of exports for SADC members outside of SACU. In some cases (Malawi, Mozambique, Zimbabwe) the top 10 products account for over 90 percent of export volumes.

The concentration of SADC exports, however, is higher with the Rest of World than with SADC members. In most cases the export concentration ratio is 15 percentage points or more greater for exports to the rest of the world than it is for exports to other SADC members. This is consistent with the aggregate analysis of intra-SADC trade and corroborates the finding that SADC trade is regionalized.

**Figure 4: Share of top 10 export products in total exports to SADC and Rest of world (ROW), 2008**

<!-- Start of picture text --> 120<br>100<br>80<br>60<br>40<br>20<br>0<br>Exports to SADC Exports to ROW<br>Zimbabwe Zambia Tanzania Seychelles SA Mozambique Mauritius Malawi Madagascar Namibia<br><!-- End of picture text --> Notes: Own calculations using HS 6-digit 2008 data obtained