rafmacalaba/data-use-mentions-extended huggingface dataset:
15 their own individual heating system (as opposed to one in common with the rest of the building). About 58% of those who use gas for space heating live in single-family homes, 20.7% live in apartments in multi-family buildings, and 21.6% has other living arrangements.

For 2017 and 2018, we can compute the quantity of piped natural gas consumed during the year from the expenditure on gas (and the gas HUS, if received by the household) reported in the HBS, because by 2017 the block tariff system had been removed and the gas tariffs were stable in 2017 and 2018. Table 7 summarizes the annual gas consumption levels in 2017 and 2018. They were similar, in part because the price of gas was essentially unchanged over two years and in part because the weather was very similar in those two years (bottom panel of table 7). The average household uses about 800 cubic meters of natural gas a year.

Figure 2 presents the distribution of gas consumption in 2017 and 2018 combined for households who use gas for space heating, distinguishing for households that did and did not receive HUS assistance. This figure conveys an important piece of information: The distribution of gas consumption for HUS recipients is to the right of that for the other households. In other words, HUS recipients tend to consume more gas. This raises the question whether HUS recipients get HUS assistance _because_ they consume more, or increase consumption _because_ they receive assistance, or do a little bit of both.

Table 8 begins to examine this question by displaying the frequencies of households that fall in each quartile of the distribution of (net) income and each quartile of the distribution of gas consumption. This table shows how surprisingly evenly distributed households are in terms of income and consumption: The poorest families (those in the first income quartile) are certainly not exclusively concentrated in the first or second consumption quartile, and the richest families (those in the fourth income quartile) are certainly well represented even in the
|
|Equity participation|34|37|58|88|118|145|194|337|
|Other|58|27|29|18|28|60|82|89|
|Foreign Liabilities|3,796|**3,688**|**6,385**|**9,379**|**3,271**|**5,307**|**8,110**|**11,442**|
|Interbankdeposits|504|82|116|354|328|1,272|1,556|2,398|
|Loans|2,837|2,962|5,465|8,100|1,869|2,100|4,254|5,907|
|Non-residents FCDs|247|412|571|731|953|1,806|2,028|2,931|
|Others|209|232|232|193|121|128|271|205|

Source: own calculation based on Central Bank data.

||Table 17<br>**Commercial**<br>**Banks' Exposure**<br>**to Nonresidents**<br>**(%**of total assets)<br> <br>|
|---|---|
||Claims on nonresidents<br>Liabilities to nonresidents|
|1981|0.1<br>0.1|
|1982|0.4<br>0.3|
|1983|0.9<br>0.8|
|1984|1.8<br>2.0|
|1985|0.2<br>0.2|
|1986|6.4<br>3.8|
|1987|6.6<br>3.9|
|1988
<!-- Start of picture text --> Figure 28: Foreign Exchange Reserves of Developing Countries<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>Other Developing China Russia India Brazil<br>US$, trillions<br><!-- End of picture text --> Source: World Bank, World Development Indicators.

45. Third, if indeed the RMB undervaluation was the main reason for the trade deficit of the United States and China‘s trade surplus with other countries – such as Japan; Korea; Taiwan, China; and Germany – China‘s trade surplus with these countries should have widened. However, to the contrary, China‘s trade surplus with other advanced countries turned into a deficit (Figure 29).

35
20 where V(i) and Y(i) are investment and GDP in year i.<sup>8</sup> Note that this procedure cannot account for the loss of existing capital stock through depreciation.

Government Investment (M(g/Y): The change in government capital is simply the difference between accumulated total capital and accumulated private capital, or A/(1-A) times the LIS of (12).

Labor Supply Growth (L): Population growth (denoted Population) is used to proxy for the change in labor supply.

Inflation: The inflation rate is measured as the average annual growth rate in the GDP deflator. This variable is used in the investment regressions to proxy for a measure of real interest rates. If nominal rates are fixed, higher inflation rates could lead both to lower real borrowing costs, and higher returns to physical capital accumulation.

Other Variables: It is important to control for as many additional non-tax factors as possible that may affect the output of the economy. For example, a sharp decline in the terms-of-trade will lead to - fall in real GDP, independent of the tax system or of investment behavior. The terms of trade variables (expressed in percentage terms) were derived rom the GDP price deflators used in the World Tables and exchange rates. Using the alternative UN definition of the terms of trade in the regressions did not appreciably affect the other coefficients.

.L There is an alternative procedure for calculating AK/Y, which is to accumulate real investment over the 9-year period, and then divide by the initial year GDP. However, such a procedure introduces simultaneity bias, since even if all countries had constant investment to GDP ratios, countries which happened to enjoy high growth rates would also experience a higehr ratio of accumulated investment to initial GDP, leading to a spurious correlation between capital accumulation and output growth.
A1.7 Gallup International Gallup International Association is a group of 49 marketing agencies around the world. Gallup was founded in 1947 and is headquartered in Switzerland. As of May 1999, Gallup International can be reached at httrp://www.iallup-international.com/surveyl.html.

In 1997, Gallup Intemational celebrated its 50th anniversary with a survey of around 30,000 people in 44 countries. Opinions were collected on a variety of topics including corruption in society. For corruption, Gallup asked respondents "From the following groups of people can you tell me for each one of them if there are a lot of cases of corruption, many, few cases or no cases of corruption at all: 1) politicians, 2) trade unionists, 3) public officials, 4) businessmen, 5) judges, 6) ordinary citizens, 7) clergy/priests, and 8) journalists." We construct a country index: of corruption in the public sphere as the average responses regarding politicians, public officials and judges. We assign a value of 2 to "a lot", a value of 1 to "many" and a value of 0 to "few or none". For each of these three categories, we construct a weighted average of these three numbers using the fraction of respondents in each category. We then construct a simple average across the three categories as the country index.

Table A1.7 Gallup Intemational 50th Anniversary Survey <!-- Start of picture text --> From the followingi groups of people can you tell me for<br>each one of them if there are a lot of cases of corruption,<br>many, few cases or no cases of corruption at all:<br>1) politicians<br>2) trade unionists<br>3) public officials<br>4) businessmen<br>5) judges<br>6) ordinary citizens<br>7) clergy/priests<br>8)
perspective: the trend in international and government debt securities outstanding since 1994 for a similar, but smaller, subset of 22 countries tracked by BIS (Figure 2).<sup>ii</sup> Again, the rapid relative and absolute increase in domestic sovereign borrowing (this time specifically in the securities markets) is clear.

**Figure 1: Domestic and external debt of 26 EMs**

**Figure 2: Total government securities outstanding of 22 EMs**

<!-- Start of picture text --> USD trn Domestic sovereign debt % GDP USD trn<br>6<br>External sovereign debt<br>9 50%<br>Total govt. debt (RHS)<br>8 5<br>Domestic<br>7 40% securities<br>4<br>6<br>30%<br>5 3<br>4<br>20%<br>3 2<br>2 10% International<br>1 securities<br>1<br>0 0% 0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012(f)<br><!-- End of picture text -->

**Source: Calculations based on JP Morgan EM Debt and Source: Calculations based on BIS securities data Fiscal Indicators (2012) and the IMF World Economic Outlook (April 2012)**

**3**
*|-0.0753|-0.2161***|-0.1337***|
|**Panel (c)**||Hour|ly wages||
||All|Teenagers<br>(15-19)|Young adults<br>(20-24)|“Young old”<br>(25-29)|
|**Unconditional**|-0.0489*|-0.1025|-0.0454|-0.026|
|**Conditional on: education**|-0.0536**|-0.0976|-0.0455|-0.0340|
|**Conditional on: hours of work, education, actual work experience**|-0.0353|-0.1565*|-0.0460|-0.0205|
|<br>**Conditional on: previous variables, plus worked while studying, migration**<br>**status, job search method, goal in life, rural area**|-0.0823**|-0.1412|-0.1154**|-0.0909*|
|**Number of observations**|1896|316|703|877|

_Note: * p<.1; ** p<.05; *** p<.01. Source: own elaboration on the ILO School-to-work transition survey of Azerbaijan._ 25
What matters for economic growth are the spillovers to other firms withiin and across industries. Evidence on this is much less robust. The case study literature has argued that positive spillovers are significant. It has also documented the importance of the availability in local skills and in-house technological capacity in adapting and using techniques developed elsewhere (Lall, 1992; Evenson and Westphal, 1995). Conversely, recent micro-econometric studies using panel datasets of enterprises have come to more ambiguous conclusions. Some analysts have found a statistically significant negative relationship between the size of foreign investment in an industry or economy and the productivity performance of domestic firms (e.g., Harrison, 1996; Haddad and Harrison, 1993).

This paper investigates the impact of foreign investment on productivity performance of firms in the Czech Republic during the initial post-reform period (1992-96).i We distinguish between firms that established partnerships with foreign firms-either through a joint venture or through direct sale of a majority equity stake-and those that did not, and ask whether total factor productivity (TFP) growth rates of these groups of firms differ. TFP is used as an indirect measure of technology transfer. Data constraints prohibit using more direct measures, such as R&D effort or the turnover of managers and highly skilled labor. Our results suggest that TFP growth is higher in firms with foreign partnerships, and that there is a clear hierarchy: firms that have been acquired by foreign owners have the highest TFP growth, followed by firms with joint ventures. Firms without foreign partnerships have the lowest TFP growth as a group. This result

> ' A separate but related literature on technology diffusion has focused largely on two issues: (i) analysis of the determinants of the number of firms or the proportion of industry output produced by a new technology (aggregate diffusion); and (ii) analysis of the determinants of the time at which a firm adopts a new technology relative to other firms (so-called duration models) (See e.g., Ray, 1964; Karshenas and
with 915 from Scopus and 353 from Web of Science, we retrieved a diverse set of publications, including conference proceedings, book chapters, and other materials (table 2).

Both the number and geographical spread of the articles on green literature have expanded since 2009. In 2009, there were 44 articles, and in 2023, there were 162 articles (figure 2). Moreover, the articles in 2009 almost exclusively covered North America (table 3). However, the scope gradually diversified. Notably, by 2015, the number of articles covering European countries had surpassed those covering North American countries in cumulative terms; by 2022, the same had occurred for Asian countries.

In the early years of our analysis period, there were no articles focusing specifically on green jobs in South America and hardly any in Africa or Oceania. This situation has changed dramatically, with substantial increases in the number of articles covering these continents over time. This trend indicates that research on green jobs is becoming increasingly globalized, encompassing a broader range of geographical settings.

# **Green Job Titles**

We initially identified 799 potential green job titles using the RAG pipeline (see file in <u>GitHub</u> for the list of titles). We excluded 66 titles, as they were not job titles but referred to green activities.

7
1st to January 31st and aggregated them at the individual level, across different waves. The final experience sample contains information from 5,205 individuals, equally split between treatment and control districts.

The cross-sectional sample measured the accumulated and medium-terms impact of the campaign on malaria incidence, risk perceptions, and bed net-related actions and intentions. Some questions from the longitudinal survey were reiterated, with variations in the time periods for respondents to report on. For example, while the experience surveys inquired whether participants or their family members had experienced malaria in the past two weeks, the cross-sectional survey posed a similar question but with reference to the last 6-8 months, i.e. since August 2020, when the study campaign went live. We also asked about malaria risk perceptions, and intentions to buy or treat bed nets with insecticide. The cross-sectional sample comprises 3,052 observations. As discussed in Section 6, in the cross-sectional study we also nested an individual-level study to explore the mechanisms behind the lack of program impacts on households living in non-solid dwellings.

For both samples, recruitment was stratified across districts and dwelling type, as described previously. The enrollment questionnaire started asking participants about their preferred language (English, Hindi, or Odia), followed by providing study details to obtain individuals’ informed consent for their participation in the research. Only individuals aged 18 or above and whose consent was obtained are part of our survey samples. Prior to analysis, we standardized and refined the samples to enhance data integrity. Specifically, we removed respondents who hastily completed the survey<sup>24</sup> and those reporting to be above 90 years old and indicating a household size exceeding 30 members, retaining only observations where all outcome and control variables were present.

We complemented survey data with administrative health facility data collected at the subdistrict-month level from the Indian Health Management Information System (HMIS).<sup>25</sup> This dataset provides monthly information from health facilities at the sub-district level, detailing the number of malaria
br>450<br>400<br>400<br>350 350<br><!-- End of picture text --> Source: OECD Programme for International Assessment (PISA) and Trends in International Mathematics and Science Study (TIMSS) The time trend in academic performance indicates that Malaysia and Thailand have not caught up with others in the region; indeed they may have fallen even further behind. Mean scores in both mathematics and reading from PISA and TIMSS declined between 1999 and 2007. Thailand improved slightly in 2009 but not enough to return to the level of 1999. The World Bank’s (2012c) analysis shows that the decline in Thai students’ performance is mostly explained by the deterioration of the quality of the education system rather than by changes in students’ backgrounds.

Non-cognitive skills as well as cognitive skills determine an individual’s success (Heckman, Stixrud and Urzua 2006). As mentioned earlier, internationally comparable data on noncognitive skills do not yet exist. As a substitute, we present information collected by the World
Cox transformation. As previously discussed, the primary goal of this transformation is to normalize regression residuals and mitigate heteroskedasticity. However, as Savin and White (1978) highlight, neglecting autocorrelation in the context of a Box-Cox transformation could result in misleading conclusions due to the potential presence of functional form misspecification. To ensure robustness, we implement the method proposed by Savin and White (1978), which was later extended by Seaks and Layson (1983).

Another important consideration is the assessment of model goodness-of-fit. When applying the FGLS method to correct for autocorrelation, the statistic—which measures the proportion of variance in the dependent variable explained by the regresRR<sup>2</sup> sors—may not be directly comparable across models, as the dependent variable is transformed based on the autocorrelation coefficient estimated with different sets of regressors (Wooldridge 2016). Similarly, when using the OLS coefficients with Newey-West errors, the traditional maybe biased, since the variance formula for the dependent variable is no longer valid in the presence of a trend. Instead of the , we report stationarity statisRR<sup>2</sup> tics to evaluate whether the residuals exhibit stable statistical properties over time—a RR<sup>2</sup> crucial assumption for ensuring that the error terms are iid. For this purpose, the PhillipsPerron test is employed to evaluate stationarity. Additionally, we report the Akaike Information Criteria, a model selection metric that estimates the relative amount of information loss by a given model, as described in Banks and Joyner (2017) for least-squared analyses.

# **_3.5 Data_**

Area and production data for 144 food commodities—including grains, oilseeds, fruits, fibers, and other crops—were collected FAOSTAT for the period 1961-2021. Together, these commodities represent over 98 percent of the global agricultural land use during the sample period. Calorific data for most commodities are sourced from the FAO’s Food Balance Sheets. Figures 1 and 2 present the calories per kilogram and
to obtain 100 values of the coefficients β̂GLS and of the error components η̂C and ε̂ch (the latter based on assumptions about their distribution and estimates of their variances σ̂η2 and σ̂ε,c2 from previous stages).13 Using R these estimates and explanatory variables from the target survey, xch, we obtain 100 imputed values of per capita household consumption for household h in cluster c:

> 11 For an introduction to variable selection and regularization methods in general, and of the LASSO and Stepwise selection methods in particular, see chapter 6 of James et al. (2013).

> 12 Having a smaller number of clusters reduces the likelihood of heteroskedasticity in the cluster component of μch. 13 See Nguyen et al. (2018) and Newhouse and Vyas (2019) for more details on the distributional assumptions.

8
followed by labor availability and the quality of formal education and training related to the production of relevant skills (World Bank, 2019a). From the firm perspective, skill deficits related mainly to new labor market entrants who were primarily jobless youth (World Bank, 2019a).

Skill constraints affected all hiring firms, across type of occupation and across sector. Depending on the specific position to fill, between 59 percent and 77 percent of hiring firms encountered problems because of applicants’ lack of skills or experience (Figure 5). Skill constraints affected all hiring firms but had a stronger effect on large, dynamic and innovative firms.<sup>9</sup> Therefore, the firms that are more likely to be competitive and productive are also the ones facing the largest skill constraints on recruitment.

Among Western Balkan countries, Kosovo stands out when it comes to skill gaps (Figure 5). A majority of firms in Albania and Serbia also experienced skill-related problems in recruiting workers for higher skill occupations, but the share of firms affected in Kosovo is even higher.

The low level of skills is related to the poor quality of the education system. Although enrollment in secondary education is high in Kosovo, a large majority of students have not acquired basic cognitive skills: 70 to 80 percent of students are not proficient in math, science and reading according to PISA tests.<sup>10</sup> In 2014, Kosovar children completed an average of 12.8 years of school by age 18. Once the quality of learning is taken into account, however, this is equivalent to just 7.7 years.<sup>11</sup> On the employer side, between 40 and 50 percent of firms report that general education does not adequately prepare students for the workplace – either in terms of up-to-date knowledge or soft skills (World Bank, 2019a). Consequently, education credentials are not thought to accurately reflect skills during recruitment.<sup>12</sup> Our analysis in
the proposed minimum wage in the<br>EUSILC is tiny, significantly limiting the sample size for this analysis. The EU-SILC measures income annually.|
|Respondents report their income in a previous calendar year. EU-SILC 2021 provides information on annual income<br>in 2020. The sample includes 5,519 full-time employees who worked for 12 months last year. If we simulate the<br>inflation-linked – adjustment based on the EU-SILC data, the increase in minimum wage affects only 1.31% of full-|
|time employees<sup>33</sup>. The number of affected employees is 72., which makes any investigation of the profile of the impact|
|impossible. Given the potential for more significant standard errors in the estimates, this study does not report these<br>estimates.|

> 33 The share of affected employees is lower firstly because of lower inflation in 2021 than in 2022 (5.1% vs. 13.8%). Moreover, the share of minimum wage earners in the EU-SILC is lower than in the administrative tax data (Robayo-Abril, Balaban and Wronski., 2024).

44
10 On the other hand, the goal of this study is to disaggregate the sources of income inequality as finely as possible. For this reason, it seems essential to clistinguish in rural Egypt between agricultural and livestock income, because these two income sources have very different effects on inequality. According to the data, the simple correlation between agricultural income and total income in Egypt is the highest of all five income sources: 0.844. By contrast, the simple correlation between livestock income and total income is one of the lowest: 0.232."6 It is unfortunate that data for livestock income in Jordan are lacking, in order to compare these results with those of Jordan.

Table 1 presents summary data for the various sources of income. The table shows quite clearly the importance of rural income other than agricultural income. In both countries, nonfarm income represents the single most important source of income, accounting for 42.2 and 50.6 percent of total rural household income in Egypt and Jordan, respectively. Although definitions of nonfarm income vary widely," these percentage figures for nonfarm income are comparable to those recorded in other studies. For example, a recent review of rural household budget surveys in 13 African, Asian and Latin American countries found that nonfarm income accounts for between 13 and 72 percent of total rural household income." The Gini coefficients of per capita rural income for Egypt and Jordan are 0.532 and 0.408, respectively. On the one hand, it is a bit surprising that the Gini coefficient for rural Egypt is much higher than that for rural Jordan. This large difference may reflect the absence of data on crop income in Jordan. In many countries crop income -- since it is dependent on land access -- is unequally distributed, and so the inclusion of this source of income would probably increase the Gini coefficient for rural Jordan.'<sup>9</sup> However, it should be noted that the Gini coefficients for Egypt and Jordan are