rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Table 1 Socioeconomic Characteristics of the Survey Population in the OCC Survey (Percentages may not sum to 100% due to rounding, nonresponses, or the omitted category "other")**

|**Characteristics of the Survey Population**|**% of combined**<br>**survey popula-**<br>**tion **|**% of the NYC**<br>**survey popula-**<br>**tion **|**% of the LA**<br>**survey popula-**<br>**tion **|
|---|---|---|---|
|**Age**||||
|18to 29 years old|29.8|22.2|37.5|
|30 to 64years old|59.3|64.1|54.5|
|65or older|8.0|10.2|5.8|
|Male|45.7|44.2|47.3|
|**Highest completed level of education **||||
|Less than high school degree|37.8|36.1|39.5|
|High school degree or equivalent|38.0|44.0|32.0|
|More than high school|24.2|19.9|28.4|
|**Household composition **||||
|No children in household|40.7|45.0|36.4|
|One or two children|41.6|41.6|41.7|
|Three or more children|17.5|13.3|21.8|
|Other adults in household|81.6|75.0|88.2|
|**Housing status**
skill content of jobs across countries but not over time. They find that ICT capital intensity, robot use and the position of the country in the global value chain (i.e. a high share of foreign value added in the production of final goods and services) are negatively correlated with the share of routine jobs.

This research contributes to this literature by analyzing trends in the skill content of jobs and their drivers, as well as the consequences for employment creation in developing economies. The use of multiple survey years per country allows us to increase the number of observations substantially, and thereby to increase the precision of our estimates in a cross-country regression setting and to control for unobserved heterogeneity across countries.

# **3. Data**

The empirical parameters are estimated using several data sets. First, it relies on the STEP surveys to measure the task content of jobs. In addition to socio-economic, demographic, employment, education and family background information, the surveys contain a series of harmonized questions on specific tasks that the respondent uses in his or her job. We use the STEP surveys for 11 developing countries (Armenia, Bolivia, Colombia, Georgia, Ghana, FYR Macedonia, Philippines, Serbia, Sri Lanka, Ukraine and Vietnam), collected between 2012 and 2016.<sup>2</sup> These surveys are representative of the working age population in urban areas. While it collects information on all individuals in the household, it randomly selects an individual between 15 to 64 years old to answer the complete questionnaire, which includes detailed employment and skills questions.

This research is also based on data from the International Income Distribution Data Set (I2D2). The I2D2 is a data set of harmonized household surveys which are comparable across countries and time. It currently covers more than 150 countries and has more than 1,000 surveys. The time coverage goes from 1960 until 2016, but it varies by country. Appendix 1 shows the country and time coverage of the sample used in this paper
**_Population:_** Population data are from the Gridded Population of the World v4. GPWv4 depicts the distribution of human population across the globe. Source data are provided in 30 arc‐second (~1 km) grid cells (CIESIN 2016).

**_Land area:_** Land areas are extracted from European Space Agency data, with land types reported in 300m square pixels. These are aggregated into our land areas. Rural land is defined as irrigated and rain‐fed land, and mosaic cropland. Urban land includes all built areas (Defourny 2017).

**_National Accounts_** _:_ National Accounts data on Gross Value Added by sector are from United Nations accounts data for 2016 http://data.un.org . Total populations and GDP are given below in Table 1. There is large variation between the countries in the region, with per capita GDP nearly 10 times as large in Kazakhstan as it is in Tajikistan. The share of each sector in GVA is shown in Table 2 below. China‐3 and Kazakhstan have the highest share of GVA in manufacturing, while primary sectors are relatively important in Turkmenistan and Tajikistan.

Table 1: GDP and Population of sample countries.

||GDP (Billion USD)|GDP per capita (USD)|Population (Millions)|
|---|---|---|---|
|China‐3|280.5|5066|55.4|
|Kazakhstan|135.0|7585|17.8|
|Kyrgyzstan|6.6|1101|6.0|
|Pakistan|282.5|1580|178.8|
|Tajikistan|7.0|796|8.7|
|Turkmenistan|36.2|6712|5.4|
|Uzbekistan|67.8|2128|31.9|

Table 2: Sectoral shares of GDP (percent)

||Primary|Manufacturing|Non
employment, labor force participation, and wages in local labor markets in the United States. Acemoglu et al. (2016) also estimate large negative employment effects, exacerbated through input-output linkages between various US industries. Pierce and Schott (2016) also confirm these large negative employment effects. The fact that these studies all rely on different identification and estimation strategies increases confidence in their findings. However, it is important to note that the manner in which the gains of trade materialize is highly dependent on the structure of economies regarding their resources and production technologies. While labor is an expensive factor in production in the United States, its cost is very heterogeneous across countries. Therefore, this type of adverse employment substitution effects must not be expected from enacting lower trade barriers in other country settings.

Despite the negative employment effects documented upon trade liberalization for the US, opening domestic markets to trade and foreign investments is often considered a sensible option to improve countries’ level of competitiveness. This is particularly true for LMICs, which tend to be most restrictive in these dimensions than higher-income countries. Country research by the OECD (2018a) documents skewed entry conditions for domestic versus foreign firms in Kazakhstan: while domestic firms enjoy highly facilitated registration processes, foreign firms and suppliers are hindered by burdensome procedures, low automation, and poor information availability. Besides entry requirements, there are sometimes differences in operational rules. In Indonesia, foreign companies are required to hold higher minimum capital requirements, to comply with stringent conditions on the employment of foreigners in key management positions, and to be subject to limitations on branching and land access (Lewis et al., 2022). In addition, some activities are entirely reserved for domestic companies. In a recent report, Vitale et al. (2020) rely on the 2018 edition of OECD PMR indicators on 35 OECD and 11 non-OECD countries to illustrate the potential of liberalizing countries’ trade and FDI regulations. The authors point out that, compared to the OECD average, countries in Latin America and the Caribbean
# _Choice of initial capital stock_

Three sources have been chosen as giving apparently reliable estimates of the capital stock by Chow, Liu and Leh and Chen. Two further, often quoted sources are also considered: The Penn World Tables (Feenstra _et al.)_ and those from Wu (Table 4). The estimates by Chow and Liu and Yeh are firmly based on original sources. They differ markedly in their estimates of the capital stock in the industrial sector. The reason for the difference appears to be that Chow has taken data for the original value of industrial fixed assets (see Chen, 1966, Table 3.2). In Chinese economic series, such data correspond to the concept of the gross capital stock (that is, the original cost of all assets currently in use). Such a definition ignores the impact of depreciation of the value of the asset. However, Liu and Yeh start from a 1955 official estimate of the net capital stock and work backwards to the end of 1952 by deducting net investment. Their estimate can be compared to the original value of the assets and implies a deduction for depreciation of 45%. The estimates for the business sector and infrastructure made by Liu and Yeh have been used in this paper.

These studies have to be complimented by estimates of the capital stock in the housing and government sectors. Estimates for the _residential housing stock_ are taken from Liu (1968) based on an extrapolation of estimates of the 1933 rural housing stock together with the ratio of urban to rural rents. His estimate corresponds almost exactly to an estimate of the replacement cost of urban and rural housing after allowance of an arbitrary 50% for depreciation (Herd et al, 2013). Finally, an estimate has been made of the capital stock used in the _government sector_ . It is based on estimates of the capital-output ratio for the Indian economy in 1975 (Arestoff and Hurlin). Overall, the preferred estimate for the initial capital stock is CNY 147 billion in 1952 prices, representing
INDEC Census Argentina 2010. _Notes_ : Weighted Least Squares. Standard errors in parentheses. * _p <_ 0.10, ** _p <_ 0.05, *** _p <_ 0.01

# **6 Conclusion**

We contribute to the study of differences in socioeconomic outcomes across Native and non-Native people by using new Argentine census data to describe the economic outcomes of Native Argentines. We find that, on average, Native Argentines obtain worse outcomes than non-Natives. These differences cannot be explained only by differences in location or educational attainment. We also find differences in the transmission of education that suggest that differences in educational attainment will not disappear with time. Interestingly, we find that differences among Native groups are much larger than the differences between Natives and non-Natives. We find that the economic outcomes of groups correlate with the practice of agriculture before the arrival of the Spanish.

These results suggest the importance of not considering the Native groups in Argentina as a homogeneous group, but stress the diversity of experiences among Natives. We hope that future research will help us understand the causes of these differences in outcomes and their implications for public policies aimed at improving the lives of Native people in Argentina.

24
square meters or larger than 500 square meters was eliminated. The lower bound was obtained using data on average housing size in rural areas from the Bangladesh Integrated Household Survey (2015). The available imagery was contrasted with the buildings obtained from OSM to ensure reasonable coverage (Figure 5.B).<sup>29</sup> We took the number of households in a mauza (according to the 2011 national census) and calculated how many segments would need to be created to obtain 100 households per segment. At the same time, the number of buildings in the segment was calculated. Given these two numbers, it was assumed that households were uniformly distributed in the buildings available in the data set. The team then proceeded to calculate the number of buildings needed per segment to obtain the same number of segments as if segments had been defined based on population.<sup>30</sup> This procedure was undertaken to connect the number of households in the 2011 census data to the buildings observed in the OSM data set. If the underlying assumption was reasonable, dividing the segments using buildings would produce segments with roughly the same number of households<sup>31</sup> within a particular mauza.

Using the centroids of each building, the team generated non-overlapping polygons within the mauza (Figure 5.C). To generate the polygons, the team minimized the total squared distance from the center of the segment to each building in the segment, given the minimum and the maximum number of buildings that are allowed in each subset.<sup>32</sup> The segments were then checked, to solve any inconsistencies due to irregular shapes of particular mauzas. Once this process was complete, the team moved forward with a ground-truthing exercise to verify assumptions, followed by listing of the selected segments (Figure 5.D).

This process described had to accommodate two detours: First, for some rural northern mauzas the availability of demarcated buildings in OSM data was low, so in
conditions at the village-level in Benin, the surveys offer several proxies for village incomes. Most are based on village averages of responses to the household survey: the percentage of households that report their income as belonging to the lowest of the income brackets; the percentages of sampled households that report owning a mobile phone and a television; the percentage of households surveyed with brick walls; and the percentage with houses with more than five rooms. With 20 observations per village, these averages are only precise enough to test whether larger income differences separate villages with more and less radio access. From the village surveys, it is also possible to compare villages according to the number of private schools serving the village.

Finally, village linguistic characteristics could affect which radio markets the village is in and be independently related to cultural norms that influence health practices or to the ability of the village to attract government programs. Based on the household survey, it is possible to compare villages according to, first, the probability that any two of these households would speak the same language at home; and, second, an indicator variable for whether the most common language among the sample of 20 households in a village is different from the most common language among all the households surveyed in the commune in which that village resides.

Table 2 reveals no statistically significant association between any of these variables and either the number of community radios to which villages have access or the share of community radios as a fraction of all radios to which a village has access. The variable closest to significance is the distance of a village from the nearest urban center, in the first column, but its effects are small. A one standard deviation increase in distance (18.7 kilometers) is associated with access to approximately 0.11 fewer radio stations. Brick walls are more common in villages with a larger share of community radios but, the effect is not quite significant and, again, its magnitude is small. A one standard deviation in the fraction of households with brick
Table 3: Impact of IOp on investment behavior depends on Earnings Rank

|**Dep var:**Binary = 1 if subject|(1)<br>chooses to invest|(2)|(3)|
|---|---|---|---|
||All<br>With|out rank feedback|With rank feedback|
|Pre-investment earnings rank|0.0337|-0.0161|0.0528|
||[0.618]|[0.912]|[0.488]|
|IOp treatment|0.427|-0.0206|0.612|
||[0.362]|[0.983]|[0.249]|
|IOp X Pre-investment rank|-0.0827|-0.0288|-0.104|
||[0.284]|[0.861]|[0.231]|
|Constant|1.066***|1.564*|0.859*|
||[0.00630]|[0.0609]|[0.0536]|
|Observations|499|164|335|
|Pseudo R-squared|0.006|0.004|0.014|

**Dep var:** Portion of earnings invested, conditional on being _>_ 0

||All<br>W|ithout rank feedback|With rank feedback|
|---|---|---|---|
|Pre-investment earnings rank|0.512|0.838|0.361|
||-[0.345]|[0.471]|[0.550]|
|IOp treatment|9.419**|4.188|11.00**|
||[0.0182]|[0.612]|[
# **1. Introduction**

The escalating impacts of climate change underscore the urgency of a green transition—a pivotal shift toward sustainable practices that is essential for our planet’s future. This transition is expected to accelerate rapidly, necessitating that policy makers analyze its impacts on national labor markets and develop effective strategies to navigate the evolving landscape. Understanding the scope and nature of green jobs is crucial for informing public policy, enabling governments and organizations to develop tailored and targeted strategies for education, training, and employment to support a sustainable economy.

Worldwide, the most widely used source of green job titles is the Green Occupations list, constructed by the U.S. Department of Labor’s Occupational Information Network (O*NET) in 2009 (Dierdorff et al. 2009). O*NET’s original approach involved reviewing publications covering a wide array of workplace topics pertinent to the green economy. In assessing green jobs, research predominantly employs two methods: top-down approaches, which categorize entire sectors or industries as green, and bottom-up approaches, which focus on specific occupations, defining green jobs based on the green nature of the tasks or skills associated with those roles (Valero et al. 2021). O*NET’s classification is the most often used source for occupation retrieval in the bottomup approach to green jobs analysis (OECD 2023).

The green job taxonomy developed by O*NET has been instrumental in shaping quantitative research on the green economy. In the United States, its impact is reflected in studies by researchers such as Consoli et al. (2016), Popp et al. (2020),Vona, Marin, and Consoli (2019), and Vona et al. (2018). The taxonomy has also been adapted for various regions, including the European Union (Bowen and Hancké 2019), the Netherlands (Elliott et al. 2021), the United Kingdom (Valero et al. 2021), Organisation for Economic Cooperation and Development (OECD) member
income incentive in attracting Italians and Spaniards, France had a generous open door policy for Portuguese nationals and it was particularly welcoming to illegal immigrants in the early 1960s at the time of the Angolan colonial war.<sup>5</sup> During the same period, in Yugoslavia, the Tito government which had been actively discouraging emigration for work purpose until the early 1960s, suddenly changed its approach as unemployment was growing. Starting in 1965, a new policy actively encouraged workers to go abroad primarily as guest workers and, in 1973, there was 1.1 million Yugoslavs living abroad, with 900,000 of them in Western Europe, mostly in Germany and Austria (Curtis, 1992). Nevertheless, 31,578 people were registered as permanent residents in Switzerland that is, held work/residence permits for more than one year and 3,362 held seasonal permits.<sup>6</sup> Moreover, seasonal workers from Yugoslavia clearly were coming to Switzerland before the change in policy in their home country as in 1973, 344 of them obtained the conversion of their seasonal permits (OFE, 2001a, Table 112.2R), implying they had been seasonal workers for at least 10 years without interruption.

From the early 1980s and during the following 20 years, the evolution of the Yugoslav population in Switzerland diverged from that of the other main source-countries for permanent residents mostly because of an unprecedented conjunction of events in Switzerland as well as in Yugoslavia. From about 44,000 permanent residents in 1980, the population rose to more than 337,000 in 2000 that is from hardly 5% to almost 25% of the foreign population in Switzerland. Open emigration policy by the Yugoslav government, the possibility of

> 5  See Blanc-Chéard (2001), Chapter 6, for more details. According to census data, in 1962, there were about 50,000 Portuguese nationals in France (i.e., 2.3% of foreign population); in 1968, there were more
**_Correction for inflation._** Egypt has experienced rapid inflation between 2005 and 2008.  It is therefore important to rely on real values for comparisons over time.   The CPI index disaggregated by regions and into food and non-food component was used. The second way was to use the poverty lines for each household re-estimated in actual prices as deflators (consumption is then measured in terms of poverty baskets a household can purchase in the current month, see El Laithy and Lokshin, 2003, for methodology).

# **IV. Economic growth, inequality and poverty over 2005-08: A cross-sectional perspective**

Egypt witnessed rapid and sustained economic growth during 2005-08. This episode followed a period of economic turmoil (large depreciation of national currency) and slow, at times almost zero, growth in per capita consumption. In contrast, real GDP annual growth averaged over 6 percent, leading to an accelerated growth in total final household consumption expenditure. In per capita terms, private consumption grew at an average rate of almost 4 percent per year in this period. The HIECPS data for 2005-2008 shows remarkably similar picture. Households' real average per capita consumption<sup>14</sup> increased by 12.3 percent between 2005 and 2008 (3.9 percent per year) – practically identical to macroeconomic estimates from the National Accounts (Table 1). But price increases for goods and services consumed by households were also staggering. Inflation over the period was very uneven: prices of food and other basic goods and services increased much faster than other prices. The cost of the subsistence minimum food basket increased by 47 percent, far more than the overall increase in the CPI (31 percent over the three years).

**Table 1: Survey Results – Growth in Monthly Mean Consumption**

|||**Consumption per**|**capita in 2005 LE**||
|---|---|---|---|---|
particularly in the case of India. Consequently, the true or “inherent” comparative advantage of South Asian countries may somewhat, are different than the following empirical results suggest.<sup>24</sup> Table 8 provides, for each of the South Asian countries, their import structure<sup>25</sup> and the corresponding RCAs of South Asian partners. A review of complementarity by each SAC’s major imports and partner’s comparative advantage does not seem encouraging for the potential success of the FTA. For example, India, the major market in the region, shows that its major imports can be identified mostly in semi-manufacturing (which is a consequence of its import policies) for which the rest of the region largely shows neither the capacity nor comparative advantage. This is reflected in the meager share of imports from the regional members on each item and the corresponding RCAs. India’s sugar imports (SITC 0612) reported a substantial (70 percent) share of its total imports from the region. However, sugar represents a mere 0.4 percent of India’s global imports. The prospects of the rest of the SACs substantially increasing their current low share of exports to India seems less promising based on their current trade structures. However, with sufficient reduction in India’s existing trade barriers—the new incentives bringing about an adjustment of resources among competing industries between India and its neighbors, enhancing current, as well as new capacities, its neighbors could make progress. In the meantime, prospects that may exist are confined to what are often termed “niche” markets (identified at HS 6-disaggregated level), which are unlikely to have a substantial impact on transforming their meager export shares with India unless quality and product features are compatible with those imported from elsewhere by India.

The statistics of the second largest economy, Pakistan, suggest a similar scenario. The region, which barely accounts for 2 percent share of Pakistan’s imports, overall demonstrates a negligible capacity to fulfill