rafmacalaba/data-use-mentions-extended huggingface dataset:
The emphasis in this paper so far has been on changes in the quantity of labor demanded or supplied as a consequence of adopting ‘green growth’ policies. But the skills needed in the labor force are likely to be affected as well. If the availability of the skills required along green growth trajectories is inadequate, that could place a major obstacle in the way of the transition to green growth.

Fankhauser et al. (2008), in their review of the literature on climate policy and jobs, note that there is relatively little information on the productivity of jobs created and destroyed by climate-change policies. There is even less on their pecuniary and non-pecuniary attributes. CEDEFOP (2009) complains that “We have not paid enough attention to the social dimension of sustainable development: its implications for employment, training and skills.” Even in countries with relatively good labor market data, it is difficult to identify the jobs the content of which is most likely to be affected by green growth policies. For example, Hatfield-Dodds et al (2008) note that in Australia “current information on green skills and workforce capabilities is very poor;” there are some data for renewable energy and construction trades but little for transport and agriculture.

Perhaps the most thorough study of green growth and skills so far is ILO/CEDEFOP (2011), which reports the results of 21 country reviews and provides a synthesis of key findings. The study notes that the demand for skills is being affected in three ways by the transition to green growth.

First, there is induced structural change across industries, increasing the demand for the skills specific to expanding industries such as renewable energy and reducing the demand for skills such as those associated with coal-mining. This ‘green restructuring’ brings with it the usual challenges to policy-makers trying to facilitate restructuring and to reduce the labor market adjustment costs, including those resulting from a changing skill mix. Many of the expanding industries are likely to be using new products and processes, reflecting the transition to low-carbon technologies,
a three-item scale developed by the Child Trends Positive Indicator Project (Lippman et al., 2014). Example items include, “I expect good things to happen to me” and “I am excited about my future.” Response options were the same as those used for selfefficacy. Answers to these three items are aggregated into a standardized z-score for use in our regression analysis.

In order to mitigate the known problems of using Likert-style response options with low-literacy and non-Western populations (Church, 2010), the survey included piloted visual aids that increased respondents’ comprehension and response times. The multiitem standardized scales all demonstrated satisfactory internal consistency for the Jharkhand survey sample of girls ages 15-24 (Cronbach’s α = 0.89 for GSES, 0.78 for PHQ-9, and 0.84 for hope). The present study utilizes data only for the sample of respondents ages 15-24, as validity of some psychosocial instruments used for younger girls was unknown and suspect and therefore key measures for this study were not used with these girls. The remaining sample was 2,425.

We also collected data on the adolescents’ social support and networks. We assess positive connectedness of the adolescent to local adults and to schools using brief items developed by Blum and Ireland (2004). Drawing on the Nepal Adolescent Girls Employment Initiative trial baseline items (Chakravarty et al., 2015), the survey also asks about sources of support for different types of problems.

We also collected data on several variables at the household level, including rural/urban location, caste status, religion, gender of head (male/female), primary language,

> 2 This study used the National Institutes of Health Toolbox adapted and validated version (CAT Ages 8-12) of the GSES given simpler language for a young sample population with generally low literacy. The version was retrieved from <u>http://www.nihtoolbox.org/WhatAndWhy/Emotion/Pages/default.aspx.</u>

5
changes in enterprise control often destroying relationships in their wake. This forces firms to bear the costs of rebuilding trust relationships and architectures more frequently.

Second, in conditions where financial markets are underdeveloped and access to credit is limited the opportunity cost of capital is high and firms have an incentive to take profits today rather than wait for profits tomorrow. Firms have a high discount rate on the future. Poverty and culture (e.g. family and community-based risk-pooling) reinforce such inducements to take current profits and tend to undermine the value of future gains from business relationships, making it harder to establish and sustain cooperation.

Weakened incentives for cooperation, together with extraordinarily high costs of searching, screening, and deterring opportunism in SSA, increase the importance of business networks for market exchange. In these circumstances, there is an added need for the information and collectivist system of enforcement that a network can provide to help make and sustain relational contracts. This is especially true for labor, credit, and other factor market transactions, which are even more susceptible to opportunistic behavior than product market transactions. Given the importance of relation-based governance of business transactions in the region, features that make it harder to establish and sustain cooperative relationships in factor markets create barriers to efficiency and growth. In helping to improve the possibilities for relational contracting, business networks play a supporting role in market development and enhance firm performance.

The RPED surveys allow us to examine the importance of networks in SSA in some detail. In the next section, we look at the effects of networks on SME performance and consider the consequences of the forms that networks take for entry, efficiency, and growth.

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of higher female employment, like slower population growth.

> 9 Note that because the World Bank Human Capital Index (HCI) used in the construction of the full GEGI is forward looking only, the full GEGI presented in Section 3 only has the second interpretation.

> 10 Downloaded from https://www.ilo.org/shinyapps/bulkexplorer7/ on 13 Dec 2019. Tonga uses updated ILO data from 2018. 11 The JOIN employment rate is sometimes sourced from “Employment to Population Ratio, aged 15-64, (Column V) of the public JOIN spreadsheet (downloaded 21 October 2019, link), and is sometimes calculated from more recent microdata that were acquired through private correspondence.

> 12  These changes stem from definitional changes following the 19th International Conference of Labor Statistics (2013), though the implementation of those definitional changes is slow and not uniform across countries.

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**Source** : Authors’ calculations based on HIECS 2015 and 2017/18. **Note** : The official methodology is run based on total expenditures.

# **Section 5: Concluding remarks**

Several countries rely on non-standard poverty measurement methodologies, which may complicate the interpretation of poverty changes across time or space. This paper focuses on one element, which is the use of non-standard poverty lines, particularly lines that are neither absolute nor relative. There is limited evidence on how these departures may impact the interpretation of poverty empirically, beyond obvious conceptual differences. This paper aims to fill this gap by looking at the case of Egypt. The analysis presented in this paper has provided several insights from comparing Egypt’s hybrid poverty lines with more standard lines.

The analysis first shows that consumption poverty increased in Egypt over the period 2015 to 2017/18, mostly due to the reduction in real consumption. The hybrid nature of the poverty lines inherent in the official poverty measurement methodology leads to a smaller increase in the poverty rate, compared to an approach where the poverty lines are updated in real terms. Yet, the profile of the poor has not been 17
features at a national scale requires constructing a cloud-free mosaic of Sentinel imagery, considerable computing power, and the expertise to implement software to calculate spatial features. Second, the data on Tanzanian building footprints are proprietary. However, data on building footprints are increasingly being released in the public domain and it is not difficult to envision information on building footprints becoming freely available in the coming years, potentially through Open Street Map as it continues to improve in accuracy and coverage. Finally, access to subarea survey identifiers and shapefiles, or access to EA geocoordinates, is necessary to link survey data to geospatial indicators. This is not feasible in all contexts, but the growing popularity of geospatial analysis and CAPI data collection is making geospatial survey analysis more common. A conservative estimate is that the time and expertise required to generate these types of estimates costs $50,000 to $150,000. This is minor compared to the value created by even doubling the effective sample size of nationally representative household surveys that often cost at least a million dollars to field.

The results could be improved by further refinements in software and methods. One avenue for further research is to explore the performance of a subarea-level model, an extension of the Fay-Herriot model specified at the subarea level with an area-level mixed effect (Torabi and Rao, 2015). Estimating such a model at the subarea level makes it easier to properly account for sample design effects. However, modeling poverty rates as a linear function of predictors may generate less accurate estimates, especially when poverty rates are low. It would be useful to better understand how the results of a properly specified subarea model compare with a household-level model using subarea predictors.

A second line of research could focus on improving the household-level model by estimating uncertainty more accurately. The estimated variance of the area random effect, for example, could be adjusted to 41
frequently embedded within products and services, leading to significant under-reporting as respondents may not explicitly recognize or disclose their use of AI. The data gap is even more pronounced in developing countries, which lack the capacity and resources to conduct in-depth surveys. These challenges hinder the collection of accurate, comparable, and up-to-date data on AI adoption, further obscuring the global landscape of AI adoption.

This paper aims to fill this data gap by utilizing novel website traffic data from Semrush. Semrush collects raw data through clickstream analytics, tracking codes, and server log files. It processes approximately 25 billion URLs daily across a global database that contains over 43 trillion 3
any, accrue. Rents under imperfect competition can be highly relevant. Theoretically, it is possible that liberalization of a highly concentrated market leads primarily to a shift of domestic rents to foreign providers, with a resulting loss in domestic welfare (Francois and Wooton, 2001b). An extreme example would be a situation where liberalization entails a shift from a domestic monopoly to foreign monopoly. Less extreme would be a shift from a purely domestic monopoly or oligopoly duopoly to a situation where the market remains highly concentrated but with ownership now shared between domestic and foreign firms. Such outcomes can in fact be the objective of a specific policy reform if this is conceived and implemented as part of a bilateral or regional preferential trade agreement.<sup>23</sup> Of course, this is not a services-specific issue, but

> 22See for example Molnar and Bottini (2006). Working with firm-level data for the OECD, they find large variations over industries and across countries in estimated mark-ups that are associated with differences in the restrictiveness of policies in these sectors. See also Inklaar, Timmer and van Ark, 2008.

> 23Jensen and Tarr (2011) and Balistreri and Tarr (2011) find exactly this type of result in their modeling of liberalization in Armenia and Kenya, respectively, where the potential gains for Armenia from preferential liberalization with the EU proves lower if Armenians lose rents from barriers against foreign service-providers that are then shifted abroad. See also Konan and van Assche (2007) for an analysis of telecommunications in Tunisia.

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<!-- Start of picture text --> 80<br>70<br>60 BAU<br>50<br>40<br>Actual—<br>30 through April<br>2009<br>20<br>10<br>0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br><!-- End of picture text --> Panel A: China <!-- Start of picture text --> 80<br>South: BAU<br>70<br>60<br>South: Actual—<br>50<br>through April<br>40 2009<br>30 North: BAU<br>20<br>10 North: Actual—<br>0 through April<br>2009<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br><!-- End of picture text --> Panel B: South (top lines) and North China (bottom lines) Data source: Authors’ own data (CNRS dataset) Figure 2. Share (%) of rural labor force with off-farm employment in China, actual and under the assumption of Business as Usual (BAU), 2000-2009 33
share of total public expenditure and the expenditure per student or per school‐age child. While public education spending as a share of GDP or as a share of total government expenditure reflects government efforts towards education financing given the available resources, it does not perfectly capture the actual flows of public financial resources into the education sector. These variables do not account for the demand for education in countries as reflected by the size of the school‐age population. Given the differences in the demographic structures, the demand for education as well as the resources needed may differ across countries. In the current paper, total public expenditure (government expenditure + education aid received) per school‐age individual (population of pre‐primary, primary, secondary and higher education age) is used as an input variable. To control for differences in purchasing power across countries as well as the inflation, public expenditure is expressed in constant PPP terms.

# _Output variable (y)_

The ideal output variable should capture both access to education and learning. The World Bank’s Human Capital Project introduced a Human Capital Index (HCI) that measures the productivity of the labor force in countries, taking into consideration three key dimensions of human capital: Survival, health and quality education (Kraay, 2018; 2019). The learning‐adjusted years of schooling (LAYS) is the indicator used for the education component. The LAYS captures the number of years of quality schooling a child can expect to obtain by age 18. It includes two components: Expected years of schooling and a harmonized test score. Learning is measured by a relative learning score calculated from global and regional learning assessments (Altinok, Angrist, & Patrinos, 2018; Patrinos & Angrist, 2018). The harmonized learning score is currently the only comparable measure of learning that covers a large number of countries.<sup>7</sup> Without ignoring the limitations surrounding the LAYS, this appears to be one of the most reliable indicators of quality education that is
# **Appendix A: Data Sources for Cross Country Regression**

This appendix provides further details on the data used in the essay, and in particular discusses the time-series employed to construct a measure of remittances. Throughout the essay, regional classifications follow the current IMF groupings. However, for the thought experiments in the econometric section, we redefined the Middle East region to include the North African countries of Morocco and Tunisia.

The most reliable information available on remittance flows is the one published by the IMF in its annual Balance of Payments Statistics. Using this source, Ratha (2003) argues that, to arrive at a more accurate estimate of remittances, one should add the “compensation of employees” and “workers’ remittances” items from the current account, and the “migrants’ transfers” item from the capital account. Unless otherwise indicated, remittances were defined as the sum of three items in the IMF’s _Balance of Payments_ statistics: “Compensation of Employees” (part of the income component of the current account), “Workers’ Remittances” (part of current transfers in the current account), and “Migrants’ Transfers” (part of the capital account). Specifically, the IMF’s _Balance of Payments Manual, Fifth Edition_ defines “Workers’ Remittances” as current transfers made by migrants who are employed and _resident_ in another economy. This typically includes those workers who move to an economy and stay, or are expected to stay, a year or longer. “Compensation of Employees” instead comprises wages, salaries, and other benefits (cash or in-kind) earned by _nonresident workers_ for work performed for residents of other countries. Such workers typically include border and seasonal workers, together with some other categories, e.g., local embassy staff. Finally, 29
|||Oral|**Table**<br>**Savings**|**4. Transpare**<br>Printed|**ncy Index**<br>material||Oral|**Credit**|Printed|material|
|---|---|---|---|---|---|---|---|---|---|---|
||Documentation||TI|Provided|TI|Documentation||TI|Provided|TI|
||required|All|Voluntary|(1=yes)||required|All|Voluntary|(1=yes)||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|HL * HI|0.007|0.247***|-0.049|0.065|-0.080|-0.023|0.255***|-0.074|-0.025|0.104|
||(0.048)|(0.085)|(0.077)|(0.068)|(0.087)|(0.065)|(0.079)|(0.057)|(0.067)|(0.070)|
|HL * LI|0.037|0.143|-0.054|0.022|-0.054|0.034|0.289***|-0.018|0.049|0.122*|
||(0.043)|(0.101)|(0.084)|(0.065
tion (PMR) index project has produced indexes of regulatory regimes, in terms of their impact of efficiency and competition (OECD 2006). These include indexes on specific issues like market access for foreign firms, and regulation of pricing and entry conditions, as well as composite indexes. The literature cited above links these measures of regulation to productivity, export performance, and the sector composition of employment.

A stylized representation of a basic lesson to be taken from this literature is presented in Figure 1. This plots two different OECD PMR indexes of regulatory restrictions in services for several OECD countries against an index of absolute productivity in services. The two PMR indicators include an index of policy discrimination against foreign firms, and an index of domestic regulation of prices. These indexes range from 0 to 7, where a low number implies a an open, ”good practice” regime while a high number implies an over-regulated and relatively closed regime. On the horizontal axis, we have plotted estimates of absolute productivity levels for firms in commercial services, relative to the United States.<sup>12</sup> What can be seen in the figure is that openness in services appears to be linked to observed patterns of productivity. Greater openness and better regulation (meaning lower index values) is associated with higher productivity. Given downstream linkages discussed in Section 3.1, such productivity effects should in turn matter for downstream, sectors as well. Francois and Hoekman (2010) survey a number of empirical papers along these lines, investigating the productivity effects of services trade and FDI liberalization. This work provides more evidence that greater availability of services has a significant positive impact on productivity of the sectors that use the services.<sup>13</sup>

# **3.3 Delivery modes and market access channels**

Delivery of services often has an element of ”jointness in production” in the sense – – that complementary inputs including other services are needed to allow effective exchange of (trade in) a service
similarly, China and India are clearly not. They rank at 35 and 40 respectively on the CPI. On the aggregate ranking derived from enterprise surveys they rank 69 and 100 – just two countries, but over 35 percent of humanity. Two other intriguing cases are Belarus and Venezuela. The CPI ranks them at 70 and 99 respectively; the enterprise ranking yields place 34 and 13. For countries like Venezuela and Belarus it might be that firms are afraid that their responses might not remain confidential or outside perception is affected by other views about the economy. For China and India we can only speculate about rosy perceptions of foreign respondents captured in the CPI.

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# **5. Methodology**

## **5.1 Geospatial Analysis**

In this analysis, our land-use / land cover (LULC) classification problem was to identify six landuse/ land cover (LULC) types: forests, settlement/developed, cultivated/agricultural areas, cultured fisheries ponds, bare ground, and surface water for all selected years, 2010, 2014, 2017, and 2020. The geospatial analysis was carried out using the ArcGIS Pro software (Version 2.4.0, 2019) for image classification and map visualization. We used a pixel-based supervised classification method for image processing.<sup>10</sup> To execute the classification, three different algorithms, namely random forest (RF), maximum likelihood estimation (MLE), and support vector machine (SVM), were tested and compared to consider their relative accuracy. In this analysis, the random forest (RF)<sup>11</sup> produces a relatively better result than MLE and SVM. Using the image values of land cover maps created using the RF algorithm, we calculated the aggregate change for various LULC types between the periods 2010, 2014, 2017, and 2020 (Table **_2_** ).

## **5.2 Econometric Analysis**

Our econometric analysis uses a linear probability model with a difference-in-differences framework to capture changes in land cover trends in response to camp openings. We focus on pixels 0-5 km from the nearest camp because we expect these pixels to be the most exposed to refugee activity as well as any camp-related changes in economic activity. As discussed in the previous section, we assign pixels 10-15 km from the nearest camp to the control group. We estimate the following regression:

> 10 A schematic model of the step-by-step GIS-based analysis is shown in Appendix A1.3 (contact author). 11 The RF algorithm, a nonparametric machine learning classifier, has widespread application in remote sensing because of its high classification accuracy, insensitivity to