rafmacalaba/datause-encoder-data huggingface dataset:
**Cazzaniga, M., Jaumotte, F., Li, L., Melina, G., Panton, A.J., Pizzinelli, C., Rockall, E., and Tavares, M.M.** 2024. Gen-AI: Artificial Intelligence and the Future of Work. IMF Staff Discussion Note, SDA/2024/001, 2024.

**Czarnitzki, D., Fernandez, G.P., Rammer, C.,** 2023. Artificial intelligence and firm-level productivity. **Journal of Economic Behavior and Organization** , Vol. 211.

**Eloundou, T., Manning, S., Mishkin,P. and Rock, D.** 2023. Gpts are gpts: An early look at the labor market impact potential of large language models, arXiv preprint arXiv:2303.10130.

**Felten, E., Raj, M. and Seamans, R.** 2018. A method to link advances in artificial intelligence to occupational abilities. AEA Papers and Proceedings 108:54–57.

**Felten, E., M. Raj, and Seamans, R.** 2021. Occupational, Industry, and Geographic Exposure to Artificial Intelligence: A Novel Dataset and Its Potential Uses. Strategic Management Journal 42 (12): 2195– 217.

**Felten, E., Raj, M., & Seamans, R** . 2023. How will language modelers like chatgpt affect occupations and industries?. arXiv preprint arXiv:2303.01157.

**Frey, C.B. and Osborne, M.A.** 2017. The Future of Employment: How susceptible are jobs to computerisation?, _Technological Forecasting and Social Change,_ 114, pp. 254-280.

**Gmyrek, P., Berg, J., and Bescon, D.** 2023. Generative AI and jobs: A global analysis of potential effects on job quantity and quality, ILO Working Paper 96.

**<mark>Gmyrek,P., Winkler-Seales, H.J., and Garganta,S.</mark>** <mark>2024. Buffer or Bottleneck? Employment Exposure</mark> _<mark>to Generative AI and the Digital Divide in Latin America (English).</mark>_ <mark>Policy Research working paper; Washington, D.C. : World Bank Group.</mark>

**Hazan, E., Madgavkar, A., Chui, M., Smit, S., Maor, D., Dandona, G.S., and HuyghuesDespointes, H.** 2024. A new future of work: The race to deploy AI and raise skills in Europe and beyond, McKinsey Global Institute.

**Hatzius, J., Briggs, J., Kodnani, D., and Pierdomenico, G.** (2023). The potentially large effects of Artificial Intelligence on Economic Growth, Goldman Sachs Economics Research.

**<mark>IEA</mark>** <mark>2023. World Energy Outlook 2023, IEA, Paris, available online: https://www.iea.org/reports/world-energy-outlook-2023.</mark>

**<mark>Lou, B., Sun, H., and Sun, T.</mark>** <mark>2024. GPTs in the Developing Economy: Impact on the Labor Market and the Role of Geography and Policy.</mark> _<mark>Available at SSRN 4786527</mark>_ <mark>.</mark>

**Min, B., O’Keeffe, Z.P., Abidoye, B., Gaba, K.M, Monroe, T. Stewart, B.P., Baugh, K., Sánchez-Andrade Nuño, B.** 2024. Lost in the dark: A survey of energy poverty from space, _Joule_ , Volume 8, Issue 7.

**Maslej, N., Fattorini, L., Perrault, R., Parli, V., Reuel, A., Brynjolfsson, E., Etchemendy, J., Ligett, K., Lyons, T., Manyika, J., Niebles, J.C., Shoham, Y., Wald, R., and Clark** , **J.** 2024.

27
_Table A3: Diversification Herfindahl–Hirschman Index (HHI) with Japan_

|**Panel A: All**|(1)|(2)<br>Au|(3)<br>to||(4)|(5)<br>Electr|onics|(6)|
|---|---|---|---|---|---|---|---|---|
||HHI|HHI|HHI|HHI|HHI|HHI|HHI|HHI|
|(Dummy share 2004-2010 > 15)|-0.007|||-0.021|0.017***|||0.013**|
||(0.013)|||(0.019)|(0.005)|||(0.006)|
|(Dummy share 2004-2010 > 10)||0.005||0.022||0.013***||-0.000|
|||(0.013)||(0.020)||(0.005)||(0.006)|
|(Dummy share 2004-2010 > 5)|||-0.001|-0.006|||0.015***|0.012**|
||||(0.014)|(0.015)|||(0.005)|(0.005)|
|Observations|5,562|5,562|5,562|5,562|52,584|52,584|52,584|52,584|
|R-squared|0.847|0.847|0.847|0.847|0.799|0.799|0.799|0.799|
|**Panel B: Auto**||Intermed|iate auto|||Final|auto||
||HHI|HHI|HHI|HHI|HHI|HHI|HHI|HHI|
|(Dummy share 2004-2010 > 15)|0.000|||-0.019|0.003|||-0.073|
||(0.015)|||(0.022)|(0.031)|||(0.046)|
|(Dummy share 2004-2010 > 10)||0.019||0.035||0.014||0.031|
|||(0.016)||(0.022)||(0.026)||(0.043)|
|(Dummy share 2004-2010 > 5)|||-0.001|-0.012|||0.036|0.039|
||||(0.017)|(0.017)|||(0.025)|(0.033)|
|Observations|3,818|3,818|3,818|3,818|1,744|1,744|1,744|1,744|
|R-squared|0.899|0.900|0.899|0.900|0.783|0.783|0.783|0.773|
|**Panel C: Electronics**||Intermediat|e electronics|||Final ele|ctronics||
||HHI|HHI|HHI|HHI|HHI|HHI|HHI|HHI|
|(Dummy share 2004-2010 > 15)|0.025**|||0.016|0.014**|||0.013*|
||(0.010)|||(0.012)|(0.006)|||(0.007)|
|(Dummy share 2004-2010 > 10)||0.022**||0.011||0.009*||-0.006|
|||(0.009)||(0.012)||(0.005)||(0.007)|
|(Dummy share 2004-2010 > 5)|||0.012|0.003|||0.016***|0.016***|
||||(0.010)|(0.010)|||(0.005)|(0.005)|
|Observations|14,320|14,320|14,320|14,320|38,264|38,264|38,264|38,264|
|R-squared|0.791|0.791|0.790|0.791|0.804|0.804|0.804|0.804|

Note: Robust standard errors clustered at the cluster importer-product level are in parentheses. All specifications include importer-year, importer-product, and product-year fixed effects. The samples include the top 15 producers of automobile or top 15 exporters of final electronics other than Japan.

*** p<0.01, ** p<0.05, * p<0.1

26
# **6. Empirical Findings and Interpretation**

We first present regression results for the final models for urban areas and comment on some (ex post) tests for aggregation and pooling. Second, we discuss the factor productivity of labor, expenses, and capital, and explain the distributions of productivity overall and by type of enterprise. Finally, we report some simulation experiments in which we provide loans to selected groups of firms. We use information from the estimated revenue model is to assess the firms' ability to repay these loans. We also consider simulations that change the level of schooling and the number of labor hours available to the firm.

The exact form of the final regression model is given by: where:

- **bo + b1 EXPENSES_1 + b2EXPENSES_2 + b3EXPENSES_3 + b4CAPITAL_1 + bcCAPITAL_2 + b61n(LABOR) + b7TRAINING +** b8SCHOOL_PRIMARY **+ b9SCHOOL_SECONDARY + bloSCHOOL_POSTSEC + b.11 XPERIENCE + b12EXPERIENCE2 (16)**

**The** introduction of spline variables increased the number of expenses **variables to** three and the number of capital variables to two. The capital variable now **includes** the values of both physical capital and stock. Finally, we consider two versions of the model: one where the schooling variables are **measured** as splines, and the other where binary variables are used to distinguish **levels of schooling.**

Tables **6** and 7 present the regression results. The t-statistics suggest that most of the parameters are significantly different from 0 at the 1 percent level. In addition, each model explains about 60 percent of the -'--iation in the **dependent variable.** This is high by cross-section standards.

38
**Figure 10: Entrepreneurship and Political Stability – The Peruvian Case**

<!-- Start of picture text --> Presidential Elections<br>30%<br>25% New<br>Government<br>20% Transition<br>Political<br>Government<br>15% Crisis Political<br>Crisis<br>10%<br>5%<br>0%<br>1995 1997 1998 2000 2001<br>-5%<br>-10% 1999<br><!-- End of picture text --> Note: Bars show the year-on-year percentage increase of new businesses. Source: World Bank Group Entrepreneurship Database and World Bank (2007).

Case-based evidence also suggests that government policy in the areas of taxation and enforcement can have a large impact on business registrations. For instance, in the posttransitional period (1992-1996), the Ukraine imposed high marginal tax rates, tax legislation was vague and non-specific (i.e. included complex exemptions and deductions), regulators did not enforce compliance, and the system was perceived as unfair and corrupt (Kravchuk, 2002). However, in July 1996 the government enacted a simplified procedure for patenting many types of entrepreneurial activities, and in September 1998 enacted a simplified (unified) system of taxation, accounting, and reporting for small business entities (with less than 50 employees and less than 1 million UAH in revenue).<sup>20</sup> These reforms have contributed to the increase in new small business registrations (Figure 11). Interestingly, the increase in registrations of new small enterprises happened mostly due to the establishment of newly operating enterprises – 91.7% in 2004 – rather than the splitting of larger enterprises (1.12% in 2004), established to take advantage of the small business tax exemptions.

> 20 “Law of Ukraine, ‘On Patenting Certain Kinds of Entrepreneurial Activity;”, July 1, 1996; “Decree of the President of Ukraine, ‘On the Simplified System of Taxation, Accounting and Reporting for Small Business Entities’”, September 9, 1998.

22
|**The Sustainable Trade Theory**|<br>Sustainable products are|<br>Leading greener and sustainable|
|---|---|---|
|Except for a few exceptions, trade in|generally better for the|livelihoods is likely to fuel|
|the cultural sector naturally promotes<br>goods and services, which are<br>greener.<sup>17</sup>|environment.|meaningful human development.<br>Sustainable trade and growth can<br>also be counted under this<br>umbrella.|

While the above concepts are basic, they are not easily achieved — at least in developing countries. The next discussion goes on to consider some reasons why this is the case, starting with barriers for ‘creative women’ in ‘creative trade.’

# _Barriers for Women in Creative Trade_

First, generally speaking, in any kind of work (and jobs, or employment), the global perception that any meaningful employment is for men, not women, has not entirely abated.<sup>18</sup> (This issue is worth recalling, as it is historical and present world-wide — albeit with variations from culture to culture.) In creative work, this perception presents a double whammy for women: their efforts in crafts, ceramics, weaving and other areas are often seen as ‘lowly’ jobs for women.<sup>19</sup> Hence, with a few exceptions, no national strategies have been made to effectively see how women’s creative output can be part of the parcel for competitiveness in international trade.

Second, in the context of information asymmetry, women tend to have less information on such provisions as market structures and trade opportunities, even those provided by the Internet. This is because of the structural issues like education and digital divides between men and women that prevail in many countries.

Third, women tend to have less mobility than men.<sup>20</sup> In our context, this may limit their ability to participate in local and international cultural trade fairs, for example.

Fourth, some aspects of trade in services that encompass creative work — cultural tourism comes to mind — may actually end up facilitating the exploitation of women. Whereas men and children are not entirely immune, in societies that objectify women, females may be asked to dress or appear in ways that potentially exploit their bodies, for instance.

> 17 See David Throsby, _The Economics of Cultural Policy_ .

> 18 Michelle Bachelet, “Free Trade Needs Free Women,” _International Trade Forum Magazine_ , July 01, 2012, accessed April 6, 2015, http://www.tradeforum.org/article/Free-trade-needs-free-women/. 19 See related discussion here: Moira Vincentelli, “Trajectories: Survival, Revival, and Innovation in Women’s Ceramics,” Carleton College | World Ceramics: Transforming Women’s Traditions, accessed March 4, 2015, <u>https://apps.carleton.edu/campus/gallery/worldceramics/essays/vincentelli/.</u>

> 20  United Nations Inter-Agency Network on Women and Gender Equality (IANWGE), “Gender Equality and Trade Policy,” IANWGE, accessed April 6, 2015, <u>http://www.un.org/womenwatch/feature/trade/gender_equality_and_trade_policy.pdf.</u>

7
<!-- Start of picture text --> Vietnam<br>2<br>Correlation: 0.74<br>8 correlation: 0. ssP4/SSP5. ° °<br>nn 8 i i fe [90 °<br>£|H°6<br>#a: OF foH |H o%Gwe o° 8<br>§ on o o<br>SB 0, 90S OF,<br>So 6 Oo, o8°o °<br>33 ofo "goBeg.Goo 8<br>ereoS 5 °, PsPee es, co<br>SPOS a0. oo °<br>2 3t eeneicetege! vyhmeas heli) [6 oo<br>a Saab stiles, Ore R:)<br>: Restaod rs cestata rise ape[oom 8o Zo cs)<br>© 2} BBS “tes PF<br>2@ G0 "e Bam9 " S ° °<br>Pa a) } i}<br>0.02 0.04 0.06 0.08 0.10 0.12<br>Average income growth between 2007 and 2030<br><!-- End of picture text -->
rea, Poland and Morocco. In most of these countries, the women’s share varies from 48 to 52 percent. However, women’s share is particularly high for the Philippines (62.2 percent), Germany (57.4), Korea and Poland (around 56 percent).

Focusing on skilled emigrants, the ranking unsurpisingly shows that rich countries with highly educated population have better educated diasporas. The elasticity of skilled emigration to population size at origin amounts to 65.7 percent. The largest skilled diasporas originate from the United Kingdom (1.487 million), the Philippines (1.111 million) and India (1.034 million). Germany and Mexico send more than 0.9 million skilled natives abroad. Four other countries have diasporas above 0.5 million: China, Korea, Canada and Vietnam. In these top-countries, the share of women among skilled migrants is large in Jamaica (62.1 percent), the Philippines (60.3) and other countries such as Japan, Russia, Ukraine, Poland and Colombia.

Figure 3. Annual average growth rate of total/skilled stock of emigrants Data by region (1990-2000) <!-- Start of picture text --> 20%<br>18%<br>16%<br>Women total emig.<br>14% Women skilled emig.<br>12% Men skilled emig.<br>10%<br>8%<br>6%<br>4%<br>2%<br>0%<br>Central Asia Western Africa Southern Asia Southern Africa Central America Middle Africa Eastern Europe South America Northern Africa South-East. Asia Eastern Asia Eastern Africa Austr + New Zeal. Western Asia Caribbean Others Oceania Southern Europe Northern Europe North America Western Europe<br><!-- End of picture text --> 14
> FlICUR .10

<!-- Start of picture text --> I<br>BRAZIL SAVING RATES (DIFFERENT RATES)<br>NRRi=l BY 2010<br>0.10-<br>0.09<br>0.08<br>0.07-<br>0.06<br>0.05<br>0.04<br>0.03<br>0.02-<br>0.01<br>1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100<br><!-- End of picture text --> INt'TEREST RATE= _1-5,_ t'TERFST RATE=3X 0 NTEREST RATE=5%.
that the firm is not scale efficient, i.e. does not produce at a scale exhibiting local CRTS. In this<sup>context,</sup> (1-SE) can be interpreted as the relative decrease in average cost<sup>obtainable from rescaling outputs to the</sup> point of (locally) constant returns to scale. Tables 9 and 10 provide the results for the Eastern Orange<sup>Free</sup> State (all four surveys).

**Table 9: Summary of Efficiency Results, Eastern Free State (1.00=efficient)**

|Efficiency|Year|Average|St Dev|Median|Mode|Min|Max|
|---|---|---|---|---|---|---|---|
||1979/80|<br>0.85|0.23|0.91|1.00|0.38|1.00|
|Technical|1982/83|0.85|0.18|0.91|1.00|0.38|1.00|
|(TE)|1985/86|0.90|0.15|1.00|1.00|0.39|1.00|
||1988/89|0.89|0.16|1.00|1.00|0.48|1.00|
||1979/80|0.87|0.23|0.93|0.98|0.24|1.00|
|Scale|1982/83|0.84|0.14|0.89|1.00|0.51|1.00|
|(SE)|1985/86|0.79|0.21|0.86|1.00|0.08|1.00|
||1988/89|0.83|0.16|0.88|1.00|0.33|1.00|
||1979/80|0.73|0.23|0.72|1.00|0.21|1.00|
|Allocative|1982/83|0.74|0.17|0.76|1.00|0.25|1.00|
|(AE)|1985/86|0.78|0.15|0.78|1.00|0.39|1.00|
||1988/89|0.66|0.20|0.66|1.00|0.30|1.00|

**Table 10: Average farm size of efficient versus inefficient farms (scale, technical and allocative efficiency), Eastern Free State (ha)**

|Item*|1979/80|1982/83|1985/86|1987/88|
|---|---|---|---|---|
|SE-eff|1208.3|766.4|697.3|475.3|
|SE-ineff|1369.3|1349.1|1624.6**|1221.8|
|TE-eff|1265.2|1205.3|1312.1|1226.5|
|TE-ineff|1283.9|1293.9|1329.7|1215.4|
|AE-eff|1258.2|1246.3|1285.1|1387.3|
|AE-ineff|1267.5|1253.2|1309.4|1188.8|

Notes: SE = scale efficiency; TE = technical efficiency; and AE = allocative efficiency ** The average is relatively high due to two very large farms The results are, to a large extent, similar to those obtained with the TFP analyses. In addition, the methodology used here isolates scale efficiency from technical and allocative efficiency, while<sup>TFP</sup> measurements do not differentiate between them. The results yielded statistically significant differences between average farm sizes of scale efficient (SE-eff) and inefficient (SE-ineff) **farns (p<O.**<sup>10) for</sup> 1982/83, 1985/86 and 1988/89. No similar trend was encountered for technical efficiency<sup>(TE) and</sup> allocative efficiency (AE), implying that there is no meaningful relationship between TE or<sup>AE and farm</sup> size. This implies that the farm size-efficiency relationship has its origin in scale efficiency<sup>and not</sup> 32
**Table A1: African Development Bank Country Policy and Institutional Assessments (ADB)**

|**Data Provider**<br>African Development Bank.||||||||||
|---|---|---|---|---|---|---|---|---|---|
|**Description**<br>Multilateral development bank|headquar|tered i|n Abid|jan, Co|te D'Iv|oire||||
|**Website**<br>www.afdb.org||||||||||
|**Data Source**<br>Country Policy and Institutional<br> <br>|Assess|ments||||||||
|**Type**<br>Expert Assessment<br>||||||||||
|**Respondents**<br>African Development Bank<br>comparability|country|econo|mists,|subjec|t to|central|ized|review|for|
|**Frequency**<br>Annual since 1998||||||||||
|**Country Coverage**<br>African Development Bank clie|nt count|ries||||||||
|**Public Access**<br>Since 2005||||||||||
|**Description**<br>Indicators on 16 dimensions of<br>on a 6-point scale. CPIA indic<br>Development Bank.|policy a<br>ators are|nd inst<br>used t|itution<br>o alloc|al perfo<br>ate con|rmanc<br>cessio|e. Re<br>nal loa|sponse<br>ns by|s are co<br>the Afr|ded<br>ican|
|**Voice and Accountability**|2007|2006|2005|2004|2003|2002|2000|1998|1996|
|NA|..|..|..|..|..|..|..|..|..|
|**Political Stability and Absence of Violence**<br>NA|..|..|..|..|..|..|..|..|..|
|**Government Effectiveness**||||||||||
|Policies to improve efficiency of public sector|X|X|X|X|X|X|X|X|..|
|Budget Management|X|X|X|X|X|X|X|X|..|
|Efficiency of Public Expenditures|X|X|X|X|X|X|X|X|..|
|Management of public debt|X|X|X|X|X|X|X|X|..|
|Quality of Public Administration|X|X|X|X|..|..|..|..|..|
|**Regulatory Quality**||||||||||
|Trade policy|X|X|X|X|X|X|X|X|..|
|Competitive environment|X|X|X|X|X|X|X|X|..|
|Labor Market Policies|X|X|X|X|X|X|X|X|..|
|**Rule of Law**||||||||||
|Property rights|X|X|X|X|X|X|X|X|..|
|**Control of Corruption**||||||||||
|<br>Transparency / corruption|X|X|X|X|X|X|X|X|..|
|**Country Coverage**|**52**|**52**|**52**|**52**|**50**|**50**|**51**|**51**|**..**|
|**Year of Publication**|**2007**|**2006**|**2005**|**2004**|**2003**|**2002**|**2000**|**1998**|**..**|

39
13

# **Figure 2 Mechanisms for Natural Gas Trading**

<!-- Start of picture text --> Producer 1 Producer 2<br>Gas trader 2 Spot market<br>---------------- L - --- - --- - --- - ------------_<br>Shipper 1 1 0` Shipper 2<br>| British Gas TransCo<br>On-system<br>market<br>British Gas TransCo<br>Flexibility<br>mechanism<br><!-- End of picture text -->

Bilateral contracts between producers and shippers. Spot marke **t** rading among producers, traders, and shippers.

-~ >.On-system trading of gas and imbalances between shippers; prior notification of BGT is required. - Flexibility mechanism trading between shippers and BGT.

Source: Authors compilation

## _Bilateral contracts_

The traditional way of trading natural gas in the U.K. is through bilateral contracts, medium- and long-term natural gas supply contracts, between producers and shippers. The provisions in these contracts vary widely, depending on the characteristics of the parties' demand and supply. Parties typically specify the total volume of gas covered by the contract; the unit price of the gas, which can be fixed or linked to the price of other fuels (such as oil); and the financial settlement.

Long-term contracts specify delivery of gas for a period longer than one year, mediumterm contracts for a period between three months and one year. A typical example of a long-term contract is a take-or-pay contract under which the buyer agrees to pay regularly for a specified volume of gas regardless of whether it actually takes the entire amount. The U.K. alternative to the take-or-pay contract is a depletion contract, under which the buyer finances a share of the producer's field development costs in exchange for future gas deliveries.

Historically, bilateral supply contracts were concluded between British Gas and its customers: British Gas acquired gas from producers at the onshore terminals and sold it
ERR analysis at Appraisal and Completion, but there are variations across regions (Table 2). While SAR and EAP have 48.5 and 47.3 percent, respectively, of projects that report an ERR in the PAD and ICR, in the Africa Region 33.3 percent of projects have reported an ERR in the PAD and ICR.

**Table 2: Percent of projects with ERRs at Appraisal or Completion, by Region, 1975‐2015**

||||**Percen**<br>|**t of total proje**<br>|**cts (%)**<br>|||
|---|---|---|---|---|---|---|---|
||**Projects**<br>**with**<br>**Appraisal**<br>**ERR and**<br>**Completion**<br>**ERR**|**Projects**<br>**with**<br>**Appraisal**<br>**ERR only**|**Projects**<br>**with**<br>**Appraisal**<br>**ERR (with/**<br>**without**<br>**Completion**<br>**ERR)**|**Projects**<br>**with**<br>**completion**<br>**ERR only**|**Projects**<br>**with either**<br>**Appraisal**<br>**or**<br>**Completion**<br>**ERR**|**Projects**<br>**with no**<br>**ERRs**|**Total**<br>**number**<br>**of**<br>**project**|
|**AFR**|33.6|10.0|43.6|2.0|45.6|54.4|2,186|
|**EAP**|48.4|6.8|55.2|2.4|57.6|42.4|1,260|
|**ECA**|32.7|8.2|40.9|3.0|43.9|56.1|892|
|**LCR**|35.1|9.0|44.1|3.1|47.2|52.8|1,501|
|**MNA**|41.4|9.4|50.8|1.5|52.3|47.7|660|
|**SAR**|47.4|6.2|53.6|2.6|56.1|43.9|937|
|**World**<br>**Bank**|38.7|8.5|47.2|2.4|49.7|50.3|7,436|

Source: IEG data set and staff calculation In the SAR and EAP regions, a little over half of all projects have reported ERRs in the PAD or ICR (56.1 and 57.6 percent, respectively). This is higher than for other regions where this ranges from 45.2 to 52 percent. In contrast, in SAR and EAP, about 43.9 and 42.4 percent, respectively, of the projects do not report an ERR either at the appraisal or at the completion stage. This number ranges from 48 to 55 percent for other regions (Table 2).

Model 1, described above, examines whether the frequency of ERRs has been declining in recent years. Figure 1 shows a long‐term declining trend, but also suggests there has been a slight uptick over the past decade. Table 3 shows the declining trends by “high‐CBA” and “low‐CBA” sectors. The IEG study (World Bank 2010) found that the changes were driven mainly by changes within the “high‐CBA” sectors (i.e. transport, energy, water, agriculture, and urban development) and also a shift toward “low‐CBA” sectors. This paper updates that analysis and finds that this result still holds (Table 4).

15