rafmacalaba/datause-encoder-data huggingface dataset:
# **2. Conceptual Framework and Definitions of Skills**

There are intuitive reasons why cognitive and socioemotional skills would be complements to digital skills. Cognitive skills linked to analytical and critical thinking help make sense of the information accessed through digital skills, say surfing the internet or analyzing data. Learning and problem solving can help to develop more digital skills and take advantage of complex computer processes. Socioemotional skills may help workers collaborate through digital technologies, use these technologies to produce creative outputs, and adapt as technologies evolve.<sup>3</sup> As such, we can think of cognitive and socioemotional skills as building blocks of digital skills, which themselves are divided in several levels of complexity. The levels of digital skills may also be pyramidal: tasks requiring more complex digital skills, for example, may not explicitly use less complex ones, but the underlying knowledge and skills encompassed in basic digital skills can be seen as pillars of more complex digital skills.

## _Defining digital, cognitive, and socioemotional skills_

We define three main skills categories, each with three subsets of skills. The main categories are digital, cognitive, and socioemotional. Skills that do not fit these categories are defined as other technical and language skills.

Digital skills are the skills that are needed to work with information and communications technology (ICT) software and devices. Digital skills allow people to access and use digital technologies. Digital skills are often mistaken for a narrow set of specialized skills used by ICT workers, such as programming or data science. However, digital skills encompass a wide range of complexity and are increasingly used across many kinds of tasks in a wide range of occupations (UNESCO 2017, IFC 2019, ITU 2020, DE4A 2021). Figure 1 presents the decision tree that we use to identify and classify digital skills.

> 3 Cognitive and socioemotional skills are themselves complementary to each other. In the U.S., job requirements for cognitive and social skills are positively correlated with pay and firm performance, and more so when both skills are required (Deming and Kahn 2018). Longitudinal data of adults, also for the U.S., show that wage returns to cognitive and socioemotional skills are increasing (Weinberger 2014, Deming 2017). Online job postings collected in Ukraine show that the shares of postings requiring cognitive and socioemotional skills within an occupation are very similar (Muller and Safir 2019).

5
# **Table 1. Results for the Fixed Effects Gravity Model, Excluding Non-Reporters.**

||OL|S|PP|ML|Feasibl|e GLS|To|bit|Wtd T|obit|EK|-H|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Coeff|t-stat|Coeff|t-stat|Coeff|t-stat|Coeff|t-stat|Coeff|t-stat|Coeff|t-stat|
|Log distance|-1.35|-43.1|-0.75|-18.5|-1.28|-44.6|-1.88|-44.9|-1.66|-42.3|-1.47|-26.1|
|Contiguity|0.17|1.3|0.37|4.1|-0.01|-0.1|-0.57|-3.2|-0.68|-4.7|-0.60|-3.1|
|Common language|0.41|6.0|0.38|4.1|0.39|6.2|0.79|9.2|0.70|8.4|0.65|6.7|
|Colonial tie|0.67|9.5|0.08|0.6|0.68|10.4|0.98|10.9|1.09|12.3|0.99|10.5|
|FTA|0.31|3.2|0.38|4.9|-0.07|-0.8|-0.36|-2.5|-1.03|-8.4|-1.38|-9.5|
|Fixed Effects|Yes||Yes||Yes||Yes||Yes||||
|Observations|9602||16290||9602||16290||16290||16290||
|R<sup>2</sup>|0.75||0.93||0.80||0.30||0.31||||
|Hetero, ln|||||||||||||
|_a_|1.97|43.0|2.7|105.3|||||||2.6|50.7|
|_b_|-0.12|-19.4|-0.09|-15.6|||||||-0.12|-14.5|
||||_Mean_|_SD_|||||||_Mean_|_SD_|
|Ln Predicted Trade|||8.0|2.9|||||||4.3|4.9|

Source: Author’s estimates using Stata 16.0. The log of trade is the dependent variable in all but the PPML model. WLS and Weighted Tobit used heteroscedasticity parameters shown under the OLS heading obtained by applying the Cameron and Trivedi (2009, p155) technique to residuals from the OLS regression. Heteroscedasticity parameters for the PPML were estimated using an EK-H model to avoid truncation of the residuals in estimating the heteroscedasticity function, with the log of the predicted value from the PPML as the explanatory variable, the log of actual trade in the density function in equation (4) and the log of the threshold in the CDF.

15
-

- Rao, S.. 2016. “London’s new late-night alternative: The Night Tube + Uber”, https://medium.com/uber <u>under-the-hood/londons-new-late-night-alternative-the-night-tube-uber-8f38e56de983.</u>

- Roberts, Mark, Frederico Gil Sander, and Sailesh Tiwari. 2019. _Time to Act: Realizing Indonesia’s Urban Potential._ <mark>Washington, D.C.: World Bank Group. https://doi.org/10.1596/978-1-4648-1389-4_ov</mark>

- Saffan, A. F., and , M. Rizki. 2018. “Exploring the Role of Onli‘e 'O’ek' In Public Transport Trips: Case of Jakarta Metropolitan Area Rail Users,” _IOP Conference Series: Earth Environmetal Science,_ 158, 012024.

- <mark>Sunitiyoso, Y., Rahayu, W. A., Nuraeni, S., Nurdayat, I. F., Pambudi, N. F., and Hadiansyah, F. 2022. “Role of ride-hailing in multimodal commuting."</mark> _<mark>Case Studies on Transport Policy</mark>_ <mark>,</mark> _<mark>10</mark>_ <mark>(2), 12831298.</mark>

- <mark>Trahjono, Tri, Andyka Kusuma and Ahmad Septiawan. 2020. “The greater Jakarta area commuters travelling pattern.” Transportation Research Procedia, 47, 585-592.</mark>

28
of a possible exchange rate and interest rate adjustments. The wage-price dynamics that underlie this process have yet to be properly explored but earlier estimations pinned down a clear feedback from wages to prices **16.** The current guidelines system may not only sanction an unnecessarily tight association of wage and price changes but may -- given the absence of any penalties on excessive wage deals -- permit financially troubled firms to indulge in predatory wage setting.

These aggregates likely encompas **s** ignificant variation in wage settlements across sectors and bargaining loci. For 1992, we can more directly observe industrial branch wage agreements. These were largely concerned with establishing a floor for annual wage increases. In almost all cases, this floor ranged between 13-18%; in those cases where the agreement was conditioned on a maximum increase, that corresponded to the 28% norm set under the thenoperating incomes policy. For firm level agreements, the majority specified an increase in basic or average wages over the range 17-21%. These numbers would be consistent with a wage setting constrained by the incomes policy, though the causality is far from clear.

For 1993 industrial gross wages increased by around 27%, with net wages increasing in step with consumer prices (23 %). As yet, we lack information that is sufficiently disaggregated. However, for 1993.ql we can contrast projected wage increases with actual settlements for 700 industrial firms. As a significant part of the annual wage round is concluded in the first quarter of the year, this provides a reasonably complete indication of the annual wage outcome for 1993. Table 13 shows that actual wage settlements have a somewhat bi-polar structure. A third of fms have actual settlements from 0-10% nominal wage increases for 1993.ql over 1992.ql. Over 40% of firms have wages increasing by more than 20%, with nearly a quarter giving wage increments in excess of 30%, with half of this sub-sample being in excess of 40%. The employment distributions for this sample do not suggest that firm size is strongly positively correlated with wages. But we lack direct information on other attributes -- such as ownership - - of 1bis population of firms

**1;** Commander and Coricelli (1992)

n Branch level data allow us to pick up the outliers; the most striking being mining where wages settled 8.5% above national averages, while employment continued to decline at a far higher rate dtn the industry average; a likely insider story.

29
Gridded Population of the World (GPWv4) dataset, developed by Columbia University based on population and housing censuses (CIESIN, 2016).

Data are available in five-year steps for the period from 2000 to 2020 with a spatial resolution of 30 arc-seconds (which is equivalent to about 1 kilometer at the equator). We use the 2015 version for further analysis.

Income data is obtained from Kummu et al. (2018) who provide gridded global datasets for GDP, GDP per capita, and Human Development Index (HDI) for every year from 1990 to 2015. GDP and HDI estimates are provided at 5 arc-minutes resolution, while GDP per capita (PPP) has a resolution of 30 arc-seconds. To generate these datasets, Kummu et al. (2018) use World Bank datasets, the Central Intelligence Agency’s (CIA) World Factbook, and subnational GDP per capita data developed by Gennaioli et al. (2012), and employ country-specific interpolation and extrapolation. These gridded datasets are consistent worldwide and comparable across countries and years. Figure 2 shows gridded GDP maps for six years. Areas that do not have any population are dropped from our analysis.

# **3.2 Model**

We simulate the wind-dispersion of air pollutants from coal power plants in four steps.

First, we use the Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT) to simulate the movement trajectories of particles originating from power plants, in line with Du et al. (2020). We generate 96 forward trajectories for 6,321 power generation units. Four such trajectories are visualized in Figure 3. Pollutant movement is calculated based on the Global Data Assimilation System at a 1 arc-degree resolution. We assume each pollutant particle has a four-day (i.e. 96 hours) lifespan and an initial altitude of 100 meters. Starting from the originating plant, the location of particles is simulated beginning from 11 am on January 1st, 2013, in hourly increments until 11 am on January 5th. Each location is a three-dimensional coordinate, specifying longitude, latitude, and altitude.

The second step is to account for particle dispersion along the simulated trajectories. This reflects the fact that air pollution is not a single particle but an air mass, and the affected area is not a trajectory line but an area with a concentration gradient. Thus, we translate each simulated point location from the HYSPLIT modeling into an affected area. We follow the Calpuff model and simulate three-dimensional Gaussian dispersion along the modeled trajectories. Dispersion is specified as follows (Agency, 1995): 7
# **1. Introduction**

This paper assesses corruption levels and trends among countries in the Eastern Europe and Central Asia (ECA) region, based on data from several sources that are both widely used and cover most or all countries in the region. To make sense of these data, the paper also examines the properties of the corruption indicators themselves. The ECA region is the most appropriate one for this analysis as it has the richest set of available country-level data on corruption. Because the various data sources are not always in agreement, we examine definitional and methodological issues in a “primer on corruption indicators” before reporting evidence on levels and trends.

Section 2 discusses definitional and methodological differences among data sources that may account in large part for the apparently conflicting messages they often provide. Section 3 assesses claims that the solution to disagreement among sources is to aggregate them, on the assumption that they collectively are more informative than is any single source. Section 4 presents evidence from the various data sources on corruption trends in the ECA region. The range of corruption measures available in firm surveys are exploited in section 5 to show that broad, perceptions-based corruption assessments appear to measure primarily administrative corruption. Section 6 concludes with recommendations. The main points of the paper are summarized below for convenience:

- Existing corruption indicators differ importantly in the aspects of corruption they purport to measure, in clarity and breadth of definition, and in the methods and transparency of their assessments.  For these reasons, _no one indicator or data source is best for all purposes_ .

- Aggregating corruption indicators from numerous sources – with the goal of increasing precision in measurement - does not always produce a more appropriate measure than using a single indicator or data source.  One cost that should be considered is the loss of _conceptual_ precision through aggregation.

3
# 1. Introduction

Many countries levy withholding taxes on<sup>interest payments on</sup> international bank loans and other<sup>debt instruments. These withholding taxes</sup> can be credited against taxable<sup>income in the major creditor nations, such as</sup> the United States and the United<sup>Kingdom. International bank loan contracts</sup> conventionally state the interest<sup>rate or spread above the benchmark rate net</sup> of withholding taxes. For a given<sup>net interest rate, an increase in the</sup> withholding tax rate increases the<sup>withholding taxes paid in the borrower</sup> country as well as creditable in<sup>the creditor country. Hence, international</sup> bank loans become more profitable<sup>to the banks the higher the rate of</sup> withholding tax imposed by the borrower<sup>country. As banks compete for loans,</sup> one expects these institutions to<sup>offer low interest spreads to countries</sup> that impose high withholding taxes.<sup>This paper shows empirically that</sup> international differences in withholding<sup>tax rates indeed to a large extent</sup> are reflected in bank credit terms.

Using a sample of 510 loans to 14<sup>debtor nations originated during the</sup> years 1971-1981, we find that the developing<sup>countries have been able to</sup> reduce their interest expense by an estimated<sup>56 cents for every dollar of</sup> tax withheld at the source. U.S. banks<sup>are shown to have passed on close to</sup> 100 per cent of their potential U.S. income<sup>tax credits to developing</sup> countries by way of lower interest<sup>spreads during the go-go lending years of</sup> 1976 to 1978. In particular, tax<sup>credits are shown to be fully reflected in</sup> loans with maturities of four years or less,<sup>but only partially in longer</sup> term loans. The rationale appears to be that<sup>banks have doubts whether tax</sup> credits flowing from long term loans will still<sup>be allowed in the future, and</sup> they may doubt whether thev will have sufficient<sup>taxable income to actuallv</sup> 1
**Table A.1.2. Similar occupations used for imputations**

|**#**|**Occupation name**|**Code**|**Code of occupations used for**<br>**imputations**|
|---|---|---|---|
|1|Legislators and senior officials|111|112, 121, 122, 134, 141, 142, 143, 232,<br>242, 243, 261, 331, 332, 333, 335, 422,<br>522,541|
|2|Traditional,and,complementary,medicine, professionals|223|222,224,225,226|
|3|Traditional,and,complementary,medicine,associate, professionals|323|321,322,325|
|4|Veterinary,technicians,and,assistants|324|321,324,325,516,532|
|5|Secretaries, (general)|412|334,421,422,441|
|6|Street,and,market,salespersons|521||
|7|Street,and,related,service,workers|951|512, 522, 524, 911, 941, 962|
|8|Street,vendors, (excluding,food)|952||
|9|Market, gardeners,and,crop, growers|611||
|10|Animal, producers|612||
|11|Mixed,crop,and,animal, producers|613||
|12|Forestry,and,related,workers|621||
|13|Fishery,workers,,hunters,and,trappers|622|921|
|14|Subsistence,crop,farmers|631||
|15|Subsistence,livestock,farmers|632||
|16|Subsistence,mixed,crop,and,livestock,farmers|633||
|17|Subsistence,fishers,,hunters,,trappers,and, gatherers|634||
|18|Wood, treaters,, cabinet-makers, and, related, trades, workers|752|313, 711, 722, 723, 731, 732, 751, 752,<br>811,815,818,821,921,932|
||||311, 312, 313, 315, 325, 421, 422, 441,|
|19|Other, craft, and, related, workers|754|512, 513, 522, 524, 621, 711, 721, 722,<br>723, 732, 741, 742, 753, 813, 818, 821,<br>833,932|
|20|Metal, processing, and, finishing, plant, operators|812|313, 512, 711, 722, 723, 732, 751, 811,<br>813,815,818,932|
||||265, 313, 512, 711, 712, 721, 722, 723,|
|21|Rubber,, plastic, and, paper, products, machine, operators|814|731, 732, 751, 753, 811, 813, 815, 818,<br>821,932|
|22|Food, and, related, products, machine, operators|816|313, 722, 732, 751, 811, 813, 818, 821,<br>932|
|23|Wood, processing,and, papermaking, plant,operators|817|512,722,732,815,818,821,932|
|24|Locomotive,engine,drivers,and,related,workers|831|511,711,811,832,833,931|
|25|Ships', deck, crews, and, related, workers|835|313, 711, 742, 751, 811, 813, 833, 834,<br>931|
|26|Vehicle,,window,,laundry,and,other,hand,cleaning,workers|912|815|
|27|Refuse,workers|961|441,711,712,832,834,921|

_Sources_ : Own elaboration based on ILO (2012) and O*NET (2021). _Notes_ : ILO (2012) lists some related occupations to a given one. O*NET (2021) lists occupations in the United States that have similar skills and experience and which workers could transfer between with minimal additional preparation.

41
# **Appendix B. GAMS code of stylized CGE model**

``` $title Illustrative CGE Model of Incentive-Compatible CDM at the Sector Level ```

|`set r`|`Regions /oecd`|`,china,row/;`|
|---|---|---|
|`alias (r,rr);`|||
|`parameter`|`c0(r,rr)`|`Consumption (=trade matrix),`|
||`ls0(r)`|`Leisure demand,`|
||`le0(r)`|`Labor + leisure endowment,`|
||`ke0(r)`|`Capital endowment,`|
||`r0(r)`|`Resource endowment,`|
||`y0(r)`|`Output,`|
||`l0(r)`|`Labor demand,`|
||`k0(r)`|`Capital demand,`|
||`f0(r)`|`Capital demand,`|
||`es0(r)`|`Energy supply,`|
||`ed0(r)`|`Energy demand,`|
||`evs(r)`|`Energy value share,`|
||`kvs(r)`|`Capital value share,`|
||`sigma`|`Cross-price elasticity of energy demand /0.5/,`|
||`eta`|`Cross-price elasticity of energy supply /0.5/,`|
||`gamma`|`Cross-price elasticity in consumption demand /4/,`|
||`cdmv(r,rr)`|`Value of CDM purchases,`|
||`elim(r)`|`Emissions limit,`|
||`target`|`Emissions target /0/;`|

``` table bmkdata(*,r) Stylized benchmark data oecd china row y0 100 40 50 evs 0.05 0.08 0.12 kvs 0.40 0.50 0.40 zeta 1.75 1.25 1.25 es0 0.4 0.1 0.5 dvs 0.8 0.9 0.6; ``` ``` evs(r) = bmkdata("evs",r); y0(r) = bmkdata("y0",r); ed0(r) = evs(r)*y0(r); es0(r) = bmkdata("es0",r)*sum(rr,ed0(rr)); f0(r) = 0.5*es0(r); r0(r) = 0.5*es0(r); kvs(r) = bmkdata("kvs",r); k0(r) = kvs(r) * (y0(r)-ed0(r)); ke0(r) = f0(r) + k0(r); l0(r) = y0(r)-ed0(r)-k0(r); ls0(r) = 0.5 * l0(r); le0(r) = l0(r) + ls0(r); cdmv(r,rr) = 0; c0(r,r) = bmkdata("dvs",r)*(le0(r)-ls0(r)+ke0(r)+r0(r)); ``` ``` parameter mchk; mchk(r,"d0") = c0(r,r); mchk(r,"m0") = le0(r)-ls0(r)+ke0(r)+r0(r) - c0(r,r); mchk(r,"x0") = y0(r)-c0(r,r); mchk("total","d0") = sum(r,c0(r,r)); mchk("total","m0") = sum(r, mchk(r,"m0")); mchk("total","x0") = sum(r, mchk(r,"x0")); ``` 21
# 3.1 GHG emissions

There are _N_ abatement options, indexed by _i_ . The model is run on a period that goes from 2000 to 2050 with a time step, ∆ _t_ , of one year. At each time step _t_ , emissions are computed from the baseline emissions _Ebase_ ( _t_ ) and the abatement achieved with each activity _i_ .

We assume constant baseline emissions, that is _Ebase_ ( _t_ ) = 5 GtCO 2 /yr.

The cumulative emissions _M_ ( _t_ ) are then computed as the sum of emissions:

# 3.2 Potentials, costs, and inertia

Abatements eorts in each sector are subject to two restrictions. First, each activity _i_ has a technical ceiling, i.e. a maximum abating potential _amax_ ( _i_ ) , expressed in avoided annual emissions, i.e. in MtCO 2 /yr. This potential is commonly represented by the rectangles width in MAC curves (see Fig. 1). For instance, switching to more ecient thermal engines for personal vehicles could save a fraction of GHG emissions associated with private mobility, but not more.

In the MAC curve, each activity _i_ is qualied with a constant marginal cost of abatement _c_ ( _i_ ) (see Fig. 1). Here, we assume that marginal abatement costs are independent of cumulative abatements and of time. In particular, we do not model technical change and learning-by-doing. Therefore, abatements _a_ ( _i, t_ ) achieved thanks to activity _i_ at time _t_ have a cost _I_ ( _i, t_ ) which reads: The innovation here is the introduction of inertia in the MACCs. Inertia is dened by a cost in time: a given amount of abatement requires to spend a positive amount of time for its implementation. This cost could be expressed in years per MtCO 2 , and is assumed independent of the nancial cost of the option. In other words, there is a maximum amount of supplementary abatement that is achievable with each activity during a given time interval, which is expressed in MtCO 2 per year.<sup>3</sup> Let _α_ ( _i_ ) , in MtCO 2 /yr<sup>2</sup> , be the maximal incremental amount of GHG emissions abatement achievable with activity _i_ over a year. Note that _α_ ( _i_ ) is the algebraic inverse of the cost in time. For

> 3This modeling diers from the time-to-build à la Kydland and Prescott (1982). Time-to-build would reect the idea that there is an incompressible lag between investment decisions and actual abatements. With time-to-build, an arbitrary large amount of abatements would require as much time to be implemented as a small abatement (if achieved through the same activity). With our cost in time, in contrast, the time expenditures are proportional to the amount of abatement, in the same way as nancial expenditures.

7
**Figure 9: Female labor share premium (percentage points), trading vs. non‐trading firms, by worker type**

<!-- Start of picture text --> 6<br>5<br>4<br>3<br>2<br>1<br>0<br>Exporting vs. non‐exporting Importing vs. non‐importing GVC participants vs. non‐ FDI vs. non‐FDI firms<br>firms firms participants<br>Total workers Production workers Non‐production workers<br><!-- End of picture text --> Note: The graph shows mean differences in female labor share between trading and non‐trading firms for total, production and non‐production workers. All regressions control for output, capital intensity, TFP (all in natural logarithms) as well as country‐ sector, subnational region and year fixed effects. All results shown are significant at the 10% level. For the underlying regression results, see Appendix 4.

# **4.3 Robustness Tests**

In a next step, Table 3 splits the sample into two periods, namely 2010‐2013 and 2014‐2017, to detect if the findings hold throughout both periods. Surprisingly, the correlation between exporting and the female labor share more than twice as high for the earlier period 2010‐2013. While the female labor share premium is 5.4 percentage points relative to non‐exporting firms for the period 2010‐13, it was only 2.5 percentage points for the period 2014‐17. Moreover, the female labor share premium for importing versus non‐importing firms, GVC participants versus non‐participants and FDI firms versus non‐FDI firms is only significant over the period 2010‐2013. While the finding may be related to global GVC trade slowdown, Appendix 1 shows that the 2014‐17 surveys contain many Latin American, Sub‐Saharan African and some Middle East and North African countries that are less integrated into GVCs and trade, which could have driven the results.

In order to rule out that the overall findings are driven by female‐intensive sectors only, we exclude wearing apparel (ISIC 18) and office, accounting and computing (ISIC 30) in the regressions. Figure 10 shows that the female labor share premium for trading firms becomes somewhat smaller for exporters and GVC participants, but not for importers and FDI firms. For the underlying regression results, see Appendix 5 and 6.

One potential omitted variable bias could stem from the lack of gender‐specific wage data in the Enterprise Surveys. We are able to address this issue by using gender‐specific wage data that are available 19