rafmacalaba/datause-encoder-data huggingface dataset:
the sales of the services by the foreign-owned entities, generally termed foreign affliate trade in services (FATS), and not the value of the associated foreign direct investment flow or stock. The fourth mode of supply, mode 4 or presence of natural persons, refers to a process through which individual service suppliers temporarily move to the country of the consumer or client to provide the service. An example would be management consulting services provided by an employee of a firm based in the UK to a client in Russia. Although technology is making mode 1 increasingly feasible, for many services the need for provider and client or consumer to be in the same place (the proximity burden) remains so strong that delivery must be local, so that foreign ownership (establishment) or temporary movement of provider or consumer is required (modes 2, 3 and 4).

The reason why trade in services is defined to span these four modes reflects the characteristics of services. Most services are intangible and they are often difficult to store. As a result they are inherently more difficult to trade than tangible goods. Many services can be exchanged only if a provider and consumer – or client are in the same place at the same time which will imply that for trade to occur use will need to be made of modes 2, 3, and 4. An implication for modeling and analysis is that ideally account needs to be taken of all four modes, the factors that affect the costs of using alternative modes and the degree to which the modes are complements or substitutes.

Services trade data are available from a number of sources. The OECD, Eurostat and the UN all provide data in some form on bilateral services trade flows (both imports and exports) by partner for up to 24 sectors and subsectors. The most comprehensive coverage of reporting countries among the three sources is the UN, which at present count provides data on roughly 190 countries. Eurostat and OECD provide data for a more limited number of reporters, though with more sector detail than the UN. Eurostat covers 27 EU members plus Croatia, Iceland, Japan, Norway, Turkey, Switzerland, and USA, while the OECD covers 28 countries (all the OECD members apart from Chile, Iceland, Israel, Slovenia, and Switzerland). Time coverage is the deepest with Eurostat, which reports data starting from 1995. IMF data cover almost as many countries as the UN, and for a longer time span, but only for trade with world.<sup>5</sup> The construction of datasets for both econometrics and CGE work has required reconciliation and merging of these data in some form. For econometrics, the main issue is reconciling multiple sources, and mirroring is often used to deepen coverage. This means that where country _x_ reports trade with country _y_ , this can be used as a replacement where country _y_ does not itself report its trade with country _x_ . Reconciled bilateral datacubes, expanded to include countries that do not report services trade, are available on this basis.<sup>6</sup>

> 5See Francois and Pindyuk (2010).

> 6 Again see Francois and Pindyuk (2010).

4
Table 7 Olley and Pakes Regressions: Allowing for Differential Spillovers with Differences in Trade Exposure

|Dependent Variable|lnTFP<br>(1)<br>Domestic nonSOEs|lnTFP<br>(2)<br>Domestic nonSOEs|lnTFP<br>(3)<br>Non-exportingfirms|lnTFP<br>(4)<br>Exporters|
|---|---|---|---|---|
|State share|<br>-0.00174|-0.00240|-0.000923|-0.0108|
||(0.00332)|(0.00318)|(0.00406)|(0.00822)|
|Horizontal|0.194**|0.550**|0.211**|0.121|
||(0.0897)|(0.230)|(0.0992)|(0.0727)|
|Backward|0.765***|0.987|0.764***|0.752***|
||(0.262)|(0.712)|(0.286)|(0.235)|
|Forward|0.0867|0.502|0.0687|0.130|
||(0.0882)|(0.369)|(0.0912)|(0.0827)|
|lnTariff|-0.0375**|0.0124|-0.0364*|-0.0422***|
||(0.0171)|(0.0309)|(0.0192)|(0.0141)|
|lnbwTariff|-0.0354*|-0.0285|-0.0392*|-0.0133|
||(0.0207)|(0.0384)|(0.0218)|(0.0219)|
|lnfwTariff|-0.00458|0.000476|-0.00262|-0.0145|
||(0.00742)|(0.00578)|(0.00764)|(0.00881)|
|Horizontal*lnTariff||-0.154*<br>(0.0809)|||
|Backward*lnbwTariff||-0.189<br>(0.332)|||
|Forward*lnfwTariff||-0.226<br>(0.140)|||
|Constant|1.663***|1.541***|1.855***|1.669***|
||(0.0598)|(0.0777)|(0.0448)|(0.0616)|
|Observations|1,073,001|1,073,001|856,297|216,704|
|R-squared|0.173|0.174|0.163|0.204|

_Notes:_ In this table, we implement several extension based on the results shown in column 5 in Table 6. In column 1, we reproduce results from column 5 in Table 6. In column 2, we add three interaction terms. In column 3, we restrict the sample to non-exporting firms. We re-do the results with exporting firms in column 4. Firm fixed effect and year dummy variables in all regression. Robust clustered standard errors in parentheses.

45
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Finally, we need to control for financial contagion and other "guilt by association" characteristics of emerging debt markets. To gauge the incidence of such shock propagation mechanisms on Ras Gas credit spreads, we use the JP Morgan emerging market bond regional indices (EMBI family), i.e., Asia, Middle East, Europe and Latin America. The following table summarizes the predicted direct and indirect effects acting through Kepco of the various variables on Ras Gas yield spreads:

|Dependent Variab|le|_Ras Gas Yi_|_eld Spread_|_Changes in_|_RG Spread_|
|---|---|---|---|---|---|
|Effect||Direct|Indirect|Direct|Indirect|
|Variable|Description|Coefficient|Coefficient|Coefficient|Coefficient|
|RGS(-I)|Lagged Ras Gas spread|persistent||||
|BRENT|(Log) oil price|indeterm.|+|indeterm.|+|
|BRE.NT<14|(Log) oil price below USD 14|insign.|+|indetern.|+|
|BRENT: 14-23|(Log) oil price: USD 14 to 23|indeterm.|+|+|+|
|BRENT>23|(Log) oil price above USD 23|indeterm.|+|indeterm.|+|
|KORELES|Kepco yield spread|+||||
|KORELES(-1)|Lagged Kepco spread|insign.|persistent||persistent|
|KEPCO|(Log) Kepco stock price|insign.|-|indeterm.||
|KRW|(Log) Korean Won FX rate|+|+|insign|+|
|KOSPI|(Log) Korea Stock Price Index|insign||insign||
|KRR|Korean country rating index|+|+|insign.|+|
|ASIA|Emerging debt returns Asia|||||
|EUR|Emerging debt returns Europe|||||
|LAT|Emerging debt returns Latin Am.|||||
|MEA|Emerging debt returns Middle East|||||

Exhibit 4. Explanatory Variables and Their Coefficients' Predicted Sign 19
The “high digital” occupational cluster includes 13 occupations (Table 5). All require all levels of digital skills. All also have a high share of digital skills among total skills, ranging from 28 to 58 percent. The high-digital cluster includes, for example, software developers, a high-skilled occupation, and keyboard operators, a medium-skilled occupation. Both have a similar share of their skills that is digital (56-58 percent) but software developers mostly require advanced digital skills while keyboard operators mostly require basic digital skills. The bulk of these 13 occupations are consistent with the ISCO taxonomy of information technology occupations and most of these 13 occupations also have high scores on an index of digitalization created for occupations in the United States based on O*NET data (Muro et al. 2017).<sup>24,25</sup>

**Table 5. Digital skill and number of occupations of digital levels based on clusters**

|||**Av**|**erage share **|**of digital skills (**|**%)**||
|---|---|---|---|---|---|---|
||||||||
||||||||
|**user**<br>**number**|**ga**<br>**level**|**Total**|**Advanced**|**Intermediate**|**Basic**|**umer o**<br>**occupations**|
|0|Very Low|4.8|0.8|1.0|2.9|38|
|1|L|8.1|2.1|1.7|4.2|20|
|2|ow|11.5|1.1|3.5|7.0|21|
|3|Medium|15.0|3.0|5.0|7.1|35|
|4|High|38.3|15.8|13.3|9.3|13|

_Source:_ Authors’ calculations based on World Bank-Burning Glass online Malaysia job postings data (2016-18). _Notes_ : Darker red colors show higher percentage.

Thirty-five occupations map to the “medium digital” occupation cluster. These occupations have low requirements for advanced digital skills, but a moderate demand for intermediate- and basic-digital skills. This category includes machine operators, technical education teachers, and office clerks, who regularly use specialized software to carry out their jobs.

Most occupations can be classified as low digital. To provide some nuance to this group, we maintain the two clusters that emerged from the exercise and name them “low digital” and “very low digital” occupations. The exercise maps 41 occupations to the low-digital occupation category. These occupations require a moderate use of basic-digital skills. A broad range of occupations are in this category, including transport specialists (like Grab drivers who use digital phone apps to identify their next transport gig job), chief executives who may use MS Office for daily work, or salespeople who operate cash registers and inventory control software. The very low digital jobs include 38

> 24 Eight of the thirteen occupations are information technology occupations as defined by the ISCO occupation classification (ILO 2012, pp. 25-26). The two occupations we define as high digital that are not ICT occupations are mathematicians, actuaries, and statisticians (ISCO code 212) and engineering professionals (excluding electrotechnology) (ISCO code 214). 25 The occupation digital score is based on occupations’ three digitalization aspects as measured by O*Net for occupations in the US —computer & electronics, programming, and interacting with computer— following the methodology by Muro et al. (2017).

19
Table 6. Global demand and Indonesian Exports at the province level. 2005-2010 and 2011-2015 Panel A: 2005-2010

|Dependent variable:|IDN exports<br>per worker|IDN exports<br>per worker|IDN exports<br>per worker|IDN exports<br>per worker *<br>ICT|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Global demand for foreign products<br>Global demand for foreign products *|0.00142<br>(0.000175)***|0.00142<br>(0.000174)***|0.00105<br>(0.000133)***<br>0.00265|-0.00004<br>(0.000)<br>0.00166|
|Share of hhlds with computer|||(0.00122)**|(0.000275)***|
|ICT at the province level|No|Yes|Yes|Yes|
|Province level controls|Yes|Yes|Yes|Yes|
|Province and year FE|Yes|Yes|Yes|Yes|
|Observations|195|195|195|195|
|R-squared|0.983|0.983|0.987|0.982|
|F-stat|73.88|74.42|57.41|18.96|
|Panel B: 2011-2015<br>Dependent variable:|IDN exports<br>per worker|IDN exports<br>per worker|IDN exports<br>per worker|IDN exports<br>per worker *<br>ICT|
||(1)|(2)|(3)|(4)|
|Global demand for foreign products|0.000259<br>(0.000)***|0.000264<br>(0.000)***|0.000226<br>(0.000126)*|-0.00002<br>(0.000)|
|Global demand for foreign products *|||0.000190|0.000240|
|Share of hhlds with computer|||(0.000463)|(0.000109)**|
|ICT at the province level|No|Yes|Yes|Yes|
|Province level controls|Yes|Yes|Yes|Yes|
|Province and year FE|Yes|Yes|Yes|Yes|
|Observations|165|165|165|165|
|R-squared|0.979|0.979|0.979|0.981|
|F-stat|10.66|11.03|5.75|4.06|

Source: Authors’ calculations based on Sakernas 2005-2010 and 2011-2015, Susenas 2005-2010 and 2011-2015, and UN Comtrade database 2005-2010.

Notes: OLS regressions. Indonesian exports per worker in thousands of USD at 2010 prices and global demand for foreign products in millions of USD at 2010 prices. ICT variable at the province level is the share of households with computer. Province controls include the age structure of the population, share of urban population, real GDP per capita, minimum wage level, gender and educational composition of employment. All models control for province and year fixed effects. Robust standard errors between parentheses clustered by province. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively.

27
30. **The cost of running the corporate business line is relatively low** . Even though the cost of funding is higher than for retail due to the lack of low-cost savings deposits, operational costs are much lower. The main contributing factor is the nature of the delivery model (no need for extensive branch network, combined with lower-volume, higher-value transactions), although the extensive scale and type (e.g., rural credit) of directed lending operations might add to operational costs.

31. **Another important feature of the corporate market is its relatively lower (but more volatile) credit expenses** . As previously shown, non-performing corporate loans are closer to international averages than those in retail. Although the information needed to assess the credit risk of firms in Brazil – particularly larger ones – is more widely available, the impact of individual credit exposures is larger and more correlated to the macroeconomic environment, requiring customized risk management models with professional financial analysis. In addition, there is room for improvement in the quality of Brazilian corporate financial statements<sup>35</sup> , including issues of transparency and disclosure, which would enhance the reliability of the input data in those models. The inability to include positive information (including by public banks) in the borrower reports produced by the credit reporting industry also hinders the predictive ability of such models. Moreover, much of corporate lending (especially to the middle market) is collateral-driven and is therefore more reliable on the credit infrastructure, making it vulnerable to related problems with weak creditor rights such as judicial debt collection difficulties. In fact, in spite of recent reforms that have improved the perfection of collateral and reduced the cost of foreclosure procedures, Brazil fares below many Latin American countries in terms of creditor rights<sup>36</sup> . Finally, the inter-linkage among different credit registries is not yet complete, while links between credit registries and payments systems remain imperfect<sup>37</sup> .

> 35 Examples include overlapping mandates for accounting and reporting standard-setting, as well as gaps between Brazil’s generally accepted financial and accounting practices and International Financial Reporting Standards (IFRS).

> 36 According to the World Bank Group’s Doing Business 2009 statistics, Brazil’s legal rights index score is 3 (versus 5.6 for the region and 6.8 for the OECD).

> 37 See Western Hemisphere Credit and Loan Reporting Initiative (2005) for more details.

21
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54
we consistently find no significance for any of these variables across all specifications whether we consider a linear or quadratic fit.

From the regional panel models, we can conclude that results indicate no effects of changes in GDP per capita over FLP rates. This is consistent with findings in Verme (2014) which, using the ILO-EAPEP data, did not find any significance of the GDP per capita or its squared value when regressed against FLP rates.<sup>6</sup> It is also clear that a middle education level leads to lower FLP rates than no education or higher education (see more on education in the next section) and that the third and fourth quarters of the year are the most difficult for women in terms of participation due to the agricultural season.

**Table 1 – Regional Panel Medium-term Models**

|VARIABLES|(1)<br>OLS-IV|(2)<br>AB-GMM|(3)<br>AB-GMM|(4)<br>AB-GMM*|(5)<br>AB-GMM|
|---|---|---|---|---|---|
|L.flpr|0.481***|0.474***|0.317***|0.0854**|0.471***|
||(0.0315)|(0.0344)|(0.0377)|(0.0410)|(0.0344)|
|L2.flpr|||0.137***|||
||||(0.0365)|||
|L3.flpr|||0.0478<br>(0.0367)|||
|L4.flpr|||0.397***|||
||||(0.0365)|||
|L5.flpr|||-0.136***|||
||||(0.0370)|||
|ln_PIB_pc|-83.82|-86.74|-172.6*|-94.35|-28.63|
||(99.20)|(104.1)|(99.06)|(84.21)|(112.7)|
|ln2_PIB_pc|-7.598|-7.886|-15.93*|-8.442|-2.758|
||(9.060)|(9.508)|(9.069)|(7.686)|(10.26)|
|middle education|-0.132**|-0.141**|-0.121**|-0.203***|-0.136**|
||(0.0558)|(0.0600)|(0.0524)|(0.0557)|(0.0601)|
|upper education|0.290***|0.284***|0.241***|0.334***|0.292***|
||(0.0769)|(0.0819)|(0.0721)|(0.0755)|(0.0816)|
|quarter 2|-0.315|-0.292|0.223|0.432|-0.371|
||(0.424)|(0.444)|(0.399)|(0.363)|(0.449)|
|quarter 3|-2.265***|-2.246***|-1.171***|-1.523***|-2.332***|
||(0.423)|(0.443)|(0.409)|(0.362)|(0.446)|
|quarter 4|-1.427***|-1.422***|-0.876**|-1.409***|-1.620***|
||(0.425)|(0.444)|(0.403)|(0.358)|(0.466)|
|secondary sector|||||25.10|
||||||(21.04)|
|tertiary sector|||||-14.14|
||||||(14.83)|

ln VA per capita primary sector ln VA per capita primary sector squared 6 This may be explained by one of two factors. Either the U-shape hypothesis does not apply to Morocco or the country is transiting through and lags on the saddle point of the curve.

11
Table 8s Malawi Studys Variable Definitions and Descriptive Statistics

|Variable|Moan|SD|
|---|---|---|
|Raw Pupil Exam Scores<br>|||
|Chichewa (10 items)<br>|5.5<br>|2.3<br>|
|Maths (10 items)|3.8|2.1|
|Pupil Exam Z-Scores<br>|||
|Chichewa<br>|0.00<br>|0.99|
|Maths|0.00|1.00|
|Exogenous Predictors<br>|||
|Urban school (2-urban)|1.54|__|
|Region's wealth,Zlabor force<br>employed in non-agricultural<br>jobs|32.0|4.4|
|Pupil sex (2-girl)|1.49|__|
|Pupil's grade-level|5.5|1.5|
|Pupil's work tasks, five possible<br>farm related jobs (e.g. fted animals)|3.1|0.5|
|Pupil's mother tongue is Chichewa,<br>language of high-status tribe (2-yes)|1.6|--|
|Pupil's father's work<br>(4-point scale by skill level)|2.7|1.1|
|Pupil's mother's schooling level<br>(2-yes, completed standard 4)|1.7|0.5|
|Pupil's mother's reading practices<br>(10-point scale, e.g. reads books)|6.6|1.2|
|Pupil's house has electricity<br>(2-yes)|1.4|--|
|Pupil's parents own radio<br>(2-yes)|1.8|--|
|Headmaster's estimate I pupils from<br>farming families|42.7|32.9|
|Headmaster's estimate I pupil living<br>in thatch-roof huts|56.2|37.0|

Note: Standard deviations not reported for dichotomous variables.

- 31 -
the latter differs from the figure in Table 1.5, 850,000 against 113,000. 1967 apparently has by the SPO been used as a bench-mark year after which a slow downward trend is assumed, bringing the number down to 665,000 in 1984.

More interesting from our point-of-view is, however, that the slack se&sons appear to be March-June and September-November, December-February being relatively busy (soil preparation for the winter wheat?) with July-August the peak (harvest). If that really is the case, an average of unemployed seeking jObs-in June,. October and April mig'-t overestimate annual averageunemployment in rural areas.

If now the rural or, at least, the agricultural labor market in Turkey, as experience from other LDCs gives us some reason for believing clears almost instantaneously, certainly at the peak season and possibly at any time during the year, rural wages being sufficiently flexible, there should be no rural unemployed seeking work at any time or, more realistically, the number should at any time be small. People employed in rural areas or not being in the rural labor force might still be seeking employment in urban areas or abroad if they planned to migrate but such questions are not asked by the surveys which in this regard are deficient as is the '-lted States model. Rural unemployment according to the labor force surveys of 1973-74 amounted to 0.2 - 0.7 percent of the rural labor force in June, October and April. This is consistent with the assumption of

-7-