rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Afghanistan<br>8 . ° . Rr<br>z, =z. . e<br>2008 2008 io 2012 ou 2006 208 10 12 21a<br>Sion<br>@NRVA-RP:month<br><!-- End of picture text --> <!-- Start of picture text --> Bhutan<br>a “<br>2 ° .2a<br>dbe<br>°<br>gSe28 =Qsws °<br>s 5<br>2002 2004 2006 2008 2010 mre 202 2008 2006 2008 2n10 2012<br>Yer Year<br>@ LSS-RP:week A LSS-RP:year<br><!-- End of picture text --> <!-- Start of picture text --> Nepal<br>e<br>«<br>a .a a.<br>.<br>g=of €23 °<br>2= &E °<br>8 &<br>2002 2004 2006Year 2008 2010 2002 2004 2006Year 2008 2010<br>@ LFS-RPweek @ NLSS-RP:week<br>A LFS-RP:year I NLSS- RP-year<br><!-- End of picture text --> <!-- Start of picture text --> Bangladesh<br>g °<br>oa ° oe<br>e Rk<br>_z, ao=<br>°<br>°<br>° ° °<br>. . e<br>2000 2005 Yer 2010 2015 2000 2005 ver 2010 2015<br><!-- End of picture text --> <!-- Start of picture text --> India<br>38<br>e dbseeo ry °. 4 6 R<br>= z<br>3 éé<br>a a eg°..2°a<br>2000 08, 2010 2000 205 2010<br>@ Thick -RPoweek — @ Thin-RP:week<br>A Thick-RP:year @ Thin- RP:year<br><!-- End of picture text --> <!-- Start of picture text --> Pakistan<br>:<br>e Po ae | atafta<br>g=08 z22ra<br>= fi5<br>8 8} a@ go ogt tacts<br>ot ° , 2 &<br>2000 2005 Year 2010 2015 2000 2005 Year 2010 2015<br>@ LFS-RP:week @ Other-RP:week<br>A LFS-RPyear Ml Other-RP: month<br><!-- End of picture text -->
Financial literacy programs and disclosure requirements alone are not sufficient for ensuring good financial consumer protection regulations that correct for information asymmetries and market failures. Regardless of financial literacy levels, service providers, if left unchecked, often have incentives to take advantage of information asymmetries and adopt unfair selling practices that allow for quick and large gains in profits. Hence, fair treatment provisions are also of importance in this regard. While all of these are, in general, aimed at correcting the information asymmetries in the market for financial products and services, another aspect of financial consumer protection is the existence and accessibility of third-party recourse mechanisms. There is not much written on fair treatment practices and recourse mechanisms in the academic literature. The policy paper by Brix and McKee (2010), however, considers these two issues in the context of low-access environments. Brix and McKee (2010) caution the policymakers that fair treatment provisions should be designed in such a way so as not to deter access to financial services. They also suggest starting with a discussion of standards for internal dispute resolution mechanisms, which is more widely feasible than third-party recourse mechanisms.

Two recent papers are particularly relevant for our research presented here. Rutledge (2010) provides a detailed analysis of financial consumer protection in banks and non-bank financial institutions in nine countries in Europe and Central Asia. In these transition economies, financial services have developed rapidly in recent years, presenting unique challenges. The assessment draws on the experience of these countries and provides lessons learned from developed economies. Another study by Brix and McKee (2010) considers the specific challenges faced by low income countries. Our analysis draws heavily on the results of these two papers and provides a high level assessment of consumer protection framework in 140 countries.

# **3 Data on Financial Consumer Protection**

This section provides a description of the cross-country data set on consumer protection laws and regulations collected for the _Financial Access 2010_ database by CGAP and the World Bank Group. The survey covered the following main aspects of a consumer protection framework: (i) scope of existing legal framework; (ii) supervisory and enforcement powers; (iii) recourse mechanisms.

## **3.1 Legal Framework**

## **_3.1.1  Existing legislation_**

Among the 142 countries responding to the survey, 118 report having laws containing provisions relevant for consumer protection in financial services, most of which were passed or revised in the last two decades. Figure 1 shows that these laws and regulations usually fall into one of the 6
In pursuance of this paragraph the Cabinet of Ministries issued a resolution no 845 of 22.10.2018. It states that accounting of the citizen at the place of stay in Tashkent city and Tashkent region can be allowed to the period of 5 days till 1 year without discharge from the permanent place of residence. The registration of citizens from other regions who would like to be engaged in seasonal work (construction, agriculture and other) in Tashkent region and Tashkent is simplified. According to the resolution citizens who do not have a permanent propiska in Tashkent city and Tashkent region can purchase property (house or apartment) only in new buildings through bank transactions. At the same time these buyers (who do not have permanent registration in Tashkent city and region) have to pay a state fee equal to 5% of the amount of the agreement but not less than 10 minimum wages (2 027 300 UZS). The same tax for residents of Tashkent city and region who have permanent propiska is 202 700 UZS.

Administrative responsibility for recruitment of citizens without permanent or temporary registration in Tashkent city and Tashkent region as well as regulation justifying employers to refuse in employment in case if the candidate does not have permanent or temporary registration is abolished.

Despite these changes the issue related to purchase of housing at the secondary market by persons without permanent or temporary registration is not solved. This leads dissatisfaction of citizens.

Through its telegram channel called “Jamoatchilik Fikri” or “Public Opinion”, “Yuksalish” movement conducted a survey on issues of permanent propiska in Tashkent and Tashkent region from June 3 – 11 2019. Around 17 600 citizens took part in the survey and almost half of them, i.e. 49%, responded that regulation related to permanent propiska in Tashkent city and Tashkent region contradicts constitutional rights of the citizens. The findings of the survey show that implementation of this paragraph of the State programme that was approved by the President is crucial. If the paragraph had been implemented timely and in full it would not have become a reason of dissatisfaction of citizens. It would contribute to the profile and prestige of Uzbekistan worldwide, increase rapid urbanization in the country, social development and hider brain drain. Taking into consideration all these factors, “Yuksalish” proposes the following:

|•|To replace the system of permanent registration in Tashkent city and Tashkent region by system of accounting at the place<br>of stay. The procedure should be simplified and completed though state services centers or Single interactive state services<br>|
|---|---|
||portal;|
|•|To recognize the right of ownership of the citizens who do not have permanent or temporary registration in Tashkent city<br>and Tashkent region not only in the primary real estate market but also in the secondary market (it is also offered to<br>additionallyinclude agreements such aspresentingthe immovableproperty).|

69
5 gradually implementing structural adjustment policies,<sup>assisted by foreign</sup> commercial borrowing.<sup>3</sup> Table 5 shows that EIAL countries suffered severe reductions in non-ofcicial flows of external financing<sup>after 1982, while the</sup><sup>**HAL**</sup> countries continued to receive an important amount<sup>in terms of GDP. That fact</sup> helps explain why the NAL countries ui-d not use adjustment lending **even** when the total external shock they faced in 1981-84 was also majo.

# Country Performance

To compare the performance of the EIAL, OAL and NAL<sup>countries, we</sup><sup>**analyze**</sup> four indicators of progress in macroeconomic adjustment--real<sup>GDP growth,</sup> domestic caving over GDP, investment over GDP, and<sup>exports over GDP--in three</sup> periods, 1970-80 (first), 1981-84 (second) and 1985-88<sup>(third). Although</sup> judgment of the effectiveness of an adjustment program cannot be<sup>**based**on a</sup> simple comparison of observed performance, nevertheless the before-after approach is useful in underlining what happened in the country groups.<sup>4</sup> Table 2 shows that for the EIAL countries the rate<sup>of GDP growth dropped</sup> substantially from 1970-80 to 1981-84 but then recovered in 1985-88.<sup>In the OAL</sup> and NAL countries, the rate of growth dropped on average<sup>between the first and</sup> secon,d periods and then stayed almost constant between the second<sup>and third.</sup> A drastic reduction in the rate of growth of GDP between<sup>the first and second</sup> periods was common to all the country groups but was greatest<sup>for the EIAL.5</sup> 3 The World Bank introduced adjustment lending in 1980 Lo<sup>facilitate this</sup> adjustment. The main rationaie was that a substantial<sup>adjustment could be made</sup> easier and its cost reduced, by spreading it over time In particular, to expand exports requires time to build export capacity and<sup>develop marketr. (See Corbo</sup> and Fischer 1990.) 4 As we explain in Section 3, the before-after comparison is a very poor measure of program effects. H,wever, it does provide<sup>measures of actual changes</sup> in given indicators.

5 Table A.2, which presants the performance of each EIAL<sup>country in the</sup> three periods, shows that only 13 of the 25 ELAL zountries<sup>hadahigher average</sup> growth rate in the third period than in the first period.<sup>This table also shows</sup> a wide variation in performance, indicating that a<sup>variety of factors seem to</sup> have been determining performance. Whl'e, a comparison of the rates of growth of GDP for the second and third periods tell us that 22 out<sup>of 25 EIAL countries</sup> improved performance. Often, the improvement in the external<sup>environment</sup> accounted for part of this improvement, but the effect of the programs could have been just as important.
arguably will tend to be more accurate in their assessments. At a minimum, one can debate meaningfully the appropriateness of the rating and the validity of the methods and information underlying them. Where definitions are brief, vague, and broad, and ratings are not accompanied by justifications for each country, such debate is impossible.

Corruption indicators also differ in attempting to assess either:

- a) the relative incidence of corrupt transactions, or

- b) the impact of corruption on business, or

- c) the existence of government and other mechanisms believed to affect the prevalence of those transactions.

The ICRG is an example of type (a), while type (b) is illustrated by the NIT corruption index. Appendixes B2 and B4 provide the criteria used by ICRG and NIT. The World Bank’s CPIA question 16 (see appendix B3) is a mix of types (a) and (c). Most questions in the BEEPS and WEF are of type (a), but each source contains type (b) questions also. One BEEPS question asks how problematic is corruption “for the operation and growth of your business.” Two other BEEPS questions ask about the “impact on your business” from other firms’ payments to Parliamentarians or government officials to influence laws and regulations. The WEF similarly asks whether or not “other firms’ illegal payments to influence government policies, laws, or regulations impose costs or otherwise negatively affect your firm.” Sources of corruption indicators may have varying constituencies or audiences, with potential implications for what their ratings are measuring. Some sources, such as Freedom House (which produces Nations in Transit) are advocacy NGOs. Others, such as the ICRG, are marketed by for-profit companies to multi-national investors and other paying subscribers. Most subscribers to the ICRG are more interested in conditions 11
leverage ratio is a step in the right direction, it is not clear whether 3% will be enough to cover bank losses should a crisis of a similar magnitude as the GFC occur again.

Reliance on internal models to compute risk-weights continues under Basel III. Figure 15 shows the percentage of countries in each ECA group adopting different approaches for calculating capital requirements for credit risk. Russia, Turkey and all countries in the CE and BC regions use an internal model for computing credit risk which determines risk-weights used in regulatory capital. Other developing countries in the ECA region have been selective in adopting some of the Basel II/III provisions, opting to use a standardized approach to calculating credit risk. As our analyses have shown, this is not necessarily a bad thing. It makes more sense to focus on a framework that is transparent and robust for computing credit risk. It is also important to keep in mind the notion of proportionality and incorporate the framework that reflects the characteristics of the local financial systems and refrain from incorporating unnecessarily complex and costly elements of Basel II/III.

**Figure 15: Approach for Calculating Capital Requirements for Credit Risk as of 2016**

<!-- Start of picture text --> EU CE & BC WB SC CA Russia Turkey EE RoW<br>Simplified standardized Standardized Internal ratings-based (IRB)<br>100<br>80<br>60<br>40<br>20<br>0<br><!-- End of picture text --> Source: Bank Regulation and Supervision Survey conducted by the World Bank.

Note: **EU** stands for Western Europe, Southern Europe, Northern Europe; **CE & BC** for Central Europe & Baltic Countries; **WB** for WesternBalkans; **SC** for South Caucasus; **CA** for Central Asia; **EE** for Eastern Europe; **ROW** for the rest of the world **.**

# **III Regulation of Systemically Important Financial Institutions in ECA**

One of the important objectives of the new capital regulations introduced after the GFC has been to protect the public from the economic damage caused by the failure of the so called systemically important financial institutions (SIFIs). Because of the market’s perception that these institutions are too-big and too-interconnected to fail, there is a concern that SIFIs may engage in excessive and correlated risk-taking. The governments are then forced to step in and provide capital and liquidity 19
40 It is important to recognize just how successful the heterodox approach is. Given enough foreign exchange and a depressed economy, expansion of dogestic demand can work. In fact, the success is broadly shared because the recovery of demand can raise firms' profitability by raising capacity utilization. That was, in fact, the case. A year after the program started Garcia was celebrated by the business class for the success of his recovery strategy. Private investment increased by 24 percent in 1986 and another 18.6 percent in 1987.27 By early 1987, the program was at the peak of its success: real CDP had grown cumulatively more than 20 percent since the third quarter of 1985. Inflation had been reduced from 188 percent to only 75 percent. But while the success was startling, the strain was starting to appear in rising cost pressures and a growing loss of foreign exchange. But these strains, while obvious to economists, were far from alarming to policy makers or the public.

The Turning Point: The turning point came in July 1987, ahead of the economic unravelling of the program, as a result of a proposal to nationalize the banking system. On July 27th, 1987, President Garcia declared:<sup>28</sup> 27See Centro de Economia Applicada (1988) and especially Iguinez for an evaluation of the first three years of the program. 28See Presidencia de la Republica (1987b,p.1).
# II. The Shadow Economy and Countering Policy Measures

## 1. What Is the Shadow Economy?

Although _shadow economy_ has been variously defined depending on research objectives and data, one widely accepted definition is “concealed production and the underground economy,” adopted by the System of National Accounts in 1993 and subsequently readopted in the Organisation for Economic Co‐Operation and Development handbook, _Measuring the Non‐ Observed Economy_ (2002), as follows: Certain activities may be both productive in an economic sense and also quite legal (provided certain standards or regulations are complied with) but deliberately concealed from public authorities for the following kinds of reasons: (a) To avoid the payment of income, value added or other taxes; (b) To avoid the payment of social security contributions; (c) To avoid having to meet certain legal standards such as minimum wages, maximum hours, safety or health standards, etc.; (d) To avoid complying with certain administrative procedures, such as completing statistical questionnaires or other administrative forms.

(Commission of the European Communities et al. 1993, 153) This paper focuses on the shadow economy comprising productive legal activities concealed from tax authorities to avoid the payment of income, value‐added, or other taxes, that is, category (a) of the definition cited above. However, category (a) sometimes, or even usually, overlaps with (b), (c), or (d); therefore, any tax policy measures intended to reduce category (a) may have effects on other subcategories of the shadow economy as a positive byproduct. Unregistered microfirms, for example, because they operate completely outside the formal economy, attempt to avoid meeting minimum wage standards or paying social security contributions, nor do they pay income or value‐added tax as a matter of course. Therefore, any tax measures aimed at bringing such unregistered firms into the formal economy and getting them to pay taxes will simultaneously reduce instances of the type of noncompliance described in subcategories (b), (c), or (d). Tax compliance measures aimed at registered firms that partly underreport their sales to avoid income or value‐added taxes would also have but limited effect on the other categories in the shadow economy.

5
The index for period _t_ is Given that _y_ and **_X_** contain monthly percent changes, we can interpret _It_ as the linear combination of search terms which best explains the series that we are forecasting, in a linear least squares sense. The expanding nature of the construction of the index allows for the factors in the trends that explain the changes in our price series to change over time. This is certainly something we might be interested in given the heterogenous character of the included terms. Figure 7 contains an example of an index created using the expanding linear OLS. That is, this is the last out-of-sample fitted value of each index created for a single price series.

We anticipate two potential problems with this approach for the current exercise, and we construct this linear index using two other methods from the statistical learning literature. Both of these techniques were implemented using the scikit-learn Python package [Pedregosa et al., 2011]. First, we have a high number of variables relative to the number of observations, especially in the early years of the index. To improve the degrees of freedom of our fit, we are interested in obtaining sparse models. To this end, we applied the lasso technique introduced in Tibshirani [1996].<sup>8</sup> The lasso is a penalized least squares method that allows both continuous shrinkage and variable selection through the imposition of an _L_ 1 _−_ penalty on the regression coefficients _β_ . That is, the coefficients are pushed both towards and to zero when appropriate. The optimization function for the lasso is where _α_ is chosen via _K−_ folds cross-validation with _K_ = 5 and the _LP −_ norm is defined _∥X∥p_ =<sup>�</sup><sup>_n_</sup> _i_ =1<sup>(</sup><sup>_|xi|p_)1</sup><sup>_/p_.Figure8containsanexampleofanindexcreatedusing</sup> the expanding lasso linear model. The fit is much more conservative than the linear OLS fit given the sparse nature of the solution.

> 8We also considered the more general LARS estimator introduced by Efron et al. [2004]. The results of this estimator were comparable, though slightly worse than the lasso. It should also be noted that we ran the computationally efficient LARS algorithm variant for the lasso solution path.

19
# **The Demand for Digital and Complementary Skills in Southeast Asia**<sup>∗</sup>

Wendy Cunningham, Harry Moroz, Noël Muller, and Aivin Solatorio The World Bank

**JEL codes** : J21, J23, J24, J63.

**Keywords** : Digital skills, cognitive skills, socioemotional skills, employment, Cambodia, Malaysia, Thailand, Vietnam.

> ∗ We are grateful to Burning Glass Technologies for their assistance with data collection, to the Digital Development Partnership for financing this work, and to Victoria Levin, Andrew Mason, and Achim Schmillen for their feedback.
established on the basis of past observed behavior in a sample of countries and extrapolated for the future using predicted income growth.

Using Yepes (2008) as starting point, we constructed an annual panel of infrastructure stocks, macro variables, and demographics for 145 countries for 1960–2012. Due to missing data the numbers of periods observed for each country differ and therefore, the database is an unbalanced panel. (See Annex IV for data sources.) The infrastructure variables gathered are:

- Telephones (mobile and landlines) subscribers per 1,000 persons;

- Kms of paved roads per Km2 of land

- Kms of unpaved roads per Km2 of land

- Kms of rail per 1,000 persons

- Total TEU handled

- KW of installed electricity generation capacity per capita;

- Percentage of households with access to electricity

- Percentage of households with access to water and sanitation

- Percentage of households with access to sanitation

- Percentage of households with access to wastewater treatment

After obtaining the annual database, we followed Yepes (2008) by taking five-year averages of all variables for each country, and obtaining a panel that contains the fiveyear average of annual flows (for flow variables) or the average stock held over the five-year period (for stock variables).

The main motivation for this transformation comes from the lack of annual data on infrastructure stock and the fact that infrastructure accumulation is a slow process that happens over long periods of time. Therefore, we are not able to capture the short-term dynamics of the investment process. Instead, Yepes’s (2008) model aims to reveal the relationship between income flows and infrastructure stocks at low frequencies. Therefore the model is estimated on a panel with five-year periodicity.

The benchmark GDP growth scenarios are created by extrapolating the 5-year-long regional growth rate to predict future GDP. This approach provides a region-specific scenario that is tailored to forecast. Then, the model applies the average growth rate of 5 years (2010–2015) GDP series, including the forecasted value of year 2014 and 2015, to predict the GDP trend for 2014–2020, which results in an implied annual growth rate for regions GDP projection.

**Table 2. Regional GDP growth rate used to forecast GDP 2014-2020**

|Region|EAP|ECA|MENA|LAC|SAR|SSA|
|---|---|---|---|---|---|---|
|GDP Growth rate|7.79%|3.95%|1.64%|3.32%|6.32%|4.63%|

Source: Authors’ estimation based on Global Economic Prospects, WDI 7