rafmacalaba/datause-encoder-data huggingface dataset:
Table 3.5: Domestic Implicit Subsidy by the Electricity Generation Sector

||**Viaarrears**<br>**($ billio**|**Vlabrt**<br>0<br>**( billion)**|**Totalas%ofC0D**<br>;t|
|---|---|---|---|
|**1993**|**3.0**|**2.0**|**2.5**|
|1994|**3.9**|**2.8**|**2.6**|
|1995|**3.7**|**4.8**|**2.2**|
|**1996**|3.5|**5.9**|**2.4**|
|**1997**|0.5|**6.7**|**1.6**|
|**Total**|**14.6**|**22.2**||

_Source:_ staff calculations

# _Cazprom and RAO UES - Policy Issues_

The first serious problem is that the implicit subsidies transmitted via Gazprom cloud its "true" taxation rate, thereby strengthening its bargaining position in relation to the government. Thus, Gray (1998, Appendix 3) argues that Gazprom's domestic tax compliance is only 40 percent, so that it is under taxed. Applying this compliance rate to the gas excise, VAT, Road Fund and Social Fund mandatory contributions, the average tax rate would equal 17 percent of total sales.Z7 But the situation changes dramatically when implicit subsidies are included. Taking arrears to Gazprom at 30 percent of sales, NCS share at 55 percent with a mark-up of 30 percent, and cash sales at 15 percent, and noting that the marginal implicit tax rate on arrears is 100 percent, and on NCS sales 23 percent, the effective tax rate works out to be:

## 0.3(1 + 0.17) + 0.55(0.23 + 0.17) + 0.15 x 0.17 = 0.597, or

60 percent, much higher than assumed by Gray. The effective rate of 60 percent consists of an explicit rate of 17 percent and an implicit rate of 43 percent, the latter flowing from arrears and the NCS subsidy.<sup>28</sup> This rate is much higher than the 42.6 percent statutory rate (see footnote 27). Of course, the question of whether the statutory rate is high enough arises; but the implicit taxes cloud the issue. Therefore, eliminating nonpayments is a pre-condition for regularizing the taxation of Gazprom.

Second, the taxation situation becomes even more murky when one considers that Gazprom manages to transmit a substantial, but unknown part, of the implicit subsidy to the public exchequer. Client arrears have been more than offset by Gazprom's own arrears to the budget, and, increasingly, extra-budgetary funds. Federal and regional tax-offsets, which typically involve some tax

> 27 The statutory gas excise rate is 30 percent of producer price, which translates into 23.1 percent of sales. The same applies to other turnover taxes, translating VAT of 20 percent into 16.6 percent of sales, and 3 percent of Road and Social Fund contributions into 2.9 percent, giving a total statutory rate of 42.6 percent. Assuming 40 percent compliance, we end up with a 17 percent explicit rate (total tax actually paid/total sales including arrears). 2' The assumed mark-up of 30 percent on NCS is probably a minimum, as Gazprom's offer of 30 percent cash discounts has not met with much response.
8 lilkel, to be reappointed in office. The citizens realize that reappointing a government who created policy surprlnes means higher expected inflation in the future, and hence lower social welfare. Thus, they are less likely to reappoint him. If the government cares about being in office, this "punishment" creates incentives not to engage in policy surprises.

The Persson-Tabellini model of reputation yields two central pofit.ve implications, which can, In printiple, be subject to some form of empirical testing. First, the equilibrium inflation rate is higher the more the citizens disagree about which governrent they prefer to hold office. In ether words, more polarized and "heterogeneous" societies encounter greater difficulty in enforcing low inflation through reputatio%Pl forc"s. Second, the equilibrium inflation rate tends to be higher the more unlikAly it is that the government currently in office will be reappointed. In ot.er words, reputation is not very effective if the government is "weak". Intuitively, the threat of being thrown out of office becomes less powerful if society is very polarized, or if the government is already weak. In the last case, this occurs because a weak government has little to lose (since it is already likely to be thrown out of office anyway). In the former case, it occurs because if society is very polarized, citizens are unwilling to switch party and punish a government just because it created policy surprises.

A serious problem in testing the main implications of the PerssonTabellini model (and, for that matter, of most political economy models) resides on finding empirical counterparts for the key political variables, such as political instability, weakness atnd polarLzation. In this paper we have tackled this problem by using two broad data sets on political and institutional characteristics of countries, assembled by Banks and Taylor and Jodice (1983), to construct proxies for these theoretical concepts. These
**Table 5: GKO yield, SRP and DRP for key dates (% per year)**

|**Date/Key event**|GKO yield|SRP|DRP|
|---|---|---|---|
|**May 15:**Fiscal sustainability clearly in question|39.3|4.8|23.0|
|**July 13:**IMF-led $22.6 bn crisis package and GKO-<br>Eurobond swap announced|102.3|8.5|82.3|
|**July 14**|58.2|8.1|38.6|
|**July 20:**IMF Board approves package. disbursement size<br>reduced from $5.6 billion to $4.8 billion as parliament stalls<br>on key expenditure controland tax measures|51.6|7.8|32.3|
|**July 23:**Day preceding completion of GKO-Eurobond swap|54.2|8.2|34.4|
|**July 24:**GKO-Eurobond swap completed. GKO Yields<br>return to [crisis levels of mid-June], SRP jumps 180 basis<br>points|66.4|10.0|44.9|
|**August 6:**World Bank Board approves crisis package-<br>related structuraladjustmentloanand disburses $300million|77.7|12.0|54.1|
|**August 10:**A Monday, exactly one week before meltdown|99.0|20.0|67.5|
|**August 14:**Friday preceding meltdown. $1.7 billion in<br>reserves lost as portfolio investors exit, bringing total loss<br>July 10 – August 14 to $4.5 billion. CBR bails out SBS-<br>Agro with$100million loan|144.9|23.8|109.5|
|**August 17:**Meltdown Monday. Devaluation, default and<br>privatefinancialpaymentsmoratoriumannounced.||||

Source: Data from Brunswick-Warburg Moscow. Calculations and commentary by authors.

Three things are worth noting from the table: first, GKO yields and the devaluation risk premium, DRP, fell substantially on July 14, the day after the IMF-led rescue package was announced; but the sovereign risk premium, SRP, fell only marginally. This pattern held on July 20, the day the IMF approved the package and released $4.8 billion: by then, the GKO yield was about half the level of July 13—but still above 50 percent. And the DRP was much less than half the level of July 13. But the SRP fell only by a little. This is consistent with the idea that the IMF-led rescue package would alleviate short-run liquidity by boosting reserves and therefore temporarily lower devaluation risk; but not have time to seriously allay default concerns.<sup>30</sup>

> 30 Sturzenegger and Zettelmeyer ((2006, p. 98) argue that because GKO yields came down sharply, the combination of the IFI liquidity injection and swap must therefore have been good for GKO holders.  They describe the view in Kharas, Pinto and Ulatov (2001) that this combination actually triggered the crisis as “too extreme”.  Three things to note: (i) GKO yields came down to around 60 percent on July 14, meaning that ex ante real interest rates were still above 50 percent and not about to help the Russian economy turn around; (ii) of course GKO yields would come down as the DRP was being reduced in view of the liquidity injection; and (iii) Sturzenegger and Zettelmeyer fail to note that the SRP barely budged after the announcement of the package, which therefore failed to lower assessments of default risk (SRP).

28
**Qualitative data** We collected longitudinal qualitative data to better understand the context, intervention, and mechanisms. First, a Liberian research assistant acted as a participant-observer during the Phase 1 therapy. Second, we interviewed facilitators for their impressions of the intervention and participants. Third, three Liberian research assistants conducted semi-scripted interviews, 14 pre-treatment and 130 post-treatment, with 66 men in the sample.<sup>29</sup> Interviews covered job satisfaction, investments, economic challenges, plans, antisocial behaviors, and perceptions of the interventions.

# **5 Empirical strategy and estimation**

We estimate intent-to-treat (ITT) effect on outcomes, _Y_ , via the OLS regression: where _TherapyOnly_ , _CashOnly_ , and _Cash_ & _Therapy_ are indicators for random assignment to treatment arms: therapy only, cash only, or both therapy and cash. We control for a vector of baseline characteristics, _X_ , and fixed effects for each of the _j_ randomization blocks, _γj_ . _Yij_ is the average of the two proximate survey rounds (e.g. the 2- and 5-week surveys for short term effects). To reduce sensitivity to outliers, we top-code continuous variables at the 99th percentile. We test sensitivity to alternative approaches in Appendix E.1.

**Key outcomes and multiple comparisons** Our theory emphasizes five major outcome families: (1) anti-social behaviors, including crime and various forms of aggression; (2) income; (3) skills of self regulation and control; (4) economic time preferences; and (5) anticriminal and antiviolent values. We identified the latter three as prime mechanisms. For each of the five outcomes we typically have multiple measures (e.g., earnings, assets, and consumption as measures of income). To reduce the number of hypothesis tests, we combine related measures into mean effects summary indexes, one for each major outcome.<sup>30</sup> We do

> correlates of attrition. Of the 298 non-responses (of 3,896), we (i) had no location information (75%); men were mentally incapacitated (1%); died (8%, or 9 men); were in prison (12%); or refused (3%). Covariates associated with higher attrition include better mental health and income.

> 2919 in control, 16 in therapy, 15 in cash, and 16 in therapy then cash. Sampling was purposeful, based on variation in key baseline measures: economic success, crime, drug use, and present bias.

> 30We take averages of our outcome measures, coded to point in the same direction, akin to the approach by Kling et al. (2007). Note that the outcomes used to create the summary index may themselves be composites of many survey questions, such as consumption (a composite of many goods) or an aggressive behavior index (a composite of many types of aggressive behavior, a standard way that psychologists measure aggression). We do so because it is typically the composite itself rather than its component survey questions that we have theoretical interest or priors. In most cases this is reflected in the survey design, where the survey questions in each composite measure comprise a separate survey section. Also, to create an index by averaging the

15
# **C. Illustrative decomposition of the level and change in the nominal bank lending rate**

In the decade 2007-2017, the average<sup>12</sup> nominal interest rate in EMDEs fell 2.7 percentage points, from 13.7% to 11%. We decompose the level of the nominal lending interest rate for 2017, and the change observed of the rate during those 10 years among macro-fiscal conditions, banking sector characteristics, and business environment (Figure 6).

The lending interest rate in 2017 (11%) can be decomposed as follows: 2.2 percentage points explained by macro-fiscal conditions; 3.2 percentage points by banking sector characteristics, and 0.35 percentage points by the business environment. Other components are the expected mean of lending interest rate for EMDEs (6.4 percentage points – the constant in the regression), other country-specific unobserved conditions, and the error term.

In the decomposition analysis (Box 4), our calculations show that the main drivers are public debt in the macro-fiscal factors, overhead costs, bank concentration, and private credit in the bank sector factors, and long insolvency processes in the business environment factors. For example, the average public debt is 46.8% of GDP for EMDEs in 2017, which explains 1.5 percentage points of the interest rate. In addition, overhead costs, bank concentration, and the private credit to GDP impact lending rates via banking characteristics, explaining 3 percentage points of the lending rate. Finally, longer insolvency processes increase rates in 1.1 percentage points while better coverage of credit bureaus reduces the rate in 0.75 percentage points.

The reduction of nominal interest rates between 2007 and 2017 has been led by improvements in the business environment, followed by macroeconomic factors, while bank characteristic factors have driven rates up. For example, increments of the credit bureau coverage (to 44.2% of total adults in 2017 from 17% in 2007, on average) and the reduction in the number of years to resolve insolvency processes (to 2.4 years from 3.2 years in the same period) reduced nominal lending interest rates in 1 percentage point.

> 12 To ensure consistency with the regression framework which produces a conditional mean, we focus on the average lending rate.

17
- 66 -

# APPENDIX TABLE 4 **(Continued)**

BARBADOS' TRANSPORT COST PROFILE FOR EXPORTS TO THE UNITED STATES IN 1986

|||U.S.|BARBADOS<br>N|<br>OMINAL FREI|<br>GHT RATE|SARBADOS<br>TARIFF|-OTHERS"|NOMINAL FRE|<br>IGHT RATE|"OTHERS"<br>TARIFF|SHARE IN<br>BARBADOS|
|---|---|---|---|---|---|---|---|---|---|---|---|
|OES|TSuSA|<br>IMPORTS($|) TOTAL|vESSEL|AIR|RATE|TOTAL|VESSEL|AIR|RATE|EXPORTS|
|131|<br>7725136|<br>17,353|<br>10.05|10.05|0.00|4.00|9.13|9.17|82.52|3.96|0.016|
|132|<br>7745595|<br>17,500|<br>0.00|0.00|0.00|5.70|8.90|7.74|14.65|4.58|0.016|
|133|<br>7930000|<br>83.528|<br>7.03|9.38|0.52|0.00|15.16|20.37|4.46|0.00|0.077|
|134|<br>8000035|2,623,078|<br>3.66|4.81|3.06|0.00|2.53|4.65|1.97|0.00|2.408|
|135|9999500|<br>146,279|<br>0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.134|

OBS DESCRIPTION 131 New radial bus tires for on the highway (not including recaps) 132 Articles nspf, of plastic or rubDer 133 Waste and scrap, nspf 134 U.S. products returned after export without Doing advanced in value or improved in condition abroad, nes 135 Under 5250 formal and informal entries
the urban ( _LSU_ ) and rural ( _LSR_ ) sectors is a function of the expected wage in the urban sector ( _auw_ ) and the observed wage in the rural sector ( _nwr_ ). This function incorporates an elasticity of mobility ( _ε1_ ) of labor from rural to urban areas. The expected urban average wage is a weighted average of the wages in the urban sector (informal, formal private and public) multiplied by the probability of finding a job in the urban sector (1- _u_ ). We have: where _TLDi_ and _PLDi_ are the total labor demand of temporary workers and permanent workers in sector i, as defined above; _nwii_ are wages in the informal sector, _nwfi_ wages in the formal sector, and _nwg_ the wage in the government sector.

Regarding the split between formal and informal sector, we assume that the supply of labor in the latter results from a cost-benefit analysis made by workers and is not simply a residual. This view is consistent with the recent empirical evidence that shows that informality is both the result of exclusion (the traditional view) and strategic decisions that respond to perceive benefits (e.g., flexibility, no taxation) and perceived costs (lower wages, lack of access to formal institutions). In this framework, an increase in labor productivity in the formal sector or stronger public institutions would reduce informality by increasing the opportunity cost of not participation in the formal sector of the economy (see World Bank, 2007). Thus, we model labor mobility between the formal and informal sector in the same way as we model mobility between rural and urban sectors: the ratio of labor supply in the formal and informal sectors is a function of the expected wage in the formal sector and the observed wage in the informal sector. The mobility elasticity ( _ε2_ ), however is assumed to be lower than _ε1_ given that the cost of migration from the rural areas to the urban areas are likely higher than those related to the movement between informal and formal sectors. We have: 11
# **VIII.   References**

ADB (2002), Report and Recommendation of the President to the Board of Directors on a Proposed Loan and Technical Assistance Grant to India for the Railway Sector Improvement Project, Asian Development Bank, Manila.

Baker, Judy, Rakhi Basu, Maureen Cropper, Somik Lall and Akie Takeuchi, (2005), “Urban Poverty and Transport: The Case of Mumbai,” _Policy Research Working Paper Series_ 3693, The World Bank, Washington, DC.

BEST (2006): <u>http://www.bestundertaking.com/finance0506.pdf</u> (2005-06) BEST (2005): <u>http://www.bestundertaking.com/finance.asp</u> (2003-05) Carruthers, Robin, Malise Dick and Anuja Saurkar, (2005), “Affordability of Public Transport in Developing Countries,” _Transport Paper 3_ , Transport Sector Board, World Bank, Washington, DC.

Foster, Vivien (2004), “Toward a Social Policy for Argentina’s Infrastructure Sectors. Evaluating the Past, Exploring the Future,” _Policy Research Working Paper Series 3422_ , The World Bank, Washington, DC.

Gomez-Lobo, A. (2007), “Public Transport Affordability and subsidy Policies; A Case study of Santaigo, Chile,’ World Bank Indian Railways (2006), Railway Production Units, Workshops and Maintenance of Rolling Stock, Seventeenth Report, Standing Committee on Railways 2005-06, Ministry of Railways, India.

Ramasaamy, N. (2006), “A Quick Review of STUs (2005-06),” _Indian Journal of Transport Management_ , April-June 2006.

Sahai, Vivek (2006), “Mumbai Suburban Rail Network: An Overview,” presented at the International Conference on Alternative Technologies for Public Transport, Institute of Urban Transport, New Delhi, India. <u>www.iutindia.org/</u> **<u>pdf</u>** <u>/rt/</u> **<u>Vivek</u>** <u>%20</u> **<u>Sahai</u>** - **<u>Mumbai</u>** <u>%20Urban%20Rail.</u> **<u>pdf</u>** H Takeuchi, Akie, Maureen Cropper and Antonio Bento (2007), “The Impact of Policies to Control Motor Vehicle Emissions in Mumbai, India,” _Journal of Regional Science,_ 47 (1).

World Bank (2002), India’s Transport Sector; The Challenges Ahead, Vol. 1 & Vol. 2, The World Bank, Washington DC.

Shrivastava, Prabhat and S.L. Dhingra (2006), “Operational Integration of Suburban Railway and Public Buses—Case Study of Mumbai,” _Journal of Transportation Engineering_ 132(6).

22
**Figure 27: Herfindahl-Hirschman Index for intra-regional air travel (2013)**

<!-- Start of picture text --> Haiti 5,506<br>Suriname 6,118<br>Guyana 7,367<br>Dominican Republic 4,497<br>Jamaica 4,917<br>Barbados 5,088<br>Bahamas 7,259<br>Trinidad and Tobago 7,641<br>Grenada 4,670<br>St. Lucia 5,302<br>Antigua and Barbuda 7,035<br>St. Kitts and Nevis 10,000<br>St. Vincent and the Grenadines 10,000<br>Dominica 10,000<br> - 2,000 4,000 6,000 8,000 10,000 12,000<br>Herfindahl Index<br>Country<br><!-- End of picture text --> _Source: Authors’ calculation based on data by Diio (2014)._ Market concentration in the extra-regional market with the Caribbean takes on a more complex and nuanced dimension (Figure 28). The largest tourist markets, the Dominican Republic, Jamaica, and The Bahamas, show by far the lowest concentration, with Jamaica and the Dominican Republic being in the fully competitive range of the scale, and The Bahamas on the bottom side of being moderately concentrated. Two airlines have a market share of more than 20% in Barbados: American Airlines and Virgin Atlantic.

The more concentrated markets (HH above 1,800), are all due to one airline having a market share of 40% or above. The higher level of concentration in Grenada and St. Kitts can be traced to the fact that in both markets American Airlines holds a share of 50% or higher – the only markets on the small island states where any airline plays such a dominant role. Haiti is also dominated by American Airlines (55%). In the other three highly concentrated markets, Caribbean Airlines has the largest market share in Trinidad & Tobago and Guyana, while Suriname Airways holds the largest share in Suriname (Box 3).

58
potential hires, and how such information contributes to job matching. Babajob data can contribute to an analysis of the role of information on non-cognitive skills in the job-search process.

# **6. Additional Uses of Online Job-Portal Data and Job-Search Platforms**

Beyond the five potential applications described above, there are a number of additional possibilities for using big data to improve labor market outcomes. The following section describes other prospective avenues for using online job-portal data and job-search platforms to improve skills-matching and generate more policy-relevant information.

# **_Understanding the Emerging Skills Requirements of New Technologies_**

Rapid technological change is driving the evolution of business practices and shaping the demand for workforce skills. New technologies often create new job types that do not fit into the existing scheme of employment categories and workforce skills. In this dynamic environment, analyzing trends in job titles can help to identify emerging skills requirements.

The US Department of Labor has developed an Occupational Information Network (O*NET), which comprises a regularly updated database of occupational characteristics and employee information.<sup>25</sup> It describes the knowledge, skills and abilities required for different positions, as well as the specific tasks and activities involved in each job type. O*NET collects this information by randomly sampling businesses and workers in each job category. Online job-portal data can complement this type of database by providing real-time information on new jobs, employee requirements and scopes of work. In case of India, it is useful to apply this analysis to review the skills classification and fit them against the National Skills Qualification Framework<sup>26</sup> for improving the relevance of training.

Text analysis can be especially useful for linking emerging occupations with their required skill sets. Moreover, algorithms based on closely related skill descriptions can allow for more precise classifications of jobs with ambiguous titles. For example, “computer programmer” can be refined to “application developer,” “web programmer,” “web designer,” etc. based on the job description.

# **_Advanced Matching Services for Employers and Job Seekers_**

> 25 See <u>http://www.onetcenter.org/ for more information.</u>

> 26 National Skills Qualification Framework (NSQF) is a competency-based framework that organizes all qualifications according to a series of levels of knowledge, skills and aptitude.

25