rafmacalaba/datause-encoder-data huggingface dataset:
increase in Singapore’s skilled labor abundance relative to the parent country increased total inbound FEI stocks from all industrialized countries.

The basic results are reinforced by the regressions in Table 6, which break the aggregate inbound and industrialized country inbound samples into periods before and after 1988 (1984-1987 and 1988-2003).<sup>15</sup> As noted earlier, Singapore’s relative endowment of occupational skills rose rapidly in the mid- to late-1980s compared to the industrialized countries, with the relative difference tending to become negative (for Singapore as host) around 1988. The implication of Table 6 seems clear. Singapore’s inbound investment shifted from a vertical, labor-seeking orientation (positive coefficient on skill differences) to a more skill-seeking orientation (negative on skill difference and the interaction with GDP differences). This finding is supported again by the breakdown of bilateral investment stocks with the industrialized countries (columns 3 and 4). Here, once again the direct coefficient on skill differences switches from significantly positive to significantly negative. In the latter case, a reduction in relative skill differences, which would imply an increase in Singapore’s skill endowment compared to the parent, would suggest a positive impact on inward FEI.

Regression results for the outbound sample did not show any significant differences regarding the skill-differences variable between 1984-1987 and 1988-2003 periods and are not reported here. As suggested by the regressions in Tables 1, 2 and 3, however, these coefficients were positive and significant in the full sample, suggesting that outbound investment in developing countries have been motivated by vertical incentives throughout the period. Regression results controlling for the Asian financial crisis did not show any major

> 15 Because the manufacturing data only begin in 1994 it is not feasible to repeat this exercise on those stocks.

30
45 private sector lending actually declined. This can be explained by the fact that banks started building provisions and tightening their procedures and controls in response to regulatory pressures. Notwithstanding this caveat and while quantity is certainly not be equated with quality, the lack of a medium-term increase in Private Credit to GDP following the financial sector reform programs is somewhat disappointing and matches the development of demandside indicators, as we will discuss below.

# **Figure 14: Private Credit to GD in Egypt over time**

<!-- Start of picture text --> Private Credit / GDP (%)<br>60.0<br>50.0<br>40.0<br>30.0<br>20.0<br>10.0<br>0.0<br>2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>Value Observed Expected median<br><!-- End of picture text --> Source: Global Financial Development Indicators, own calculations

# **7.3. Gauging Egypt’s financial system with micro-data**

While aggregate data and following the benchmarking model described above paint a favorable picture of Egypt’s financial system, with actual levels well above predicted levels, micro-level indicators paint a different picture. Figure 15 shows that the share of firms with a credit from a formal institution has been consistently below the predicted level across three survey waves (2006, 2008 and 2011).<sup>18</sup> In addition, Figure 16 shows a positive, though nonlinear relationship between the level of Bank Credit to GDP and the share of enterprises that

> 18 While the gap seemingly closes in 2011, this last survey has to be treated with caution as it relies on a smaller sample than the two previous surveys.

45
# **BENCHMARKING PRODUCT MARKET POLICIES IN BULGARIA**

In the aftermath of the 1996-1997 crisis Bulgaria pursued an ambitious reform effort, stimulated by the prospect of EU accession. Macroeconomic stability was established as a result of prudent fiscal and income policies centered on a Currency Board Arrangement. Fiscal consolidation reduced the overall fiscal deficit from 15.2 percent of GDP in 1996 to a balanced budget in 2003 and fiscal surpluses thereafter while public debt level declined from over 100 percent of GDP in 1997 to currently 27 percent. As a result of these policies and deep structural reforms, average growth reached about 5 percent per year in 2000-05 (and is estimated at close to 6.5 percent in 2006) and inflation has declined to single digits from hyperinflation levels. Unemployment also declined from 18 percent in 2000 to 8 percent currently.

Solid macroeconomic performance supported by accelerated structural reforms improved the environment for doing business in Bulgaria. The private sector’s share in the economy increased substantially and is now comparable to, and in some cases even higher, than among other EU new member states (NMS). Domestic investment surged to close to 30 percent of GDP in 2006 compared to less than 10 percent in 1996-1997 while foreign direct investment has averaged 13 percent over the last four years. Beginning in 1998, most of the non-infrastructure enterprises and banks were privatized or liquidated, banking restructuring was completed and banking supervision strengthened; trade and prices were liberalized; reforms in the energy sectors were implemented in both the district heating and electricity sectors; major public sector and institutional reforms have been implemented. Steps were also taken to reform the regulatory framework. Nevertheless, important aspects of the regulatory environment still need to be improved, both in terms of the quality of the rules and regulations, and of their efficient enforcement.

A crucial element of the second generation of reforms to be pursued by Bulgaria is the implementation of less restrictive product market policies, which will encourage a more efficient allocation of resources and improve labor productivity. This is especially important for Bulgaria given the significant income and productivity gaps the country has with other EU members: Bulgaria joined the EU with an estimated per capita income level of about 35% of the average for EU25, the lowest among the new member states.<sup>7</sup> In addition, employment and participation rates, albeit improving substantially recently, remain among the lowest in the EU.

Enhancing competition in product markets has been found to help increase GDP per capita by providing incentives to firms to reallocate resources to more productive activities, increase innovation and technological diffusion. In addition, less restrictive regulations may positively affect employment by reducing the rents that some firms extract from overregulation and force firms to expand their activities.<sup>8</sup>

> 7 Eurostat projection. 8 See Conway, Janod, Nicolletti (2005).

9
# **3.2.1 Observable Characteristics**

To measure how much of the association between household income and informal consumption shares can be explained by observable characteristics, we estimate the following regression separately for each country: where _i_ indexes a household, _Xi_ are household characteristics and each observation is weighted by the relevant household survey weight. Table 2 shows the average of the slope coefficients _β_ across countries, the average upper and lower bounds of the 95% confidence intervals, and the number of countries for which the coefficient is statistically significant at the 5% level. Column 1 displays results from the specification without controls. Column 2 controls for household demographics: household size and age, education and gender of the household head.<sup>16</sup> We find that these characteristics do not explain the correlation between informal expenditure and income and, if anything, the IEC slopes become slightly steeper.

Columns 3 and 4 add controls for households’ location, either with an indicator for whether the household lives in a rural area (column 3) or with survey block fixed effects (column 4).<sup>17</sup> These controls allow us to test whether the association between informal budget shares and household expenditure is due to poorer households living in areas with worse access to formal retailers. Despite large differences in average informal budget shares between rural and urban households, controlling for rural locations only explains 13% of the slopes.<sup>18</sup> Controlling for survey blocks explains just over 20% of the differences in informal budget shares between poor and rich households.

In columns 5 to 8 we test whether non-homothetic across preferences goods play a role – when richer households spend more on goods predominantly sold in formal stores. We run a product-level version of specification (2) with goods fixed effects and compute an average goods-level estimate of _β_ for each country.<sup>19</sup> We gradually consider variations within narrower goods categories: we first consider food vs non-food (column

> 16Household size controls for economies of scale across households of different size which could affect where households choose to shop - see Deaton and Paxson (1998).

> 17The survey block is the most granular location information and contains on average 74 households in our surveys. The median survey block is representative on average of 52,900 households.

> 18The informal budget share is on average 67% in rural areas versus 52% in urban areas and IEC slopes are steeper in urban locations. Figure A7 shows the IECs separately for rural and urban areas.

> 19Formally we run the regression: _Share In f ormalig_ = _β ∗ ln_ ( _expenditureg_ ) + _αg_ + Γ _Xi_ + _εig_ where _Share In f ormalig_ is the share of household _i_ ’s informal expenditure on good _g_ and _αg_ are goods fixed effects. Each observation is weighted by household survey weights and goods expenditure shares.

12
<!-- Start of picture text --> Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.97*** 0.98*** 0.93***<br>H<br>Kendall tau-b 0.89*** 0.94*** 0.78***<br>Spearman 0.97*** 0.8*** 0.45<br>A<br>Kendall tau-b 0.89*** 0.61** 0.33<br>Spearman 0.92*** 0.98*** 0.93***<br>M0<br>Kendall tau-b 0.78*** 0.94*** 0.78***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>C9. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All<br>indicators included. Alternative scenario: Confidence in government institutions indicator removed (weights within the<br>Social Cohesion dimension were scaled from 6.25% to 8.33% so all weights add up to 1).<br>Peru<br>C9.1. FOSD: Counting Vector C9.2. Dominance Analysis<br>Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>C9.3. SOSD: Social Sustainability Index C9.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.96*** 0.96*** 0.78***<br>H<br>Kendall tau-b 0.85*** 0.87*** 0.66***<br>Spearman 0.94*** 0.83*** 0.82***<br>A<br>Kendall tau-b 0.82*** 0.65*** 0.7***<br>Spearman 0.97*** 0.96*** 0.78***<br>M0<br>Kendall tau-b 0.87*** 0.87*** 0.66***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br>.1<br>1<br>.8<br>.05<br>.6<br>CCDF<br>.4 Difference<br>0<br>.2<br>0 -.05<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _C9. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All indicators included. Alternative scenario: Confidence in government institutions indicator removed (weights within the Social Cohesion dimension were scaled from 6.25% to 8.33% so all weights add up to 1)._ South Africa C9.1. FOSD: Counting Vector C9.2. Dominance Analysis 57
Appendix C).<sup>8</sup> In the most extreme cases, the richest quintile receives around 30 times as much as the poorest quintile; and even in those countries where energy subsidies are the least regressive, the richest quintile still receives twice as much as the poorest quintile.

Energy subsidies contribute to environmental damage in these 12 countries, particularly by contributing to climate change and air pollution, and this is the basis for the environment argument for reducing subsidies. The IMF (2023) produces estimates of the implicit costs of energy subsidies (i.e., costs beyond the actual costs paid by the government) and finds that on average in the 12 study countries, the cost of climate change and air pollution is 3.6 percent of GDP and 3.7 percent of GDP, respectively (see Figure C3 in Appendix C). These estimates vary considerably across countries and in terms of the relative share of the cost due to climate change and air pollution. The most extreme cases are the cost of air pollution in Egypt, which is estimated to be almost 20 percent of GDP, and the cost of climate change in Kazakhstan, which is over 10 percent of GDP. Even in the two countries where environmental costs as a share of GDP are lowest (Ghana and Nigeria), the absolute monetary value is still nontrivial.

8These are calculated based on the distributional incidence of the predominant energy subsidy (either fuel or electricity) by income quintile in each country. Estimates are from Amjad, Carrasco and Meyer (2023) for Pakistan; Faizuddin et al (2013) for Bangladesh; World Bank (2022B) for Indonesia; Ladronis, Lopez de la Valle, and Martinez (2019) for Argentina; Paz Arauco, Jimenez and Yanez (2020) for Bolivia; Lara Ibarra et al (2019) for Egypt and World Bank (2023B) for Angola. The estimates for the following countries were produced by Bank staff: Ecuador (using Encuesta Nacional de Ingresos y Gastos de los Hogares Urbanos y Rurales 2011 (ENIGHUR)), Ghana (using the 2017 Ghana Living Standard Survey-7), Kazakhstan (using the 2021 Household Income and Expenditure Survey), Nigeria (using the 2015/16 Nigeria General Household Survey), and Vietnam (using the 2018 Vietnam Household Living Standards Survey (VHLSS)). In countries where fuel (or electricity) is the predominant type of subsidized energy, the distributional incidence was estimated for fuel (or electricity) subsidies.

15
# **Figure 21.  Remaining gaps compared to the OECD**

<!-- Start of picture text --> Scope of public enterprise<br>sector<br>4.0<br>3.0<br>Sector specific administrative<br>Size of public enterprise sector 2.0<br>burdens<br>1.0<br>0.0<br>Direct control over business<br>Price controls<br>enterprises<br>Use of command and control<br>Bulgaria OECD<br><!-- End of picture text --> In fact, the Bulgarian state continues to exercise a greater amount of control over the economy than is the norm among OECD countries – at least as measured by the PMR approach. The size and scope of the public sector are above the average, yet the two indicators that standout concern the extent of direct control over business enterprises and the use of command-and-control (as opposed to incentive-based) regulation. (Table A2. 3 and Table A2. 5 show the detailed composition of these indicators). Actions to improve Bulgaria’s performance on both counts could include: • **Increase reliance on incentive-based regulation.** In a narrow sense this entails requiring regulators to consider alternatives to any regulation they seek to introduce as is now the norm in OECD countries. More substantively, this requires changing the regulatory and administrative culture so that new regulation is not the default option to modify economic behavior. Among other things, this will require providing guidance and training on what may constitute valid alternatives to regulation (Box ).

• **Reduce control over business enterprises in which the state has interest.** As shown in Table A2. 3, the practice of holding golden shares has been eliminated by the majority of OECD countries. Of those that still have golden shares, only about half use them to affect strategic decisions of firms, as is still the case in Bulgaria. More generally, responses to the PMR questionnaire suggest that the strategic choices of publicly controlled firms have to be reviewed or cleared by a national or sub-national legislature. This practice has now been abandoned by about half the countries in the OECD.

28
28

# **Figure 1**

<!-- Start of picture text --> Emerging countries: workers' remittances (credit)<br>(millions of US dollars)<br>75000 100<br>workers' remittances (credit)<br>60000 number of emerging countries included (right scale) 80<br>45000 60<br>30000 40<br>15000 20<br>0 0<br>1976197719781979198019811982198319841985198619871988198919901991199219931994199519961997199819992000200120022003<br><!-- End of picture text -->

**Figure 2**

**Emerging countries: workers' remittances and net FDI inflows** _(millions of US dollars)_

<!-- Start of picture text --> 150000 100<br>Net FDI inflows<br>Workers' remittance<br>120000 number of emerging countries included (right scale) 80<br>90000 60<br>60000 40<br>30000 20<br>0 0<br>1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br><!-- End of picture text -->
- Bucci, A. and Guerrini, L.: 2009, Transitional Dynamics in the Solow-Swan Growth Model with AK Technology and Logistic Population Change, The BE Journal of Macroeconomics 9(1), 43.

- Cai, Y.: 2010, Dynamic Programming and its Application in Economics and Finance, PhD thesis, Stanford University.

- Cai, Y. and Judd, K. L.: 2010, Stable and Ecient Computational Methods for Dynamic Programming, Journal of the European Economic Association 8(2-3), 626634.

- Cai, Y. and Judd, K. L.: 2014, Advances in Numerical Dynamic Programming and New Applications, in K. L. Judd and K. Schmedders (eds), Handbook of Computational Economics, Elsevier.

- Cai, Y., Judd, K. L., Thain, G. and Wright, S. J.: 2013, Solving Dynamic Programming Problems on a Computational Grid, Working Paper 18714, National Bureau of Economic Research.

- Chape, S., Harrison, J., Spalding, M. and Lysenko, I.: 2005, Measuring the Extent and Eectiveness of Protected Areas as an Indicator for Meeting Global Biodiversity Targets, Philosophical Transactions of the Royal Society B: Biological Sciences 360(1454), 443455.

- Costanza, R., d'Arge, R., De Groot, R., Farber, S., Grasso, M., Hannon, B., Limburg, K., Naeem, S., O'Neill, R., Paruelo, J. et al.: 1997, The Value of the World's Ecosystem Services and Natural Capital, Nature 387(6630), 253260.

- Craneld, J., Eales, J., Hertel, T. and Preckel, P.: 2003, Model Selection when Estimating and Predicting Consumer Demands using International, Cross Section Data, Empirical Economics 28(2), 353364.

- Cubbage, F., Koesbandana, S., Mac Donagh, P., Rubilar, R., Balmelli, G., Olmos, V., De La Torre, R., Murara, M., Hoeich, V., Kotze, H. et al.: 2010, Global Timber Investments, Wood Costs, Regulation, and Risk, Biomass and Bioenergy 34(12), 16671678.

- Ehrlich, P. and Mooney, H.: 1983, Extinction, Substitution, and Ecosystem Services, BioScience pp. 248254.

- FAO: 2010, Global Forest Resources Assessment 2010, FAO Forestry Paper 163, Food and Agriculture Organization of the United Nations.

23
- _9_ -

16; Writh its location close to EU markets, and its low labor costs, Albania should be able to expand trade and attract some foreign mvestment enabling the country to better exploit its natur endowments, especially in naturl resources, tourism, water resources and agriculture.

# **Se _es for D** Policies

17. Albania remains the poorest state in Central and Eastem Europe, with per capita GDP of rougly $340 in 1993. There are perhaps more graphic indicators of the country's standard of living than even these rather bleak income statistics. FoX instance, it is estimated that there are only _7.5_ square meters of housing per person in Albania, and the country with the next lowest housing stock in Central and Eastern Europe is Romania which has 15 square meters per person. By way of contrast, Hngary has the largest stock of housing in Central and Eastern Europe with 26 square meters per peson.18. Difficulties in tortaton and communications were alluded to above as makidng it difficult for a higbly centralized form of government to be efficient The country is mountainus and many villages are inaccessible by motor vehicle. About 400 villages with an estimated population of 1.5 million people are not accessible by motor vehicle for part of the year. In addition, there is no telephone service to many municipalities. Thus, not only is Albania poor, but there are significant _regional dixparides_ within the country, with some areas served much better than others.

19. The poverty of this country, and its previous mimanagement by the fonner communist regime, are reflected in other statistics for local infrastructure. For instance, of the 250 water supply systems in Albania, only one provides 24 hour service to its customers. In Durres, which is
- Prioritizing the issues that need the attention of the Board to ensure focus on items of material nature and issues related to its governance responsibilities, reducing time consumed with side issues or staff briefings "for information";

- Undertaking Board evaluation to identify existing mix of competences and skills, and specify new profiling for new Board positions;

- Establishing clear terms of reference for Board Committees/ Sub-Committees and how they report to the Board with particular reference to the Audit and HR Committees;

- Undertaking capacity building initiatives for senior management and directors on the required governance changes within the context of unbundled power sector structure;

- Eliminating over time other conflicts of interest inherent in the composition of the Board of Directors, including the introduction of a strict limit to the number of companies in which Board of Directors member can sit;

- Strengthening the process of selection and appointment of BoDs, through structured and skill‐ based nomination process, transparent and based on merit. Nomination Committees can also be established at the company level or for all SOEs. A database of qualified candidates can also be considered to help enlarging the pool of potential experts for SOE Boards, especially to bring in more private sector experience, thereby improving Boards’ professionalism.

25