rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 8288

# **Abstract**

This paper introduces key findings from a new cross-country survey on state-owned financial institutions in Europe and Central Asia. It covers 41 such institutions operating in the region as of end 2016, and considers variables in the areas of mandates, instruments, performance, governance, and monitoring/evaluation. It presents results over the period following the global financial crisis up to 2015, distinguishes between state-owned commercial and development financial institutions, and looks at differences among three geographical sub-regions.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group and the Finance and Markets Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at dmare@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
simplicity disappears quickly under any kind of scrutiny. First of all, the GATT provides that the price of the exported good be compared with the price of a "like product" sold in the home market. The Korean electronics industry exports basic, no-frills TV sets, while its home-market sales are concentrated in expensive, top-of-the-line models, with wooden cabinets and all the bells and whistles that can be installed. The intent of the investigation process is to compare apples to apples not apples to pears, so the investigator must adjust for the different product characteristics to make the price of a fancy 27-inch set that retails in the neighborhood of $2,000 comparable to that of a 13-inch set in a metal cabinet that retails for $169.95.

Another complication is that, for a fair comparison, the two prices should be at the same level of trade, normally at the ex-factory level. Again, Korean TV's provide an example. Korean manufacturers built their export business by selling in large vc'umes to American and European retailers. Distribution, retailing, and advertising were "added" by Sears or K-Mart. But in Korea, the manufacturing companies are parts of industrial conglomerates. These conglomerates own wholesale companies and department stores, which are the major retail outlets. Thus in Korea, the first sale that is not an intracompany transfer is the final sale to the consumer. But on exports, the first such "arms-length" sale is from the factory to the distributor-retailer. suppose that the auitidumping investigation _eastablshed_ fr_m 4port documents that Sears paid $100 for the sets it retails for $169.95. The dumping margins the U.S. government found in the Korean TV case were about 15 percent, indicating that the "comparable" price of the sets sold in Korea was $115. This means that the adjustments for different product characteristics and different ways of doing business in Korea and in the United States reduced the $2,000 retail price to a comparable ex-factory price of $115. In other words, theset administrative adjustments provide 95 percent of the information on which the eventual finding is based.

Even when the case seems to be straightforward -- identical products sold in the home and export market, at the same level of trade -- modern 32 -
Table 6: 2SLS Estimation of Labor Supply by Skill Level

||Wage Em|ployment|Self Em|ployment|
|---|---|---|---|---|
||(1)<br>Low Skilled|(2)<br>High Skilled|(3)<br>Low Skilled|(4)<br>High Skilled|
|Panel A: Labor market par|ticipation||||
|Household with migrant|-0.205**|-0.486|0.199***|0.324|
||(0.098)|(0.305)|(0.069)|(0.346)|
|Observations|14054|2825|14054|2825|
|Wald _χ_<sup>2</sup>|3577.578|997.890|3864.717|2175.469|
|_F_-test 1stage|68.765|8.179|68.765|8.179|
|Coecient 1stage|1.925***|1.460***|1.925***|1.460***|
|Panel B: Hours supply|||||
|Household with migrant|-15.028***|1.032|-13.064***|5.048|
||(5.754)|(11.830)|(4.454)|(14.563)|
|Observations|4714|1131|10319|1199|
|Wald _χ_<sup>2</sup>|2170.635|505.787|1130.111|1815.099|
|_F_-test 1stage|33.235|7.061|63.906|6.082|
|Coecient 1stage|1.820***|1.665***|1.921***|1.503**|
|Controls|||||
|Individual characteristics|Yes|Yes|Yes|Yes|
|Household characteristics|Yes|Yes|Yes|Yes|
|VDC characteristics|Yes|Yes|Yes|Yes|
|Region Fixed Eect|Yes|Yes|Yes|Yes|

Note: High skilled adults are dened as adults with 11 years or more of education. Sample in Panel B is conditioned on being employed in that particular sector. Log of hourly wage is added as an extra individual control for column (1) and (2). 2SLS estimates are reported in the table. Instrument used for 2SLS is share of international migrants in a VDC in 2001 (IV1). Sample is working age (18 to 60) adults and standard errors are clustered at VDC level. Standard errors in parentheses.<sup>_∗_</sup> _p <_ 0 _._ 1 ,<sup>_∗∗_</sup> _p <_ 0 _._ 05 , _∗∗∗_ 2 _p <_ 0 _._ 01 . Individual controls are age age , years of education, gender, household head identier, and marital status. Household controls are female HH head, share of male and female adults, share of elderly, share of children, amount of land-owned, house ownership and social caste. Similarly VDC level controls are, unemployment rate, poverty rate, illiteracy rate, inequality (Gini), and urban/rural location. Regions are Kathmandu, other urban areas, Western hills, Eastern hills, Western Terai, and Eastern Terai. Coecient 1stage is the estimated coecient on the exogenous instrument in the rststage.

32
<!-- Start of picture text --> Policy Remarach<br>Debt and International Finance<br>WPS 916<br><!-- End of picture text -->

- This paper a product of the Debt and International Finance Division, International Economics - Department is part of a larger effort in the departnent to investigate the benefits and costs to debtor countries and creditors of voluntary, market-based debt and debt service reduction arrangements. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Sheilah King-Watson, room S8-040, extension 31047 (June 1992, 21 pages).

The price of debt on the secondary market reflects the risk that the debtor country might default on its external debt.

Using the option-pricing theory, Chesney and Morisset identify the factors that influenced the risk of default in six highly indebted countries from 1986 to 1990.

In particular, they provide a measure of the debtor countries' willingness to pay. They identify the parameters of the stochastic process followed by this variable, so this approach can be used to prdict the future price of debt.

Their model also emphasizes that a debtreduction operation may lead to a significant increase in the price of debt on the secondary market. This effect appears to be linked to the initial stock of extemal debt, as suggested by the "debt overhang" hypothesis.

Finally, Chesney and Morisset show empirically that a country's _willingness_ to pay is significantly influenced by changes in indicators of the country's _ability_ to pay (for example, by an increase in reserves or in GDP growth), and by exogenous events such as the increase in commercial banks' loan reserves in mid-1987 or the Brady Plan announcement in 1989.

ThePolicy ResearchWorking PaperSeriesdissetinates thefmdings of work under way in theBank. Anobjectiveof the series is to get these findings out quickly, even if presentations are less than fully polished. The fmdings, interpretations, and conclusions in these papers do not necessarily represent official Bank policy.

Produced by the Policy Research Dissemination Center
**<u>Table 4: Major Cross-border Mergers and Acquisitions Completed, 2008–12</u>**

|**Value**<br>**(US$,**<br>**billions)**|**Year**|**Acquired Company**|**Industry of Acquired**<br>**Company**|**Acquiring**<br>**Company**|**Head-**<br>**quarters**|**Industry of**<br>**acquiring**<br>**company**|
|---|---|---|---|---|---|---|
|3.2|2008|Territorial Generation Co. No.<br>10(TGC-10)|Electricity and heating|Fortum Oyj|Finland|Electric services|
|4.0|2009|OJSC “Severneftegazprom”|Crude petroleum and<br>natural gas|E ON AG|Germany|Electric services|
|1.2|2009|OJSC “Polvus Zoloto”|Gold ore|Wandle Holdings<br>Ltd.|Cyprus|Holding company|
|6.3|2011|OJSC “Polyus Zoloto”|Gold ore|Kazakh Gold<br>GroupLtd.|Kazakhstan|Gold ores|
|5.5|2011|OJSC “Polimetall”|Gold ore|PMTL Holding<br>Co.|Jersey|Holding company|
|4.2|2011|OJSC “Pervaya Gruzovaya<br>Kompaniya”|Railroads, line-haul<br>operating|LLC“Nezvaisimay<br>a Transportnaya<br>Kom-paniya”<sup>*</sup>|Russia|Courier services|
|4.0|2011|OJSC “Novatek”|Crude petroleum and<br>natural gas|Total SA|France|Crude petroleum<br>and natural gas|
|5.2|2012|OJSC “MegaFon”|Radiotelephone<br>Communications|Investor Group|Cyprus|Investors, nec|
|3.3|2012|OJSC “TelekomInvest”|Radiotelephone<br>communication|AF Telecom<br>Holding|Cyprus|Investors, nec|

_Source_ : UNCTAD _World Development Report_ , various years, and company information. Includes deals worth over $3 billion, except for 2009 for which deals over $1 billion are included.

Notes: nec = not elsewhere categorized; OJSC = open joint stock company; LLC= limited liability company.

* This firm is owned by Universal Cargo Logistics Holding BV (Netherlands), which is in turn owned by Fletcher Group Holdings Ltd. (Cyprus)

0
7 measures are not significant at the five percent level (See Table 3). When squared Log GDP per capita is added to the regression, the regressions' R-Squared term becomes significantly larger and the coefficients on both Log GDP per capita and Squared Log GDP per Capita become highly significant"2 (See Table 4). This result agrees with previous studies which find that in cross country comparisons, the average level of inequality is lower in both very poor and rich countries, than in moderately poor countries." The turning point, where average inequality appears to begin to decrease with increased wealth varies between $1433 for the coefficient of variation, and $1826 for the ratio measure in 1985 prices. This empirical regularity does not necessarily prove the existence of either an intertemporal, or structural relationship between level of development and inequality. The data suffers from the usual problems relating to poor quality, as well as additional problems caused by comparing income distribution data for households and for individuals and using approximations of inequality measures. The "Inverted U" relationship for the Gini Coefficient is shown in figure 2.

**Table** 3: Regression of Inequality variables on Log of **ODP** per Capita

|**Dep.**Variable|**GENI**|COEFFVAR|THE8L|RrP40|
|---|---|---|---|---|
|No of**Obs**|**72**|**72**|72|77|
|Constant<br>l___________________|0.6102<br>(6.85)|1.6801<br>(6.53)|0.7177<br>(4.42)|7.3076<br>(3.24)|
|LODPPCIY|-0.0220<br>(-1.94)|4.0950<br>(-2.89)|-0.0461<br>(-2.22)|-0.4076<br>(-1.42)|
||0.05|0.11|0.07|0.03|

> 12 Third and **fourth** powers of log GDP per capita **are** insignificant when added to **the** regression.

" **See for example** Ahluwahlia (1976).
# **Table A4. Survey representativeness checks, by country**

|Dependent variable:|Program is included in<br>the survey|Program is an<br>effective survey|Program is an effective<br>survey, excluding closed<br>programs|
|---|---|---|---|
|**_Panel A. Brazil_**|(1)|(2)|(3)|
|HEI is public|0.028**|0.042*|0.042*|
|Obs.|2,367|2,367|2,101|
|HEI is for-profit|0.039|0.032|0.042|
|Obs.|2,362|2,362|2,096|
|Total enrollment|-4.953|-2.232|-5.738|
|Obs.|2,362|2,362|2,097|
|Total graduates|-1.249|-0.506|-1.504|
|Obs.|2,359|2,359|2,094|
|Annual tuition cost|419.1*|288.3|305.0|
|Obs.|2,388|2,388|2,122|
|**_Panel B. Colombia_**||||
|Technical program|0.008|0.011|0.009|
|Obs.|2,130|2,130|2,015|
|Distance (virtual) program|0.011|-0.003|-0.001|
|Obs.|2,130|2,130|2,015|
|Annual tuition cost (in COP)|-28,285.2|80,234.1|87,502.3|
|Obs.|2,130|2,130|2,015|
|Total enrollment in 2017|66.7|-65.3|-88.9|
|Obs.|2,130|2,130|2,015|
|HEI is SENA|0.003|-0.014|-0.019|
|Obs.|2,130|2,130|2,015|
|Program has high quality||||
|accreditation|-0.006|-0.003|-0.002|
|Obs.|2,130|2,130|2,015|
|**_Panel C. Dominican Republic_**||||
|<br>HEI is a university||-0.376**|-0.038|
|Obs.||209|93|
|HEI is public||0.073|-0.039|
an inspection (which can occur more than once a year) but the fi ne is reduced by 50% if the fi rm pays within 10 days of the date of noti fi cation<sup>12</sup> (other types of violations of the labor code are also punishable with fi nes, although we do not describe them here).

In summary, Brazil has very restrictive labor market regulation, which provides strong incentives for noncompliance on the part of fi rms and workers. Since law enforcement is imperfect there is a large amount of informal employment. Furthermore, enforcement is highly decentralized which results in substantial variation in enforcement across areas.

# 2.2. Informal Employment and Economic Performance: Theoretical Considerations

In this paper we examine how variation in the enforcement of the law in fl uences illegal behavior on the part of fi rms and, as a result, how it affects their productivity. Underlying our empirical work is a simple model that relates enforcement of labor regulation, the demand for informal labor and fi rm productivity. In this section we brie fl y describe such a model and its implications for our empirical work.

We consider a fi rm choosing simultaneously two types of labor (formal and informal), capital and the technology of production. Firms can choose different types of technology depending on the prices of different inputs. Technology, capital and formal labor are quasi fi xed inputs, while informal labor can be fl exibly adjusted every period. The additional fl exibility of informal labor is due, for example, to lower hiring and fi ring costs (Oi, 1962), or to the lack of regulation in working hours. We assume there are no other differences between these two types of workers (for example, differences in education or other measures of skill). There is no distinction between formal and informal fi rms, but only between formal and informal workers (our assumption is that all fi rms are fully registered, although part or all of their workers may not be legally registered).

We consider informal workers to be unregulated workers who coexist with formal workers in a single labor market, as opposed to assuming segmented labor markets.<sup>13</sup> However,

> 12In the fi rm level data we use in the paper the number of inspections in a fi rm in 2001 varies between zero and 60. The median number of annual labor inspections is 1 and the mean is 1.33.

> 13Maloney (2004) (and others) argue for a uni fi ed labor market approach as opposed to a dual view of the labor market.

8
-18-

We :an use the same framework to estimate the NPV of the flows resulting from divestiture. However, we should use this framework as a checklist to which ac9ditional items should be added and subtracted. For example, following divestiture, explicit transfers from the Treasury to the divested firm are not likely to include operational subsidies or capital injections. Yet, they should include the budgetary outlayo necessitated by divestiture (the cost of the administrative process, financial and physical restructuring, labor severance payments, re-training schemes, etc.). Similarly, implicit Treasury transfers to the divested firm are not likely to include tax and import duty exemptions and capital subsidies, unless concessions were made when the deal was struck. Explicit transfers in the opposite direction (i.e., from the divested firm to the Treasury) will include the selling price, which corresponds in some sense to the scrap value under the above framework. Similarly, divested firms will not remit dividends to the Treasury, unless of course the government had decided to retain some equity following divestiture. Under both frameworks, corporate taxes will appear as a transfer to the Treasury, provided firms were making profits.

We can get most of the information we need to calculate the two NPVs largely from the accoun.s of the enterpr-.e and the government budget records. We must also estimate three parameters; the lifetime of the firm, the relevant discounting factor in a given country, and the shadow multiplier for
climate variables, inclusion of these variables could result in consistent estimates of the effect of business environment and agglomeration (Xu, 2011). The main concern when using local averages is that there might be some omitted local variables that are correlated with employment growth and the included variables. To avoid this, we include many local variables and test whether the results are robust to the inclusion of additional local variables. In addition, we compare the patterns of the correlation of our key variables with the outcomes for different types of firms to see if they form a coherent pattern that can be explained by simple and intuitive conjectures.

# **Econometric Model and Results**

The basic model is: The dependent variable is employment growth for firm i in sector j and period k. Following Davis and Haltiwanger (1999), we calculate growth rates by dividing change in employment between the year of the survey and three years earlier by the simple average of employment in the survey year and three years earlier (see Table 1). This bounds growth between -2 and 2 and reduces the influence of outliers. All regressions include sector and time dummies ( γ j and α k) to control for sector differences that might affect firm growth and worldwide macroeconomic factors such as the great recession.

The regressions also include several variables to control for firm characteristics (FC), the business environment (BE) and agglomeration effects/trading costs (Agg) (see Table 1). The firm characteristics include two dummies indicating firm age, two dummies indicating firm size based on initial employment, and two variables representing state and foreign ownership. The regression also includes several business environment variables representing macroeconomic stability (inflation), the quality of infrastructure (the number of power outages), the institutional environment (control of corruption and crime), labor flexibility (the share of employees in unions and an index indicating difficulty of firing), and labor skills (average years of schooling and the share of workers that are skilled). Finally, the regression includes several agglomeration 11
18

# **Figure 11: Production Volumes of Selected Wood Products in Gabon (in 1,000 m**<sup>**3**</sup> **)**

<!-- Start of picture text --> 600<br>500<br>400<br>300<br>200<br>100<br>0<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007<br>Sawnwood Veneer sheets Plywood<br><!-- End of picture text --> _Source:_ Calculated from FAOSTAT ForesSTAT data, accessed November 2009.

The external pressures on Gabon’s timber industry explicitly targeted two related objectives. The first, exerted by external donors as a direct pressure on the Gabonese government, was designed to promote greater transparency in the management of the forestry sector in order to facilitate the sustainable management of this key global resource and to widen the distribution of benefits from the exploitation of the forests. The second set of pressures were indirect in that they were not aimed directly at Gabon. They involved a series of standards which global buyers set to ensure the sustainable management of the forests and the legality of wood products that they sourced. This, as we will see below, led to a combination of both private and public standards governing the procurement of wood from Gabon and other sources of tropical hardwoods.

# **_3.2 Market Requirements for Gabon’s Timber Products_**

Two distinct export markets can be identified for Gabon’s timber products – the EU, particularly France its prior colonial ruler, and China.

# <u>The EU</u>

Gabon’s timber exporting industry is a “creation” of French industry, reflecting the industrial phase in the Forest Transition trajectory in which northern firms – squeezed out of domestic sources of timber supply – indentified cheap sources of raw material supply in ex-colonies. Timber exports from Gabon to France (the major external market) started with colonial settlements during the 1850s-80s, and persisted after independence when French interests shifted towards mining activities (Wunder, 2003). Over time, total export volumes to France have risen from around 300,000 m<sup>3</sup> in 1989 (from when reliable records are available) to nearly 600,000 m<sup>3</sup> in the peak year of 2002. But, as processing capacities rose in Gabon, these log exports were complemented by exports of sawnwood, veneers and plywood, jointly reaching a roundwood equivalent of 230,000m<sup>3</sup> , exceeding the export of logs to France in 2006 (220,000m<sup>3</sup> ) (Figure 12).
|**HS**<br>**code**||||**Year**|||
|---|---|---|---|---|---|---|
|**2 digits**|**_Type of products_**|**2001**|**2002**|**2003**|**2004**|**2005**|
|||53|53|53|53|53|
|26||3.7|3.6|3.2|3.4|3.3|
||_ORES, SLAG AND ASH_|_(2.1)_|_(2.1)_|_(2.3)_|_(2.2)_|_(2.3)_|
|||111|111|111|111|111|
|27|_MINERAL FUELS OILS_|4.2|4.0|4.0|3.9|3.8|
||_,_|_(1.7)_|_(1.8)_|_(1.8)_|_(1.9)_|_(2.0)_|
|||267|267|267|267|267|
|28|_INORGANIC CHEMICALS_|4.3|4.4|4.5|4.4|4.4|
|||_(2.0)_|_(2.1)_|_(2.3)_|_(2.2)_|_(2.1)_|
|||570|570|570|570|570|
|29|_ORGANIC CHEMICALS_|4.9|4.9|5.0|5.0|5.0|
|||_(1.1)_|_(2.1)_|_(3.0)_|_(2.1)_|_(1.3)_|
|30|_AAA_|79<br>8.8|79<br>8.8|79<br>8.8|79<br>8.9|79<br>8.8|
||_PHRMCEUTICL PROD_|_(3.3)_|_(3.5)_|_(3.5)_|_(3.5)_|_(3.4)_|
|31||37<br>8.4|37<br>9.4|37<br>8.8|37<br>9.1|37<br>8.9|
||_FERTILIZERS_|_(3.3)_|_(1.9)_|_(3.1)_|_(2.8)_|_(3.0)_|
|||67|67|67|67|69|
|32|_TAN DYE EXTRACTS_|5.0|5.1|5.1|5.1|5.2|
||_,_|_(1.8)_|_(1.8)_|_(1.8)_|_(1.8)_|_(2.2)_|
|||59|59|59|59|59|
|33|_ESSENTIAL OILS_|8.4|8.5|8.5|8.5|8.5|
|||_(4.6)_|_(4.8)_|_(4.8)_|_(4.8)_|_(4.8)_|
|34|_SOAPS LUBRICANTS_|36<br>11.9|36<br>12.1|36<br>12.6|38<br>11.9|38<br>12.3|
||_,_|_(4.5)_|_(4.3)_|_(4.8)_|_(5.0)_|_(4.3)_|
|||35<br>|35<br>|35<br>|35<br>|35<br>|
|35||4.9|5.2|5.4|5.4|5.3|
||_MODIFIED STARCHES_|_(0.2)_|_(1.1)_|_(1.9)_|_(2.0)_|_(2.3)_|
|36||10<br>17.3|10<br>19.4|10<br>18.0|10<br>18.7|10<br>19.1|
||_Glues_|_(5.4)_|_(1.9)_|_(3.8)_|_(2.4)_|_(2.3)_|
|||61|61|61|61|61|
|37||<br>11.2|<br>11.5|<br>11.7|<br>11.8|<br>11.8|
||_Pyrotechnic materials and products_|<br>_(4.7)_|_(4.7)_|_(4.5)_|_(4.5)_|_(4.4)_|
|||136|136|136|136|136|
|38|_MISC CHEMICAL PRODS_|5.7|5.9|5.9|5.9|6.1|
|||_(3.4)_|_(3.4)_|_(3.5)_|_(3.4)_|_(3.4)_|

49