rafmacalaba/datause-encoder-data huggingface dataset:
Figure 11. Lorenz Curve before and after Pension Transfers 2006 2016 <!-- Start of picture text --> 1 1<br>0.9 0.9<br>0.8 0.8<br>0.7 0.7<br>0.6 0.6<br>0.5 0.5<br>0.4 0.4<br>0.3 0.3<br>0.2 0.2<br>0.1 0.1<br>0 0<br>0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1<br>Total Family Income Social Pension Total Family Income Social Pension<br>Contributory Pensions Contributory Pensions<br>Accumulated Percentiles Accumulated Percentiles<br>0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1<br><!-- End of picture text --> Source: author elaboration based on ENEMDU In summary, the income protection system for older people has a slight incidence on poverty, though it does not affect income distribution.

# 3.4 Financial situation of the contributory pension scheme

The financing of the contributory pension scheme administered by the IESS has three main sources: (1) current income, composed of statutory contributions made by workers and employers; (2) capital income, which mainly relates to the sale of assets in general managed by the technical reserve fund and, (3) public transfers. The latter source of resources was defined as 40% of total expenditures on pensions and was used until year 2015.

Figure 12 presents expenditures, income level and financial results of the contributory pension scheme expressed as a GDP percentage. From 2003 to 2012, the information available allows differentiating the resource sources in two main categories: own resources (current income and capital) and public transfers. From year 2013 to 2017, own resources, in turn, may be divided into current income (income per contributions) and capital income.

On average, for the whole period taken into account, it is noticed that the financial result of the pension system was positive and equal to 0.5% of the GDP. However, it is possible to differentiate two well-defined periods. Between years 2003 and 2015, both current income as well as public 19
Table 1: Distribution of Foreign Affiliates by Region and Country, 2016

|Region/Country|# of Subsidiaries|Share|
|---|---|---|
|_Panel A. Asia_|3,237|78.89|
|China|1,851|45.11|
|Vietnam|488|11.89|
|Indonesia|174|4.24|
|India|165|4.02|
|Japan|81|1.97|
|Thailand|80|1.95|
|Hong Kong SAR, China|68|1.66|
|Malaysia|60|1.46|
|Philippines|58|1.41|
|_Panel B. North America_|356|8.68|
|United States|343|8.36|
|_Panel C. Europe_|315|7.68|
|Poland|44|1.07|
|Russian Federation|40|0.97|
|Germany|40|0.97|
|_Panel D. Latin America_|161|3.92|
|Mexico|91|2.22|
|Brazil|39|0.95|
|_Panel E. Rest of the World_|34|0.83|
|Total|4,103|100.00|

_Notes:_ The data comes from the Survey of Business Activities (SBA) in 2016. The sample is restricted to Korean parent firms in manufacturing. There are 1,939 Korean manufacturing firms that have 4,103 foreign affiliates. Regions are classified into five areas (Asia, North America, Europe, Latin America, and Rest of the World). The top 15 countries are also presented.

Table 2: MNCs and Non-MNCs Observable Characteristics, 2016

||MNCs|Non MNCs|Difference|
|---|---|---|---|
|Employees (number)|602|153|449|
|Share of production workers (percent)|55|63|-8|
|Wage bill (million Korean won)|46,689|8,752|45,814|
|Capital stocks (million Korean won)|212,424|28,859|183,565|
|Average wage (million Korean won)|58|53|5|
|Capital stocks per worker (million Korean won)|197|175|22|
|Sales (million Korean won)|510,629|73,191|437,138|
|Purchases (million Korean won)|286,926|40,968|245,958|
|Exports (million Korean won)|194,215|12,653|181,562|
|<br>Imports(million Korean won)|105,913|11,097|94,816|
|Observations|1,939|3,977||

_Notes:_ The data come from the Survey of Business Activities (SBA) in 2016. The sample is restricted to Korean parent firms in manufacturing. There are a total of 5,916 Korean manufacturing firms. The values indicate the average observable characteristics for MNCs and non MNCs.

12
(IMF 2018c). The country is in debt distress, and several payments to external borrowers have been missed.

Zimbabwe is also classified as being in debt distress. Over the last five years, government debt has risen substantially from just over 48 percent of GDP in 2013 to an estimated 82 percent in 2017. Persistently large fiscal deficits have partly been the result of an elevated public wage bill, which absorbed 90 percent of revenues in 2017 (IMF 2017). In addition, revenues remain subdued amid weak growth and structural rigidities, while transfers to the agricultural sector have kept non-wage expenditure elevated. Moreover, the deficits have partly been financed through an overdraft facility at the Reserve Bank of Zimbabwe that, given insufficient reserves, has led to money creation and exacerbated foreign-currency shortages.

**Risk of debt distress.** Higher levels of public debt, much of it external, and an increased reliance on commercial loans make many LICs vulnerable to currency, interest rate, and refinancing risks (Devarajan 2018; Gill and Karakülah 2018a,b).<sup>6</sup> LIC vulnerabilities are reflected by the fact that almost all LICs have the lowest or second lowest grade in the OECD’s country credit risk classification.<sup>7</sup> Because of rising arrears or the need for debt restructuring, 11 LICs were assessed as being in debt distress or at a high risk of debt distress as of November 2018, compared to only six in 2015.<sup>8</sup> For LICs assessed at low or moderate risk of debt distress, safety margins have eroded.

# **III. Other LIC vulnerabilities**

**Private debt.** Due to shallow domestic capital markets and limited access to international finance, the median LIC has total private debt equivalent to only 18 percent of GDP, significantly less than the 41 percent ratio for the median non-LIC EMDE (Figure 3).<sup>9</sup> Nonetheless, LIC private sector debt has been on a steady upward trend since 2005, rising by almost 8 percentage points. Excess

6 Separately, some countries such as The Gambia are vulnerable to rollover risk because of heavy reliance on shortterm domestic debt (IMF 2018d).

7 There is one exception: The credit rating for Senegal has improved recently in the OECD credit risk classification, improving from 6 to 5 in a 0-7 rating system, with a higher number indicating higher credit risk (OECD 2018).

8 A country is considered to be in debt distress if it is experiencing difficulties in servicing its debt, as evidenced, for example, by the existence of arrears, ongoing or impending debt restructuring, or if there are indications that a future debt distress event is probable. LICs in debt distress are The Gambia, Mozambique, South Sudan, and Zimbabwe. LICs at high risk of debt distress are Afghanistan, Burundi, Central African Republic, Chad, Ethiopia, Haiti, and Tajikistan. There is a total of 30 LICs that have a debt sustainability analysis (DSA) available under the Joint World Bank / IMF debt sustainability framework (DSF).

> 9 Private sector debt refers to the sum of commercial banks’ and other financial corporations’ claims on the nonfinancial private sector, in percent of GDP.

7
are slightly younger, except in Armenia and Georgia. With regard to the distribution of men and women across three broad groups of occupations— _high-skilled_ white-collar jobs, _low-skilled_ white-collar jobs, and blue-collar jobs—we find that a larger proportion of men than women report working in blue-collar jobs. In Colombia and Ukraine, about 50 percent of the male sample is in blue-collar occupations. The distribution of men and women in white-collar jobs varies by country. In Armenia, Georgia, and Ukraine, a larger proportion of men and women are in _high-skilled_ whitecollar occupations than in _low-skilled_ white-collar occupations. In Colombia, Kenya, and Vietnam, we find that a larger share of men and women are in _low-skilled_ white-collar jobs than in _highskilled_ white-collar jobs.

# **V. Results**

We begin by reporting findings from the mean decomposition analysis for two sets of equations: one set without controlling for occupation and the other with occupation controls added. Assuming that there is occupational segregation in our sample of countries, the first specification without occupational controls provides the “true” gender wage gap. When controlling for occupation, we expect that occupation partially mediates the relationship between skills and earnings. As mentioned above, we use the Heckman selection correction method for these mean decompositions. Results are reported by country. Then, we discuss results from the quantilebased decomposition analysis separately for each country in our sample.

# **_Mean Decomposition Analysis with Selection Correction_**

In order to decompose the wage gap between men and women into the part explained by observed differences in characteristics and the returns to these characteristics, we begin with mean decompositions using the pooled method from Neumark (1988) and Heckman’s selection correction method.<sup>15</sup> The contributions to the gender wage gap of observed characteristics (experience and type of employment) and schooling are estimated first. Building on this model, we subsequently add skills measures in four separate steps. The results are reported in Table 8. Panel A of Table 8 shows results when schooling is included in the model, controlling for potential experience and type of employment. In Panel B we add noncognitive skills. Cognitive skills as measured by reading proficiency scores and complexity of computer use on the job are added in

> 15 The decomposition was carried out with and without selection correction. The latter results are not shown in the paper but are discussed in the Results section and are available upon request.

20
Policy Research Working Paper 10533

# **Abstract**

The recent global crises, such as the COVID-19 crisis, remind us of the importance of efficient transportation and logistics. Notably, however, even before the crises, some regions were already experiencing a gradual increase in freight costs, with more and more empty trucks observed. The paper recasts light on the question of how road freight costs are determined using large, unique shipping data from Eastern European and Central Asian countries. It finds that economies of scale are significant in both freight weight or load factor and distance. The elasticity with respect to freight weight is particularly high at about 0.3 to 1.0 in absolute terms. Thus, to contain trucking costs, it is important to maximize the load factor through freight consolidation at origins and destinations. The elasticity with respect to distance is relatively modest at 0.04 to 0.16 in absolute terms but still statistically significant, indicating that distance may not necessarily be a constraint on trade and regional integration. Trucking costs also decrease with driving speed, a proxy for efficiency of movements or road conditions. The elasticity is significant for food products (−0.03) and other consumer goods (−0.11). Finally, the paper finds that border crossing adds 3–4 percent to freight costs.

This paper is a product of the Transport Global Practice. 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://www.worldbank.org/prwp. The author may be contacted at aiimi@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
||||**Table 19: D**|**ID Regressio**|**n(5)**||||
|---|---|---|---|---|---|---|---|---|
||Bus for ho<br>Linear pro|spital<br>bability|Probit||Bus for sch<br>Linear pro|ool<br>bability|Probit||
|D|0.077|(0.023) ***|0.325|(0.098) ***|0.016|(0.009) *|0.362|(0.204) *|
|T|-0.010|(0.023)|-0.132|(0.130)|-0.005|(0.006)|-3.651|(0.182) ***|
|D*T|0.066|(0.031) **|0.342|(0.148) **|0.027|(0.012) **|3.969|(0.234) ***|
|lnHH size|0.014|(0.022)|0.063|(0.091)|0.004|(0.010)|0.076|(0.167)|
|Adult share|-0.040|(0.047)|-0.154|(0.193)|0.003|(0.017)|0.028|(0.333)|
|HH head sex: Male|-0.001|(0.026)|0.020|(0.106)|-0.002|(0.010)|-0.011|(0.212)|
|HH head education: Elementary|-0.011|(0.019)|-0.051|(0.076)|-0.012|(0.008)|-0.187|(0.126)|
|HH head education: Secondary|-0.080|(0.029) ***|-0.393|(0.159) **|-0.030|(0.008) ***|||
|HH head education: University|-0.134|(0.039) ***|-0.914|(0.451) **|-0.028|(0.008) ***|||
|Appliance ownership: Refrigerator|0.004|(0.027)|0.020|(0.110)|0.001|(0.012)|0.000|(0.203)|
|Appliance ownership: Color TV|-0.045|(0.026) *|-0.179|(0.101) *|-0.013|(0.012)|-0.208|(0.183)|
|Appliance ownership: Radio|-0.015|(0.017)|-0.058|(0.070)|0.010|(0.007)|0.199|(0.140)|
|Appliance ownership: Gas stove|-0.035|(0.025)|-0.126|(0.090)|0.009|(0.011)|0.186|(0.171)|
|Appliance ownership: Washing|||||||||
|machine|-0.035|(0.021) *|-0.156|(0.101)|-0.013|(0.008)|-0.316|(0.209)|
|Gas use for cooking|0.028|(0.021)|0.128|(0.092)|-0.010|(0.006) *|-0.355|(0.217) *|
|Power use for lighting|-0.019|(0.026)|-0.069|(0.101)|0.012|(0.010)|0.260|(0.169)|
|Tap water for cooking|0.010|(0.017)|0.054|(0.072)|-0.008|(0.007)|-0.177|(0.136)|
|Constant|0.203|(0.070)***|-0.917|(0.287)***|0.009|(0.030)|-2.451|(0.555)***|
|Obs.|2,064||2,064||2,064||1,882||
|R-squared|0.043||||0.020||||
|F-stat|6.35||||2.91||||
|Pseudo-R2|||0.050||||0.102||
|Wald-stat|||85.74||||3,361.7||
|Z-statfor interactioneffect (mean& s|td.dev.)||2.484|(0.178)|||1.753|(0.355)|

Note: Standard errors are shown in parentheses. *, **, *** indicate the statistical significance at 10%, 5% and 1%, respectively.

38
5

# _<u>Countries covered in the database</u>_

The database encompasses information from a total of 103 countries, of which 79 are non-OECD countries, henceforth called developing countries, and 24 are OECD countries, together broadly representing all regions and income groups in the world.<sup>3</sup> For the full list of countries covered, please see Annex Table 1. First-hand information for non-OECD countries was collected by administering a questionnaire, whereas similar information for OECD countries was obtained from publicly available sources. To ensure data accuracy, policy information was subjected to review by government officials, which led to confirmation and/or update of the data for most of the OECD countries and a number of developing countries. The full list of governments which confirmed the policy information is available in Annex Table 1.

# _<u>Sectors and modes covered in the database</u>_

The database focuses on five major services sectors, namely financial services (banking and insurance), telecommunications, retail distribution, transportation<sup>4</sup> and professional services (accounting and legal), with each sector further disaggregated into subsectors as applicable. The choice of sectors was based primarily on our assessment of their economic importance from a development perspective, on the existence of meaningful restrictions on services trade, and on the feasibility of collecting relevant policy data.

We have not at this stage covered two important areas of export interest for developing countries: cross-border trade in business processing services (associated with the “outsourcing” phenomenon) and the international movement of less-skilled individuals to deliver services in fields such as construction. The rationale for not including the former is that such cross-border trade is largely free from explicit restrictions; the rationale for not including the latter is that the immigration policies which most significantly affect such movements of people are notoriously opaque. However, we hope to be able to address these gaps in future data collection exercises. We recognize

> 3  The regional affiliation of countries in this database follows the official World Bank country classification, which groups all nonhigh income countries into world regions.  For ease of exposition there are two exceptions to this rule: Trinidad and Tobago is presented as part of the Latin America and Caribbean (LAC) region, and Bahrain, Kuwait, Oman, Qatar and Saudi Arabia are referred to as Gulf Cooperation Council (GCC) countries.  All six are high-income countries in official World Bank terms. 4  Regarding policies governing cross-border (mode 1) trade in international air passenger transportation services, we draw on the WTO‟s QUASAR database since it represents the most comprehensive source currently available on bilateral air services agreements, covering over 2000 agreements.
> clearly shown in Table **13** which reports, for the period between June **1981** and December **1990,** the investment returns on the total pension fund for each AFP as well as the average real rates of return

> for three different sizes of accounts: pensionable income equivalent to **_5_ UF, 15 UF** and **60 UF.**

Table **13**

||Real|RatesofReturn||
|---|---|---|---|
||(June**1**|**981-**<br>December**1990)**||
||**'j.P.**<br>**5**|**U.F.**<br>**U.P.**<br>15<br>**60**|Total<br>Fund|
|Cuprum<br>|**9.22**|11.22<br>**11.92**|**16.3**|
|Invierta<br>|**7.06**|10.25<br>**11.31**|**13.9**|
|Union<br>|7.49|**10.26**<br>11.20|14.2|
|Planvital|**7.29**|10.12<br>**11.08**|**13.7**|
|Summa<br>|**7.61**|**10.00**<br>10.82|13.4|
|Concordia|8.48|10.20<br>**10.80**|14.3|
|Habitat|**9.35**|**10.35**<br>**10.71**|12.8|
|**El**Libertador<br>|**7.38**|**9.80**<br>**10.63**|12.4|
|Magister|**6.83**|**9.21**<br>**10.03**|**13.1**|
|SantaMaria|**6.87**|**9.11**<br>**9.89**|**12.6**|
|Provida|6.48|8.94<br>**9.78**|**13.1**|
|Weightedaverage|<br>**7.53**|**9.69**<br>10.43||
|Source:<br>Habitat|**(1991)**|||

The difference between the return on low income and high income accounts was on average nearly **3%.** Compounded over a forty year period this would cause very large differences in the accumulated capital for different groups of affiliates. Thus, **100** pesos compounded at **7.5%** per year (the average return for low income accounts) would accumulate 1804 pesos after 40 years, while the same amount compounded at **10.5%** per year (the average return for high income accounts) would amass _5426_ pesos or slightly over **3** times as much. **100** pesos paid annually over 40 years and compounded at **_7.5%_** would grow into a capital fund of **22,725** pesos, whereas compounded at **10.5%** the capital fund would reach **_50,725._**

> It should, however, be stressed that the results reported in Table **13** are based on average

> performance over the first ten years of the system. As the management fee has been eliminated and the flat fee has declined in real terms, the difference in performance has also become smaller. In fact, since **1988,** the gap in investment returns between low and high income accouats has narrowed significantly. On average, it is now less than **I** percentage point.

# **3.13 High** Concentration, Competition, **and** Corporate **Governance**

The high concentration of the pension fund sector is causing some concern because of its potentially adverse impact on competition and efficiency. It should, however, be stressed that high concentration need not result in lack of competition or high costs. First, the relaxation of entry criteria has increased the threat of potential competition. Second, large AFPs may benefit from economies of scale that could be reflected in lower commission charges. In fact, the three largest AFPs charge lower commission rates than the average.

29
# 1 Introduction

When Eastern Europe opened up in 1990. there was widespread agreement that the existing State Owned Enterprises (SOEs) were doomed. Some claimed they were actually value-subtracting and would be wiped ouE by trade liberalization (McKinnon (1993)). While this extreme view had little basis in fact. most would have agreed with the view expressed in van Wijnbergen (1991): " After the crumbling of central authorty that used to exercise both ownership and control. ownership of -SOEs remains ineffective and control diffuse. ( ... ) [With changes in ownership announced but implemented managers andno t workers councils alike have every incenive to decapitalise the enterprise and increase its debts". Similar misgivings were expressed in Lipton.and Sachs (1990 a.b) and Frvdman and Wellisz (1991).

While this may be a reasonable description of what is now going on in theFormer Soviet Union. the SOEs in most of Central Eastern Europe (CEE) have done much better than predicted. Poland grew faster than any country in Europe in 1993. at least partly because its state enterprise sector pumin a robust performance after the decline in 1990 and 1991. Eixports to the West shot up. mostly because SOEs successfully sought new export markets after the coliapse in interal demand in 1990 and the demise of CMEA trade in 1991: and industrial production. almost 70 percent of which came from SOEs, grew at 4 percent in [992 and 5.6 percent in 1993 (GUS Statistical Bulletin, various issues) with prospects of 6-7 percent for 1994.' This raises what is in our view the most intriguing question posed by Poland's growth experience: how come the state sector has defied the odds and not collapsed? It is no surprise that the new private sector is growing at.breakneck speed: but given the incentive problems and poor governance in the SOEs. why did the disaster that was widely predicted in the early reform years not materialize?

There is remarkably little empirical work to base an answer on. Most of the survey work has been -focused on the rapidly growing private sector (cf Webster (1992) for interesting examples). While that focus is understandable. it is no great surprise that private enterprises are growing fast. difficult circumsnances notwithstnding. The service sector, which is where most of privateenterprises arestarring up. was largely neglected under the communist regimes. by and large requires little up front capital outlay and faces a huge demand. Much more intriguing is the question why, given the incentive problems and lack of corporate control mechanisms rightly pointed out in the early literamre., state industries seem to be recovering in much of Central and Eastern Europe (CEE). This is the question we addressAggregate statistics do show a shrinking state sector in 1993. This is however pardy because of privatization and pardy because of asset sales by SOEs to private operators. There is a growing body of survey evidence shDWing **that** SOEs are **turning** around. See Hume and Pinto (1993) for examples.