rafmacalaba/datause-encoder-data huggingface dataset:
**TABLE 7: Investment financial performance and additional country-level risk factors in year before first cash flow.** Explanatory variables have been normalized as Z-scores by subtracting off the sample mean and dividing by the sample standard deviation. *** ** * Standard errors are clustered at the country-year. p<0.01, p<0.05, p<0.1

||(1)|(2)|(3)<br>Qu|(4)<br>antile Regr|(5)<br>ession|(6)|
|---|---|---|---|---|---|---|
||OLS|0.1|0.25|0.5|0.75|0.9|
||PME|PME|PME|PME|PME|PME|
|Panel A) Direct investments, vintage pre-2010|||||||
|Political Risk (PRS Group)|2.182|-0.003|-0.011|-0.116|-0.368|-0.827***|
||(2.235)|(0.054)|(0.070)|(0.106)|(0.256)|(0.318)|
|Corruption Perception (Transparency Int’l)|-0.334|-0.005|0.108|0.215*|0.417|1.008***|
||(0.734)|(0.111)|(0.111)|(0.117)|(0.267)|(0.215)|
|Economic Freedom (Heritage Foundation)|-2.102|-0.002|-0.059|-0.099|-0.253|-0.793***|
||(2.405)|(0.065)|(0.066)|(0.104)|(0.168)|(0.233)|
|Constant|2.432***|0.013|0.334***|0.962***|2.076***|4.061***|
||(0.598)|(0.082)|(0.082)|(0.101)|(0.301)|(0.340)|
|R-squared|0.00880|0.00005|0.00033|0.00030|0.00050|0.00004|
|Number of observations|346|346|346|346|346|346|
|Panel B) Fund investments, vintage pre-2010|||||||
|Political Risk (PRS Group)|-0.090|-0.025|0.060|0.101|0.225|-0.400|
||(0.368)|(0.093)|(0.120)|(0.170)|(0.155)|(1.018)|
|Corruption Perception (Transparency Int’l)|-0.139|0.079|0.011|0.023|-0.409**|-0.010|
||(0.214)|(0.175)|(0.131)|(0.206)|(0.189)|(0.316)|
|Economic Freedom (Heritage Foundation)|-0.063|-0.083|-0.016|-0.058|0.271|-0.427|
||(0.222)|(0.165)|(0.134)|(0.163)|(0.186)|(0.673)|
|Constant|1.252***|0.144|0.363***|0.659***|1.138***|3.446***|
||(0.381)|(0.114)|(0.108)|(0.147)|(0.179)|(0.871)|
|R-squared|0.02253|0.00009|0.01136|0.00587|0.00043|0.01980|
|Number of observations|67|67|67|67|67|67|
|Country fxed efects|No|No|No|No|No|No|

38
# _(e) The New Foreign Trade Policy_

In 1992, Fujimori carried out a “civil coup” and closed the Congress. The legislative void created by this coup and the already existing climate of liberalization created conditions for the government to remove all the pieces of the protectionist scaffold, to subscribe WTO agreements, to create institutions to regulate free competition, and to decree the necessary rules.

The new foreign trade policy aimed at productive efficiency but it also expected to favor the anti-inflationary strategy and to reinforce public finances. In August 1990, the exchange rate was unified and in September, the first great tariff reduction established only three levels, 15%, 25% and 50%, but maintained the 10% surcharge for certain items.<sup>5</sup> The banned imports list was eliminated, and the items subject to a Previous License were reduced from 285 to 13. The No Competition requirement and the remaining para-tariff restrictions were discontinued, except for sanitary ones.

|**Table 3**<br>**.**<br>|**Proportion of Imports covered by the New Tariff Rates ,**<br>|**September 1990**<br>|
|---|---|---|
|Tariff|Items|Proportion|
|10%|Inputs with no domestic substitutes, iron and steel, capital<br>goods for the industry, metal mechanics, agriculture,<br>medical equipment, some medicines, CKD packages,<br>vehicle parts/pieces, tires.|41.3 %|
|15%|Other capital goods, inputs with domestic substitutes,<br>reduced incidence on production costs,and ad-valorem<br>rate over 25%. Agricultural products for final<br>consumption.|36.9%|
|25%|Other consumption goods, inputs with tariffs over 50%<br>that did not represent an important share of the cost of<br>other productive activities, capital goods with rates over<br>50% andwithdomestic substitutes.|21.8%|

Source: Carlos Boloña Behr and Javier Illescas Mucha, _Políticas Arancelarias en el Peru, 1980-1997_ Free Market Economy Institute and San Ignacio de Loyola University, November 1997, Lima, Peru.

The average tariff, including a temporary surcharge, went from 66% in July 1990 to 32%, and the average dispersion, from 25 to 13 points. The maximum rate dropped from 84% to 50% and most exemptions were eliminated. In November, the surcharge was removed, the average tariff falling to 26%, and the subsidy to non-traditional exports was also removed. In December, a schedule for future annual reductions to reach a uniform flat rate of 15% was published.

> 5 Carlos Boloña Behr and Javier Illescas Mucha, _Políticas Arancelarias en el Perú, 1980-1997_ , Free Market Economy Institute and San Ignacio de Loyola University, November 1997, Lima, Peru, pp. 52-60.

5
# COSTS OF INFRASTRUCTURE DEFICIENCIES IN MANUFACTURING: INDONESIA. THAILAND. AND NIGERIA

# I. BACKGROUND OF THE RESEARCH PROJECT

In 1992, the World Bank completed a research project, mlmpacts of Infrastructure Deficiencies on Nigerian manufacturing. " This research project produced a systematic empirical study which investigated the relationship between deficient public infrastructure services (as inputs in the production process of manufacturing firms) and the productivity of industry. This issue is important for policy and Bank lending operations because infrastructure affects economic growth and development in all facets of the development process.

In the 1980's, the operational staff of the Bank's Africa Region and policy makers in member countries became seriously concemed with the misallocation of resources manifested in duplications of capital investments in infrastructure by public enterprises on the one hand and private individual end-users on the other. In these countries, infrastructure provisions suffered from two extreme aes of inefficiency: (1) The non-performing public sector, which had a high level of capital investment in place but was unable to provide steady and reliable flows of infrastructural services; (2) the users, both firms and households, who found it necessary to provide their own facilities in whole or in part to substitute or complement the deficient public supply by incurring high private costs.

The study on Nigeria documented empirically (i) the extent of public service deficiencies in electric power, water supply, transport, telecommunications, and waste disposal; (ii) responses
Taxes by activity explicitly considered are the following:

- Corporate income tax IRES (“Imposta sul reddito delle società”) and the regional production tax IRAP (“Imposta regionale sulle attività produttive”) are added together and disaggregated by activity using tax return data available at DF (the Department of Finance). The implicit tax rates are calculated using the operating surplus as a tax base and will increase the cost of capital used by each firm.

- Social Security contributions paid by the employer (SSCs Employer) are retrieved from the use table, defined as the difference between employee income of regular workers and gross wages by sector of economic activity; subsequently, this value is classified according to the type of skill category: low skilled, skilled, and high skilled.

- Other taxes on production are published by Istat by sector. As IRAP is considered with IRES as a separate entry in the SAM, its amount is subtracted to obtain the other taxes on production as the residual value.

Finally, households’ direct taxes are included in the model and treated as a single entry that combines income taxes (imposta sui redditi delle persone fisiche (IRPEF)) and IRPEF surcharges, substitutive taxes (forfeit mixed, rents, capital income taxes etc.) and other special regimes and social security contribution paid by the employee.

# **2.2 The IRENCGE-DF Model**

## **_Model Overview_**

The Italian Regional and Environmental Computable General Equilibrium of Department of Finance (IRENCGE-DF) Model is a (recursive) dynamic single country computable general equilibrium (CGE) model that is developed by the Italian Ministry of the Economy with technical assistance from the World Bank. It is based on the MANAGE-WB model of the World Bank (World Bank, 2023), which is in turn based on the MANAGE model that is documented in van der Mensbrugghe (2021).

IRENCGE-DF is designed to focus on energy, emissions and climate change. In addition to the standard features of a single country CGE model, the IRENCGE-DF model includes a detailed energy specification that allows for capital/labor/energy substitution in production, intra-fuel energy substitution across all demand agents, and a multi-output multi-input production structure. Furthermore, the model introduces household heterogeneity to better analyze the impacts of environmental and energy policies on welfare and inequality and disaggregates the representative household into ten household groups. The sources of income and consumption structure reflect the 6
as the Demographic and Health Surveys (DHS), though researchers have reduced under-reporting by providing special training to interviewers, placing greater emphasis on respondents’ privacy and safety, and allowing women multiple opportunities to disclose their experiences (Ellsberg et al., 2001a; Garcia Moreno et al., 2003; Ellsberg et al., forthcoming).

# **_Estimates of the magnitude of the problem_**

Population-based surveys have found that between 10-70% of women report being physically assaulted by an intimate male partner at some point in their lives (Heise, Ellsberg and Gottemoeller, 1999). See Annex A for estimates from many recent population based studies (Ellsberg et al., forthcoming). Findings from a multi-country study on domestic violence and women’s health carried out by the World Health Organization in fifteen sites and ten countries found that between 13-62% of women had experienced physical violence by a partner over the course of their lifetime, and between 3-29% of women reported violence within the past year (Figure 1.1).

**Figure 1.1.  Percentage of women who reported sexual violence by an intimate partner (ever), physical violence by an intimate partner (ever), and physical violence by an intimate partner in the past 12 months.**

<!-- Start of picture text --> 37%<br>Bangladesh (Urban) 40%<br>19%<br>50%<br>Bangladesh (Province) 16% 42%<br>10%<br>Brazil (Urban) 27%<br>8%<br>14%<br>Brazil (Province) 34%<br>13%<br>59%<br>Ethiopia (Province) 49%<br>29%<br>6%<br>Japan (Urban) 13%<br>3%<br>17%<br>Namibia (Urban) 31%<br>16% 23%<br>Peru (Urban) 50%<br>17%<br>47%<br>Peru (Province) 62%<br>25%<br>30%<br>Thailand (Urban) 23%<br>8% 29%<br>Thailand (Province) 34%<br>13%<br>23%<br>Tanzania (Urban) 33%<br>15%<br>31%<br>Tanzania (Province) 47%<br>19%<br>6%<br>Serbia 23% sexual violence ever<br>3%<br>20% physical violence ever<br>Samoa 41%<br>18% physical violence past 12 months<br><!-- End of picture text -->

**Source:** Unpublished data from the WHO Multi-Country Study on Women’s Health and Domestic Violence Against Women.  The final published comparative report is forthcoming. Cited with permission.

Prevalence data on sexual violence is even more limited than physical violence. However, evidence suggests that a substantial proportion of girls and women have experienced child sexual abuse, forced sex and other forms of sexual coercion in virtually every setting of the world. For example, population-based studies have asked about “forced” sexual debut among sexually experienced young people and found rates from 7% (New Zealand), to 46% (in the Caribbean) (Heise and Garcia Moreno, 2002).

10
One relationship that would seem important to control for in our test of the market failure hypothesis is scale. It is possible that with a larger population, there would be a larger pool of entrepreneurs, leading to more experiments and a higher number of export discoveries. It is also possible that a larger pool of imitators, although increasing the social value of discovery, would reduce incentives for individual entrepreneurs to experiment, resulting in a lower frequency of discovery. Scale variables such as total population, working-age population, and number of person-years of education in the country were not found to be statistically significant in any estimation of the model, which is discussed in the following paragraphs.<sup>6</sup>

# _Testing the Market-Failure Hypothesis_

As a measure of barriers to entry, we use a set of objective indicators rather than subjective surveys. These indicators are drawn from the World Bank’s _Doing Business_ database (World Bank 2004). Although only available for 2003, these are the best measures available, and it is unlikely that the regulatory regime changed significantly during the sample period. Rather than arbitrarily selecting a single indicator, we construct an index based on five of them, encompassing costs and delays in starting a new business, enforcing contracts, and hiring employees. The indicators for registering a new business would be most appropriate if discovery is undertaken by new entrepreneurs, whereas the measures of labor-market rigidities would be most appropriate if discovery is the work of existing firms. It is not clear which is the case, which could also vary by country and by industry. Moreover, all these regulatory measures are highly collinear. A composite index of these variables is consequently the most appropriate measure, which

> 6 Nor were measures of financial system development, initial exports, institutional quality, or infrastructure.

10
17

# **Table 8 SIile** of Emlovees **by** Maior **Occuvational Group and** Nationality, 1983

|Occ. Group|Zambian<br>(1)|Non-Zambian<br>(2)|Total<br>(3)|(2) as %<br>of (3)|
|---|---|---|---|---|
|Professional,<br>technical&<br>related|37,676|5,285|42,961|12.3|
|Admin.&<br>managerial|2,563|744|-<br>3,307|<br>22.5|
|Clerical|39,925|816|40,741|<br>2.0|
|Sales|4,583|321|4,904|<br>6.5|
|Service|35,580|752|36,332|<br>2.1|
|Agriculture,<br>Forestry, etc.|<br>16,021|527|16,548|<br>3.2|
|Production,<br>Transport<br>&<br>related|114,886|4,188|119,074|<br>3.5|
|Not stated|900|35|935|<br>3.7|
|Total|252,134|12,668.|264,802|<br>4.8|
|Estimated % of<br>Employment|total<br>72|82|73||

Sources: GRZ 1983; GRZ 1985.
individual OGIC). Furthermore, the initially disadvantaged groups of the population seem to benefit more from growth than those that were initially advantaged (decreasing type OGIC). When the two processes are compared, the dominance of the 2002–05 growth episode over the 2005–08 episode is evident for every perspective adopted.

Hence, we contribute to the literature by showing how it is possible to extend the existing frameworks proposed for the distributional assessment of growth to make them consistent with the EOp approach. The empirical analyses conducted in the paper show that the evaluation of growth may differ if the opportunity inequality perspective is adopted instead of the standard income inequality perspective.

The rest of this paper is organized as follows. Section I introduces the models used in the literature on the distributional effect of growth and in the EOp literature. It then proposes the opportunity growth incidence curves and summary indexes to assess the distributional impact of growth in terms of opportunity. Section II provides the empirical analyses based on Italian and Brazilian data. Section III concludes.

# **I. THE INCIDENCE OF GROWTH IN THE SPACE OF OPPORTUNITIES**

A well-developed body of literature has proposed a number of tools that can be used to evaluate the distributive impact of growth<sup>4</sup> in the space of final achievements. After a brief survey of these tools, this section will propose a set of formal tools that can be used to evaluate the impact of growth in the space of opportunities.

## **_Growth and Income Inequality_**

Let _F_ ( _yt_ ) be the cumulative distribution function of income at time _t_ , with mean income _μ_ ( _yt_ ), and let _yt_ ( _p_ ) be the quantile function of _F_ ( _yt_ ), representing the income corresponding to quantile

> 4In what follows, we focus, in particular, on those tools that will be extended to the EOp model in the next section. For a detailed survey of other existing measures of growth, see Essama-Nsaah and Lambert (2009) and Ferreira (2010).

6
**Figure 7: Product Space Differentiated by Exporting Status in Penang, 2000**

<!-- Start of picture text --> 5 10 15 20<br>invdensity<br>exporting not exporting<br>20000<br>10000<br>0<br>prodyminusexpy<br>-10000<br><!-- End of picture text --> Source: authors’ calculation

**Figure 8: Product Space Differentiated by Exporting Status in Penang, 2007**

<!-- Start of picture text --> 5 10 15 20 25<br>invdensity<br>exporting not exporting<br>20000<br>10000<br>0<br>prodyminusexpy<br>-10000<br>-20000<br><!-- End of picture text --> Source: authors’ calculation 15
# **Annex Tables**

- 1 IDA-only countries: Key economic indicators, 1985-88, 1995, 2000

- 2 IDA-only countries: Financing and debt relief, 1985-88, 1995, 2000

- 3 Nigeria: Key economic indicators, 1985-88, 1995, 2000

- 4 Nigaria: Financing and debt relief, 1985-88, 19935, 2000

- _5_ Selected IBRD borrowers: Key economic indicators, 1985-88, 1995, 2000

- 6 Selected IBRD borrowers: Financing and debt relief, 1985-88, 1995, 2000

# **Annex Charts**

- 1 Sub-Saharan Africa: Terms of trade index, 1989-2000

- 2 IDA-only countries: Gross financing requirements (US$ billions), 1989-2000

- 3 IDA-only countries: Gross financing requirements (percent of GDP), 1989-2000

- 4 Nigeria: Gross financing requirements (US$ billions), 1989-2000

- _5_ Nigeria: Gross financing requirements (percent of GDP), 1989-2000

- 6 Selected IBRD borrowers: Gross financing.' quirements (US$ billions), 1989-2000

- 7 Selected IBRD borrowers: Gross financing requirements (percent of GDP), 1989-2000
# Table of Contents

|1.|Introduction ............................................................................................................................. 3|
|---|---|
|2.|Intervention Description.......................................................................................................... 5|
|3.|Data ......................................................................................................................................... 7|
|4.|Methodology ........................................................................................................................... 9|
|5.|Results .................................................................................................................................... 13|
|6.|Conclusion ............................................................................................................................. 20|
|Ref|erences .................................................................................................................................... 23|
|App|endix ....................................................................................................................................... 25|
# Figure 5.  Country correlations for commodity exporters versus non-commodity exporters, 2007-2020

<!-- Start of picture text --> Commodity exp Non-Commodity exp<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>Commodity Average = 0.26***<br>-0.2<br>Non-Commodity Average = -0.03<br>-0.4<br>-0.6<br>-0.8<br>-1.0<br>NicaraguaGuyana<br>YemenMarshall IslandsTurkmenistanOmanEquatorial GuineaFijiLiberiaNamibiaAfghanistanArgentinaLebanonCôte d'IvoireTimor-LesteCongo, Rep.BangladeshNepalUruguayEcuador<br>UkraineAzerbaijanZimbabwe<br>PakistanSolomon IslandsThe GambiaAlgeriaMaliAntigua and BarbudaBoliviaHungaryGreeceQatarTuvaluSenegalChadMicronesiaIran, Rep. of BrazilCyprusCentral African RepublicMadagascarTrinidad and Tobago<br>IcelandKiribatiAngola<br>LesothoSyriaMontenegroMozambiqueNigerSt. Kitts and NevisCongo, Dem. Rep.GabonEstoniaLatviaSaudi ArabiaColombiaBelizeCroatiaSerbiaBeninRomaniaSudanTongaSurinameTurkeyLibyaBurkina Faso<br>IndiaSouth AfricaNorwayBarbadosComorosMongoliaMaltaVanuatuCambodiaHaitiPeruPanamaMauritaniaArmeniaCzech RepublicBulgariaKyrgyz RepublicEthiopiaGuinea-BissauRwandaMaldivesSierra LeoneJamaicaBotswanaPhilippinesBahrainBhutanSan MarinoMexicoCabo VerdeJordanCameroonGrenadaLithuaniaIraqSt. Vincent and the GrenadinesGhanaUgandaPolandBosnia and HerzegovinaBurundiSouth SudanTanzaniaHondurasSwedenChinaNigeria<br>Samoa<br>Kuwait<br>PalauPortugalMorocco TajikistanTunisiaChileDjiboutiLao P.D.R.VietnamAlbaniaLuxembourgNew ZealandMauritiusThe BahamasMoldovaSt. LuciaPapua New GuineaIrelandBelarusUzbekistanNauruEswatiniMyanmar<br>Costa Rica Taiwan, China<br>Zambia KenyaSri LankaThailandTogoSloveniaKazakhstanIsrael SpainIndonesia KosovoSeychelles Dominica São Tomé and Príncipe<br>Italy Malaysia<br>GeorgiaEritreaMalawi Dominican Republic Brunei DarussalamRussian Federation United Arab Emirates<br>El Salvador Netherlands North Macedonia<br>GuatemalaGermany United States<br>Slovak Republic<br>EgyptKoreaFrance<br>Japan Canada ParaguayFinlandGuineaBelgiumSomalia<br>Austria<br>Correlation between cyclical components of real government expenditure and real GDP 2007-2020 AustraliaDenmark Switzerland<br>Singapore<br>Hong Kong SAR, China<br>United Kingdom<br><!-- End of picture text --> Note: Each bar represents the correlation between the (HP) cyclical components of real GDP and real government spending for commodity exporters versus non-commodity exporters. *, **, and *** denote significance at the 10, 5, and 1 percent level, respectively. Sources: Authors’ calculations based on WEO (IMF) data. Commodity classification from World Bank..
- 16 -

33. As a source of funds, chonsei typifies the value of housing as a collateral. However, instead of borrowing from a bank against this collateral, the owner is extracting a loan from his tenant (or tenants). In terms of bargaining power, the lack of adequate housing finance and the scarcity of housing favor landlords. With chonsei, tenants assume all default risk since the landlord receives the total deposit in advance. On the other hand, the imputed rents tend to be competitively priced and reflect the cost of capital on the curb (UMM) markets. The chonsei system may be widespread but it is not financially efficient either for the landlord or the tenant compared to the possibilities offered by financial institutions under liberalization of the financial system.

# C. Financial Strategies to Achieve Housing Ownership

# 1. Sources of Funds for Housing Purchases: Overview

34. The 1986 KRIHS survey of household assets directed by Kyu-Bang Lee provides the first reliable view of general housing finance conditions in Korea. Table 2, originally published by KRIHS, reports the share of the total funds used to finance housing by 13 household income groups. This table shows how regressive the present housing finance system is, with the lowest income categories relying the most extensively on costly and short-term informal sources: kye (rotating credit associations), curb market loans, and chonsei from renting out part of the unit. Lower income households also have little access to loans from their work place or other institutions, unspecified sources are also large. The minimal role of the housing bank is also clear: as much financing comes from direct chonsei rental (column 10) asfrom KHB loans (column 6). Tne total volume of chonsei claims is of course greater (see columns 2 and 10).

# 2. Households Receiving Partial Bank Financing

35. The lack of mortgage lending in Korea forces households into complex funding strategies as shown by the annual surveys of units partially financed by the Korea Housing Bank. This subset covers a more fortunate minority of home buyers. Table 3 ranks in order of frequenjc, the most common strategies currently used by Korean households.There are for these borrowers eight major sources of

- 12/ These tables were prepared from the 1985 KHB Survey at my request by Jae-Hye Han. Ms. Han has recently completed an analysis of the Korean tenure choice which draws on a variety of recent housing surveys. See Jae-Hye HAN, (1987). The present analysis was suggested by French surveys of household wealth and mortgage borrowers, the French market being subjected to financial repression and a scarcity of mortgage funds until the early 1970s.