rafmacalaba/datause-encoder-data huggingface dataset:
development, and as documented by La Porta et al. (1998), a relationship between the judicial and legal development and the ownership structures of individual corporations.

# **VII. Conclusions**

In most East Asian countries, wealth is very concentrated in the hands of few families and links between government and business are extensive. Legal and regulatory developments may have been impeded by the concentration of corporate wealth and the extensive links between corporations and government, either directly or indirectly. Consequently, relationships between patterns of ownership and legal and other institutional variables are not necessary casual, as has been suggested, at least for some other countries. The possible endogeneity of the legal systems implies that future legal and regulatory reform in some East Asian countries may not be independent of changes in ownership structures and concentration of wealth.

Insider-control may also have contributed to the weak performance and risky investment of many East Asian corporations prior to the crisis. Our results allow for a reexamination of the relationships between ownership structure and corporate performnance, since previous studies only looked at the immediate owners and not the ultimate, controlling owners. The finding that many firms in East Asia belong to the same group and/or are controlled by a single family also suggests that further research may be warranted on the performance of firms belonging to the same group or controlled by the same family and on the corporate governance mechanisms used within such groups. These relationships between performance and ownership structures can be researched using data from before the crisis. One can also investigate whether the perfornance of firms during the 1997 financial crisis depended on their ownership structures and possible affiliations to a group. The large shocks many firms experienced as a result of the East Asia financial crisis provide an unique opportunity to understand how resources are allocated within groups, especially as access to external financial markets was sharply diminished. This line of research may in turn offer several important insights as to how corporate governance and corporate restructuring in East Asia can be improved. Finally, the large role played by a few families in East Asian corporate sectors suggest that an investigation of the evolution of legal and judicial systems in relation to wealth concentration may be particularly insightful for some of these countries.

25
|(6)<br>Non-Agri.|-0.371**<br>(0.185)<br>7282<br>858.504<br>41.195<br>-13.505*<br>(7.730)<br>3189<br>1475.447<br>31.814<br>Yes<br>Yes<br>Yes<br>Yes<br>rument for the<br>n parentheses._∗_<br>head identier.<br> of land-owned,<br>quality (Gini),<br> Eastern Terai.<br>xtra individual|
|---|---|
|Male<br>(4)<br>(5)<br>Any Wage Emp.<br>Agri.|-0.311*<br>0.027<br>(0.175)<br>(0.114)<br>7282<br>7282<br>941.219<br>476.251<br>41.195<br>41.195<br>-13.564*<br>2.955<br>(7.066)<br>(4.996)<br>3623<br>773<br>1538.916<br>314.929<br>37.343<br>20.071<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>n a VDC in 2001 is used as an inst<br>ed at VDC level. Standard errors i<br>ion, marital status, and household <br> elderly, share of children, amount <br>ate, poverty rate, illiteracy rate, ine<br>s, Eastern hills, Western Terai, and <br>g of hourly wages is added as an e|
|(3)<br>Non-Agri.|-0.221**<br>(0.088)<br>9597<br>1744.580<br>70.938<br>-26.255*<br>(13.613)<br>1264<br>920.637<br>7.140<br>Yes<br>Yes<br>Yes<br>Yes<br>ional migrants i<br>errors are cluster<br> years of educat<br> adults, share of <br> unemployment r<br>eas, Western hill<br>cular sector. Lo|
|emale<br>(2)<br>Agri.|-0.049<br>(0.083)<br>9597<br>576.071<br>70.938<br>7.292<br>(6.134)<br>1069<br>205.487<br>16.894<br>Yes<br>Yes<br>Yes<br>Yes<br> of internat<br>d standard <br>re age age2,<br> and female <br>ontrols are, <br>er urban ar<br>n that parti|
|F<br>(1)<br>Any Wage Emp.|Panel A: Labor market participation<br>Household with migrant<br>-0.205*<br>(0.104)<br>Observations<br>9597<br>Wald _χ_2<br>1236.455<br>_F_-test 1stage<br>70.938<br>Panel B: Hours supplied<br>Household with migrant<br>-15.219**<br>(6.826)<br>Observations<br>2222<br>Wald _χ_2<br>2004.359<br>_F_-test 1stage<br>18.791<br>Controls<br>Individual characteristics<br>Yes<br>Household characteristics<br>Yes<br>VDC characteristics<br>Yes<br>Region Fixed Eect<br>Yes<br>Note: 2SLS estimates are reported in the table. Share<br>estimations. Sample is working age (18 to 60) adults an<br>_p <_ 0_._1, _∗∗p <_ 0_._05, _∗∗∗p <_ 0_._01. Individual controls a<br>Household controls are female HH head, share of male <br>house ownership and social caste. Similarly VDC level c<br>and urban/rural location. Regions are Kathmandu, oth<br>Sample in Panel B is conditioned on being employed i<br>control.|

29
21 _Table 7: Results for 1998 Health VIII baseline survey_

||‘High’ p|ayments|Catastroph|ic payments|Out-of-pock|et payments|
|---|---|---|---|---|---|---|
|Variable|Probit|IV Probit|Probit|IV Probit|OLS|IV|
|CMS|-0.481|-0.749|-0.968|-1.040|-41.652|-48.269|
||(12.03)|(10.40)|(16.05)|(11.89)|(9.05)|(5.83)|
||_-0.190_|_-0.289_|_-0.258_|_-0.269_|||
|GIS, LIS, etc.|0.078|0.599|0.073|0.536|19.361|90.995|
||(1.33)|(4.15)|(1.20)|(3.67)|(2.92)|(5.82)|
||_0.030_|_0.209_|_0.027_|_0.208_|||
|Chronic|0.724|0.717|0.803|0.797|93.843|93.197|
||(16.86)|(16.63)|(20.47)|(20.28)|(21.65)|(21.45)|
|Per capita income|186|187|-950|-953|21099|20710|
||(16.00)|(15.96)|(53.67)|(53.50)|(18.02)|(17.45)|
|Gender|-0.031|-0.033|-0.016|-0.018|-0.357|-0.679|
||(2.26)|(2.42)|(1.14)|(1.28)|(0.23)|(0.43)|
|Han|0.133|0.132|0.081|0.080|0.492|0.416|
||(7.04)|(6.94)|(4.03)|(3.99)|(0.22)|(0.19)|
|Married|0.000|0.000|-0.023|-0.024|-7.121|-7.336|
||0.00|(0.02)|(1.05)|(1.10)|(2.90)|(2.98)|
|No. children aged less than 5|0.045|0.045|0.033|0.033|2.116|2.246|
||(3.95)|(3.92)|(2.73)|(2.76)|(1.59)|(1.68)|
|Elementary school|0.032|0.030|0.005|0.001|1.650|0.965|
||(1.62)|(1.53)|(0.25)|(0.05)|(0.73)|(0.43)|
|Primary middle school|0.147|0.144|0.048|0.043|1.310|0.461|
||(7.17)|(6.97)|(2.22)|(2.00)|(0.55)|(0.19)|
|Senior middle school|0.278|0.267|0.180|0.168|11.195|9.112|
||(9.21)|(8.78)|(5.74)|(5.30)|(3.24)|(2.61)|
|Vocational school|0.204|0.137|0.099|0.033|10.891|0.660|
||(2.90)|(1.88)|(1.30)|(0.42)|(1.36)|(0.08)|
|Junior college|0.172|0.059|0.275|0.178|43.513|28.253|
||(1.39)|(0.47)|(2.15)|(1.36)|(3.08)|(1.95)|
|Constant|-0.203|-0.195|0.211|0.216|59.852|60.682|
||(6.21)|(5.94)|(6.01)|(6.15)|(15.83)|(16.00)|
|N|36616|36616|36616|36616|36616|36616|
|Log likelihood|-24400|-24400|-21700|-21800|||
|Chi squared|-1001|-983|-4445|-4227|||

Note: Equations include age and age squared in addition to variables listed. The first number for each covariate is the coefficient, the second (in parentheses) is the _t_ -statistic, and the third (in italics, indicated only in the case of the insurance dummies) is the marginal effect.
International Migration, Remittances and<sup>the Brain Drain:</sup> A Study of 24 Labor-Exporting Countries* Richard H. Adams, Jr. PRMPR World Bank 1818 H Street, NW Washington, DC 20433 Phone: 202-473-9037 E-Mail: radams(aworldbank.org

*I would like to thank Maureen Lewis and<sup>Zafiris Tzannatosfor useful commentson an</sup> earlier draft.
|Fourth quartile of<br>saturation|(0.306)|(0.087)|(0.171)|(0.183)|
|---|---|---|---|---|
|Program village*After|0.051|0.006|0.062|-0.019|
||(0.156)|(0.03)|(0.052)|(0.059)|
|Program village*fourth|-0.604*|-0.136|-0.357*|-0.498**|
|quartile of saturation|(0.36)|-(.118)|(0.212)|(0.221)|
|After*fourth quartile of|-0.292|0.049|0.027|-0.037|
|saturation|(0.247)|(0.048)|(0.055)|(0.089)|
|Program|0.547|0|-0.014|0.161|
|village*after*fourth<br>quartile of saturation|(0.355)|(0.055)|(0.071)|(0.106)|
|Control Observations|418|417|409|398|
|Treated Observations|451|447|445|433|
|Control Mean|4.488|0.625|1.208|1.189|

note: *** p<0.01, ** p<0.05, * p<0.1 All specifications include municipality fixed effects. Standard errors are clustered at the village level; there are 130 villages.

Source: 2011 _Pantawid_ impact evaluation survey 80
Figure 3.1. Poverty and under-five mortality -- deviation in 2030 from base <!-- Start of picture text --> 2<br>0<br>-2<br>-4<br>-6<br>-8<br>-10<br>ser-eq+fg ser-eq+db ser-eq+tx mdg-eq+fg mdg-eq+tx mdg-eq+fg6<br>U5MR - rural (‰ pts) U5MR - urban (% pts)<br>Poverty - rural (% pts) Poverty - urban (% pts)<br><!-- End of picture text --> Figure 3.2. Water and sanitation access -- deviation in 2030 from base <!-- Start of picture text --> 2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>% -0.5<br>-1.0<br>-1.5<br>-2.0<br>-2.5<br>-3.0<br>ser-eq+fg ser-eq+db ser-eq+tx mdg-eq+fg mdg-eq+tx<br>Water access - rural Water access - urban<br>Sanitation access - rural Sanitation access - urban<br><!-- End of picture text --> 13
|**Table 1. S**|**ummary Stati**<br>Mean|**stics**<br>Median|Std Dev|# Obs|
|---|---|---|---|---|
|**Panel A. Loan level data- working capital**|**loans of recipi**|**ent firms**|||
|Spreadlfbt|27.13|20.69|22.97|397,427|
|Volumelfbt|11.11|10.90|1.53|397,427|
|Provision ratelfbt|1.75|1.00|1.56|397,427|
|Earmarkfbt|0.61|1.00|0.49|397,427|
|Collaterallfbt|0.79|1.00|0.41|397,427|
|Maturitylfbt|16.06|12.00|12.48|397,427|
|BF Relationfbt|3.03|2.45|2.47|397,427|
|WC Loan Ratingft|8.13|8.00|0.95|259,993|
|First Ear Ratingf|7.73|7.00|1.03|353,224|
|HHfy|15.82|14.58|5.79|397,427|
|**Panel B. Firm-bank-year level data**|||||
|Probability of new earmarked loanfby|0.04|0.00|0.19|1,990,576|
|Firm agefy|1.83|1.91|1.02|1,990,576|
|Firm sizefy|1.84|1.61|1.45|1,990,576|
|BF Relationfby|1.72|0.94|2.02|1,990,576|
|WC Loan Ratingfy|8.47|9.00|0.95|1,990,576|
|HHfy|15.81|14.78|5.34|1,990,576|
|**Panel C. Firm-bank level data at the start**<br>|**of an earmark**|**ed relatio**|**nship**||
|Bundlefb|0.15|0.00|0.35|50,239|
|Firm agef|1.63|1.65|1.08|50,239|
|Firm sizef|2.28|2.08|1.62|50,239|
|BF Relationfb|1.89|1.29|1.89|50,239|
|HHf|18.30|16.42|7.85|50,239|
|WC Loan Ratingf|8.17|8.00|0.97|36,918|
|First Ear Ratingf|7.78|8.00|1.04|44,272|

Notes: The table displays the summary statistics of our sample for the period between January 2005 and December 2016. All variable definitions are listed in Appendix Table A1.

19
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27
# Individual Diversity and the Gini Decomposition

_Lidia Ceriani_<sup>1</sup> _and Paolo Verme_<sup>2</sup>

**JEL:** D31, D63

**Keywords:** Gini, Inequality, Decomposition

**Sector Borad:** Poverty (POV)

> 1EconPubblica, Bocconi University, Milano

> 2World Bank. The authors are grateful to Ernesto Savaglio, Casilda Lasso de la Vega, Chiara Gigliarano and participants to a seminar held at the Universit ́e d’Aix-Marseille in 2011 for useful exchanges during the preparation of the paper.

1
**Figure 2.5 b. Current Account**

**(Percentage of GDP)**

<!-- Start of picture text --> 5<br>-C-Argentina<br>3<br>5 &LAC GDP Weighted Mean<br>0) 0~~0)0 0 0<br>N N 1 N N<br>-3<br>-5<br>-7<br><!-- End of picture text -->

**Source:** Balance of Payments from domestic sources via HAVER. Latin America average includes Arg, Bra, Chi, Col, Mex, and Pe.

Table 2.3. Current Account Adjustment

||asshareo|fGDP|asshareof|Imports|
|---|---|---|---|---|
||1998/99|2000/01|1998/99|2000/01|
|Argentina|0.77|1.29|7.16|14.51|
|Bolivia|1.44|1.54|4.96|7.83|
|Brazil|1.11|-0.55|13.83|-7.33|
|Chile|5.56|-0.86|22.38|-3.64|
|Colombia|5.20|-3.14|34.37|-22.55|
|**CostaRica**|-0.91|-0.34|-2.07|-0.83|
|**Ecuador**|15.84|-12.20|56.03|-41.16|
|Mexico|0.37|0.31|1.13|0.92|
|Peru|3.19|0.50|20.75|3.37|
|Venezuela,RB|7.24|-5.56|42.99|-41.67|
|Average|3.98|-1.90|20.15|-9.06|

# Note:

Current Account adjustment is defined as the year on year difference in the Current Account surplus as share of GDP or imports.

**Sources:** Imports (US$) from Direction of Trade (IMF); GDP and Current Account Balance (US$) from WDI (World Bank).

12
In the simulations we use four alternative ages of retirement, at 55, 60, 65 and 70. Although 60 and 65 are common retirement ages in many pension schemes, 55 is used to indicate the likely pension cost if a further decline in the average age of retirement occurs, and 70 is used to indicate the pension cost if retirement ages are increased to take account of past and projected improvements in mortality experience.

We also use four average ages of death, at 80, 81, 83 and 85. Our annuity calculations are made on a gender-neutral basis, which is calculated according In age and gender-specific life-expectancies, which are then weighted by the appropriate sex ratios for each retirement age. The assumption of gender-neutrality in the annuity market is a deviation from pure actuarial principles and so implies some eedistribution from men to women, but is consistent with European Community sex equality legislation. We produce an average gender neutral life expectancy at each of the four retirement ages, which then indicates the average age of death for all people surviving to that retirement age. On the basis of the 1987-8 UK life tables, the average age of death is as follows:

|For people surviving to:|Average age at death:|
|---|---|
|55|80|
|60|80|
|65|81|
|70|83|

We provide additional annuity estimates for death at age 85 since these indicate the potential pension cost of a significant future improvement in the life expectancy of older people.

26
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21
It is important that the EPA discussion should not be undertaken in isolation from the range of other trade and development policy issues facing Nigeria. For example, Nigeria is facing opportunities to pursue more effective regional integration, including the adoption of the ECOWAS common external tariff. While an analysis of the likely consequence of the complete implementation of the ECOWAS CET is beyond the scope of the present study, the current analysis shows that sequencing an EPA with reform that reduces the level of external protection in Nigeria can lead to considerably different estimated EPA impacts. Finally, the scope of the EPA goes well beyond reciprocal tariff reduction to cover issues such as trade facilitation and standards, services and investment. In this context, it is important that an EPA should not be seen as a goal in itself but rather it should be consistent with the attainment of clearly defined broader trade and development objectives.

The paper is organized as follows. Section 2 describes the structure of Nigeria’s imports, with special attention to those from the EU. Section 3 discusses the nature of Nigeria’s tariffs and other trade barriers. Section 4 contains the main part of the paper and presents the findings from the analysis carried out with the tariff simulation tool TRIST to provide estimates of the potential effects of preferential import liberalization with respect to the EU. Finally, Section 5 concludes by discussing the issue of how an EPA could be leveraged to address key constraints to integration into regional and global markets. It also discusses potential complementary measures for boosting “supply response” and making the resulting EPA a tool for development.

# **2.  Structure of Nigerian Imports**

Nigeria imported almost US$23 billion worth of goods in 2006 according to official figures from Customs. Of those imports, US$9 billion or 38 percent came from the European Union (Figure 1). This suggests that preferential trade liberalization with the EU could have a significant impact on the Nigerian economy. However, while the EU is still the largest import supplier to Nigeria, its share has been steadily decreasing over the last two decades (from around 60 percent in the late eighties) as more efficient Asian suppliers gained market shares over their European counterparts. Indeed, starting from around 1 percent in the mid-eighties, the market shares of China and India rose, respectively, to 14 percent and 5 percent in 2006. The United States was the second largest source in 2006, accounting for 16 percent of Nigeria’s merchandise imports.

4
**Table 2: Assumptions for analysis of distributive impact of taxes and transfers in Mongolia**

||**MONGOLIA 2016**|
|---|---|
|**Use of Input-output matrix for indirect taxes/subsidies**||
|Indirect taxes calculated with an input-output matrix?|No|
|Indirect subsidies calculated with an input-output matrix?|No|
|**Take-up assumptions**<br>||
|Take-up of direct cash transfers-as reported in survey|as reported|
|Take-up of direct in-kind transfers-as reported in survey|as reported|
|**SSC,taxes and subsidies (shifting assumptions)**||
|SSC: only those paid by employee, or both those paid by employee and<br>employer are assumed to be borne by employee?|employee only|
|Direct taxes: burden of PIT assumed to be borne entirely by the<br>recipient of income?|Yes|
|Indirect taxes: burden of VAT/excises assumed to be borne entirely by<br>the consumer?|Yes|
|Indirect subsidies: benefit assumed to be borne entirely by the<br>consumer?|Yes|
|**Tax evasion assumptions**<br>||
|Direct taxes|Assume that taxes on all reported income is paid|
|Indirect Taxes|No assumption on VAT avoidance|
|**Scaling-up/down assumptions**||
|In-kind education services scaled up or down?|Scaled down|
|In-kind health services scaled up or down?|Scaled down|
|Taxes and subsidies scaled up or down?|No|
|Cash transfers include adminnistrative costs?|No|
|In-kind transfers include adminsitrative costs?|Yes|
|In-kind transfers include capital expenditures?|Yes|
|In-kind transfers include regional dispersion?|Yes (pre-school, primary and secondary average<br>expenditure per student are Aimag specific, tertiary<br>education and public heatlh are national average<br>expenditure)|
|**Treatment of special contributions**||
|contributory pensions are treated as tax/transfer?|Direct Transfer|
|public sector fees are treated as taxes?|No included|

28 MONGOLIA Distributional impact of taxes and transfers / November 2018
<!-- Start of picture text --> a |<br>& Ss Se & we & Pia & RK AZ »& Ro<br>QReiS ©<i3 »SSs &@2 L¥= AXe,& Ny;a >\o NSBSS &fo)< Re@& BSoy’cS<br>Gg » > Ke oO R »y s & »~ ©<br>SS rac So M ra<br><!-- End of picture text -->

<!-- Start of picture text --> g Vulnerable to poverty based on .29 probability threshold<br>bd<br>So3<br>2<br>a8<br>-2<br>8wo<br>o —_-° -<br>3 4 5 6 7 8<br>predicted log per capita income<br>GR[J chronic povertynon-poor, non-vulnerable HR vulnerable to poverty<br>Note: red vertical line is the poverty line(PL), yellow line is 1.25°PL, and green line is 1.5°PL<br><!-- End of picture text --> <!-- Start of picture text --> Distribution of the vulnerable population by<br>income quintile (2019)<br>43.8<br>28.6<br>17.9<br>77<br>ma ——at<br>Ql Q2 Q3 a4 Qs<br><!-- End of picture text -->