rafmacalaba/datause-encoder-data huggingface dataset:
# **I. Introduction**

Over thle years, "capital flight" has received much attention from researchers and policy makers (see, for example, World Bank, 1985 and 1993). Using estimates of capital flight from developing countries, authors have drawn attention to the magnitude of this phenomenon--particularly for Latin Amer 'a. It is generally agreed that these large private capital outflows have represented an important macroeconomic problem for many developing countrics in the past two decades. The reversal of capital flight in the early-1990's for some Latin American countries has added a new dimension to this phencmenon. Yet, capital flight remains a little understood aspect of these countries' economies and it even remains difficult to ascertain the magnitude of capital flight. Consequently, a better understanding of the extent of past capital flight, as well as a reliable measure of possible capital flight reversals in recent years, may be a useful input to a realistic assessment of current prospects for renewed invetrment and growth in developing countries.

Papers on capital flight try to answer a number of questions. What is the best way to measure capital flight? Can the occurrence and magnitude of capital flight be explained by (economic) variables? And what policy implications follow (for example, how to rev erse capital flight)? This paper is not intended to debate what causes capital flight or the best measure; nor will it draw policy implications. Its sole purpose is to provide a set of consistent figures on capital flight using existing measures for 84 developing countries over a long period (1971-91). T'his should be useful for researchers, since much detailed data used here originates from the World Bank and IMF and may not be generally available. ' Since capital flight -' calculated for many developing countries, no attempt was made to correct the figures in cases where data problems are known (or appear) to 2 exist. Nor was any adjustment made for country-specific information or circumstances. For individual countries or time periods, researchers may want to make adjustments to the data calculated here.

Section II describes different concepts and methodologies used for measuring capital flight. Section m describes the data and daa sources used. Some summary figures are provided in section IV on the various measures for 84 countries and for groups of developing countries (regional) and compares the capital flight measures to other (macro-) variables. Section V lists recent papers on capital flight. Detailed capital flight numbers ale found in Annex 1, while country coverage and notts on data are in Annexes 2 and 3. Annex 4 provides the relative coverage fcr the 84 countries compared to all developing countries.

Ilndividual country data are available from the World Bank on a floppy disk (contact: Ms. Shelly Fu, Tel. (202) 473-3885, Fax. 477-0661).

> 2Eggerstedt et al. (1992), for example, draw attention to the fact that most capital flight measures do not correct for the foreign currency deposits held by state-owned companies.
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# **D. Results**

The results are illustrated in Table 12. For a country such as Mauritius, the impact can be summarized as follows'<sup>4</sup> :

# **2001-2004**

The impact of AGOA during the period 2001 and 2004 will be to raise exports relative to the pre-AGOA situation by about 5 percent.'<sup>5</sup>

* Had there been no rule of origin requirement on Mauritius, the increase in exports due to the tariff preferences accorded by AGOA would have been 36 percent, substantially higher than with rules of origin.

# **2005-2008**

* In 2005, when the MFA quotas on Mauritius' competitors are eliminated, its exports will be about 26 percent lower than they otherwise would have been. But if AGOA is modified to eliminate the rules of origin requirement, the decline in exports would be 18 percent.

For a least developed country such as Madagascar, the results are more dramatic both on the up side and down.

# **2001-2004**

* The impact of AGOA during the period 2002 and 2004 will be to increase exports relative to the pre-AGOA situation by about 92 percent.'<sup>6</sup>

industry is determined by unskilled labor costs, which as Table 11 shows continues to remain low in much of SSA, except for Mauritius and South Africa.

'4 The following assumes that Mauritius, prior to AGOA, was not quota constrained and will only benefit from the tariff preference effect. This is supported by data for 1999 and 2000 which show that quota utilization by Mauritius was less than 50-60 percent for all categories except two. These two categories accounted for about 25 percent of total exports by value.

'5 This is for the most plausible scenario characterized by an export supply elasticity of 1.

16 This is for the most plausible scenario characterized by an export supply elasticity of 5.
# **Table 1: Descriptive statistics**

||Mean|Standard<br>deviation|
|---|---|---|
|District characteristics (2001 census)|||
|District population|2,955,445|1,727,678|
|District population density (persons per square kilometer)|808|2,458|
|Age profile (working age population/non-working age population)|1.32|0.26|
|Share of population with a graduate education|5.9%|2.7%|
|Index of infrastructure quality for district (0-4 scale)|2.98|0.68|
|Travel time to closest of ten largest cities in driving minutes|446|240|
|Stringency of labor adjustment laws for district's state|0.69|0.84|
|<br>Stringency of labor disputes laws for district's state|-0.41|1.24|
|Female literacy rate|51.7|15.1|
|Total fertility rate based on age-specific district fertility rates|3.3|1.1|
|Sex ratio measured as females per male|0.939|0.057|
|Establishment characteristics, unorganized manufacturing:|||
|Total female-owned establishments in district-industry|866|6,237|
|Total employment in female-owned establishments in district-industry|1,068|7,939|
|Total male-owned establishments in district-industry|1,455|4,276|
|Total employment in male-owned establishments in district-industry|3,234|10,944|
|Female business-ownership share|0.13|0.28|
|Female entrepreneurship share|0.21|0.37|
|Index of labor market strength|0.09|0.11|
|<br>Index of input market strength|-1.71|0.24|
|Index of output market strength|0.04|0.06|
|Index of labor market strength, female-owned businesses|0.08|0.17|
|Index of input market strength, female-owned businesses|-1.79|0.29|
|Index of output market strength, female-owned businesses|0.02|0.03|
|Establishment characteristics, unorganized services:|||
|Total female-owned establishments in district-industry|152|801|
|Total employment in female-owned establishments in district-industry|216|1,041|
|Total male-owned establishments in district-industry|1,773|5,651|
|Total employment in male-owned establishments in district-industry|2,569|7,628|
|<br>Female business-ownership share|0.09|0.21|
|Female entrepreneurship share|0.11|0.24|
|<br>Index of labor market strength|0.10|0.11|
|Index of labor market strength, female-owned businesses|0.10|0.15|

Notes: Descriptive statistics taken from National Sample Statistics. Output index values are multiplied by 10 for presentation.
of variables at the steady state. The respective restrictions are reported in the last column of Tables B2 and B3.

- The consumption utility scaling parameter is set to ensure that GDP is equal to one at the steady state. This is done for convenience only as most other steady-state values then have a clear interpretation as ratios to GDP.

- The share parameter in RoW wholesale production function is restricted to give real exchange rate equal to 1 at the steady state.

- The ratio of high-skilled and low-skilled labor hours is roughly 1, taken from main labor force indicators by education level data of TurkStat. The corresponding consumption ratio is around 2, calculated using distribution of consumption over income quantiles data of TurkStat under the assumption that low-skilled households constitute the lowest 50% of the income distribution. Hence, H-households consume twice as much as L-households while they provide the same number of labor hours. This is possible because the formers’ real wage is higher, and they receive capital income.

- The share of private non-energy, renewable energy and fossil energy investment to GDP ratios are around 23.3%, 0.3% and 0.4%, respectively. Total investment share of GDP comes from GLORIA database. We use World Input Output Database (WIOD) to split investment to core and energy.

- The annualized public net debt-GDP, corporate net debt-GDP, and RoW net debtGDP ratios are around 33%, 57%, and -40%, respectively, from the Financial Accounts Reports of the Central Bank of the Republic of T ̈urkiye (CBRT).

- The ratio of renewable and fossil energy is around 0.2, averaged over 2019-2021 from the energy balance of IEA<sup>27</sup> . We assume the ratio of final use (residential, public, and half of transportation) and intermediate use (industry, and half of transportation) of the TFC is 0.94, average value over 2000-2022. Finally, about 87% of the Turkish TPES is imported, coming from GLORIA database. In the model, this corresponds to the share of imported carbon in total carbon input.

- Wage share of output in core goods and energy sectors are around 39% and 0.08%, calculated from GLORIA database.

- Renewable to fossil energy employment ratio is 0.15, calculated from Global Trade Analysis Project (GTAP)-Power and Gender Disaggregated Labor Database (GDLD) of the World Bank.

> 27To obtain the renewable-fossil energy ratio, total final consumption (TFC) would be the appropriate measure. Yet, it is unclear to what extent electricity is generated by renewables and fossil energy. Hence, we use the ratio of total primary energy supply (TPES) as a proxy.

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# **Contents**

|**Introduction ............................................................................................................................................................ 1**|
|---|
|**Part I—The Contextual and Analytical Challenges of Pay Reform ....................................................... 2**|
|Defining the Challenge and Assembling a Game Plan ......................................................................................... 3|
|Wage Bill Analysis and Modeling ................................................................................................................................. 6|
|Civil Servant Earnings Analysis .................................................................................................................................... 7|
|Comparing Civil Service Earnings to Alternatives .............................................................................................. 11|
|Qualitative Research to Understand Incentives .................................................................................................. 14|
|**Part II—Putting the Pieces Together: The Lao PDR Case Study......................................................... 17**|
|The Analytical Puzzle ...................................................................................................................................................... 17|
|The Lao Civil Service in Perspective ......................................................................................................................... 19|
|How Much Are Civil Servants Compensated? ....................................................................................................... 21|
|Are Civil Servants Compensated Adequately? ..................................................................................................... 24|
|A Civil Service Incentives Assessment ..................................................................................................................... 29|
|Ways Forward: Core Civil Service Reform Principles for GOL to Consider ............................................. 30|
|A Postscript: What Happened Next and What We Could Have Done Differently .................................. 33|
In terms of missing or incomplete data, the Australian spreadsheet lacks details on final antidumping duties imposed such as Australian Customs Dumping Notice (ACDN) documents, dates on which antidumping duties were imposed, and the size of duties imposed for certain cases (especially those initiated between 1996 and 2004). Furthermore, the Australian data does not contain the outcomes of reviews.

# **3.3 Brazil (BRA)**

Brazilian antidumping investigations are carried out solely by the Department of Commercial Defense (DECOM), which is part of the Secretariat for Foreign Trade (SECEX). The Secretariat itself is under the Brazilian Ministry of Development, Trade, and Industry. The outcomes of investigations are published by the Secretariat (SECEX) in circulars, some of which are available online at the Secretariat’s website. The Brazilian data was provided by Honorio Kumé and is based on Kumé and Piani (2005), which includes a history and analysis of the use of antidumping in Brazil. See also Caetano (2005).

The Brazilian spreadsheet contains the standard data items described in section 2, as well as a few additional data items described below.

**Table 3.3.1 : Additional Brazilian Data Included in AD-BRA-v1.0.xls**

|**Column**|**Variable**|**Description**|
|---|---|---|
|**In AD-BR**|**A-Master :**||
|**U.**|**P_AD_DUTY**|The preliminary antidumping duty imposed. If several duties were imposed, this<br>column contains the maximum duty|
|**V.**|**F_AD_DUTY**|The final antidumping duty imposed. If several duties were imposed, this column<br>contains the maximum duty|
|**W.**|**TYPE**|An indicator of review cases (R=review case)|

In terms of missing or incomplete data, the Brazilian spreadsheet lacks data on the preliminary outcomes for certain cases. There is also no firm-level data (neither for domestic nor foreign firms).

18
Table 3. Contributions of global shocks to global CPI inflation factor: 5-year windows

|Time eriods||Str|uctural Shocks||
|---|---|---|---|---|
|p|Oil Price|Global Supply|Global Demand|Global Interest rate|
|Fll id|38.4|17.6|27.8|16.2|
|u pero|[12.9 66.9]|[2.4 33.3]|[4.5 59.5]|[1.2 34.4]|
|1970-74|29.1|24.7|28.4|17.8|
|1975-79|24.7|28.4|25.5|21.4|
|1980-84|18.1|20.3|37.6|24.0|
|1985-89|30.3|16.1|33.9|19.7|
|1990-94|18.5|13.8|40.6|27.0|
|1995-99|27.0|19.5|27.3|26.1|
|2000-04|20.8|10.1|41.8|27.4|
|2005-09|31.7|8.8|34.6|24.9|
|2010-14|37.7|7.3|35.8|18.3|
|2015-19|37.8|10.0|35.2|18.1|
|2019-22|44.4|13.4|26.5|15.6|

Note: The results are based on the global FAVAR model based on 5-year rolling windows. The estimates of variance decompositions (in percent) are based on 2-year forecasting horizon when the results mostly converge to the long-term level.

32
6 Taken jointly, the three institutional innovations mentioned above contributed to preventing what could have otherwise been a banking system meltdown unleashed by the Tequila. The increase in moral hazard that may have resulted from the bailout of large depositors that these innovations afforded in some cases, can be rightly considered to have been a small price paid in order to limit contagion risk under the turbulent financial environment unleashed by the Tequila. Moreover, a systemic crises was avoided with very little use of public sector resources, particularly when compared to international experiences. In part due to these institutional innovations, since 1995 the authorities have broadly succeeded in promoting an orderly process of consolidation and strengthening of the banking system,<sup>8</sup> with striking results.<sup>9</sup> In the period July 1995 to April 1999 the Argentine authorities resolved 18 failures of financial intermediaries using the powers under Article 35 bis (Table 1). The liabilities of the resolved institutions added to around US$5 billion, with the median size bank in the group featuring liabilities of around US$210 million and the largest case (Banco Mayo) with liabilities for some US$1.3 billion. In 12 of these 18 cases, SEDESA resources were used to facilitate the execution of Article 35 bis procedure -essentially by completing the asset side of the "good" bank- for a sum total of around US$750 million, equivalent to nearly 20 percent of the liabilities separated and transferred under Art. 35 bis procedures. The Capitalization Trust Fund provided, to the same end, a total of US$382 million, which were distributed over 10 cases.

8 To be sure, other factors have played a major role in the system's consolidation and strengthening. These factors include: a marked improvement in prudential oversight and its enforcement; the enhancement of market monitoring via, for instance, improved accounting and disclosure standards and the requirement that banks issue subordinated debt on a regular basis; the privatization of public sector banks; the full opening of the banking sector to foreign capital; tougher-than-Basle capital requirements; and stringent liquidity requirements. For details see Argentina: Financial Sector Review, Report No. 17864-AR, September 28, 1998, The World Bank.

9 The number of financial institutions in the Argentine system fell from 205 at end-1994 to around 130 at end-1998. Over the same period, the number of foreign-owned banks rose from 30 to 42, with their share in the system's deposits increasing from 16 percent to about 55 percent. In respect of publicly owned banks, BANADE, the former development bank, was closed, and 15 provincial banks were privatized.
c

# Table 26: EARNINGS FUNCTION FOR THE SELF-EMPLOYED

||Farm|ers||Non|-Farmers||||||
|---|---|---|---|---|---|---|---|---|---|---|
||||Al|l|Abid|jan|Other C|ities|Rural|Areas|
|Intercept|8.78|(52.5)|10.36|(55.7)|10.62|(33.3)|10.52|(34.3)|10.09|(31.4)|
|Years of Primary Schooling|.055|(2.3)|<br>.014|(0.5)|<br>.012|(0.3)|<br>.020|(0.4)|<br>.057|(1.0)|
|Years of Secondary Schooling|.069|(0.6)|<br>.133|(1.8)|<br>.209|(2.3)|-.004|(0.0)|<br>.016|(0.1)|
|Years of Higher Schooling|-|-|.337|(1.4)|<br>.216|(0.9)|<br>_<br>-||-_||
|Years of Current Job Experience<br>|.0 8|(4.5)|<br>.036|(2.3)|<br>.059|(1.9)|<br>.019|(0.7)|<br>.079|(2.5)|
|(Years of Current Job Experience)<sup>2</sup>|-.0005|<br>(4.4)|<br>-.0006|(1.5)|<br>-.0015|<br>(1.6)|<br>.0001|<br>(0.1)|-.0024|(2.6)|
|Years of Previous Job Experience<br>|.041|(2.1)|<br>.090|(3.8)|<br>.062|(1.3)|<br>.112|(2.9)|<br>.031|(0.6)|
|(Years of Previous Job Experience)<sup>2</sup>|-.0009|<br>(1.5)|<br>-.0024|(2.9)|<br>-.0007|<br>(0.3)|-.0033|<br>(2.7)|-.0009|(0.5)|
|Years of Apprenticeship|-.043|(1.8)|<br>-.036|(1.0)|<br>-.024|(0.5)|-.096|(1.5)|<br>.011|(0.2)|
|Years of VTE|.195|(1.7)|.0005|(0.0)|<br>.019|(0.1)|-.026|(0.1)|<br>.281|(0.4)|
|Sex (1-female)|-1.63|(21.5)|<br>-.406|(2.7)|<br>-.351|(1.4)|-.553|(2.1)|-.512|(1.9)|
|Non-Ivorian|.240|(2.1)|-.033|(0.3)|<br>-.356|(1.9)|<br>.017|(0.1)|<br>.348|(1.4)|
|Abidjan|-1.17|(1.6)|.272|(2.1)|<br>_-|||-|-||
|Rural Areas|-.056|(0.5)|<br>-.132|(1.0)|<br>_-||-|_|-||
|R2|.31||.15||.25||.11||.15||
|F|47.4||6.9||5.0||2.6||2.2||
|Number of cases|1,286||530||180||211||139||

Note: Absolute t - values in parentheses.
divisions within the household and its members’ labor choices. In addition to the household questionnaire, we collected information at the rural district level on issues affecting the community as a group. This community survey was collected for each rural district, and includes information about social and public infrastructure, incidences of land conflict and the presence of illegal armed groups. It also generated the necessary information for creating a conflict time line.

# **3.2 Colombia: 50 years of conflict**

Since the 20<sup>th</sup> century, Colombia has been confronted by two major internal conflicts. The first, known as _La Violencia_ , started in the middle of the 1940s, and intensified with the assassination of the populist leader Jorge Eliécer Gaitán in 1948. The conflict emerged from the political struggles between the two major, traditional political parties in Colombia, the Liberals and Conservatives. Regional land disputes and power struggles that had been latent for decades combined with political disputes to fuel the conflict in rural areas as well (Oquist 1980). In 1958, Liberals and Conservatives negotiated a power-sharing agreement that paved the way for a peace deal and halted armed confrontations.

The power-sharing agreement effectively eliminated political violence, yet land disputes and regional power struggles remained dormant. By the end of the 1960s, guerrilla movements promoting agrarian land reform emerged in the rural areas of the country. The presence of rebel groups was confined to isolated rural regions of the countries, and actions were occasional and limited to attacking government forces.

The dynamics of the conflict changed dramatically with the appearance of the illegal drug trade at the end of the 1980s. Resources from illicit drug trading provided massive funds with which rebel groups were able to operate. These groups soon began launching attacks on large land-owners and drug barons in order to extract additional resources. Kidnapping, extortions, the taxation of cocaine production, mining and cattle ranching provided additional sources of financing. These attacks and the flow of resources from illegal drug trading contributed to the creation of right-wing paramilitary groups aimed at contesting the power of the guerrilla movement. At the same time, the conflict began to expand geographically along the Colombian territory.

10
# **1. Introduction**

Publicly-supported grants and loans to poor areas have long been an important vehicle for development assistance. For example, China’s anti-poverty policies have emphasized such poor-area programs since the mid-1980s,<sup>2</sup> motivated by the observation that the country’s success against poverty over the last 25 years has been geographically uneven, with marked disparities in living standards emerging.<sup>3</sup> Advocates of such programs claim that credit constraints in poor areas perpetuate their poverty and that targeted aid can relieve those constraints. By this view, capital-market failures in poor areas entail that the investments made under such a program would be infeasible otherwise, implying both efficiency and equity gains.

It remains an open question how much impact can be expected. While not perfect, capital markets may still work well enough to assure that marginal products of capital come into rough parity between poor and non-poor areas in steady state. Then the problem of lagging poor areas is not so much lack of capital as low productivity of capital, such as due to poor natural conditions, lack of complementary knowledge or skills, or poor policies.

And even with credit constraints, some people are clearly more constrained than others. If those selected are not credit constrained, their participation is voluntary, and the interest rate is no different from other credit sources, then there will be no net gain from the extra availability of credit. Heterogeneity in the impacts of such programs can also arise from inequalities in the complementary skills or knowledge needed to derive benefits from the extra investment. Beneficiary selection will then be crucial to the outcomes. However, it is not obvious that the selection procedures found in practice would “pick the winners.” Beneficiary selection for local development programs has come to rely heavily on local community groups. This practice may well achieve greater equality in access to the aid within villages, but possibly at the expense of assuring that the aid goes to those who would benefit the most.

> 2 See ( _inter alia_ ) Leading Group (1988), World Bank (1992, 1997), Jalan and Ravallion (1998) and Park et al. (2002).

> 3 See, for example, Knight and Song (1993), Jian et al., (1996), Khan and Riskin (1998), Ravallion and Jalan (1996, 1999), World Bank (1992,1997), Kanbur, and Zhang (1999) and Ravallion and Chen (2006).

2