rafmacalaba/datause-encoder-data huggingface dataset:
- 3 -

Table **lt** Central Govornment Curront Revenues. 1982-87 (Millions **of** t) **^/**

||1982|1998|1984|1986|1986|1987|
|---|---|---|---|---|---|---|
|TaxRevenues|716.4|711.0|681.1|986.9|997.1|1,097.1|
|DirectTixee|208.2|197.8|241.9|249.4|269.6|297.1|
|Incom Tax|198.8|190.8|288.9|240.6|250.2|286.7|
|Property Tax|7.4|_7.0_|_8.0_|8.8|8.8|10.4|
|Indirect Taxes|509.2|618.7|689.4|786.6|788.8|80a.0|
|Taxes**on**Domestic Transactions|280.1|226.1|284.7|820.8|828.2|<br>858.6|
|Tax on production,<br>consumption|||||||
|and**sales**|211.8|205.7|249.9|278.1|285.5|<br>801.8|
|Boer|86.9|85.0|48.8|47.7|47.7|60.9|
|Soft Drinks|10.4|10.1|11.2|12.8|18.1|16.8|
|Liquors|26.1|28.6|28.8|28.6|28.9|27.2|
|Petroleum Products|14.6|16.2|17.0|17.0|17.7|19.9|
|Cigarettes|81.2|26.1|81.8|a8.2|88.7|<br>88.6|
|Sales Tax|91.6|87.2|116.0|129.2|184.9|<br>148.4|
|Other Consumption**Taxes**|<br>1.6|2.8|8.8|6.1|8.5|7.0|
|Others|18.8|20.4|84.8|47.6|46.7|62.8|
|Taxes on International<br>Transactions|279.1|287.6|864.7|416.9|410.4|446.4|
|Imports|178.2|201.5|268.8|816.6|801.1|<br>887.4|
|Export|98.2|77.8|87.8|92.5|108.4|<br>100.0|
|**Bananas**|45.4|84.9|86.8|41.8|88.1|<br>89.6|
|Coffee|40.8|85.8|42.8|45.8|62.6|<br>62.6|
|Other*|7.0|7.6|7.7|5.9|4.7|7.8|
|Others|7.7|8.8|8.6|6.8|6.9|9.0|
|Non-Tax<br>Revenues|64.7|67.1|68.8|78.2|164.6|171.2|
|Services|10.2|14.2|18.4|14.6|16.1|<br>16.0|
|Transfers from rest of the|||||||
|Public Sector|17.0|Ir a|17.8|18.2|18.5|17.1|
|Qovernmnt**Fees,**Othor Services|6.7|6.6|7.8|6.8|6.4|<br>6.6|
|Other Income|11.9|24.2|15.4|21.9|106.8|182.6|
|Petroleum|**(-)**|(12.4)|(6.7)|(11.1)|<br>(90.1)|(120.7)|
|**Others**|(11.9)|<br>(11.8)|(8.7)|<br>(10.8)|<br>(16.2)|(11.9)|
|Other<br>Non-Tax<br>Revenues|8.9|7.2|11.9|17.2|8.8|<br>-|
|CURRENT REVENUES|770.1|778.1|949.6|1,064.1|1,151.7|1,268.t|

Source: Ministry of Finance, Bank Staff Estimates.

3/ t a lempiras
Policy Research Working Paper 7474

# **Abstract**

This paper analyzes environmental reliance, poverty, and climate vulnerability among more than 7,300 households in forest adjacent communities in 24 developing countries. The data are from the detailed, quarterly income recording done by the Poverty Environment Network project. Observed income is combined with predicted income (based on households’ assets and other characteristics) to create four categories of households: income and asset poor (structurally poor), income rich and asset poor (stochastically non-poor), income poor and asset rich (stochastically poor), and income and asset rich (structurally non-poor). The income and asset poor generate 29 percent of their income from environmental resources, more than the other three categories. The income poor are more _exposed_ to extreme and variable climate conditions. They tend to live in dryer (and hotter) villages in the dry forest zones, in wetter villages in the wet zones, and experience larger rainfall fluctuations. Among the self-reported incomegenerating responses to income shocks, extracting more environmental resources ranks second to seeking wage labor. Given high reliance on forest and other environmental resources, a concerning finding is that, in the Africa subsample (dominated by dry forests), the rate of forest loss is more than four times higher for the income & asset poor compared with the income & asset rich. Special attention should be given to the poorest households in dry areas, predominantly in Africa. They are (already) exposed to more extreme climate conditions, they suffer the highest forest loss, and the forest benefits are at risk in global warming scenarios.

This paper was commissioned by the World Bank Group’s Climate Change Cross-Cutting Solutions Area and is a background paper for the World Bank Group’s flagship report: “Shock Waves: Managing the Impacts of Climate Change on Poverty.” 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 arild.angelsen@nmbu.no and theresedokken@gmail.com.

_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 2: Unconditional Correlation Coefficients between Main Labor Provisions**

||**C**<br>|**ategories**<br>|||
|---|---|---|---|---|
||1. Aspirational<br>labor<br>goals/objectives|2. Substance-<br>related<br>labor clauses|3. Investment|4. Cooperation|
|2. Substance-related|||||
|labor clauses|0.0415*||||
|3. Investment|-0.0748*|0.9572*|||
|4. Cooperation|-0.2540*|0.5605*|0.5574*||
|5. Institutions|0.1118*|0.6311*|0.5513*|0.6773*|

**Notes:** * indicates statistically significant correlation at the 5 per cent level.  Correlations are calculated over the country pairs with active trade agreements.

pg. 25
situation, and (4) making decisions to improve one’s health. The suggested indicators are listed in table 4 of annex 2.

Evaluating empowerment outcomes within different domains/sub-domains (household, community, labor health, and continued education) allows the evaluation team to compare whether empowerment outcomes are the same for different domains, and to assess whether modules contribute to their intended outcomes.

The impact of the project on students will be evaluated on a bi-annual basis, with the first evaluation expected to be completed in 2005. The data collected during this first evaluation will serve as the baseline for future evaluations. The project team is currently in the process of defining the control group.

# 2.3 Applying the Framework within a National Survey

At the national level, empowerment has become embedded in the language of national policy frameworks for poverty reduction.<sup>23</sup> A recent review of some 39 Poverty Reduction Strategy Papers (PRSPs) and Interim Poverty Reduction Strategy Papers (IPRSPs) (Thin 2002), for example, reveals that almost half of this sample explicitly mentions empowerment in some shape or form. The scope of discussion of empowerment issues and strategies within these PRSPs, however, is quite narrow. The three predominant thematic areas of discussing empowerment, echoed in a recent PREM review (Holland and Brook 2004) are governance, gender, and community participation (see annex 3). Governance is discussed particularly in the context of decentralization and service provision. Here, empowerment strategies focus on strengthening agency and “voice” through education, capacity building, and enhanced knowledge, while tackling “responsiveness” through public-sector reform and capacity building among service providers. PRSPs tackling empowerment were also quite likely to look at women’s empowerment, particularly in the political and economic domains, focusing on improved access to education, labor markets, credit, and political participation. Community participation is also widely addressed, emphasizing participation in the project cycle linked to ownership and sustainability, with, in some cases, specific reference to natural resource management. However, these PRSPs tend to be weak on conceptualizing what empowerment means, in some cases because empowerment is cross-cut or overshadowed by other conceptual frameworks such as rights, exclusion, or marginalization, and are also thin on the detail of strategies for empowerment.

Furthermore, although empowerment appears as an objective in many PRSPs, recent efforts to assess the extent to which these commitments have been achieved demonstrate the paucity of data available for such evaluations. The absence of instruments and reliable data for monitoring empowerment highlights an immediate need to enhance ongoing efforts to strengthen poverty and welfare monitoring systems. For example, the Government of Ethiopia’s monitoring and evaluation action plan outlines a system

> 23 This text in this section draws heavily on Holland and Brook 2004.

26
decade. Regarding beliefs and unfairness, consistent with theories of fairness, we find that people leaning to the right of the political spectrum, Catholics, and optimists are more likely to believe income distribution is fair. Finally, we analyze the link between fairness perceptions and propensity to protest, and show some suggestive evidence that people who believe inequality is very unfair are more prone to mobilize.

The rest of the paper is organized as follows. In section 2 we document some stylized facts about distributive justice perceptions and the evolution of income inequality. In section 3, we shed some light on the discussion of whether income inequality should be measured with absolute or relative measures by studying the relationship between perceptions data with different indicators of income inequality. In section 4 we analyze whether individuals’ unique backgrounds shape their perception of fairness, by analyzing how individuals’ characteristics relate with perception of distributive justice; and compare the relative importance of the demographic variables vis-à-vis aggregate trends of inequality to explain the observed changes in fairness perceptions. In section 5 we analyze the relationship between different beliefs systems and unfairness, while in section 6 we explore the link between fairness perceptions and social cohesion. Section 7 concludes.

# **2. Income inequality and fairness: Some stylized facts**

Latin America has long been characterized as a region with high levels of income inequality, among the least egalitarian regions in the world. Among the ten most unequal countries of the world for which household survey data are available, eight of them are in LA, and the rest in Sub-Saharan Africa (World Bank, 2016), probably the most unequal region in the world (Alvaredo and Gasparini, 2015). Although the disparities between the poor and rich are still large, after a period of increasing inequality during the 1990s, the region experienced a ‘turning point’ in the 2000s, when income inequality saw a widespread decrease across the countries of the region.<sup>9</sup> The social gains in terms of inequality contrast with what happened in other developing regions in the world, where the declines in inequality were much more modest (e.g., such as in the Middle East and North Africa), or even increased (such as in East Asia and Pacific, cf. Alvaredo and Gasparini, 2015, p. 29), and also contrasted with the increases in inequality experienced by developed countries (cf., Atkinson, Piketty and Saez, 2011).

In this section, we replicate the widespread decrease in income inequality in LA, and show how perceptions about fairness moved in the same direction. Our primary data set for income inequality comes from a regional data harmonization effort known as SEDLAC (CEDLAS and World Bank), which increases the cross-country comparability from official household surveys.<sup>10</sup>

> 9 See Gasparini, Cruces and Tornarolli (2011), Gasparini and Lustig (2011) and Lustig, López-Calva and Ortiz-Juárez (2013).

> 10 See Data Appendix for more details on the data sources.

4
WPS3458 Regionalism in Standards: Good or Bad for Trade?<sup>�</sup> Maggie Xiaoyang Chen<sup>y</sup> University of Colorado at Boulder Aaditya Mattoo<sup>z</sup> World Bank October 2004

# Abstract

Regional agreements on standards have been largely ignored by economists and unconditionally blessed by multilateral trade rules. We ...nd, theoretically and empirically, that such agreements increase trade between participating countries but not necessarily with the rest of the world. Adopting a common standard in a region, i.e. harmonization, boosts exports of excluded developed countries to the region but reduces exports of excluded developing countries - possibly because developing country ...rms are hurt more by an increase in the stringency of standards and bene...t less from economies of scale in integrated markets. Mutual Recognition Agreements (MRAs) are more uniformly trade promoting unless they contain restrictive rules of origin, in which case intra-regional trade increases at the expense of trade with other, especially developing, countries. We propose a modi...cation of international trade rules to strike a better balance between the interests of integrating and excluded countries.

Key words: standard, harmonization, MRA, rules of origin, scale economy JEL codes: F12, F13, F15

> �The authors have bene...ted from discussions with Paul Brenton, Carsten Fink, Kishore Gawande, Bernard Hoekman, Murat Iyigun, Jim Markusen, Keith Maskus, Robert McNown, Kamal Saggi, and participants of seminars at the World Bank, US International Trade Commission (ITC), and University of Colorado at Boulder. The views expressed here are those of the authors and should not be attributed to the World Bank.

> yDepartment of Economics, University of Colorado at Boulder, Boulder, CO 80309; Phone: (303) 859-1853; Email: xiaoyang.chen@colorado.edu.

> zDevelopment Research Group, World Bank, 1818 H Street, NW, Washington, DC 20433; Phone: (202) 458-7611; Email: amattoo@worldbank.org.

1
**Figure 21: Total Factor Productivity Change over Time for Selected Countries**

<!-- Start of picture text --> 6<br>5<br>4<br>3<br>2<br>1<br>0<br>2010 2011 2012 2013 2014 2015 2016 2017<br>Bulgaria Mexico Peru<br><!-- End of picture text -->

**Figure 22: Malmquist Indices and Sub-Components Over Time**

<!-- Start of picture text --> 14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Total Factor Productivity Pure Efficiency<br>Technological Change Scale Efficiency<br><!-- End of picture text --> Source: Authors’ elaboration.

# **3.3 Electricity Spending Trends**

As with roads, budgetary expenditure on electricity fails to fully capture resource flows to the sector due to widespread reliance on off-budget vehicles. Due to the prevalence of electric utilities, many of which are state-owned enterprises across the developing world, budget spending on electricity only tells a small part of the expenditure story for the sector. During the last 25 years, there has also been a substantial scale-up in PPPs, particularly for power generation projects. Finally, donors – from the OECD and beyond – have also contributed to spending in the sector, especially in the lower income country group. This means that budget expenditure needs to be combined with data from utility financial statements, as well as public private partnerships and donor engagements, to build-up a comprehensive picture of financial flows to the electricity sector.

By integrating off-budget data sources, a more complete picture can be provided. Over the period 20102018, public expenditure for the reporting 48 countries averages 0.35 percent of the GDP with a clear rising trend (Figure 23a). The private sector contributed 0.21 percent of GDP towards expenditure, while OECD donors contributed just 0.05 percent of GDP in the power sector during the same period (Figure 23b, c).

25
# **Table 7. Probit Marginal Effects of Extensive Margin**

||(1)|(2)|
|---|---|---|
|Dependent variable|Exporter<br>(yes=1)|New Exporter<br>(yes=1)|
|ln(value of machinery)|0.003*|0.001|
||(1.94)|(0.95)|
|ln(cost per worker)|0.026*|0.004|
||(1.82)|(0.34)|
|Innovation (yes = 1)|0.137**|0.068|
||(2.43)|(1.47)|
|ln(age of firm)|0.006|0.009|
||(0.38)|(0.63)|
|Foreign owners (yes=1)|0.133**|-0.009|
||(2.19)|(-0.25)|
|Female owners (yes=1)|0.013|0.010|
||(0.25)|(0.23)|
|Registered (yes=1)|0.018|0.071***|
||(0.44)|(3.84)|
|Training (yes=1)|0.015|-0.039*|
||(0.37)|(-1.72)|
|Foreign tech (yes=1)|0.328***|0.046|
||(4.10)|(0.87)|
|Imported inputs (yes=1)|0.135***|0.057**|
||(4.40)|(2.27)|
|Year dummies|Yes|Yes|
|Pseudo R-squared|0.2091|0.0524|
|Observations|594|594|
|No. of firms|198|198|

Notes: Probit marginal effects reported. The dependent variable in column (1) is a dummy equal to one if the firm is an exporter and zero otherwise. The dependent variable in column (2) is a dummy equal to one if the firm is a “new exporter,” that is a firm that did not export in 2013 but does in 2020. Unweighted. Robust t-statistics in parentheses. *** p<0.01, ** p<0.05, * p<0.1 31
49

# **Table 1 Correlation Matrix of Stock Returns 1980** - **2001**

||Argentina|Brazil|Mexico|Peru|Chile|Colombia|
|---|---|---|---|---|---|---|
|Argentina|1.00|0.66|0.81|0.68|0.82|0.65|
|<br>Brazil|0.66|1.00|0.50|0.42|0.62|0.32|
|Mexico|0.81|0.50|1.00|0.05|0.92|0.87|
|Peru|0.68|0.42|0.05|1.00|0.56|0.05|
|Chile|0.82|0.62|0.92|0.56|1.00|0.86|
|Colombia|0.65|0.32|0.87|0.05|0.86|1.00|

**TABLE** 2

# **Unit Root, Cointegration and Tests for Long Run Relationship between Returns and (log) Dividend Yields.**

||RALS:<br>T|Unit Root<br>est|RALS:<br>Cointegration<br>Test|Joh<br> <br>Coint<br>Vec|ansen<br>egrating<br>tors*|ARDL: Test<br>on Long<br>Run<br>Relationship|
|---|---|---|---|---|---|---|
||Total<br>Returns|Log of<br>Dividend<br>Yield||No<br>Data<br>Trend|Linear<br>Data<br>Trend||
|ARGENTINA<br>|I(O)a<br>|I(1)<br>|Not<br>cointegrated|0|2|Long-run<br>relationshipb|
|BRAZIL<br> <br>I______________|T(1)<br>________|I(1)|Not<br>cointegrated|0|0|No long-run<br>relationship|
|CHILE|I(1)|I(1)|Not<br>cointegrated|0|0|No long-run<br>relationship|
|COLOMBIA|I(1)|I(1)|Not<br>cointegrated|0|0|No long-run<br>relationship|
|MEXICO|I(1)|1(Q)|Not<br>cointegrated|0|2|No long-run<br>relationship|
|PERU|I(1)<br>I_________|I(1)<br>_<br>|Not<br>I__cointegrated|0<br>__|0|No long-run<br>relationship|

*Results based on the Trace. Two cointegrating vectors reflect the fact of stationarity in the series and therefore there is no cointegrating relation.

aResults are sensitive to the inclusion (or not) of constant and trend. The series are I(l) when constant and trend are not included in the regressions.

b Results are sensitive to the inclusion (or not) of constant and trend. There is no long-run relationship when trend is not included in the regressions.
Figure 9: Coefficient plot of PISA-based operations management index x intermediate outcomes, by country <!-- Start of picture text --> Teacher Shortage Index Teacher Motivation Index<br>COL CHL ARG PER BRA MEX URY CRI ARG COL BRA URY PER MEX CRI CHL<br>Teacher Effort Index Household Effort Index<br>ARG COL MEX BRA PER CRI URY CHL ARG COL BRA CHL MEX URY PER CRI<br>1 1<br>.5 .5<br>0 0<br>-.5 -.5<br>Coefficient on operations management Coefficient on operations management<br>1 1<br>.5 .5<br>0 0<br>-.5 -.5<br>Coefficient on operations management Coefficient on operations management<br><!-- End of picture text --> Notes: Each marker represents the coefficient (and vertical spike represents the associated 95% confidence intervals) from regressions of the operations management index on the intermediate school outcome indices for each country in Latin America. The operations management index is built out of the school questionnaire from PISA 2012 using the methodology from Anderson [2008]. All specifications include country fixed effects, school controls and PISA _school final weights_ . School controls include school location, student-teacher ratio, log of the number of students, share of government funding relative to total school funding, ratio of computers connected to the web as a proxy for school resources, and average student socio-economic status. For control variables, missing variables are replaced with a value of -99 and we include an indicator variable with a value of 1 for each imputed value, for each variable with imputed values dummies are added to the specifications.

41
A growing literature, however, examines the experiences of economic growth and institutional development in very small countries, and makes a compelling case that such countries face particular disadvantages, including as the result of capacity shortages. This literature provides a theoretical basis for our proposition that capacity shortages are the binding constraint to PFM performance in very small countries.

The literature on economic development in small states is too extensive to be summarized here. Bertram and Watters (1985), Winters and Martin (2004), and Gibson and Nero (2007) argue that PICs, in particular, face immutable barriers to economic growth and private sector development, due to diseconomies of scale in production of goods and provision of public services, and high costs of distance. The World Bank has recently contributed to this type of analysis with policy pieces emphasizing geographical constraints to economic growth in the PICs in general (World Bank 2011) and Solomon Islands in particular (World Bank 2010).

More relevantly, a small literature (Brown 2010, Baker 1992, Wittenhall 1992) specifically examines institutional development in small states, typically arguing that smallness leads to important weaknesses in public administration relative to larger countries because of the “limited pool of skilled human resources to perform the vital roles of the public service and a lack of depth in specialization which affects implementation and, by extension, absorptive capacity” (Brown 2010: 56). Without adequate skilled individuals to undertake the vital functions of government, the quality of public administration inevitably suffers. Wittenhall (1992:51) notes that, with weaknesses in education and training and very small populations, governments may simply not be able to find any staff with requisite skills. Baker (1992b) further notes that individuals who attain specialist skills within government are often attracted to broader overseas job opportunities in contexts where “only a few brains need be drained before a serious systemic crisis occurs” (Baker 1992b:16). It is not difficult to imagine how such issues could impact on PFM performance in Pacific Island states, where a recent PEFA assessment conducted in Kiribati noted the administrative problems created by the existence of nine long-term unfilled directorlevel financial management positions in the Audit Office and Ministry of Finance.

More evidence regarding PEFA performance and the potential impact of capacity gaps in very small country settings could inform improved understanding of the causes and potential cures for weaknesses in public financial management performance and other areas of public administration.

# **3. Data and Methodology**

We define “small” PICs as those with populations of less than one million. The small PICs for which PEFA data exist are Cook Islands, Fiji, Kiribati, Marshall Islands, Micronesia, Nauru, Niue, Samoa, Solomon Islands, Tonga, Tuvalu, and Vanuatu but exclude larger Pacific states (Papua New Guinea and Timor Leste).

5