rafmacalaba/datause-encoder-data huggingface dataset:
Economic growth and fiscal and external balances in Brazil have been historically affected by large variations that have sometimes resulted in fiscal, balance of payments, and debt crises. Brazil’s response to the deteriorating fiscal situation in the 1990s was a sharp fiscal adjustment policy, which relied on increasing the primary surplus to stabilize the public debt, coupled with a program of liability management designed to reduce risk. The fiscal adjustment process, however, has been characterized by drastic increases in tax revenues and cuts in infrastructure spending as a way to finance increases in current spending.

Brazil’s fiscal position has improved following significant reforms since the launching of the Real Plan in 1994. Brazil’s long history of high inflation fueled by weak fiscal policies has been reversed. The implied reduction in Brazil’s external vulnerability to foreign capital swings and the fiscal adjustment process have paved the way for lower domestic interest rates and accelerated economic growth. Improvements in the country’s fiscal position have been supported by a new fiscal responsibility law enacted in 2000 Pursuit of sound macroeconomic policies, including the generation of primary public sector surpluses that averaged about 4 percent of GDP annually between 2002 and 2007, allowed federal government debt to achieve an investment grade rating in April 2008.

**Figure 1: Primary balances of Subnational Governments, 1998-2007**

(as percent of GDP) <!-- Start of picture text --> 1,5<br>1<br>0,5<br>0<br>-0,5<br>-1<br> Source: Central Bank of Brazil<br>(% of GDP)<br>1998 01 1998 07 1999 01 1999 07 2000 01 2000 07 2001 01 2001 07 2002 01 2002 07 2003 01 2003 07 2004 01 2004 07 2005 01 2005 07 2006 01 2006 07 2007 01 2007 07<br><!-- End of picture text --> According to a recent World Bank report (World Bank, 2008a), in 2006 intergovernmental transfers in Brazil amounted to R$185 billion, equivalent to 8.2 percent of GDP and 24 percent of the tax revenues. Federal transfers to states and municipalities were at 5.3 percent of GDP (2.5 percent of GDP to states and 2.8 percent 6
# **Chapter 2: Calculating Profits**

|**Step-by-step Implementation Guidance**|
|---|
|Step 1: Calculating total daily sales and costs at the end of the day. The HB goes back to Step 9 of record-keeping. It uses idiosyncratic rules of thumb like "the sum where<br>you disregard all colored entries will now be used to calculate profits. Entries highlighted with color represent costs that are not related to business expenses, such as loans<br>to your sister". The HB illustrates with the example carried from Chapter 1|
|Step2: Calculatingdaily profits "simplytakeyour total dailysales and subtract from ityour total dailycosts." Illustrates with the example carried from Chapter 1|
|Step 3: Calculating monthly profits and profits on a “normal day”. "calculating your monthly profits is equivalent to calculating profits on a daily basis. Hence, all you have to<br>do is add up your daily profits for all days of the month."|
|**Tips **|
|Making daily profits more precise (stock-ups for multiple days): "With the records now in place, you can estimate the days it takes until the products stocked up on one<br>occasion sell out completely....Try to estimate the number of days for each product to stock-out. The way you deal with the entries in your records will be the same as with<br>costs that represent more than one day. Just as you have done already (record-keeping, step 7), every purchase that represents more than a day shall be marked with color.<br>Then add the total purchases for those products divided by the days they will last for."|
|Matching daily profits with monthly profits: "at the end of a month, calculate the monthly profits as shown above. Then, go back to each day<br>of the month and note down, on the page that you are calculating your monthly profits, both the total daily sales and that day’s total costs including those you marked with<br>color. If you now sum up the daily total sales, you receive your monthly total sales. Likewise, you can calculate the monthly total costs."|
**Table 1.  Private Sector Contribution to GDP for Selected Countries**

||**Ratio of Private Sector**|**Economic Activity to**|
|---|---|---|
|**Country**|**Gross Domes**<br>|**tic Product**<br>|
||**1995**|**2010**|
|Australia|89.65%|89.40%|
|Brazil|84.84%|84.94%|
|Canada|84.09%|86.05%|
|Estonia|89.33%|88.26%|
|Greece|87.15%|82.10%|
|India|94.05%|93.33%|
|Indonesia|96.30%|94.65%|
|Ireland|87.95%|87.55%|
|Italy|89.59%|88.31%|
|Japan|89.18%|86.52%|
|Korea, Republic|92.41%|91.17%|
|Mexico|92.65%|90.94%|
|Netherlands|86.19%|84.52%|
|Poland|90.88%|91.05%|
|Romania|95.16%|91.56%|
|Spain|89.20%|87.73%|
|Sweden|84.46%|84.94%|
|Taiwan, China|90.20%|91.23%|
|Turkey|95.21%|92.13%|
|United Kingdom|85.92%|82.74%|
|United States|83.24%|80.50%|

Source: European Commission (2014).

3
following categories of capital intensity – low, medium and high.<sup>6</sup> Indeed, certain cities seem to attract only low-capital (and hence high-labor) based industries, such as Dire Dawa. On the other hand, emerging cities such as Mekele and Sebeta seem to attract medium and high-capital intensive industries, which might be a worrying pattern. It is difficult to ascertain what model of development would be most sustainable for Ethiopia – modest capital intensity is required for firms to compete in international markets, while optimal use of labor would also help raise labor productivity across different types of industries.

**Figure 7: Town employment contribution by capital intensive sector.**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Low Medium High<br>DEBRETABOR HURUMU METU ADWA NEGELE JIMMA SODO DEBRE BREHAN DEBERE MARKOS DIRE DAWA YIRGA CHEFE GIMBI ABIYI ADI HOSAENA BUTAJIRA SHASHEMENE AXUM MEKELE JIJIGA WOLDIYA ADDIS ABABA DILA NEKEMTE GONDER HARAR SIRE AGARO ASELA ADIGRAT DURAME AMBO ALETA WONDO SEBETA ZEWAY/BATU<br><!-- End of picture text --> Source: UBAEU, CSA Is the increase in capital intensity explained by a move to capital-intensive sectors? In other words, are cities specializing in products that require additional investments in capital? I estimate a regression (panel fixed-effects) model to study the relationship between sectoral composition at the city level and firms’ capital intensity in production. The results suggest that cities are not necessarily decreasing their share of laborintensive sectors, but they are increasing the capital-intensity in employment. Although traditionally capital-intensive sectors (non-metallic minerals, publishing and printing) are increasing intensity faster, this is also true of labor-intensive industries (such as wearing apparel, tanning and dressing, etc.).

**What is the importance of firm size in job creation** ? Figure 8 shows the distribution of employment and the total number of firms for the period of 2000-2011.<sup>7</sup> Clearly, enterprises with fewer than 50 employees dominate  the account for close to 60% of the total number of firms. However, the bulk of employment (i.e. more than 60%) is

> 6 Capital intensity is based on the firm’s capital-per-labor ratio.

> 7 The data are shown for 36 cities, which account for 77 percent of the total firms, and 73 percent of the total employment generated within the LMMIS data.

9
Cochabamba was highly regressive, with all deciles losing welfare because of this price effect but with the poorest decile losing much more than the richest decile.

The last example in this discussion is from a well-known study by Galiani, Gertler, and Schargrodsky (2005), who exploited variation in ownership across time and space to identify the causal effect of privatization of water distribution on child mortality in Argentina (Figure 11). From 1993 onward, several municipalities privatized the distribution of water, whereas other municipalities kept their initial arrangement with public companies, either national or local, thus providing a control group, or counterfactual.

The evidence suggests that after 1993 child mortality rates in municipalities with privatized water distribution dropped faster than in those with nonprivatized water distribution. This is an interesting result. If we believe that changes in child mortality are mostly attributable to changes in the subgroups of the population in which child mortality is highest—often, the poorest people—this would mean that the privatization of water in Argentina had a progressive effect. But the difficulty with the approach taken in the paper is that it is a highly reduced form. We do not know through what channels the reduction in the child mortality rate took place. Did it occur because there was better access under privatized water companies? Or was it because the quality of water was better? We do not know. But this certainly is the type of evidence that is needed in order to understand the distributional impact of privatization.

# **V. Concluding Remarks**

The paucity of full evaluations of privatization operations in developing countries is surprising. There are some analyses of the efficiency gains of privatization, but there is very little assessment of the distributional impact of privatization, and most of the studies that do exist only consider the effect of price changes on consumers. Evaluations of the indirect effects of privatization—for example, the study of child mortality discussed earlier—are much rarer. The complete picture that was described in the analytical section of this paper is simply not available in the empirical analyses. Thus, one important conclusion of this brief review of privatization and development is the need to pursue this type of empirical analysis more exhaustively. If there is scope for privatization in some countries, we need to have more in-depth evaluations to guide our policies.

16
POL.IcY RElSEARCH WORKING PAPER 2526

# Summary findings

The year 1999 was important for poverty-related research and policy development in Vietnam. The General Statistics Office had collected household data **in** the second Vietnam Living Standards Survey in 1998 and made it available for analysis in 1999. And four participatory poverty assessments (PPAs) were implenmented during 1999.

Turk's case study describes how government agencies, donors, and nongovernmental organizations collaborated in implementing the PPAs. The considerable amount of qualitative information about poverty produced in Vietnam over the past 8 to 10 years has rarely grabbed the attention of policymakers, who tend to view such information as "unscientific" and lacking in credibility. By contrast, the PPAs implemented in 1999 have been widely circulated, used, and quoted.

What was different about those PPAs that led their findings to be brought into local and national policy debates, as previous findings had not been?

Working partnerships among doniors and nongovernmental organizations were important and helped build consensus on the research findinlgs, but more crucial was the active engagement of F,overnment partners from the very early stages. Establis iing a Poverty Working Group provided a structu re for implementing the PPAs, for feeding analysi. through to the poverty assessment, and for keeping government fully involved. The Poverty Working Group now supports the government in drafting its po-erty reduction strategy.

Strong World Bank leadership, financial .upport from the U.K. Department for International Devc1opment, the technical expertise and commitment of the PPA partner agencies, and the availability of recent high quality household survey data played an important part in ensuring the PPAs' credibility.

This paper-a product of the Hanoi Country Office, East Asia and Pacific Region-is part of a larger effort in the region to encourage greater participation by poor households in policymaking and programming for poverty red iction. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Herawaty S:utrisna, room MC9-242, telephone 202-458-8032, fax 202-522-1556, email address hsutrisna((iworldb. nk.org. Policy Research Working Papers are also posted on the Web at www.worldbank.org/research/workingpapers. The author may be contacted at cturk(dworldbank.org. January 2001. (25 pages)

> _The Policy Research Working Paper Series disseminates the findings of wvork in progress to encourage the exchange of ideas abouit development isszues. An objective of the series is to get the findings out quickly, even if the pr esentations are less than fully pol shed. The papers carry the names of the authors and should be cited accordingily. I he fintdings, injterpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank. its Executive DirectP_ **_rs, or_** _the countries they represent._

Produced by the Policy Research Dissemination Center
# Growth Rates and Aggregate Welfare: A;. International Comparison

# 1. INTRODUCTION

The gross national product (GNP) per head<sup>and related income measures</sup> are widely used to appraise the economic<sup>well-being of people living in</sup> different countries. These measures<sup>have been subject to much criticism for</sup> their failure to give any indication<sup>of how the total output of a country is</sup> distributed among its people.1 Recently, many economists, the most<sup>notable</sup> of them being Sen (1985), have been<sup>concerned with whether these aggregated</sup> income measures reflect the well-being of<sup>people. Despite these criticisms,</sup> the aggregate income statistics continue<sup>to be widely used to distinguish rich</sup> and poor countries, perhaps because of<sup>readily available national accounts for</sup> most countries of the world.

The present paper is concerned with the<sup>measurement of growth rates of</sup> various broad economic indicators. It<sup>is clearly important to determine</sup> whether and to what extent people are<sup>becoming better or worse off over time.</sup> Not surprisingly therefore, magnitudes<sup>of growth rates are always the focus of</sup> attention when economists discuss alternative<sup>strategies of economic</sup> development. However, how these<sup>growth rates should be computed is seldom</sup> discussed in the economic literature.2 1See Sen (1973, 1974) and Kakwani (1981) who have attempted to derive alternative welfare measures which<sup>take into account both the size and</sup> distribution of income.

2Although, we recognize that the reliability and comparability of national accounts data are subject to serious<sup>limitations, this issue has not been</sup> considered in this paper. However,<sup>the World Bank puts enormous efforts in</sup> improving these data on a continual<sup>basis.</sup>
below those provided by aggregate models (Deininger _et al._ 2023).<sup>8</sup> Moreover, less than one-fifth of the total effects is attributable to direct conflict-induced field damage while the remainder comes about through indirect effects, including damages to the electricity grid and logistics infrastructure including road transport and grain elevators (Khoshnood _et al._ 2022); limited availability of key inputs including fuel, labor, and agro-chemicals; and reduced demand as a result of higher cost for transport and marketing.

To link data on area cultivated and yield to welfare and prices or profitability, surveys at household or farm level are needed. Household surveys conducted by FAO highlight the importance of home production on garden plots dating from Soviet time as a safety net to increase resilience (FAO 2022b). In a nation-wide survey of 5,230 rural households, drops in income were reported by 55% of respondents, especially for IDPs. For the population who are not displaced three salient characteristics emerge, namely (i) 25% of the surveyed rural population reported reducing or stopping agricultural production due to the war; (ii) 72% of crop and 64% of livestock producers reported increased production costs; and (iii) more than half and around 20% of respondents reported spending more than 50% and 75% of their total expenditure on food, respectively (FAO 2022c).

While these figures illustrate the breadth and depth of war impacts, disaggregated data on small and medium scale farm production is important to understand the extent to which the invasion may deplete the productive capacity of rural areas and to design responses on the continuum between humanitarian and productive support to maintain and expand productive capacity, diversification, and employment opportunities in rural areas. This is particularly relevant as experts agree that a significant part of Ukraine’s agriculture sector operates in informality, but little is known on either its size or the nature and profitability of its operations.

# **2.2 Sample construction and level of informality**

To obtain information on changes in welfare, production, and productivity in the small and medium farm sector between 2021 and 2022, a nation-wide phone survey of such farms in areas controlled by Ukraine, was conducted in cooperation with the Ministry of Agricultural Policy and Food (MAPF), from October to December of 2022.<sup>9</sup> The original intent was to construct a sample frame using data from the State Statistics Service of Ukraine (SSSU), complemented with the company registry. However, these sources cover only registered legal entities. To capture informal farms that, based on expert estimates, cultivate 32% of Ukraine’s agricultural area (Nivievskyi _et al._ 2021), a decision was taken to use the State Agrarian Registry (SAR) as a sample frame instead.

> 8 - <u>https://downloads.usda.library.cornell.edu/usda esmis/files/3t945q76s/cc08jp14g/cr56p755q/wasde0822.pdf</u> 9 The survey was implemented by the Kyiv International Institute of Sociology (KIIS) with financial support from the European Commission.

6
# 1. Introduction

This paper provides an overview of the data sources, aggregation methodology, and main findings of the Worldwide Governance Indicators (WGI). The WGI are a long-running data product first published in 1999 (see Kaufmann, Kraay and Zoido-Lobaton (1999) for the first presentation of the methodology and aggregate indicators), with subsequent annual updates. The WGI report data on six aggregate indicators of governance: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption, covering 214 economies over the period 1996-2023. The aggregate indicators, the underlying individual indicators on which they are based, and extensive documentation of the methodology, are available at <u>www.govindicators.org.</u> The WGI do not involve new primary data collection on our part as authors of the WGI. Rather, the aggregate indicators are constructed by combining several hundred individual indicators measuring aspects of governance taken from 35 existing data sources that capture perceptions and views of the quality of governance reported by many experts and survey respondents worldwide. The WGI rely on a diverse set of data sources, including several global and regional surveys of households and firms, as well as many expert assessments produced by a range of organizations in the public, private, and NGO sectors, often scored by networks of experts living and working in the countries or regions that they assess. Five WGI data sources are produced by organizations headquartered in developing countries<sup>1</sup> , and a further eight WGI data sources are household or firm surveys directly capturing the views of survey respondents in developing as well as advanced economies.

The WGI are based on perceptions data for four reasons: (1) perceptions matter, because households and firms make decisions based on their views and perceptions of the quality of governance; (2) for some dimensions of governance such as corruption that do not leave a “paper trail”, data measuring subjective perceptions of corruption or self-reported experiences with corruption can provide valuable insights; (3) data capturing respondents’ perceptions and views can provide valuable information on the gap between _de jure_ rules and their _de facto_ implementation; and (4) unlike objective indicators that capture the existence of specific laws, rules and regulations, data capturing survey and expert respondents’ views are not susceptible to “gaming” where policy makers target reforms to narrowly

> 1 The Asian Development Bank (based in the Philippines), the African Development Bank (based in Cote d’Ivoire), Afrobarometer (based in Ghana), the Center for Democracy and Development (based in Ghana), and the African Institute for Development Policy (based in Kenya and Malawi).

1
# _The impact of HIV/AIDS on mortality:_

For the 60 countries highly affected by the HIV/AIDS epidemic, estimates of the impact of HIV/AIDS are made by explicitly modelling the course of the epidemic and by projecting the yearly incidence of HIV infection. The model developed by the UNAIDS Reference Group on Estimates, Modelling and Projections<sup>2</sup> is used to fit past estimates of HIV prevalence provided by UNAIDS so as to derive the parameters determining the past dynamics of the epidemic. For most countries, the model is fitted assuming that the relevant parameters have remained constant in the past. Beginning in 2005, the parameter PHI, which reflects the rate of recruitment of new individuals into the high-risk or susceptible group, is projected to decline by half every thirty years. The parameter R, which represents the force of infection, is projected to decline in the same manner. The reduction in R reflects the assumption that changes in behaviour among those subject to the risk of infection, along with increases in access to treatment for those infected, will reduce the chances of transmitting the virus. The rate of mother-to-child transmission is projected to decline at varying rates, depending on each country’s progress in increasing access to treatment. In addition, the component of the Reference Group model relative to the survivorship of infected children has been updated: in the 2004 Revision it is assumed that 50 per cent of children infected through mother-to-child transmission will survive to age two.

The 2004 Revision incorporates for the first time a longer survival for persons receiving treatment with highly active antiretroviral therapy (ART). The proportion of the HIV-positive population receiving treatment in each country is consistent with estimates prepared by the World Health Organization for end of 2004<sup>32</sup> . Coverage is projected to reach between 40 percent and 85 per cent by 2015, depending on the current level of coverage. It is assumed that, on average, annual survival probabilities increase to at least 80 for individuals receiving ART. Under this assumption, mean survival from the initiation of therapy is 3.1 years (median 4.5 years). In contrast, in the absence of treatment mean survival after progression to AIDS is assumed to be just one year.

> 2 "Improved methods and assumptions for estimation of the HIV/AIDS epidemic and its impact: Recommendations of the UNAIDS Reference Group on Estimates, Modelling and Projections". _AIDS_ , vol. 16, pp. W1-W14 (UNAIDS Reference Group on Estimates, Modelling and Projections, 2002).

> 3 World Health Organization. “3 by 5” Progress Report, December 2004. WHO and UNAIDS.

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