rafmacalaba/datause-encoder-data huggingface dataset:
25

# Appendix I: Data

The data used in the analysis is from BESD, the World Bank database with the following exceptions'. Per capita gross domestic product, government share of GDP, trade share of GDP, and investment share of GDP are from Summers and Heston (1991). Assassinations per million population, and revolutions and coups, are constructed from the raw data used in the Barro-Wolf data set. Primary and secondary enrollment rates in 1960 are obtained from the Barro-Wolf data set, but are supplemented with data from SOCIND, United Nations Social Indicators, which is part of the World Bank data base. The War Casualties data is from Easterly, Kremer, Pritchett, and Summers (1992) and the two financial variables, DCPT7088, the ratio of claims on the private sector by the central bank and deposit money banks to GDP, and BTOT7088, the ratio of the assets of deposit money banks to the combined assets of deposit money banks and the central bank are from raw data from King and Levine (1992). The raw income distribution data used to construct the inequality variables comes from four basic sources. The primary source is SOCIND. This is supplemented by Jain (1975), and "A Survey of National Sources of Income Distribution Statistics" published by the United Nations(1981). The quintile income distribution data, used for some points in RTP40, is from Lecallion et al (1984) and United Nations (1985).

In addition to the general problems encountered in cross country growth regressions there are additional problems specific to inequality measures.' A problem in cross country studies concerning inequality is that data on inequality tends to be very sparse. To deal with this concern, as in Alesina and Rodrik (1991) and Persson and Tabellini (1990), income distribution is measured in different years for

> 28 With the exception of the inequality data, the data used in the following analysis is from William Easterly and Sergio Rebelo, 'Fiscal Policies and Economic Growth: An Empirical Investigation', part of an ongoing World Bank research project 'How Do National Policies affect Long Term Growth?

29 See Easerly, King, Levine and Rebelo (1991) or Levine and Renelt (1992) for a discussion of common problems in growth regressions.
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Policy ReseaRch WoRking PaPeR 4452

# **Abstract**

This paper complements the cross-country approach by examining the correlates of growth acceleration in per capita gross domestic product around “significant” public expenditure episodes by reorganizing the data around turning points, or events. The authors define a growth event as an increase in average per capita growth of at least 2 percentage points sustained for 5 years. A fiscal event is an increase in the annual growth rate of primary fiscal expenditure of approximately 1 percentage point sustained for 5 years and not accompanied by an aggravation of the fiscal deficit beyond 2 percent of gross domestic product. These definitions of events are applied to a database of 140 countries (118 developing countries) for 1972-2005.

After controlling for the growth-inducing effects of positive terms-of-trade shocks and of trade liberalization reform, probit estimates indicate that a growth event is more likely to occur in a developing country when surrounded by a fiscal event. Moreover, the probability of occurrence of a growth event in the years following a fiscal event is greater the lower is the associated fiscal deficit, confirming that success of a growth-oriented fiscal expenditure reform hinges on a stabilized macroeconomic environment (through a limited primary fiscal deficit).

This paper—a product of the Poverty Reduction and Economic Management Vice Presidency (PREMVP)—is part of a larger effort in the Poverty Reduction and Economic Management anchor to increase awareness of key stylized facts associated with the design and implementation of fiscal policy in developing countries as part of the debate on fiscal space. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted directly at deMelo@ecopo.unige.ch or through PREM at aestache@worldbank.org.

_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 14: Industry groupings as a share of GDP

||Panel a|. Data|
|---|---|---|
|Aggregate|Manufacturing<br>ICT|Retail<br>Other Industries|
|100%|27%<br>10%|27%<br>36%|
||Panel b. Cali|brated Model|
|Aggregate|Manufacturing<br>ICT|Retail<br>Other Industries|
|100%|25%<br>12%|11%<br>53%|
|Panel c. Co|unterfactual - Bringing|mobility restrictions back to 1995|
|Aggregate|Manufacturing<br>ICT|Retail<br>Other Industries|
|100%|36%<br>7%|21%<br>36%|

_Sources:_ PCBS, authors’ calculations.

location and industry grouping. The overall contribution to GDP by industry grouping then depends on the prices for inputs, from wages, and from the estimated production function, including the trade shares. Table 14 compares the contribution to GDP of each industry grouping in the data (panel a) and in the model (panel b). We compare the model with data from the PCBS on national accounts for the West Bank in 2010.<sup>32</sup> The data only includes the industries covered by our survey. The model is able to replicate the relative importance of the different industry groupings in terms of contribution to GDP. It should be noted that we did not target the contribution to GDP when calibrating our model. Besides the data from the survey, we only used employment by location and industry, and an estimate of average wage by location. In particular, wages in the model do not vary across industries. This fact makes us confident on the ability of the model to be informative of the issue at hand.

Indeed, removing mobility barriers would disproportionately and negatively affect ICT relative to other industries. Table 14 reports the results broken by industry grouping. Bringing mobility restrictions back to 1995 would imply a decrease of the ICT contribution to GDP from 12% to 7%, to the advantage of manufacturing and retail. Also the other industries would experience a reduction in the contribution to GDP.

With the focus on industrial composition, the paper does not attempt to provide an estimate of the overall effect of mobility restrictions on GDP growth. For this reason, results on GDP growth are not shown. However, it is worth mentioning that, while the ICT contribution to GDP would decrease in the absence of mobility restrictions, the ICT value added would still increase, although less than other industries.

Removing mobility restrictions allows people and production to concentrate towards the middle of the West Bank, while shipping goods to the outer areas (figure 7). As a result, the region around Ramallah and Nablus would experience an inflow in population, while the regions around Jenin, Bethlehem, and Hebron would experience an outflow. As a matter of

> 32The first year in which the data include a break down for information and communication.

32
**<u>Table 5: Descriptive statistics by status of households’ land market participation</u>**

||<br>**Rent**<br>|<br>**al markets (2**<br>|<br>**005)**<br>|<br>**Sales**<br>|**markets (200**<br>|**0-2005)**<br>|
|---|---|---|---|---|---|---|
||**Rented out**|**Autarky**|**Rented in**|**Sold**|**Autarky**|**Purchased**|
|**Household characteristics**|||||||
|Household size|3.508|4.661|5.104|5.214|4.641|4.585|
|Female head|0.149|0.072|0.026|0.071|0.074|0.049|
|Members 14-60 years old|1.955|2.745|2.826|2.929|2.703|2.919|
|Dependency ratio|0.502|0.415|0.430|0.433|0.423|0.366|
|Head's age|56.493|52.426|47.652|54.786|52.328|50.951|
|Head's education (years)|8.299|7.809|8.539|7.750|7.859|8.098|
|Max. educ. In household|9.791|9.789|10.217|10.250|9.801|9.927|
|Toilet|0.687|0.503|0.470|0.786|0.484|0.764|
|Running water|0.537|0.317|0.261|0.500|0.309|0.455|
|Telephone|0.194|0.086|0.009|0.107|0.080|0.146|
|TV|0.134|0.081|0.078|0.071|0.084|0.073|
|Poverty (%)|0.045|0.186|0.165|0.143|0.183|0.138|
|**Asset ownership**|||||||
|Area of land owned (ha)|1.061|0.779|1.688|0.928|0.888|0.297|
|No. of plots owned|3.224|2.929|3.835|3.250|3.083|1.805|
|Value of all animals|160|866|1173|568|904|322|
|Any cattle|0.105|0.705|0.826|0.500|0.720|0.325|
|Any horse|0.045|0.333|0.504|0.036|0.359|0.049|
|Any minor animal|0.358|0.744|0.861|0.714|0.760|0.439|
|Any plough|0.030|0.125|0.261|0.071|0.138|0.033|
|**Land use**|||||||
|Any irrigation|0.284|0.658|0.652|0.607|0.654|0.520|
|<br>Area irrigated|0.065|0.268|0.857|0.145|0.318|0.064|
|Annual cropland rainf. (ha)|0.141|0.269|0.540|0.354|0.294|0.098|
|<br>Annual cropland irrig. (ha)|0.053|0.249|0.841|0.132|0.298|0.059|
|Perennial land rainfed (ha)|0.077|0.050|0.065|0.097|0.053|0.030|
|Perennial land irrig. (ha)|0.012|0.019|0.016|0.012|0.019|0.005|
|<br>Pasture land (ha)|0.017|0.091|0.125|0.044|0.097|0.006|
|<br>Rented out land (ha)|0.695|0.003|0.000|0.039|0.028|0.038|
|**Ag. output&productivity**|||||||
|<br>Total value of ag. output|335.8|943.2|2252.2|498.8|1042.1|615.0|
|<br>... share from livestock|0.251|0.493|0.430|0.291|0.491|0.391|
|Total profit/ha (US $)|176.2|1310.5|1327.0|229.3|1314.7|897.2|
|<br>**Income composition (%)**|||||||
|<br>...from agric.|0.145|0.443|0.520|0.267|0.458|0.201|
|<br>...from non-ag. wage|0.185|0.183|0.158|0.194|0.169|0.337|
|<br>...from non-ag. self|0.129|0.060|0.056|0.210|0.056|0.124|
|<br>...from remittances|0.540|0.310|0.267|0.328|0.314|0.339|
|**Community char.**|||||||
|<br>Dist. to nearest bank (km)|1.508|4.222|1.774|1.143|4.262|0.613|
|<br>Access to extension|0.149|0.214|0.183|0.214|0.218|0.106|
|Access to vet service|<br>0.105|0.284|0.357|0.179|0.290|0.203|
|Migration share in comm.|23.985|20.268|19.946|18.357|20.685|16.825|
|<br>Shortage of labor in comm.|0.463|0.362|0.400|0.250|0.384|0.187|
|Share of hhs with credit|0.418|0.341|0.383|0.429|0.340|0.415|
|No. of obs.|67|1614|115|28|1645|123|

_Source:_ Own computation from 2005 Albania LSMS 25
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> While our stock perspective is not new, its empirical applicability has been hampered **by** the lack of comprehensive data on foreign asset and liability stocks. In section **3** of the paper we present a new data set on foreign assets and liabilities, recently put together **by** Kraay, Loayza, Serv6n, and Ventura **(1999),** that covers a large number of developing and industrial countries and spans the years from **_1965_** to **1997.** The section also describes the variables used to measure domestic investment returns and risks as well as wealth, relative to the rest of the world.

Section 4 presents the empirical results obtained estimating our panel error-correction model for various groups of countries. The model is first implemented on the **full** available sample, and robustness checks are then performed **by** estimating it on country groups that differ in per capita income level and restrictions to portfolio diversification. The estimated parameters are then used to gauge the degree of disequilibrium in external positions --judged from a stock perspective-- over time for selected sample countries.

Section **5** summarizes the main results and concludes **by** proposing extensions for further research.

# **2. Methodology**

# **2.1 A stock approach to external sustainability**

Our approach views external sustainability as driven **by** portfolio equilibrium conditions in the long run, and **by** the dynamic forces resulting from asset reallocation in the short run. Long-run external equilibrium is achieved when international and domestic investors obtain their desired portfolio allocation of assets across countries. The current account balance is just the mechanism through which investors acquire their desired asset positions.

However, imperfections in financial and factor markets may prevent the instantaneous achievement of the optimal portfolio. Short-run external equilibrium is then given **by** the currentaccount deficit consistent with the dynamics along the adjustment path towards investors' longrun equilibrium portfolio, which reflects existing constraints to immediate portfolio adjustment. These may arise from various sources (see Bacchetta and van Wincoop **1998)** such as (i) investors' imperfect infornation (e.g., gradual learning about the state of the world, or about the permanence of reforms that initially enjoy imperfect credibility); (ii) congestion effects, such as increasing marginal costs to foreign investment due for example to its use of internationally immobile labor inputs; (iii) irreversibilities that make investment respond sluggishly to aggregate

**5**
# How to deal with exchange rate risk in infrastructure and other long-lived projects<sup>∗</sup>

Luciano de Castro<sup>†</sup> Claudio Frischtak<sup>‡</sup> Arthur Rodrigues<sup>§</sup>

**Keywords:** Exchange rate risk, infrastructure projects, Government protection, insurance for exchange rate risk, bidding for public projects, investors risk aversion, concession periods.

**JEL codes:** H4, F3, H8.

> ∗We are grateful to Eduardo Engel, Victor Tito, Cesaire Assah Meh and seminar participants at BNDES for helpful comments.

> †Department of Economics, Tippie College of Business, University of Iowa. E-mail: luciano-decastro@uiowa.edu Luciano acknowledges financial support by CNPq.

> ‡International Growth Centre (IGC). E-mail: claudio.frischtak@interb.com.br

> §Department of Economics, Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio). E-mail: arthur.mauriciorodrigues@gmail.com
takes for sedentary, light, moderate, and heavy physical activity. The factorial method can be used to generate descriptive estimates of population-level total energy expenditure using the height, weight, demography, and activitylevels observed in the Indian population using various data sources. The factorial method computes TEE for individual _i_ using the following equation: where _i_ is an individual, _REEi_ is the Resting Energy Expenditure of individual _i_ and _ALi_ represents the Activity Level of individual _i_ . REE makes up the bulk of TEE (about two thirds for the average person) and reflects the energy required for essential metabolic functions. An individual in a complete state of rest has _AL_ = 1, _TEE_ = _REE_ , and still needs a lot of calories to avoid weight loss.<sup>3</sup> An individual that does more than rest will have an AL greater than one. The factorial method implies that a given reduction in AL generates a larger reduction in TEE for individuals with larger REE. Thus, when estimating levels or changes in TEE for a population, one cannot simply average _AL_ or _REE_ across individuals but must sum up the individual _TEE_ as constructed above. Measurement of these two components for the Indian population is discussed next.

# **2.1 Resting Energy Expenditures (REE)**

Although REE can only be directly measured in a laboratory setting, there are numerous published predictive equations that are determined by regressing measured REE on more commonly observed attributes such as age, sex, height and weight. While these equations miss an important idiosyncratic element of metabolism, they typically yield an _R_<sup>2</sup> over 0.7. The equations – provided in (Henry 2005) are used here these are based on regressing REE on height and weight separately by sex and age groups (1-3, 4-9, 10-17, 1830, 31-60, 60+) for a large population of individuals drawn from around the world including tropical and developing countries.

The anthropometric micro-data used come from India’s National Family Health Survey (NFHS), a household survey that measures height and weight

> 3Basal Energy Expenditure (BEE) is closely related to REE. Measuring BEE requires a minimum period of fasting before testing and is lower than REE, which incorporates the energy cost of metabolizing food. Most laboratory measures use REE. It is possible to directly quantifying the energy cost of metabolizing different macronutrients but the differences over time and across households appear minimal.

5
# **II. The structure of taxes and social spending in Romania: 2016**

## **The Romanian Public Finance System**

Public finance in Romania consists of the central and local governments and public enterprises. The revenue structure is shown in Table 1. Total revenue amounted to 29.3 percent of GDP in 2016, with tax collections amounting to nearly 18 percent of GDP and social security contributions amounting to 8 percent of GDP. Our analysis focused on the major tax items, namely personal income taxes, VAT, and excise taxes. These items made up about 83 percent of all tax revenue in 2016. Corporate taxes were not included given the difficulty of attributing the tax burden to specific households.

### **_Direct taxes and social insurance contributions_**

Personal income tax (PIT) revenues accounted for 3.6 percent of GDP in 2016, which was shared between the central, regional and local self-governments. The personal income tax had a flat rate of 16 percent in 2016 and was paid on employment income, self-employment income and income from investments, while social benefits were exempt. The flat tax rate was reduced to 10 percent in 2018, as discussed further in section VI. For dependent employment, the tax base is the gross wage minus social contributions and tax allowances. For income from self-employment, the tax base is gross income minus deductible expenses and social contributions, and income from investments minus social contributions. Tax allowances for employees vary depending on the level of income and the number of dependents. The system also has deductions for voluntary private pension contributions, and there are other allowances and tax credits (see Appendix 1).

**Table 1. Romania. General Government Revenue, 2016**

||Fiscal Ac|counts|Portion of<br>Accounts to b|Fiscal<br>e analyzed|Amounts in<br>survey|Ratio of total<br>amounts in the<br>survey and|
|---|---|---|---|---|---|---|
||(in millions<br>of lei)|% of GDP|(in millions<br>of lei)|% of GDP|(in millions<br>of lei)|external<br>statistics, %|
|**Revenue**|**223,722**|**29.3%**|**167,663**|**22.0%**|**109,526**|49.0%|
|Taxes and social contributions|197,681|25.9%|167,663|22.0%|109,526|55.4%|
|Corporate tax|15,442|2.0%|||||
|Personal income tax|27,756|3.6%|27,756|3.6%|19,189|69.1%|
|VAT|51,675|6.8%|51,675|6.8%|21,100|40.8%|
|Excises|26,957|3.5%|26,957|3.5%|8,050|29.9%|
|Customs duties|883|0.1%|||||
|Social security contributions|61,274|8.0%|61,274|8.0%|61,187|99.9%|
|Other taxes|13,693|1.8%|||||
|Nontax revenue|17,938|2.4%|||||
|Capital revenue|769|0.1%|||||
|Grants 1/<br>_Source: National Institute of Statistics,_<br>_1/ Includes -financed capital projects_|7,332<br>_Ministry of Pu_|1.0%<br>_blic Finance,_|_MFMod, World_|_Bank staff est_|_imates_||

5
16 Edwards and Tabellini (1991) postulate that the more politically unstable a country is, the larger will be its budget deficit. Political instability will raise the frequency of government changes and lower the likelihood that a current policymaker will be reelected. Given this, consider a policymaker who is required to choose both the intertemporal profile of spending and taxes as well as how to allocate the resources acquired by issuing debt. Suppose that because of political instability in the country, the policymaker is aware that in the future he may be replaced by a policymaker or political majority with different preferences about some aspects of fiscal policy. Then he realizes that, whereas he is in control of how to allocate the proceeds of his borrowing, the allocation of the burden of repaying the debt in the future may not be under his control. This asymmetry may prevent the current policymaker from fully internalizing the costs of running a deficit, the more so the greater is the difference between his preferences and the expected preferences of the future majority. In simple terms, the policymaker may wish to borrow in excess of the optimum and let his successors "pay the bills". Thus, political instability and polarization tends to lead to a larger than optimal size of the budget deficit, even if the policymaker and the voters are rational and forward-looking.

This paper will use the frequency of government crises to proxy for political instability<sup>16</sup> . The number of government crises is defined as the number of major government crises, defined as any rapidly developing situation that threatens to bring the downfall of the present regime – excluding situations of revolt aimed at such overthrow.

# **4.4 Political Freedom**

the size of budget deficits. However, it is noted that a country that is politically polarized may not be politically unstable. For example, a country can have two very different or polarized political ideologies and yet be politically stable if the supporters of one of the political ideology form the vast majority of the voter population, assuming majority rule. For this reason we have decided to omit the concept of political polarization in our presentation of political instability as a significant determinant of budget deficits.

16 It was originally intended to follow Edwards and Tabellini (1991) and Roubini (1991) in using the frequency of government changes (both regular and irregular) as a measure of political instability. However, the data series for the frequency of government changes or total executive transfers obtained from Taylor (1985) is short, ending in 1983. As such, the number of government crises is used as a substitute. This alternative measure is crude since a government crisis does not necessarily lead to a change in the government. In addition, this measure excludes situations of revolt to overthrow the government, which should be included in a measure of political instability.