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Business cycles characteristics and fiscal pro-‐cyclicality in resource-‐rich countries1
Leonor Coutinho2
Europrism Dimitrios Georgiou3
University of Cyprus
Alexander Michaelides4
University of Cyprus and Imperial College
Maria Heracleous5
University of Cyprus Stella Tsani6
Europrism
Europrism Working Paper 13/1
December 2013
Abstract
We analyse business cycles in resource-‐rich countries, fiscal procyclicality and its links to macroeconomic volatility in a systematic manner. We find that resource-‐rich countries experience higher volatility compared to resource-‐poor ones. Among the group of countries analyzed, resource-‐rich developing countries are found to record the highest macroeconomic volatility. Fiscal policy in resource-‐rich countries is also found to be on average more procyclical than in resource-‐poor countries. Panel data estimations provide robust evidence of the latter. Investigation of the links between fiscal procyclicality and macroeconomic volatility showed that they are positively correlated. Estimations confirm that fiscal procyclicality is associated with greater macroeconomic volatility. This is illustrated to be the case particularly for resource-‐rich countries.
1 This research falls under the Cyprus Research Promotion Foundationʼs Framework Programme for Research, Technological Development and Innovation 2009-‐2010 (DESMI 2009-‐2010), co-‐funded by the Republic of Cyprus and the European Regional Development Fund, and specifically under Grant ΑΝΘΡΩΠΙΣΤΙΚΕΣ/ΟΙΚΟΝ/0311(ΒΙΕ)/04. 2 Corresponding Author, Europrism Research Centre, Office, B21, Thessalonikis 5, Nicosia 2113, Cyprus, [email protected] 3 University of Cyprus, Lefkosia, 1678, [email protected] 4 Department of Finance, Imperial College Business School, South Kensington Campus, London, SW7 2AZ, UK: [email protected] 5 Department of Economics, University of Cyprus, Lefkosia, 1678, Cyprus: [email protected] 6 Europrism Research Centre, Office, B21, Thessalonikis 5, Nicosia 2113, [email protected]
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1 Introduction Previous studies have documented with a wide range of econometric techniques that countries that are rich in oil or other natural resource wealth have failed to grow more rapidly than those that are less endowed. This phenomenon has been labelled in the literature as the “resource curse”. There have been several studies trying to identify the reasons behind the “resource curse” of endowment-‐rich economies (see Frankel, 2010, and references therein, for instance). One important possible reason identified, to which various other reasons can be tracked down, is the pro-‐cyclicality of fiscal policies, which tend to exacerbate business cycle volatility in resource-‐rich countries, contributing to an increased macroeconomic volatility which hampers economic growth.
The importance of macroeconomic volatility to economic growth has received significant attention both in the theoretical and the empirical economic literature. Recent theories have challenged the traditional approaches that have studied apart long-‐run growth and volatility (for a review of the literature see Fatás, 2002). Growing empirical evidence has established a strong connection between volatility and long-‐run performance (see among others Ramey and Ramey, 1995 and Deaton and Miller, 1996). Empirical evidence on the direction of the relationship between volatility and growth has varied, but it can be argued that comprehensive empirical evidence has been found for the hypothesis that cyclical fluctuations reduce long-‐term growth (see Priesmeier and Stahler, 2011, for a recent survey).
There is an important difficulty however in estimating the effect of volatility on growth, due to the possibility of reverse causality. Recent studies that address this issue in a more systematic way confirm the significant negative effects of volatility on growth (see Fatás and Mihov, 2006; Loayza et al, 2007 and Aghion et al, 2010). In the specific case of resource-‐rich countries, studies highlight the importance of the relationship between volatility and economic growth in explaining the “resource curse”. van der Ploeg and Poelhekke (2008) find evidence that natural resources adversely affect economic growth through the indirect negative impact of volatility on the latter.
Given this growing evidence on the importance of macroeconomic stability for growth, this paper analyses the business cycle characteristics of resource-‐rich countries, and compares them to those of resource-‐poor countries, with the aim of identifying a link between natural resource abundance and volatility. In addition we also analyse the link between macroeconomic volatility and fiscal policy procyclicality in a systematic way. For this we firstly document the stylized facts regarding output volatility in resource-‐rich countries and compare these to the statistics observed for resource-‐poor economies, using standard business cycle techniques to analyse “growth cycles”. Secondly, we study the behaviour of fiscal policy in resource-‐rich countries, following the contributions of Alesina et al. (2008) and Itzelski and Vegh (2008). Finally, we estimate the conditional correlation between macroeconomic volatility and fiscal procyclicality, to gauge the significance of the links between the two.
We use a large database comprising of 189 countries, carefully split into resource-‐rich and resource-‐poor countries, according to their ratios of commodity exports to GDP, commodity
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exports to total exports, and two main commodity exports to total exports. The data covers the period 1962-‐2011, but not all data is available for all countries for this sample period giving rise to an unbalanced panel. It is important to note also that the availability of fiscal data is particularly poor, leading to a very short sample for a significant number of countries. For this reason, the results must be interpreted with caution.
The results indicate that macroeconomic volatility (measured by the volatility of GDP) appears to be indeed higher in resource-‐rich countries. Moreover, there is evidence that this increased volatility is more of a resource phenomenon than a developing country phenomenon. Although developing countries do show higher volatility than higher income countries, the group of resource-‐rich developing countries is the one showing the highest volatility.
Graphical analysis indicates that fiscal procyclicality is positively related to resource abundance, special within the set of resource-‐rich countries. Individual countries’ regression results indicates that on average resource-‐rich countries seem to be more procyclical than resource-‐poor, however the results need to be treated with caution due to small sample limitations and their statistical significance. In order to shed more light into this issue we take advantage of the panel of countries and estimate instead a pooled regression. The pooled results indicate that fiscal policy not only tends to be procyclical in resource-‐rich countries, but also it is strongly procyclical, since the estimates point to an increase in government consumption of about 2%, following an 1% increase in GDP.
The results regarding the links between macroeconomic volatility and fiscal procyclicality indicate the existence of positive correlation between the two. Other interesting results are unveiled in this analysis. Macroeconomic volatility appears to be positively related to poor political rights. It is also positively related to initial income per capita. This result, although counter-‐intuitive is consistent with micro-‐econometric estimates which show that the variability of wealth is positively related to initial wealth, since individuals with higher wealth are also those that are able to take more risk. Finally, macroeconomic volatility appears negatively related to financial development. High levels of financial development allow hedging against different types of risk, and also borrowing in bad times, and saving/repaying in good times, in order to smooth consumption and output.
The remainder of the paper is organized as follows. Section 2 describes the methodology employed and the data used in the analysis. Section 3 summarizes the results regarding macroeconomic volatility, fiscal procyclicality and the results obtained on the link between macroeconomic volatility and fiscal procyclicality. Last section concludes.
2 Methods and data To explore the questions associated with business cycles, fiscal procyclicality and macroeconomic volatility we utilize an annual frequency7 data set that includes GDP,
7 We use annual data to avoid the selection bias that might arise from focusing on countries for which only quarterly data are available. This allows us to extend substantially both the number of countries and the time span, resulting in a relatively large dataset.
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government consumption and measures of institutional and financial variables. Sample countries are classified into resource-‐rich and resource-‐poor ones. The literature provides different approaches to defining countries as resource-‐rich. The most widely used proxy for resource dependence is the ratio of resource exports to GDP (see, among others, Sachs and Warner, 1995 and Arezki and van der Ploeg, 2011), but other measures are also used in the literature, including the ratio of commodity exports in total exports, and the ratio of resource revenues in total fiscal revenues. For instance, IMF (2010), Kalyuzhnova (2008) and Tsani (2013) define as resource-‐rich the countries where the share of resource exports (fuels, ores, minerals, metals) over total merchandise exports is equal or more than 40%. Collier and Hoeffler (2009) define as high-‐rent countries those where resource revenues account for 10% or more of GDP.
We construct the resource-‐rich sample using a combination of definitions that generate aggregate dependence on natural resource revenues. Specifically, we define resource-‐rich countries as those countries that have a ratio of commodity exports to GDP equal to, or above 8%, combined with revenues from commodity exports to total exports equal to, or above 60%, provided that the revenues from their two main commodity exports as a share of total exports are equal to, or greater than, 40%. This last condition ensures that we do not include in the sample countries that are relatively diversified in their commodity trade. Such countries might not be considered as dependent on a major revenue source and therefore might be significantly less affected by fluctuations in a particular commodity price.
Note that we consider a relatively high share of commodity exports in total exports as one of our benchmarks because our dataset include data from the 1960s and 1970s when the share of commodity exports to total exports was relatively high for all countries in general8. As a validation check, we compare the resulting classification with the IMF definition of resource-‐rich countries, provided in the Fiscal Rules Dataset 2012 (Schaechter et al., 2012), and find a similar categorization for the countries that appear in both samples.
Based on the above criteria 87 countries in the sample are classified as resource-‐rich, three of which are dropped from the analysis due to data restrictions, resulting in a set of 84 resource-‐rich countries9. The sample of resource-‐rich countries is summarized in Appendix A.
2.1 Business cycles To analyze the characteristics of business cycles in resource rich countries and compare them to those of resource poor countries we will use the concept of “growth” cycles, where the underlying idea is that the business cycle can be identified as a deviation relative to a trend (rather than in terms of absolute changes). Two types of filtering approaches that have become standard methods of removing trends in the business cycle literature are used in our analysis. The first one implements the Hodrick-‐Prescott (HP) (1980, 1997) filter, which is a model-‐free approach to decomposing a time series into its trend and cyclical components. To test the robustness of the results we also implement the Christiano-‐
8 The average share of commodity exports to total exports between 1962 and 2011 for the whole sample of countries is about 62%. 9 We drop from the resource-‐rich sample The Bahamas, because the export share in GDP is above 100%; and Greenland and Somalia, due to lack of fiscal data.
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Fitzgerald (CF) filter (2003) in detrending the data and estimating the business cycle component of the real GDP which is the variable of interest in our study.
The HP filter was proposed by Hodrick and Prescott (1997) as a trend-‐removal technique that could be applied to data that came from a wide class of data-‐generating processes. The HP filter is an algorithm that “smooths” the original time series yt to estimate its trend component, τt. The cyclical component is then defined as the difference between the original series and its trend, i.e., ct = yt -‐ τt where τt is constructed to minimize:
∑ ∑−
−+ −−−+−T T
tttttty1
1
2
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2 )]()[()( ττττλτ
The smoothness of the trend depends on a parameter λ. The trend becomes smoother as λ approaches 1, and Hodrick and Prescott (1997) recommended setting λ to 1,600 for quarterly data. For annual data, as is the case in our dataset, we follow the Ravn-‐Uhlig (2002) rule and set the smoothing parameter λ=6.25.
As an alternative to the HP filter we also apply the Christiano-‐Fitzgerald (CF) (2003), band-‐pass filter to our real GDP data. This method places two important restrictions on the mean squared error problem that this filter solves. First, the CF filter is restricted to be a linear filter and second, yt is assumed to be a random-‐walk process. The CF filter is the best linear predictor of the series filtered by the ideal band-‐pass filter when yt is a random walk. Since the real GDP may well be approximated by a random walk plus drift process, we decided to also utilize this method of decomposing our data into trend and cyclical components. The minimum and maximum periods for filtering out stochastic cycles are set to 2 and 8, as typically used in the literature.
2.2 Fiscal procyclicality To analyse the cyclical behavior of fiscal policy we estimate measures of fiscal pro-‐cyclicality following Alesina et al. (2008), Catão and Sutton (2002) and Gavin and Perotti (1997). Our methodology consists of estimating equation (1) separately for each country:
∆𝐹! = 𝛽𝑦𝑔𝑎𝑝! + 𝛾𝑍! + 𝜆𝐹!!! + 𝑒! (1)
where F is government consumption, our fiscal policy indicator; ygap is a measure of the business cycle (log-‐deviation of GDP from its trend), and Z is a set of control variables, which in the final specification include the lag of the dependent variable. The estimated coefficients β for each country provide us with country specific measures of fiscal pro-‐cyclicality.
Such a regression clearly suffers from endogeneity since GDP and government consumption are jointly determined. One possible way of identifying the causal effect from GDP growth to fiscal policy is to use instrumental variable techniques. The problem associated with the instrumental variables approach, however, is finding appropriate instruments. For an instrument to be valid, it needs to fulfill both the criteria for instrument relevance (in our case sufficiently correlated with GDP growth) and of exogeneity (that the instrument is not
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correlated with the error term, that is, the instrument has no partial effect on the fiscal stance once GDP growth is controlled for).
In the fiscal policy procyclicality literature, a number of instruments have been used. For example, Gali and Perotti (2003) analyze European countries and the US and suggest using the US output gap to instrument the output gap of EU countries, and EU GDP as an instrument for the US output gap. Jaimovich and Panizza (2007) suggest instead using as instrument the trade-‐weighted average of rest-‐of-‐the-‐world GDP. In several cases lagged GDP growth (or the lagged output gap) is also used as an additional instrument, but as pointed out by Ilzetzki and Vegh (2008) the strong serial correlation of GDP may make lagged GDP an imperfect instrument, as GDP at time t−1 may still be correlated with the error term at time t. Alesina et al. (2008) use a version of this methodology and instrument the output gap of each country with the output gap of its neighbors (regional output gap excluding the country).
To address endogeneity problems we estimate equation (1) by instrumental variables, instrumenting the output gap of each country on the regional output gap (excluding the country analysed). For resource-‐rich countries we use an additional instrument that arises naturally for this set of countries, namely the growth in the price of the main commodity export. Arguably commodity prices are determined in world markets and can thus provide a textbook-‐type exogenous variation to the income earned by a particular country. We use this exogenous variation to give a causal interpretation on how fiscal policy reacts to GDP changes. We use the lagged resource price growth to instrument for current GDP growth. We use the lagged, and not the contemporaneous commodity price growth, to account for possible delays in the transmission of commodity price shocks to the economy and to guard against the effects of serially correlated measurement error.
Given that the small sample size of many countries limits the statistical significance of the coefficients obtained, we further test for the presence of fiscal procyclicality in resource-‐rich countries using panel data analysis. Thus we estimate the following equation:
𝛥𝐹!" = 𝑎! + 𝜇! + 𝛽𝑦𝑔𝑎𝑝 + 𝛾𝐹!"!! + 𝑒!" 𝑖 = 1,2,… ,𝑁 𝑡 = 1,2,… ,𝑇! (2)
where 𝐹!" is our preferred fiscal policy indicator for year,𝑦𝑔𝑎𝑝!" is the measure of a country’s business cycle. In addition, we include the lagged dependent variable to capture empirically observed policy persistence. Country fixed effects denoted by 𝑎! are included to account for differences in the average fiscal stance across countries, while time-‐decade effects (𝜇!) are also included to control for unobserved factors that are common across countries and might be influencing fiscal policy over time. The error term is denoted by 𝑒!" .
We use as instrument the real rest-‐of-‐the-‐region GDP and for resource-‐rich countries we use the main commodity price (lagged resource price growth) as both an alternative and an additional instrument.
2.3 Volatility and fiscal procyclicality To test the hypothesis of whether countries with more pro-‐cyclical fiscal policies are also those that experience more volatile business cycles we analyse the correlation between
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business cycle volatility and fiscal policy procyclicality, controlling for a set of other characteristics including political institutions, financial development, exchange rate arrangements, and initial GDP per capita.
Evidence in the literature has been pointing on the role of institutions to macroeconomic volatility arguing that institutions that fail to control political elites are associated with higher volatility (see Acemoglu et al, 2002 and Barseghyan and DiCecio, 2009). Initial income has also been associated with macroeconomic volatility. When wealth is high economic agents’ reaction is less sensitive to shocks and the economy may be more robust to crisis. When wealth is low, the economy becomes vulnerable to fluctuations and can be more volatile (see Perri and Heathcote, 2012).
The empirical evidence tends to support the view that more flexible exchange rate regimes tends to insulate the economy better when facing terms of trade shocks. Broda (2002) based on a sample of developing countries, documents that in response to negative terms of trade shocks countries with fixed exchange regimes experience large and significant declines in real GDP. The opposite occurs in the case of flexible exchange rate regimes. Edwards and Levy Yeyati (2003) show that the terms of trade shocks are amplified in countries that have more rigid exchange rate regimes.
Further evidence indicates that financial depth may also play part in explaining macroeconomic volatility. Deeper financial systems relax borrowing constraints and promote risk sharing, thus enhancing the economy’s ability to absorb shocks. Deeper financial systems can dampen volatility by alleviating firms’ cash constraints, especially in economies with tight international financial constraints (Caballero and Krishnamurty, 2001). Aghion et al. (1999) show that economies with poorly developed financial systems tend to be more volatile, as both the demand for and supply conditions for credit tend to be more cyclical. Deeper financial systems may also facilitate greater diversification, reducing risk and dampening fluctuations (Acemoglu and Zilibotti, 1997).
We estimate a cross-‐sectional empirical model in which the dependent variable is our measure of business cycle volatility (the standard deviation of de-‐trended GDP series). The explanatory variables include the correlation of fiscal balances with the business cycle and the set of control variables mentioned above. The model is summarised by equation (3):
𝜎! = 𝑎 + 𝑏𝛽! + 𝛿𝑋! + 𝜀! (3)
where 𝜎! is the estimated business cycle volatility of country i, a is a constant, 𝛽! is the measure of fiscal pro-‐cyclicality of country i (correlation of the fiscal indicator with the cycle) and Xi is a vector of control variables for country i. With this framework we test in particular whether the estimated parameter b is positive and significant, indicating that more fiscal pro-‐cyclicality is associated with more business cycle volatility.
In this manner we assess whether the general perception that resource-‐rich countries with more pronounced business cycles also have more pro-‐cyclical (structurally adjusted) fiscal policies. To avoid working with negative numbers, we scale the measure of fiscal
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procyclicality 𝛽! using a standard methodology (𝛽!-‐min(β))/( 𝛽!-‐max(β)). This transformation yields a measure of procyclicality between zero and one.
2.4 Data We make use of a large set of data extracted from the IMF database (World Economic Outlook-‐WEO); the World Bank databases (World Development Indicators-‐WDI, Global Economic Monitor-‐GEM, Global Financial Data-‐GDF and Worldwide Governance Indicators-‐WGI); the UN COMTRADE database and the Polity IV database. The dataset covers the period 1962-‐2011 for many variables but for other variables the series extend over shorter periods. Data on macroeconomic and fiscal variables such as GDP and government consumption are obtained from the World Bank WDI database (Table 1).
Table 1: Macroeconomic and fiscal data and sources
Variable Description Source Max Sample
Countries
GDP Gross Domestic Product (local currency, current prices, units)
WDI 1962-‐2011 190
GDP (US $) Gross Domestic Product ( US Dollars, current prices, units)
WDI 1962-‐2011 188
Government Consumption
General Government Consumption Expenditure (local currency, current prices, units)
WDI 1962-‐2011 177
GDP Deflator GDP Deflator (base year varies by country-‐recalculated with 2005 base year for all countries)
WDI 1962-‐2011 181
CPI Consumer Price Index, local currency, (2005=100)
WDI 1962-‐2011 176
Notes: WDI: World Development Indicators
Real Government Consumption is constructed by deflating the nominal series taken from WDI database with the 2005 CPI deflator. We have constructed real GDP variable using the GDP deflator from WDI using 2005 as the base year. Growth rates have been generated by taking the difference of the natural logarithm of real GDP and multiplying the result by 100. For the rest-‐of-‐region GDP growth variable we have first categorized each country into a region according to the Word Bank classification. The Word Bank defines the regions in the following way: High-‐Income OECD, High-‐Income non-‐OECD, East Asia and Pacific, Eastern Europe and Central Asia, Latin America and Caribbean, Middle East and North Africa, South Asia and Sub-‐Saharan Africa. We then have calculated GDP in Purchasing Power Parity (PPP) adjusted (year 2005) terms by dividing the Real GDP by the PPP conversion factor for 2005. The Real GDP in PPP-‐adjusted terms for each region is constructed by summing up the Real GDP in PPP terms of each country within a region. To compute the rest-‐of-‐region-‐GDP for each country i, we simply subtract the real GDP in PPP of country i from the Real GDP in PPP of the region. Taking the difference of its natural logarithm times 100, produces the growth rate of real rest-‐of-‐region-‐GDP.
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Commodity export and prices data have been extracted from the UN COMTRADE database. The data on commodity exports include a group of 12 variables which contains information on the value of exports by export category (Table 2). The categories are the broad commodity categories from the UN COMTRADE and refer to the standard International Trade Classification, Revision 1 (SITC, Rev.1). The variables of this group have been used in own calculations on total exports.
Table 2: Commodity exports related variables
Variable Description Source Max Sample
Countries
Category 0 Exports, Food and live animals, US$ UN COMTRADE
1962-‐2011 191
Category 1 Exports, Beverages and tobacco, US$ UN COMTRADE
1962-‐2011 188
Category 2 Exports, Crude materials, inedible, except fuels, US$
UN COMTRADE
1962-‐2011 191
Category 3 Exports, Mineral fuels, lubricants and related materials, US$
UN COMTRADE
1962-‐2011 190
Category 4 Exports, Animal and vegetable oils and fats
UN COMTRADE
1962-‐2011 188
Category 5 Chemicals, US$ UN COMTRADE
1962-‐2011 190
Category 6 Exports, Manufactured Goods, classified chiefly by material, US$
UN COMTRADE
1962-‐2011 191
Category 6672 Exports, Diamonds, not industrial, not set or strung, US$
UN COMTRADE
1962-‐2011 128
Category 68 Exports, Non-‐ferrous metals, US$ UN COMTRADE
1962-‐2011 187
Category 7 Exports, Machinery and transport equipment, US$
UN COMTRADE
1962-‐2011 190
Category 8 Exports, Miscellaneous manufactured articles, US$
UN COMTRADE
1962-‐2011 191
Category 9 Exports, Commodities and transactions, not classified according to kind, US$
UN COMTRADE
1962-‐2011 191
Following Sachs and Warner (1995) resource intensity is defined as the ratio of primary exports to GDP. Both variables are measured in US dollars. Primary exports are defined to be the sum of the UN COMTRADE categories 0, 1, 2, 3, 4 and 68. We expand this definition to also include category 6672 -‐ Diamonds, not industrial, not set or strung. The source for primary exports data is revision 1 of the Standard International Trade Classification (SITC). Total Exports is created by adding up the main UN COMTRADE commodity categories, namely 0, 1, 2, 3, 4, 5, 6, 7, 8 and 9. These categories are from revision 1 of the SITC. Total Exports are measured in US dollars.
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Table 3: Commodity prices variables Variable Description Source Max Sample Countries Aluminium Aluminum, US$/mt, nominal US$ GEM 1962-‐2011 5 Bananas Bananas, US$, $/mt, nominal US$ GEM 1962-‐2011 7 Beef Meat, beef, US$ cents/kg, nominal US$
cents GEM 1962-‐2011 3
Coal Coal, Australia, US$/mt, nominal US$ GEM 1970-‐2011 1 Cocoa Beans Cocoa, US$ cents/kg, nominal US$ cents GEM 1962-‐2011 5 Coconuts Coconut oil, US$/mt, nominal US$ GEM 1962-‐2011 1 Coffee Coffee, average, Arabica, Robusta, US$
cents/kg, nominal US$ cents GEM 1962-‐2011 12
Copper Copper, US$/mt, nominal US$ GEM 1962-‐2011 8 Copra Copra, US$/mt, nominal US$ GEM 1962-‐2011 2 Cotton Cotton, A Index, US$ cents/kg, nominal US$
cents GEM 1962-‐2011 9
Diamonds Average One Carat D Flawless, US$/Carat, nominal US$
Ajediam10 1962-‐2011 5
Fish Fish (salmon), Price index Farm Bred Norwegian Salmon, export price, 2005=100
WEO 1980-‐2011 8
Gold Gold, US$/toz, nominal US$ GEM 1962-‐2011 6 Groundnuts Groundnut oil, US $/mt, nominal US$ GEM 1962-‐2011 3 Hides Hides, Price Index Heavy native steers, over
53 pounds, wholesale dealer’s price, 2005=100
WEO 1980-‐2011 1
Iron Iron ore,US$ cents/dmtu, nominal US$ cents GEM 1962-‐2011 3 Natgas Natural gas, average, Europe, US, US$
dollars/mmbtu, nominal US$ GEM 1962-‐2011 16
Nickel Nickel, US$/mt, nominal US$ GEM 1962-‐2011 1 Oil Crude oil, average, spot, US$/bbl, nominal
US$ GEM 1962-‐2011 31
Rice Rice, Thailand, 5%, US$/mt, nominal US$ GEM 1962-‐2011 1 Rubber Rubber, Singapore, US$ cents/kg, nominal
US$ cents GEM 1962-‐2011 1
Shrimp Shrimp, Mexico, US$ cents/kg, nominal US$ dollar cents
GEM 1975-‐2011 1
Soybeans Soybeans, US$/mt, nominal US$ GEM 1962-‐2011 2 Sugar Sugar, world, US$ cents/kg, nominal US$
cents GEM 1962-‐2011 5
Tea Tea, auctions (3) average, US$ cents/kg, nominal US$ cents
GEM 1962-‐2011 3
Timber Agr: Raw:1 Timber, Price Index, nominal US$, 2005=100
GEM 1962-‐2011 6
Tin Tin, US $ cents/kg, nominal US$ cents GEM 1962-‐2011 1 Tobacco Tobacco, US $/mt, nominal US$ GEM 1962-‐2011 2 Uranium Uranium, US$/pound, nominal US dollars GFD 1968-‐2011 1
Notes: GEM: Global Economic Monitor, World Bank; GFD: Global Financial Data
10 Ajediam, Antwerp Jewels & Diamond Manufacturers
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Data on commodity prices include nominal prices and price indices for a set of twenty nine commodities (Table 3). These variables have been used in order to calculate price indices for the first two exporting commodities of each country included in the dataset. Data on export prices have been extracted from the World Bank databases (GEM and GFD) and the IMF database (WEO). Data on diamond prices have been extracted from datastream and Ajediam Antwerp Jewels & Diamond Manufacturers.
The nominal price of each commodity is measured in US dollars (world prices). We use the exchange rate (local currency per US dollar) from the WDI to express the nominal commodity prices in local currency. We then construct an index of commodity prices with 2005 as the base year and derive the Real Commodity Price using the 2005 CPI. Finally, we construct Real Commodity Price Growth taking the difference of the natural logarithm of the Real Commodity Price times 100.
Price data for diamonds was available only for the 2002-‐2011 period via datastream. Therefore, to construct the nominal price series for diamonds we used information from a graph titled “Historical diamond trade price trend evolution graph” found on the Ajediam (Antwerp Jewels & Diamond Manufacturers) website11. The graph plots historical wholesale prices for Average One Carat D Flawless from 1960 to 2013. Comparing the last 10 observations of our constructed data with the actual data obtained from datastream we observe that they are quite similar.
To account for institutions we use the Polity2 variable obtained from the Project IV database. We average Polity2 over the available years for each country and then create the dummy variable “Democracy” which takes the value one when this time average is strictly positive and zero otherwise. Data on political rights ranking have been extracted from Freedom House Political Rights Index. The index ranges from 1 to 7 with 1 denoting most free countries and 7 least free countries.
In addition we also use a set of institutional controls in the analysis of the link between macroeconomic volatility and fiscal procyclicality, which in the final specifications include: the political rights index, the private credit to GDP ratio, and the index of exchange rate flexibility. The political rights ranking is given by the Freedom House index which ranges from 1 (most free) to 7 (least free). The democracy variable is the variable Polity2 from the IV Project database. Polity2 ranges from -‐10 (strongly autocratic) to 10 (strongly democratic). The exchange rate flexibility is measured using the average of the “coarse” classification in Ilzetzki, et al. (2008).
3 Results
3.1 Business-‐cycle volatility in resource-‐rich countries We investigate the relationship between business cycle volatility and resource dependence by plotting GDP volatility against the share of resource exports to GDP. Figure 1 plots the volatility of GDP growth against resource dependence measured as share of resource
11 http://www.ajediam.com/investing_diamonds_investment.html
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exports to GDP for the full sample of countries, while Figure 2 only plots the resource-‐rich ones, defined on the basis of resource exports, as explained in the previous section. Both panels illustrate the positive correlation between resource dependence and the volatility in output growth.
Table 4 and Table 5 provide additional evidence on the latter. We use advanced techniques, (HP and CF filter) to determine the trends in GDP and explore the characteristics of business cycles. We undertake this investigation for both resource-‐rich and resource-‐poor countries and compare the results obtained on the two groups. The variable of interest is real GDP. Resource-‐rich countries record larger expansions and contractions in terms of amplitude compared to resource-‐poor countries. Resource-‐rich countries are found to record larger gaps in good times but also to record larger downturns when times are bad. The findings are confirmed when alternative methodologies are employed (HP, CF filter). When looking into more detail into the sample countries, the results show that volatility is more of a resource-‐rich country phenomenon rather than a developing-‐country phenomenon. Volatility is found higher in countries dependent on natural resources compared to developing ones and particularly high for developing resource-‐rich countries.
Figure 1: Volatility of GDP growth and resource dependency: Evidence from full sample of countries
Notes: Resource Exports/GDP is the ratio of primary exports to GDP. Primary exports, are defined according to Sachs and Warner (1995), as the sum of non-‐fuel commodity categories (UN comtrade categories 0, 1, 2, 4 and 68) and fuels (category 3). We expand the definition by also including category 6672 (diamonds). The source for primary exports is UN Comtrade, SITC revision 1, and for GDP the WDI database from the World Bank.
IRQ LBR
LBN ARM AZE
TJK
TJK
VNMNOR
NAMSGP
LTU
SYC
KIRMDA
OMN
SAU
AGO
KAZ
QATTTO
CHE
ZWEGNB
TKM
05
1015
20
S.D
of Y
early
GD
P G
row
th
0 20 40 60 80Average Resource Exports/GDP
13
Figure 2: Volatility of GDP growth and resource dependency: Evidence from resource-‐rich countries sample
Notes: Resource Exports/GDP is the ratio of primary exports to GDP. Primary exports, are defined according to Sachs and Warner (1995), as the sum of non-‐fuel commodity categories (UN comtrade categories 0, 1, 2, 4 and 68) and fuels (category 3). We expand the definition by also including category 6672 (diamonds). The source for primary exports is UN comtrade, SITC revision 1, and for GDP the WDI database from the World Bank. We define resource-‐rich countries as those countries that have a ratio of commodity exports to GDP equal to, or above 8%, combined with revenues from commodity exports to total exports equal to, or above 60%, provided that the revenues from their two main commodity exports as a share of total exports are equal to, or greater than, 40%. Table 4: Business cycle characteristics in resource-‐rich and resource-‐poor countries
Expansions Contractions HP-‐Filter Average
Amplitude Maximum Amplitude
Average Incidence
%
Average Amplitude
Maximum Amplitude
Average Incidence
% Resource-‐Rich 2.5 8.6 50 -‐2.6 -‐9.3 50 Resource-‐Poor 2.0 6.3 49 -‐1.9 -‐7.2 51 CF-‐Filter Average
Amplitude Maximum Amplitude
Average Incidence
%
Average Amplitude
Maximum Amplitude
Average Incidence
% Resource-‐Rich 2.4 8.2 50 -‐2.5 -‐8.8 50 Resource-‐Poor 1.9 6.0 50 -‐2.0 -‐6.9 50
Notes: Business cycle is identified as a deviation in real GDP relative to the trend. Trends are determined with Hodrick-‐Prescott (HP) filter and Christiano-‐Fitzerald (CF) filter. Average Amplitude is the mean of the positive gaps; Incidence is the percentage of years with a positive/negative gap in percent of available years. We define resource-‐rich countries as those countries that have a ratio of commodity exports to GDP equal to, or above 8%, combined with revenues from commodity exports to total exports equal to, or above 60%, provided that the revenues from their two main commodity exports as a share of total exports are equal to, or greater than, 40%.
IRQ LBR
ARM AZE
TJK
GNB
NOR
KIR
SYCOMN
KAZ
AGO
BRNQAT
TKM
NAM
SAU
TTO
ZWE
05
1015
20
S.D
of Ye
arly
GDP
Grow
th
0 20 40 60 80Average Resource Exports/GDP
14
Table 5: Macroeconomic volatility and natural resources
Resource-‐rich Countries
Resource-‐poor Countries
Resource-‐rich Developing Countries
Resource-‐poor Developing Countries
Standard Deviation of Output Gap in % (HP filter)
3.5 2.7 3.6 3.1
Standard Deviation of Output Gap in % (CF filter)
3.3 2.6 3.4 3.1
Standard Deviation of the growth rate of GDP in %
6.2 4.7 6.3 5.5
Notes: Unweighted average of countries. Trends are determined with Hodrick-‐Prescott (HP) filter and Christiano-‐Fitzerald (CF) filter. We define resource-‐rich countries as those countries that have a ratio of commodity exports to GDP equal to, or above 8%, combined with revenues from commodity exports to total exports equal to, or above 60%, provided that the revenues from their two main commodity exports as a share of total exports are equal to, or greater than, 40%.
3.2 Cyclicality of fiscal policy in resource-‐rich countries Individual country regression results show that on average, resource rich countries are more pro-‐cyclical than resource-‐poor countries (Table 6). However the results should be treated with caution as in most instances they are not statistically significant and they are subject to small sample problems12. We extend the investigation of the procyclicality of fiscal policy by plotting the estimated coefficients from individual country regressions against resource dependence measured as the share of resource exports to GDP. Several plots are considered making use of alternative samples (world countries and sample of resource-‐rich countries only) and GDP filters (HP and CF). Graphical analysis shows that procyclicality is positively related to resource abundance particularly in the case of resource-‐rich countries (see Figure 4 and Figure 6).
Table 6: Average estimates of fiscal procyclicality
GDP filter
Full Sample
Resource-‐Rich Countries
Resource-‐Rich and Developing Countries
Resource-‐ Poor Countries
HP -‐28.890 1.625 0.242 -‐53.134
CF 0.369 3.630 4.463 -‐2.120
Notes: Average of β coefficients of output gap resulting from individual country regressions specified in equation (1). Dependent variable: Real Government Consumption Growth. Controls: Real Government Consumption Growth (t-‐1). IV estimations which use Rest-‐of-‐Region GDP growth as an instrument. We define resource-‐rich countries as those countries that have a ratio of commodity exports to GDP equal to, or above 8%, combined with revenues from commodity exports to total exports equal to, or above 60%, provided that the revenues from their two main commodity exports as a share of total exports are equal to, or greater than, 40%. GDP trends determined with Hodrick-‐Prescott (HP) filter and Christiano-‐Fitzerald (CF) filter.
12 To save space in the paper and for the benefit of the reader we report only average of individual regression results. Individual country regression results can be made available by the authors upon request.
15
Figure 3: Fiscal procyclicality and resource intensity, Evidence from world sample countries (GDP filter: HP)
Notes: Betas are individual country regression estimations (β coefficient) of output gap specified in equation (1). Dependent variable: Real Government Consumption Growth. Controls: Real Government Consumption Growth (t-‐1). IV estimations which use Rest-‐of-‐Region GDP growth as an instrument. GDP trends determined with Hodrick-‐Prescott (HP) filter. Resource Exports/GDP is the ratio of primary exports to GDP. Primary exports, are defined according to Sachs and Warner (1995), as the sum of non-‐fuel commodity categories (UN comtrade categories 0, 1, 2, 4 and 68) and fuels (category 3). We expand the definition by also including category 6672 (diamonds). The source for primary exports is UN comtrade, SITC revision 1, and for GDP the WDI database from the World Bank.
ALB
ARGATG
AUS
BDI
BELBENBFA
BGD BGR BHRBLZBOLBRABRB BRN
BTN
BWA
CAF
CAN
CHE
CHN CMR COG
COL
CPV CRICYPDEU
DMADNKDOM DZA
ECU
EGY
ESPETHFIN FJIFRA GABGBRGEO
GHA
GMB
GRC GRDGTMGUYHND
HRV
IDN
IND IRL IRNISLISRITA
JORJPNKEN
KGZ
KNA
KOR
KWT
LBRLCALKALSOLUX
LVA
MAR
MDGMEXMLIMLT
MOZ MRTMUS MWI
MYSNAMNER NICNLDNORNPL
NZL OMNPAK
PAN PER
PHL
PNGPOLPRT
PRY
RWA SAUSDN
SENSGPSLE
SLV SUR
SVK
SWESWZSYC
SYRTCD
TGOTHA
TJKTON
TTO
TUN
TUR
UGA
URYUSA VCTVUT
ZAF
ZAR
ZMB
-50
0
50
100
Bet
as
0 20 40 60 80Average Resource Exports/GDP (Whole Sample)
16
Figure 4: Fiscal procyclicality and resource intensity, Evidence from the sample of resource-‐rich countries (GDP filter: HP)
Notes: Betas are individual country regression estimations (β coefficient) of output gap specified in equation (1). Dependent variable: Real Government Consumption Growth. Controls: Real Government Consumption Growth (t-‐1). IV estimations which use Rest-‐of-‐Region GDP growth and commodity price growth as instruments. GDP trends determined with Hodrick-‐Prescott (HP) filter. Resource Exports/GDP is the ratio of primary exports to GDP. Primary exports, are defined according to Sachs and Warner (1995), as the sum of non-‐fuel commodity categories (UN comtrade categories 0, 1, 2, 4 and 68) and fuels (category 3). We expand the definition by also including category 6672 (diamonds). The source for primary exports is UN comtrade, SITC revision 1, and for GDP the WDI database from the World Bank. We define resource-‐rich countries as those countries that have a ratio of commodity exports to GDP equal to, or above 8%, combined with revenues from commodity exports to total exports equal to, or above 60%, provided that the revenues from their two main commodity exports as a share of total exports are equal to, or greater than, 40%.
In order to investigate further this issue and overcome sample problems associated with individual country regressions we take advantage of the panel dataset and estimate a pooled regression. The pooled results indicate that fiscal policy not only tends to be procyclical in resource-‐rich countries, but also is strongly procyclical, since the estimates point to an increase in government consumption of about 2%, following an 1% increase in GDP.
AUS
BHRBLZ
BOLBRN
BWA
CAF
CMR
COG
COLCRI DZA
ECU
FJI GAB
GHA
GMB
GTM HNDIDN IRN
ISL
KEN
KWT
LCA
LKA
MARMLI
MOZ MRT
MWI
NORPER
PNG
PRY
SAUSDN
SENSYC
SYR
TGO
TTOURY
VCTVUT
ZAR
ZMB
-20
-10
0
10
20
Bet
as
0 20 40 60 80Average Resource Exports/GDP (Resource Rich)
17
Figure 5: Fiscal procyclicality and resource intensity, Evidence from world sample (GDP filter: CF)
Notes: Betas are individual country regression estimations (β coefficient) of output gap specified in equation (1). Dependent variable: Real Government Consumption Growth. Controls: Real Government Consumption Growth (t-‐1). IV estimations which use Rest-‐of-‐Region GDP growth as an instrument. GDP trends determined with Christiano-‐Fitzerald (CF) filter. Resource Exports/GDP is the ratio of primary exports to GDP. Primary exports, are defined according to Sachs and Warner (1995), as the sum of non-‐fuel commodity categories (UN comtrade categories 0, 1, 2, 4 and 68) and fuels (category 3). We expand the definition by also including category 6672 (diamonds). The source for primary exports is UN comtrade, SITC revision 1, and for GDP the WDI database from the World Bank.
Table 7 presents the results from regressing real government consumption growth on real GDP growth. Column (1) shows that the positive correlation exists even with an OLS regression. Column (2) reports the results when the growth rate in the main commodity price export is used to instrument for GDP growth. The positive coefficient is statistically significant at the 1% level, and indicates that 1 percent increase in GDP generates a 2.7 percent increase in real government consumption growth. The weak instrumental variables (WID) hypothesis is rejected when using the Cragg-‐Donald F-‐Statistic for i.i.d. error disturbances since it exceeds the Staiger and Stock (1997) rule of thumb of ten to reject the hypothesis of weak IVs, therefore passing the instrument relevance test (see Cragg and Donald, 1993).
AGO
ALB ARG
AUSAUT
BDI
BELBEN
BFABGD
BGR BHRBLR
BLZ
BOL
BRABRB BRN
BTN
BWACAFCANCHECHN
CMR
COG
COL
CRICYPDEU DMADNK
DOM
DZA
ECU
EGYESPETHFIN FJIFRA
GABGBRGEO
GHA
GMB
GRC
GRD
GTM
GUY
HND
HUN
IDN
INDIRL
IRNISL
ISRITA
JORJPN KENKGZ
KNAKOR
KWT
LBRLCALKALSOLUX
LVA
MAR
MDGMEX
MKDMLIMLT MNGMOZMUS MWI MYS
NER
NICNLDNORNPL
NZL
OMN
PAK
PAN PERPHLPNGPOL
PRT
PRY
ROM
RWA SDN
SEN
SGPSLE
SLVSUR
SVK
SVNSWE
SWZ
SYC
SYRTCD
TGO
THATON
TTOTUN
TUR
UGAUKR
URYUSA
VCTVNM
VUT
ZAF
ZAR
ZMB
-50
0
50
100
Bet
as
0 20 40 60 80Average Resource Exports/GDP (Whole Sample)
18
Figure 6: Fiscal procyclicality and resource intensity, Evidence from the sample of resource-‐rich countries (GDP filter: CF)
Notes: Betas are individual country regression estimations (β coefficient) of output gap specified in equation (1). Dependent variable: Real Government Consumption Growth. Controls: Real Government Consumption Growth (t-‐1). IV estimations which use Rest-‐of-‐Region GDP growth and commodity price growth as instruments. GDP trends determined with Christiano-‐Fitzerald (CF) filter. Resource Exports/GDP is the ratio of primary exports to GDP. Primary exports, are defined according to Sachs and Warner (1995), as the sum of non-‐fuel commodity categories (UN comtrade categories 0, 1, 2, 4 and 68) and fuels (category 3). We expand the definition by also including category 6672 (diamonds). The source for primary exports is UN comtrade, SITC revision 1, and for GDP the WDI database from the World Bank. We define resource-‐rich countries as those countries that have a ratio of commodity exports to GDP equal to, or above 8%, combined with revenues from commodity exports to total exports equal to, or above 60%, provided that the revenues from their two main commodity exports as a share of total exports are equal to, or greater than, 40%.
Column (3) uses the regional GDP growth as an IV and the results are similar in terms of sign, as the procyclicality coefficient remains positive and statistically significant but rises from 2.7 to 3.8 (but with wider confidence intervals)13. Using both IVs in (4) we get similar results, 13 Comparing the first stage F-‐statistics across the two specifications, it looks like the commodity price IV is more relevant than the regional GDP one (the first stage F-‐statistic is 21.10 in the first case versus 4.21 in the second). Thus, the rest-‐of-‐region GDP turns out to be a weaker instrument for our particular sample of resource-‐rich countries. This is also illustrated by the p-‐value in the Angrist-‐Pischke (AP) test statistic that rises from 0 to 0.04 (still a valid IV nevertheless at the five percent level of statistical significance).
AUSBHR
BLZ
BOL
BRN
BWA
CAF
CMR
COG
COL
CRI DZA
ECU
FJI GAB
GHA
GMB
HND
IDN
IRNISL
KENKWTLCALKA
MARMDG
MLI
MOZ
MWI
NORPER
PNG
PRYSAU
SDNSEN
SYCSYR
TGO
TTO
URY
VCT
VUT
ZMB
-15
-10
-5
0
5
10
Bet
as
0 20 40 60 80Average Resource Exports/GDP (Resource Rich)
19
with the coefficient remaining strongly statistically significant at the one percent level and remaining stable at around 2.6. The Cragg-‐Donald and AP statistics illustrate how both instruments remain relevant. Also, according to the Sargan test for overidentified restrictions, where the joint null hypothesis is that the instruments are valid, the p-‐value is 0.1549, suggesting that we cannot reject instrument validity.
Table 7 : Cyclicality of real government consumption growth
(1) (2) (3) (4)
OLS IV Prices IV RR GDP IV Prices + RR
GDP
GDP Growth 0.778*** (0.057)
2.674*** (0.745)
3.806* (1.978)
2.615*** (0.721)
Real Government Consumption Growth (t-‐1)
0.103*** (0.019)
0.019 (0.040)
0.008 (0.051)
0.014 (0.037)
Observations 2317 2153 2275 2113 Number of Groups 76 72 74 71 Average Group 30.49 29.90 30.74 29.76 R2 overall 0.11 0.09 0.09 0.09 First Stage F-‐statistic -‐ 21.10 4.209 10.74 AP (p-‐value) -‐ 0.0000 0.0403 0.0000 Cragg-‐Donald F-‐statistic -‐ 21.10 4.209 10.74 Notes: Dependent variable: Real Government Consumption Growth. All regressions include country fixed effects and time-‐decade effects (not reported). Standard errors in parentheses. * Significant at 10%, ** significant at 5%, *** significant at 1%. OLS estimation is in Column (1) and IV estimations are in Columns (2), (3) and (4). Column (2) uses Real Commodity Price Growth as an instrument; Column (3) uses Rest-‐of-‐Region GDP growth as an instrument, Column (4) uses both Real Commodity Price Growth and Rest-‐of-‐Region GDP Growth as instruments. Weak identification tests are also reported. The Cragg-‐Donald Wald F-‐statistic and the p-‐value of the Angrist-‐Pischke (AP) multivariate F-‐test (the Cragg-‐Donald Wald F-‐statistic and the F-‐test from a first-‐stage regression in the case of a single endogenous regressor are equivalent).
The estimates from Table 7 are also economically significant. They indicate that a 1% exogenous rise in GDP growth leads to a 2.6% rise in real government consumption growth. This illustrates quite a large procyclical response of fiscal policy to GDP changes and justifies the focus on understanding commodity price booms and busts to guide policy makers (see for instance Deaton and Laroque, 1996). These results support the hypothesis that fiscal policy is strongly procyclical in resource-‐rich countries, both in terms of statistical significance and economic magnitude.
3.3 Fiscal procyclicality and macroeconomic volatility In order to analyse the links between fiscal procyclcialcity and macroeconomic volatility we need heterogeneity in fiscal procyclicality, hence we must rely on the individual country
20
regressions estimates of procyclicality, which were not statistically significant in many instances. Nevertheless, following the literature we use these estimates as indicators of procyclicality. To motivate the discussion on fiscal procyclicality and macroeconomic volatility we plot the volatility of GDP growth against the beta coefficients resulting from the individual country regressions discussed in the previous section. We plot volatility of GDP growth against betas for the sample of resource rich countries and for the whole sample of countries. Betas estimations when both GDP lifters i.e. HP and CF are used are reported. The plots illustrate the positive relationship between macroeconomic volatility and fiscal procyclicality particularly with regards to resource-‐rich countries.
Figure 7: Volatility and fiscal procyclicality: Evidence from world sample countries (GDP filter: HP)
Notes: Betas are individual country regression estimations (β coefficient) of output gap specified in equation (1). Dependent variable: Real Government Consumption Growth. Controls: Real Government Consumption Growth (t-‐1). IV estimations which use Rest-‐of-‐Region GDP as instrument. GDP trends determined with Hodrick-‐Prescott (HP) filter.
ALB
ARGATG
AUS
BDI
BELBENBFABGD
BGRBHR
BHS
BLZBOL
BRABRB
BRN
BTNBWA CAF
CAN CHE
CHNCMR
COG
COLCPVCRICYP
DEU
DMA
DNK
DOMDZA
ECUEGYESP
ETH
FINFJI
FRA
GAB
GBR
GEO
GHAGMB GRC
GRD
GTM
GUY
HND HRVIDN INDIRL
IRN
ISLISR
ITA
JOR
JPN
KEN
KGZ
KNAKOR
KWT
LAO
LBR
LCA
LKA
LSO
LUX
LVA
MARMDG
MEXMLIMLT
MOZMRT
MUS
MWI
MYSNAM
NERNIC
NLDNORNPLNZL
OMN
PAK
PANPER
PHLPNG
POLPRTPRY
RWA
SAUSDN
SENSGP
SLE
SLV
SUR SVK
SWE
SWZ
SYC
SYR
TCD
TGO
THA
TJK
TONTTOTUN
TUR
UGA
URY
USA
VCTVUT
ZAF
ZAR ZMB
0
.05
.1
.15
Vola
tility
of G
DP
Gro
wth
-50 0 50 100Betas
21
Figure 8: Volatility and fiscal procyclicality: Evidence from resource-‐rich countries (GDP filter: HP)
Notes: Betas are individual country regression estimations (β coefficient) of output gap specified in equation (1). Dependent variable: Real Government Consumption Growth. Controls: Real Government Consumption Growth (t-‐1). IV estimations which use Rest-‐of-‐Region GDP growth and commodity price growth as instruments. GDP trends determined with Hodrick-‐Prescott (HP) filter. We define resource-‐rich countries as those countries that have a ratio of commodity exports to GDP equal to, or above 8%, combined with revenues from commodity exports to total exports equal to, or above 60%, provided that the revenues from their two main commodity exports as a share of total exports are equal to, or greater than, 40%.
Figure 9: Volatility and fiscal procyclicality: Evidence from world sample countries (GDP filter: CF)
Notes: Betas are individual country regression estimations (β coefficient) of output gap specified in equation (1). Dependent variable: Real Government Consumption Growth. Controls: Real Government Consumption Growth (t-‐1). IV estimations which use Rest-‐of-‐Region GDP growth as instrument. GDP trends determined with Christiano-‐Fitzgerald (CF) filter.
AUS
BHRBLZBOL
BRN
BWACAF CMRCOG
COL
CRI
DZA
ECUFJI
GAB
GHAGMB
GTMHNDIDN
IRN
ISLKEN
KWTLCA
LKA
MARMLI
MOZMRT MWI
NOR
PERPNG
PRY
SAUSDN
SEN
SYC
SYRTGO
TTO
URY
VCTVUT
ZARZMB
0
.05
.1
.15
Volat
ility o
f GDP
Grow
th
-20 -10 0 10 20Betas
AGO
ALB
ARG
AUSAUT
BDI
BEL
BENBFABGD
BGRBHR
BHS
BLR
BLZ BRABRB
BRN
BTN BWACAF
CANCHE
CHN
CMRCOG
COL
CRICYPDEU
DMA
DNK
DOM
DZA
ECU EGY
ESP
ETH
FINFJI
FRA
GAB
GBR
GEO
GHAGMB GRCGRD
GTM
GUYHUN
IDNINDIRL
IRN
ISLISR
ITA
JOR
JPN
KEN
KGZ
KNAKORLAO
LBR
LCA
LKA
LSO
LUX
LVA
MAR MDGMEX
MKD
MLI
MLT
MNGMOZ
MUS
MWI
MYS
NER
NIC
NLDNORNPL
NZL
OMN
PAK
PANPER
PHL
PNG
POLPRTPRY
ROM
RWA
SDN
SENSGP
SLE
SLV
SUR
SVK SVNSWE
SWZ
SYC
SYR
TCD
TGO
THATON
TTOTUN
TURUGA
UKRURY
USA
VCT
VNM
VUT
ZAFZARZMB
0
.05
.1
.15
Volat
ility o
f GDP
Grow
th
-40 -20 0 20 40Betas
22
Figure 10: Volatility and fiscal procyclicality: Evidence from resource-‐rich countries (GDP filter: CF)
Notes: Betas are individual country regression estimations (β coefficient) of output gap specified in equation (1). Dependent variable: Real Government Consumption Growth. Controls: Real Government Consumption Growth (t-‐1). IV estimations which use Rest-‐of-‐Region GDP growth and commodity price growth as instruments. GDP trends determined with Christiano-‐Fitzgerald (CF) filter. We define resource-‐rich countries as those countries that have a ratio of commodity exports to GDP equal to, or above 8%, combined with revenues from commodity exports to total exports equal to, or above 60%, provided that the revenues from their two main commodity exports as a share of total exports are equal to, or greater than, 40%.
To test the hypothesis that countries with more procyclical fiscal policies record also greater macroeconomic volatility we look into the correlation among the latter controlling for additional characteristics of political institutions, financial development, exchange rates and initial GDP. Regression results are summarized in Table 8. Column (1) reports the results when measures of fiscal procyclicality (beta coefficients) employed result from IV regression estimations when rest-‐of-‐region GDP is used as instrument. Column (2) reports the results when measures of fiscal procyclicality (beta coefficients) employed result from IV regression estimations when rest-‐of-‐region GDP and growth in commodity price are used as instruments. Estimations on fiscal procyclicality are found positive and statistically significant in both specifications, indicating that fiscal procyclicality and macroeconomic volatility are positively correlated. Correlation is found higher for resource-‐rich countries.
Further interesting findings show that macroeconomic volatility is positively correlated to poor political rights. These results confirm earlier findings in the literature which suggest
AUS
BHRBLZ BOL
BRN
BWA CAFCMRCOG
COL
CRI
DZA
ECUFJI
GAB
GHAGMB HND IDN
IRN
ISL KEN
KWTLCA
LKA
MARMDG MLIMOZMWI
NOR
PERPNG
PRYSAU
SDN
SEN
SYC
SYRTGO
TTO
URY
VCTVUT
ZMB
0
.05
.1
.15
Vola
tility
of G
DP
Gro
wth
-15 -10 -5 0 5 10Betas
23
that weak institutions which fail to constrain politicians and political elites or to enforce property rights are associated with higher volatility (see Acemoglu et al, 2002 and Barseghyan and DiCecio, 2009). In contrast macroeconomic volatility is found to be negatively correlated to financial development. These findings add to the evidence that financial depth plays an important part in dampening macroeconomic volatility (Dabla-‐Norris and Srivisal, 2013) and support the theory that more efficient financial markets can contribute to lower macroeconomic volatility through risk amelioration, improvement of corporate governance, mobilization of savings, reduction of transaction and information costs, and promotion of specialization (Bencivenga and Smith, 1992 and Levine, 1997).
Table 8: Macroeconomic volatility and fiscal procyclicality
(1) (2)
One Instrument Combined Instruments
Fiscal Procyclicality 0.00758*** 0.00797***
(0.00212) (0.00196)
Political Rights Ranking 0.0270*** 0.0231***
(0.00690) (0.00587)
Private Credit to GDP Ratio -‐0.000164*** -‐0.000154***
(0.0000427) (0.0000444)
Democracy 0.000497 0.00219
(0.00325) (0.00285) Exchange Rate Flexibility Index
-‐0.000185 -‐0.000453
(0.00142) (0.00122)
Initial GDP per Capita (log) 0.00351** 0.00323***
(0.00137) (0.00122)
N 130 120
r2 0.280 0.281
r2_a 0.245 0.242
F 22.45 24.22
Column (1) reports the results when measures of fiscal procyclicality (beta coefficients) employed result from IV regression estimations when rest-‐of-‐region GDP is used as instrument. Column (2) reports the results when measures of fiscal procyclicality (beta coefficients) employed result from IV regression estimations when rest-‐of-‐region GDP and growth in commodity price are used as instruments. The dependent variable is the volatility of estimated output gaps (HP filter). Standard errors in parentheses. Constant Omitted. * p<0.10, ** p<0.05, *** p<0.01
Last macroeconomic volatility appears to be positively related to the initial income level. The result is consistent with evidence that when wealth is high consumers demand is not sensitive to unemployment expectations and the economy is robust to crisis. When wealth is
24
low, consumers' demand is sensitive to unemployment expectations; the economy becomes vulnerable to fluctuations and it is in general, more volatile (see Perri and Heathcote, 2012).
4 Conclusions We analysed business cycle characteristics in resource-‐rich and resource-‐poor countries and its links to fiscal policy procyclicality. Standard business cycle techniques to analyse “growth cycles” showed that volatility is indeed higher in resource-‐rich countries and that increased macroeconomic volatility is a resource-‐rich country phenomenon more than a developing country phenomenon.
The study also found fiscal procyclicality to be higher in resource-‐rich countries compared to resource-‐poor ones. Although individual country estimates of fiscal pro-‐cyclicality must be taken with reservation given the short sample available for fiscal variables in a number of countries, panel data estimates confirm the pro-‐cyclicality of fiscal policy in resource-‐rich countries.
In identifying the links between fiscal procyclicality and macroeconomic volatility the analysis presented has showed that they are positively correlated. Factors related to institutional quality and financial depth among others have also been found to be correlated to volatility. Although it is difficult to establish causality in this analysis, the positive correlation between fiscal procyclicality and business-‐cycle volatility indicates that booms and busts in fiscal spending caused by ups and downs in resource prices are likely to be exacerbating macroeconomic fluctuations in resource-‐rich countries rather than dampening it. The question is whether it may be possible to discipline fiscal policy in these countries to contain these effects.
Based on this evidence we aim to extend this research by looking at the factors that contribute to procyclical fiscal policy in resource-‐rich countries with particular emphasis on institutions. Recent studies have suggested that countries that have been able to address the fiscal policy challenges associated with the natural resource endowments have been those that have put in place strong fiscal institutions. Nevertheless empirical and country evidence on this matter remains inconclusive, and a systematic analysis of fiscal policy pro-‐cyclicality and of the fiscal arrangements which can contribute to macroeconomic stability in resource rich countries can offer valuable contributions to this debate.
25
5 References Acemoglu, D., Johnson, S., Robinson, J. and Thaicharoen, Y. (2002). ‘Institutional causes, macroeconomic symptoms: Volatility, crises and growth’. NBER Working Paper 9124. NBER Working Paper Series.
Aghion, P.; Angeletos, G.-‐M.; Banerjee, A. & Manova, K. (2010). 'Volatility and growth: Credit constraints and the composition of growth.', Journal of Monetary Economics 57, 246-‐265.
Alesina, A.; Tabellini, G. & Campante, F. R. (2008), 'Why is fiscal policy often procyclical?', Journal of the European Economic Association 6(5), 1006-‐1036.
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Appendix A: Sample of resource-‐rich and resource-‐poor countries Resource-‐rich countries Algeria, Angola, Armenia, Australia, Azerbaijan, Bahamas, The Bahrain, Belize, Bolivia, Botswana, Brunei Darussalam, Cameroon, Central African Republic, Chile, Colombia, Congo Dem. Rep., Rep. Congo, Costa Rica, Cote d'Ivoire, Cuba, Ecuador, Faeroe Islands, Fiji, Gabon, Gambia, The Ghana, Greenland, Grenada, Guatemala, Guinea, Guyana, Honduras, Iceland, Indonesia, Iran, Islamic Rep., Iraq, Kazakhstan, Kenya, Kiribati, Kuwait, Liberia, Libya, Madagascar, Malawi, Maldives, Mali, Mauritania, Moldova, Mongolia, Montenegro, Morocco, Mozambique, Namibia, New Zealand, Nicaragua, Niger, Nigeria, Norway, Oman, Papua New Guinea, Paraguay, Peru, Qatar, Russian Federation, Saudi Arabia, Senegal, Seychelles, Sierra Leone, Solomon Islands, Somalia, Sri Lanka, St. Lucia, St. Vincent and the Grenadines, Sudan, Syrian Arab Republic, Tajikistan, Togo, Trinidad and Tobago, Turkmenistan, United Arab Emirates, Uruguay, Vanuatu, Venezuela, Virgin Islands (U.S.), Yemen, Zambia, Zimbabwe Resource poor countries Aruba, Andorra, Afghanistan, Albania, Argentina, American Samoa, Antigua and Barbuda, Austria, Burundi, Belgium, Benin, Burkina Faso, Bangladesh, Bulgaria, Bosnia and Herzegovina, Belarus, Bermuda, Brazil, Barbados, Bhutan, Canada, Switzerland, China, Comoros, Cape Verde, Cayman Islands, Cyprus, Czech Republic, Germany, Djibouti, Dominica, Denmark, Dominican Republic, Egypt, Eritrea, Spain, Estonia, Ethiopia, Finland, France, United Kingdom, Georgia, Guinea-‐Bissau, Greece, Croatia, Haiti, Hungary, India, Ireland, Israel, Italy, Jamaica, Jordan, Japan, Kyrgyz Republic, Cambodia, St. Kitts and Nevis Korea, Lao PDR, Lebanon, Lesotho, Lithuania, Luxembourg, Latvia, Mexico, Macedonia FYR, Malta, Myanmar, Mauritius, Malaysia, New Caledonia, Netherlands, Nepal, Pakistan, Panama, Philippines, Poland, Portugal, French Polynesia, Romania, Rwanda, Singapore, El Salvador, Serbia, Suriname, Slovak Republic, Slovenia, Sweden, Swaziland, Turks and Caicos Islands, Chad, Thailand, Tonga, Tunisia, Turkey, Tuvalu, Tanzania, Uganda, Ukraine, United States, Vietnam, West Bank and Gaza, Samoa, South Africa Notes: We define resource-‐rich countries as those countries that have a ratio of commodity exports to GDP equal to, or above 8%, combined with revenues from commodity exports to total exports equal to, or above 60%, provided that the revenues from their two main
