Economic Growth Volatility and Resource Curse - Global Science ...

DOI 10.7603/s40932-014-0003-y ISSN: 2232-0172 Vol 4 Issue 2, August 2014 pp. 147–164 A Contemporary Business Journal

Economic Growth Volatility and Resource Curse: The Role of Financial Development Maryam Moradbeigi Department of Economics, Universiti Putra Malaysia, Malaysia Taylor’s Business School, Taylor’s University, Malaysia Siong Hook Law Department of Economics, Universiti Putra Malaysia, Malaysia © The Author(s) 2014. This article is published with open access by Taylor’s Press. Abstract: We assess whether well-developed financial systems can moderate the positive association between oil volatility and growth volatility. To this end, we follow Beck et al’s proposition (2006) and distinguish between two different kinds of volatility, that is, oil terms of trade volatility, which is referred to as real shock and inflation volatility, which is referred to as monetary shock. Using data from a sample of 63 oil-producing countries for the period of 1981-2010, the empirical analysis confirms a negative link between the volatility of oil terms of trade and growth. However, we also found weak evidence that financial development dampens the effect of oil terms of trade volatility. Key words: Resource curse, financial development, oil abundance dynamic panel data analysis EL classification: E44, G21, O13, O16

1. INTRODUCTION Do economies that rely more on natural resources experience larger volatility in economic growth? Do countries with well-developed financial systems diminish the impact of oil volatility on the economy? According to the experiences of natural resource-rich countries, this wealth can be considered as a double-edged sword. On one hand, natural resources can enhance the pace of development by increasing national income. On the other hand, it may dampen long-term economic growth by damaging the balance of growth across different sectors of the economy. While pioneering empirical cross-country studies have established *

Correspondence: Maryam Moradbeigi, Taylor’s University, Malaysia.Co Email : [email protected]

Taylor’s Business Review, Vol. 4 Issue 2, August 2014

147

Maryam Moradbeigi & Siong Hook Law

that natural resource abundance can have a negative impact on economic growth, which is known as a resource curse (Arezki and van der Ploeg, 2007; Brückner, 2010; Bulte, Damania & Deacon, 2005; Kronenberg, 2004; Rodriguez and Sachs, 1999; Sachs and Warner, 1995, 1997, 2001), the potential link between natural resource volatility and the volatility of growth has not been studied systematically yet. Based on the above literature, the aim of this study is to investigate the link between the volatility of oil terms of trade and economic growth. The results of the current study will contribute to the literature on resource curse by assessing the role of financial development in the association of growth and oil volatility. In this paper, we introduce the volatility of oil terms of trade as the real shock and investigate its impact on growth volatility using a core sample of 63 oil-producing countries and five-year non-overlapping observations between 1981 and 2010. The empirical results confirmed a robust positive relationship between oil terms of trade volatility and growth volatility. In addition, we found weak evidence that financial development can moderate the volatility impact of oil terms of trade. The reminder of the paper is organized as follows. Section 2 reviews the literature, Section 3 describes the data and econometric model, Section 4 presents the main findings, and finally, Section 5 provides a conclusion.

2. LITERATURE REVIEW Whilst previous studies have looked at different channels though which natural resource abundance may retard the growth rate in certain countries (Alexeev and Conrad, 2011; Aslaksen, 2007; Corden, 1984; Gylfason, 2004; Gylfason and Zoega, 2006; Harding and Venables, 2013; Hodler, 2006; Isham, Woolcock, Pritchett & Busby, 2005; Mehlum, Moone & Torvik, 2006a, 2006b; Oomes and Kalcheva, 2007), we are particularly interested in two channels, namely, volatility and financial development. New resource discoveries or sudden changes in the price of a resource commodity can cause changes in the wealth of a country. Since the price elasticity for resource supply is low, their revenues are highly volatile, and this can lead to boom and bust cycles. Therefore, one explanation for the resource curse is that volatile commodity prices have thrown many naturally endowed countries into debt crises. However, when debt is induced as an explanatory variable, the natural resource dependence was found to no longer have a significant negative effect on economic growth. van der Ploeg and Poelhekke (2009) extended Ramey and Ramey’s (1994) model by including natural resource abundance as an underlying determinant of unanticipated output growth volatility in the growth model. The main objective of their study was to investigate whether any direct effect of resource abundance on growth may offset the indirect impact of volatility for other determinant variables in the growth model. The cross-country evidences for the period 1970-2003 showed a positive and significant direct association between resource abundance and growth, while the indirect effect from volatility was negative and statistically significant. They concluded that commodity price volatility is the main reason for the adverse effect of natural resource on economic growth, especially for point-source natural resources and economies with weak financial institutions, current account restrictions and high degree of capital mobility. Therefore, while natural resources are a curse for volatile economies, it can be a blessing for countries with a stable growth output.

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Economic Growth Volatility and Resource Curse: The Role of Financial Development

Adopting the Instrumental Variable (IV) approach in which a resource export is included as an instrument, van der Ploeg and Poelhekke (2010) highlighted the presence of a significant and strong negative effect of macroeconomic volatility on economic growth on the one hand and the positive relationship between exports of point-source resources and macroeconomic volatility on the other. Therefore, the adverse indirect effect of the volatility of resource exports on economic growth outweighs the direct positive effect of natural resources on economic growth. This paper attempts to shed light on the association of the volatility of oil terms of trade and growth volatility, empirically by accounting for the role of financial development. Previous studies have found that financial development reduces macroeconomic volatility (Easterly, Islam & Stiglitz, 2000; Denizer, Iyigun & Owen, 2002; Hausmann and Gavin, 1996; Raddatz, 2006). Building on Beck, Lundberg & Majnoni’s (2006) theoretical model, we examined whether financial intermediaries serve as shock absorbers and mitigate the effect of oil volatility on growth volatility. The hypothesis put forward by Beck et al. (2006) argued that economic shocks alter the relative composition of investment and output, which in turn, causes output volatility. Distinguishing between two classes of entrepreneurs, that is, high wealth and low wealth entrepreneurs, the researchers pointed out that shocks affecting the real sector will change the available internal funds for both classes of entrepreneurs. Additionally, since the marginal productivity of low wealth entrepreneurs is higher than high wealth ones, this characteristic amplifies the productivity shocks in the imperfect capital market system. Therefore, a more developed financial market dampens the effect of real shocks by alleviating the cash flow constraint for low wealth entrepreneurs.

3. DATA AND ECONOMETRIC MODEL 3.1 Data We used two samples of countries with five-year non-overlapping observations for the period 1981-2010. In particular, a panel of 63 countries was utilized whereby the domestic credit to private sector was used as an index for the financial development, while for the measure of financial development that was based on the share of liquid liabilities in GDP, the number of countries was reduced to 61 (Table A-1 lists the name of all the countries used in the empirical estimation). Two different measurements of financial development was used partly as a robustness check on the empirical results, and partly because data on the ratio of liquid liabilities in GDP was lacking for Oman and United Arab Emirates. Table 1 describes the data and sources in more detail.


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Table 1. Definitions and sources of variables used in regression analysis Variable

Definition

Financial development

Real GDP per capita Trade openness

Growth volatility

Inflation volatility

1. Domestic credit to the private sector divided by GDP 2. Liquid liabilities divided by GDP

% of GDP

Ratio of GDP to population

US Dollars at 2000 prices % of GDP

Ratio of exports and imports to GDP Ratio of government consumption to GDP Standard deviation of oil terms of trade growth in fiveyear interval

Government expenditure Oil terms of trade growth volatility

Unit of measurement

% of GDP

Source

World Bank, the World Development Indicators (WDI)

% of GDP

Standard deviation of real GDP per capita growth in five-year interval Standard deviation of inflation in five-year interval

Author’s construction based on Spatafora and Tytell (2009). Author’s construction using data from World Bank

The dependent variable used for this study was five-year non-overlapping standard deviation of real GDP per capita growth. Tables 2 and 3, which present the data statistics and correlation coefficients, respectively show that growth volatility ranges from a minimum of 0.4% (Cameron in the middle period, and Hungry and Algeria in the last period) to about 22% (Equatorial Guinea in the middle period), around a mean of 3.2 (which is larger than the mean of economic growth rate around 1.7% for the sample of 63 countries). We used five-year standard deviation of oil terms of trade growth and inflation over the corresponding period to capture the real and monetary shocks. The measure for oil terms of trade (OTOT) index was adopted from Spatafora and Tytell (2009) and defined as OTOTit =

( POIL ) MUV t –––––––––

X −M i i

(1)

t

where POILt is the yearly price of oil for the period 1980-2010, MUVt is a manufacturing unit value index, and Xi and Mi are the average share of export and import of oil in country i’s GDP between 1980 and 2010. This index permits the country’s exposure to changes in oil prices to be different according to the composition of its oil export and import basket. Using equation 1, the growth of oil terms of trade index was calculated as gOTOT,it = lnOTOTit − lnOTOTit−1

150


(2)


0.2792

0.0993

–0.2676

0.2370

–0.1449

–0.1206

SD(INFLATION)

PRIV-CRED

TO

GDPC

G

SD(GROWTH 1.0000

SD(OTOT)

SD(GROWTH)

Correlations Matrix

G Number of groups N=63 Number of observations= 378

0.0616

0.0221

0.3459

-0.2266

-0.0346

1.0000

SD(OTOT)

6.4926

44.3802 35.9825 10269.48

605.1273

0. 0428

Overall Standard Deviation 2.7074

0.0190

-0.0919

-0.0582

0.0984

1.0000

SD(INFLATION)

15.7666

49.3317 67.47 8739.698

PRIV-CRED TO GDPC

% of GDP % of GDP US dollar at 2000 Prices % of GDP

78.4364

0.0244

SD(INFLATION)

3.1451

Mean

SD(OTOT)

Unit of Measurement

SD(GROWTH)

Variable

-0.2386

0.6111

0.0144

1.0000

0.2056

0.0747

1.0000

TO

3.6673

20.2347 15.8594 2275.86

549.6314

0. 0275

Within Standard Deviation 2.0289

PRIV-CRED

5.393608

39.76339 32.51521 10081.2

254.8446

0. 0330

Between Standard Deviation 1.8046

0.0386

1.0000

GDPC

1.6806

0. 9967 13.3759 86.3353

0

0.000009

0. 3589

Minimum

1.0000

G

46.75

217.7488 227.7742 50064.86

9730.509

0. 2576

21.7296

Maximum

Table 2. Summary statistics and correlation coefficients: Private credit as a % of GDP, five-year non-overlapping data (1981-2010)


151

152


0.2663

0.1028

–0.2686

0.2346

–0.1920

–0.1107

SD(INFLATION)

LIQ-LIABIL

TO

GDPC

G

SD(GROWTH 1.0000

SD(OTOT)

SD(GROWTH)

Correlations Matrix

0.0528

–0.0021

0.3397

–0.1270

–0.0316

1.0000

SD(OTOT)

6.3855

33.9368 35.7183 9812.826

614.835

0.0418

Overall Standard Deviation 2.7007

–0.0201

–0.0922

–0.0553

–0.1501

1.0000

SD(INFLATION)

15.7874

50.8421 66.2702 8315.901

G Number of groups N=61 Number of observations= 366

80.9248

LIQ-LIABIL TO GDPC

% of GDP % of GDP US dollar at 2000 Prices % of GDP

SD(INFLATION)

0.0229

SD(OTOT)

Mean

3.1123

Unit of Measurement

SD(GROWTH)

Variable

0.2447

0.5853

0.0773

1.0000

0.0693

0.0149

1.0000

TO

3.6962

13.1981 15.9378 2107.564

558.5933

0.0266

Within Standard Deviation 2.0112

LIQ-LIABIL

5.243

31.4816 32.1866 9650.151

258.6741

0.0324

Between Standard Deviation 1.815

0.3535

1.0000

GDPC

1.6806

4.1234 13.3759 86.3353

0

0.000009

0.3589

Minimum

1.0000

G

46.75

224.3888 227.7742 40943.88

9730.509

0. 2576

21.7296

Maximum

Table 3. Summary statistics and correlation coefficients: Liquid liabilities as a % of GDP, five-year non-overlapping data (1981-2010)



This equation reflects the changes in real oil prices in country i scaled by the importance of oil in the net export of the country (Xi − Mi). In order to construct the volatility in oil terms of trade growth, the five-year nonoverlapping standard deviation of the growth rate of oil terms of trade index, gOTOT,it, was employed. SD(OTOT)it,t+S =

1 S

–––

∑ (g S

S=0

1 − ––——— OTOT,it+s S+1

∑

S S=0

gOTOT,it+s

)

2

(3)

As the five-year non-overlapping standard deviation is considered here, S equals four (S = 4). As shown in both tables, both oil terms of trade and inflation volatility indicate large variations across countries. The results show that growth volatility is positively correlated with volatility in oil terms of trade growth and inflation.

3.2 Econometric Methodology The testable hypothesis suggests that the effect of oil terms of trade growth volatility on growth volatility depends on the degree of financial development. The more developed the financial market, the lower the effect of oil terms of trade growth volatility. To test this hypothesis, we estimated the following regression: SD(GROWTH)it = α1SD(GROWTH)it−1 + α2SD(OTOT)it +α3SD(INFLATION)it + βFDit + (FDit * SD(OTOT)it) + CVit + i + t + it

(4)

where SD(GROWTH) is the five-year non-overlapping standard deviation of annual growth rate of real GDP per capita, SD(OTOT) and SD(INFLATION) are the five-year nonoverlapping standard deviation of oil terms of trade growth and inflation rate, respectively, FD denotes the measure of financial development, C is a vector of other control variables, and t are the individual time-invariant effect and time dummies, respectively, and it is i the disturbance term. As proposed by Arellano and Bond (1991), the first-differenced operation was applied in order to remove individual time-invariant effect i, which is the source of inconsistency due to the correlation between SD(GROWTH)it−1 and i. Therefore, the first-differenced equation forms SD(GROWTH)it = α1 SD(GROWTH)it−1 + α2 SD(OTOT)it + α3∆SD(INFLATION)it + β FDit + (FDit * SD(OTOT)it) + CVit + t + it where

(5)

denotes the first-differenced operator.

In correlating SD(GROWTH)it−1 and it, the OLS estimation cannot still be utilized for the first-differenced equation as it will be upward biased and inconsistent. In order to deal with this problem, Arellano and Bond (1991) proposed to use the lagged dependent variable, that is, growth volatility here, in the level equation as an instrument and then, estimate the model by using the Generalized Method of Moments (GMM) technique. This method is usually termed the first-differenced GMM estimator.


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In addition, Bound, Jaeger & Baker (1995) introduced the weakness of the instrumental variable in the first-differenced GMM estimator because of the non-stationary nature of the instrumentals. Blundell and Bond (1998) instead, suggested using the system GMM estimator developed by Arellano and Bover (1995) to deal with weak instrumental variables. They included the different forms of the lagged dependent variable into the matrix of instrumental variables. Their simulation results showed that the system GMM estimator’s efficiency increased when the lagged dependent variable coefficient is near to 1. Thus, the two-step system GMM estimator was applied in this study. However, increased number of moment conditions with widening time dimensions is a feature of the GMM method, which can over-identify the model. Thus, the Sargan test was utilised to test this problem. By including all the control variables in the model, the final specification for the growth volatility equation comes in form of SD(GROWTH)it = α1 SD(GROWTH)it−1 + α2∆SD(OTOT)it + α3 SD(INFLATION)it + β FDit + (FDit * SD(OTOT)it) + 1 (FDit SD(INFLATION)it) + 2∆lnTOit + 3∆lnGDPCit + 4 lnGit + ∆ t + it

(6)

where lnTO, lnGDPC and lnG are the logarithm of trade openness, real GDP per capita and the government expenditure as a share of GDP, respectively. There are evidences that show wealthy economies are more stable, while countries with larger trade openness are more likely to be affected by changes in terms of trade (Beck et al., 2006). Additionally, the positive association between government expenditure and growth volatility is expected because some fiscal policies are driven by factors rather than macroeconomic stability considerations (Afonso & Furceri, 2010). To explore the effect of financial market development on the relationship between oil terms of trade and growth volatility, the sign and significance of the coefficient of oil terms of trade growth volatility and interaction term between oil terms of trade growth volatility and financial development have to be considered. A negative sign on is consistent with the theoretical model that financial development dampens the effect of oil terms of trade growth volatility and it varies across different levels of development in financial market. Therefore, SD(GROWTH) we are interested in ——————— = α2 + FDit. SD(OTOT)

4. EMPIRICAL RESULTS The empirical results for the growth volatility equation using the system GMM estimator are presented in Table 4, where the different measures of financial development used were the logarithm of domestic credit to private sector as a share of GDP for 63 countries and the logarithm of the share of liquid liabilities in GDP for 61 countries, with five-year averaged data over 1981-2010. For each measure of financial development, three sets of results are presented. The first set of results presents the specification without interaction terms (Model 1 and Model 4). Then, regression results with only interaction terms of financial development and oil terms of trade growth volatility (Model 2 and Model 5) and subsequently, results with

154



both interaction terms (Model 3 and Model 6) are presented. According to the first and second serial correlation tests, the one lag length of growth volatility as an instrument is used in specifications 4-6. The lagged dependent variable is found to be positive and significant with a high coefficient in all specifications. While the high coefficient of lagged growth volatility suggests significant persistence, it was statistically different than unity in all cases, indicating that the dynamic GMM is an appropriate estimator and therefore, the statistical estimations of the hypothesis of interest utilizing this method can be relied upon. Models 1 and 4 in Table 4 suggest a positive and statistically significant effect of both oil terms of trade growth volatility and inflation volatility, while the effect of financial development is not robust. Furthermore, the impact of oil terms of trade growth volatility on growth volatility is large. The negative impact of oil terms of trade growth volatility is in line with previous studies such as van der Ploeg and Poelhekke (2009, 2010) which confirms the volatility of commodity prices as a source of resource curse. We also note that the system GMM regression results suggest that more open economies and lower GDP per capita suffer larger volatility in their economic growth, as the coefficients of these variables appear to be positive. However, they are statistically significant only when the share of domestic credit to private sector in GDP is used as the measure of financial development. The empirical results also indicate that higher government expenditure leads to larger swings in economic growth. The results presented in Table 4 suggest weak evidence regarding the dampening effect of financial development on the effect of oil terms of trade growth volatility. The standard deviation of oil terms of trade growth is statistically significant when both measures of financial development are used in all specifications, while its interaction with financial development is significant at the 10% level only when the logarithm of liquid liabilities divided by GDP is utilized as the measure of development in the financial market. Therefore, more developed financial markets are likely to help countries deal better with oil terms of trade growth volatility and limit the pass-through of its adverse effect on growth volatility by facilitating borrowing constraints. While the inflation volatility was found to be statistically significant at 1% level in 4 out of 5 models, its interaction term with financial development proved to be not significant in any specifications. Therefore, the empirical evidence does not support the hypothesis that financial development amplifies the impact of inflation volatility on growth volatility. The positive effect of trade openness also suggests that more open economies suffer from volatility in their growth rates, while economies with large per capita GDP have more stable growth rates, which is captured by the negative coefficient for real GDP per capita. The positive and statistically significant coefficient for the share of government expenditure in GDP indicates that this fiscal variable can be a source of growth volatility. In addition, all the diagnostic tests in all specifications turned out satisfactory. The high p-value for Sargan statistics indicates that the null hypothesis of the over-identifying restrictions failed in being rejected across all the regressions and confirms that all specifications are well specified. Furthermore, the test results for the first-order serial correlation rejected the null hypothesis of no autocorrelation for all models, while the second-order autocorrelation test results failed to reject the null hypothesis of no autocorrelation. Thus, the first- and second-order serial correlation tests are satisfactory, indicating that utilized instruments are independent of error terms (no autocorrelation) and hence appropriate for the estimation.


155

156


SD(INFLATION)

SD(OTOT) × lnFD

G, in log

GDPC, in log

TO, in log

FD, in log

SD(INFLATION)

SD(OTOT)

lnFD

lagged SD(GROWTH)

Dependent variable: Growth volatility

–––

0. 3376*** (0.044) 9.1002** (5.133) 0. 0004*** (0.000) 0. 522 (0.337) 1.3097** (0.408) –0.6705* (0.614) 1.1574** (0.435) –––

0. 3179*** (0.046) 11.5775** (5.190) 0. 0004*** (0.000) 0. 6348* (0.355) 1.5626** (0.658) –0.5253* (0.411) 1.1702** (0.461) 2.6627 (4.504) –––

0. 316*** (0.050) 11.6315** (5.213) 0.0004 (0.000) 0. 6292* (0.356) 1.5604** (0.646) –0.5204 (0.425) 1.172** (0.466) 2.6464 (4.417) 0. 00003 (0.000)

Logarithm of Private credit as a % of GDP (PRIV-CRED) Model (1) Model (2) Model (3)

–––

0 .3071*** (0.064) 10.7145* (6.682) 0 .0003*** (0.000) 0 .5749 (0.480) 0.8918 (0.615) –0.559 (0.453) 1.471** (0.608) –––

0 .3038*** (0.065) 11.3773* (6.717) 0 .0004*** (0.000) 0 .5359 (0.479 0 .3943 (0.646) -0.5855 (0.450) 1.4114** (0.612) –14.3965* (8.283) –––

0. 2812*** (0.081) 11.78867 (7.649) 0 .0004 (0.000) 0 .6217 (0.472) 0.4101 (0.838) –0.4802 (0.518) 1.2504** (0.630) –13.609 (8.861) 0 .0004 (0.001)

Logarithm of Liquid liabilities as a % of GDP (LIQ-LIABIL) Model (4) Model (5) Model (6)

Different measures of financial development (FD)

Table 4. Growth volatility and financial development (the two-step system GMM), five-year non-overlapping average data (1981-2010)


Notes:

–3.2358 (0. 2.804) 13.4093 (0.41) –3.2326 (0.00) –0.113 (0.91) 63 315

–5.9544** (2.766) 14.5088 (0.33) –3.1868 (0.00) –0.0936 (0.92) 63 315

–5.9485** (2.817) 14.4564 (0.34) –3.1799 (0.00) –0.0898 (0.92) 63 315


–2.9761 (0.00) –0.179 (0.85) 61 305

–3.4571 (2.353) 6.032 (0.53)

–2.5859 (0.01) –0.0671 (0.94) 61 305

-0.9532 (2.847) 6.5711 (0.47)

–1.7344 (2.766) 7.8048 (0.35) –2.635 (0.01) –0.04874 (0.96) 61 305



1. Figures in parentheses are the standard errors for coefficients and p values for diagnostic tests. 2. , and indicate statistical significance at the 1%, 5% and 10% levels, respectively.

Number of groups Number of observation

Second-order serial correlation test

First-order serial correlation test

Sargan Test

CONSTANT


Table 4 (con’t)



157

158


CONSTANT

SD(INFLATION)

SD(OTOT) × lnFD

G, in log

GDPC, in log

TO, in log

FD, in log

SD(INFLATION)

SD(OTOT)

lnFD

lagged SD(GROWTH)


–9.1067* (5.020

–––

0.3566*** (0.089) 9.9644* (5.301) 0.0004*** (0.000) 0.9476** (0.400) 1.0994* (0.675) –0.1649 (0.618) 1.5419** (0.573) –––

–8.819* (4.908)

0. 3137*** (0.087) 10.8617* (5.884) 0. 0004*** (0.000) 0. 9879** (0.462) 1.3394** (0.671) –0.27 (0.661) 1.355** (0.527) –0.5488 (8.955) –––

0.3066*** (0.078) 10.9935* (5.8764) 0. 0005* (0.000) 0. 9764** (0.438) 1.343058** (0.649) –0.2522 (0.657) 1.3571** (0.517) –0.6261 (8.771) 0.0001 (0.000) –8.9216* (4.856)


–11.8868 (7.146)

–––

0.3871*** (0.135) 5.6656 (8.334) 0.0004*** (0.000) 0.4155 (0.537) 0.7608 (0.930) 0. 394 (0.752) 2.0944** (0.749) –––

-8.8252 (7.161)

0. 3831*** (0.135) 6.3833* (8.404) 0.0003*** (0.000) 0.4064* (0.538) 0 .4529 (0.953) 0.1915 (0.740) 2.0305** (0.756) –16.6674* (10.625) –––

0.3144** (0.133) 7.462524 (8.806) 0.0005** (0.000) 0.40856 (0.535) 0.1251 (1.105) 0.3834 (0.802) 1.84377** (0.780) –15.6031 (9.987) 0.0008 (0.001) –8.4041 (7.154)



Table 5. Growth volatility and financial development (two-step first-differenced GMM), five-year non-overlapping average data (1981-2010)


Notes:

11.4681 (0.25) –3.1723 (0.00 –0.3222 (0.74) 63 315

12.1133 (0.20) –3.1851 (0.00) 0.0325 (0.97) 63 315

12.1434 (0.20) –3.1783 (0.00) 0.0494 (0.96) 63 315

Logarithm of Private credit as a % of GDP (PRIV-CRED) Model (1) Model (2) Model (3) 3.2754 (0.35) –3.1248 (0.00) –0.0627 (0.95) 61 305

4.654449 (0.20) –2.8785 (0. 00) 0.13754 (0. 89) 61 305

4.2958 (0.23) –2.5878 (0.01) 0.0414 (0.69) 61 305



1. Figures in parentheses are the standard errors for coefficients and p values for diagnostic tests. 2. , and indicate statistical significance at the 1%, 5% and 10% levels, respectively.

Number of groups Number of observation

Second order serial correlation test

First order serial correlation test

Sargan Test


Table 5 (con’t)



159


To test the sensitivity of the results in regard to the econometric methodology, the firstdifferenced GMM was utilised as an alternative estimation approach. The results of the firstdifferenced GMM estimation presented in Table 5 indicate that almost all the coefficients have the exact sign. The coefficients of oil terms of trade growth volatility and inflation volatility appeared to be positive in all models while it was statistically significant in 4 out of 6 specifications for the effect of oil-terms of trade growth volatility. Although the interaction term between oil terms of trade growth volatility and financial development was negative in all models, it was only statistically significant in Model 5. This shows weak evidence for the moderating effect of financial development in the association between oil terms of trade growth volatility and growth volatility. Therefore, the empirical results are robust to the alternative estimator method.

5. CONCLUSION This paper (i) investigated the impact of oil terms of trade growth volatility on growth volatility and (ii) assessed the role of financial development as a potential channel through which the two variables mentioned earlier might be linked. The estimation results for two panels of 63 and 61 countries over the period of 1981-2010 confirmed the positive and statistically significant relationship between oil terms of trade growth volatility and growth volatility. In addition, we found weak evidence that supports the dampening effect of financial development in the propagation of oil terms of trade volatility. However, no evidence for the magnifying role of financial development in the propagation of inflation volatility was found. In conclusion, our results suggest a negative association between the volatility of oil terms of trade growth and economic growth volatility. This supports the hypothesis that oil- rich economies experience larger volatility in their growth rate. The moderating effect of financial development on the relationship between volatility in oil terms of trade and growth volatility is supported by the empirical evidence as well. Specifically, a good financial system can offset, to a certain extent, the negative effect of volatility in oil terms of trade on growth volatility. The findings that are presented here reflect strong policy implications. Governments can improve the conduct of macroeconomic policies by improving the performance of financial markets. A better financial system decreases the uncertainty among households and firms, increases government credibility and thus enhances the positive effects of oil resources on growth by channelling their revenues into more productive activities. The volatility of oil terms of trade can be better managed by export diversification and appropriate exchange rate regimes. It should be noted that as the resource curse phenomenon is not only limited to oilproducing countries, there is a need for future research to investigate the role of financial development in moderating the negative effects of volatility of other natural resources other than oil. To this end, one can classify the natural resources into point-source and diffuse resources.

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Table A-1. Core sample of 63 countries Algeria* Angola* Argentina Australia** Austria** Azerbaijan Bahrain Bolivia Brazil Brunei Cameroon Canada** Chile** China Colombia Congo Rep Congo, Dem Rep

Cote d’Ivoire Denmark** Ecuador* Egypt Equatorial Guinea France** Gabon Germany** Greece** Hungary** India Indonesia Iran* Italy** Japan** Kazakhstan Kuwait*

Libya* Malaysia Mexico** Netherlands New Zealand Nigeria Norway Oman Pakistan Peru Poland** Qatar* Romania Russia Saudi Arabia* South Africa Spain**

Sudan and South Sudan Syria Thailand Trinidad and Tobago Tunisia Turkey** United Arab Emirates* United Kingdom** United States** Venezuela* Vietnam Yemen

* member of Organization of the Petroleum Exporting Countries (OPEC) ** member of Organization for Economics and Co-operation and Development (OECD) Open Access: This article is distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0) which permits any use, distribution and reproduction in any medium, provided the original author(s) and the source are credited.


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