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Do resource dependent regions grow slower than they should?
Economics Letters 111 (2011) 194–196
Contents lists available at ScienceDirect
Economics Letters j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / e c o l e t
Do resource dependent regions grow slower than they should? Alexander G. James a,⁎, Robert G. James b a b
Department of Economics and Finance, University of Wyoming, Laramie, WY 82071-3985, United States Department of Economics, California State University, Chico, Chico, CA 95928, United States
a r t i c l e
i n f o
Article history: Received 10 April 2010 Received in revised form 19 January 2011 Accepted 31 January 2011 Available online 16 February 2011
a b s t r a c t A large literature documents a negative correlation between income growth and resource dependence. This correlation has been named the resource curse. We present evidence that suggests that the resource curse can be explained by a slow growing resource sector. Published by Elsevier B.V.
JEL classification: Q2 Q3 Keywords: Resource dependence Economic growth Resource curse
1. Introduction A large literature documents a negative relationship between resource dependence and economic growth (James and Aadland, forthcoming; Papyrakis and Gerlagh, 2007; Sachs and Warner, 2001, 1995). This negative relationship has been named the “resource curse.”1 Perhaps the most prominent explanation of the resource curse is the Dutch disease. The Dutch disease occurs when natural resource industries “crowd-out” other growth promoting industries by increasing wages, the exchange rate or decreasing the level of investment flowing to non-resource sectors (Sachs and Warner, 1995; Matsuyama, 1992; Van Wijnbergen, 1984). In this paper, we explore an alternative explanation of the resource curse: a slow growing resource sector. An implication of this is that although a resource discovery will decrease future growth, it will increase the level of income for current and future generations. When the relative slow growth in the resource sector is accounted for, we find evidence that resource dependence positively effects economic growth at the U.S. state-level. 2. A Growth Identity Assume an economy is composed of a mining and composite sector (the composite sector is the entire economy less mining). Output per capita (referred to as output hereafter) in the mining and composite ⁎ Corresponding author. E-mail address: [email protected] (A.G. James). 1 See Fig. 1 for a plot of 1980 U.S. state mining dependence on growth. 0165-1765/$ – see front matter. Published by Elsevier B.V. doi:10.1016/j.econlet.2011.01.029
sectors are r and c, respectively. Instantaneous growth in the economy (gT) is a weighted average of growth in the composite sector (gc) and the mining sector (gr)2 r
c
g r+g c r+c c c r = g − g −g
gT =
r : r+c
ð1Þ
From 1980 to 2000 composite and mining sectors among U.S. states grew at an annual average rate of 1.54% and −4%, respectively. Given the relative slow growth of the mining sector, −(gc − gr) is negative and there will be a negative correlation between growth (from 1980 to 2000) and 1980 mining dependence. To further understand growth implications of Eq. (1), consider an economy without any mining earnings prior to time t = 2. Between time t = 2 and t = 3, a resource discovery increases mining to 25% of total output. Assume growth rates in the composite and mining sectors are 1.54% and −4%, respectively. Fig. 2 plots the effect of the resource discovery on production and growth. Prior to the resource discovery growth is 1.54%. Immediately following the resource discovery growth is .75 ⁎ (.0154) + .25 ⁎ (−.04) = .155%. Eventually, resource earnings approach zero and income growth returns to 1.54%. Although the 2 Note that total production equals production in the resource industry plus production in the composite industry (i.e., T = r + c, where T is total production). Taking the total derivativeof T=r+ yields dT = ∂ r + ∂ c. Dividing by T yields c ∂r ∂c r+ c dT r c = ð∂r + ∂cÞ = ðr + cÞ = . T r+c
Growth in personal income per capita
A.G. James, R.G. James / Economics Letters 111 (2011) 194–196
0.03 0.025 0.02 0.015 0.01 0.005 0 -0.005
0
0.05
0.1
0.15
195
0.2
0.25
0.3
Mining Dependence (1980)
Fig. 1. Annual growth in personal income per capita (1980–2000) on 1980 mining dependence.
1 rt ^ = g n
+ n
ð2Þ
" n^ r # c 1 c r 1 rt e g + ct en^g −rt −ct 1 n^g c n^ n^ r t e −1 − e g −e g = n n n rt + ct rt + ct
ð3Þ c
15 10
GDP GDP net resource earnings Resource Earnings
5 0 1
6
11
16
21
26
31
36
Time (years) Fig. 2. GDP with a resource discovery.
The following regression is used to test for a negative correlation between growth and mining dependence
ri ð4Þ + i ri + ci Y00;i 1 ln where Gi = . Y00, i and Y80, i are levels of per capita 20 Y80;i ri personal income in 2000 and 1980 for state i, respectively. r i + ci measures state i's dependence on mining, where ri and ci are state i's earnings in the mining and composite sectors in 1980, respectively. A simple OLS regression was run to obtain estimates of β0 and β1. The regression results are reported in Table 1. The coefficient on mining dependence is negative (−.053) and significant at the 99% level. This result is consistent with the findings of Papyrakis and Gerlagh (2007) who test for and confirm the existence of the resource curse at the U.S. state-level. The estimation of Eq. (4) suggests that most of the resource curse can be explained by a relative slow growing mining sector. To see this, recall that −.04 and .0154 are the national average annual growth rates of the mining and composite sectors, respectively. c Substituting ^ ^r = −.04 and n = 20 into Eq. (3) yields a g = .0154, g coefficient on mining dependence equal to −.045. A t-test shows that −.045 and −.053 are not significantly different. This result is important because it suggests that common tests for the resource curse are measuring, to a degree, the difference between the growth Gi = β0 + β1
+ ct + n −rt −ct : r t + ct
Eq. (2) can be re-written as,
^ g =
20
Production
resource discovery lowers future growth it raises the level of current and future income. Alaska and Wyoming are excellent case studies for this analysis. In 1980 Wyoming was the most mining dependent state while Alaska was the seventh most mining dependent state. In 1980 nearly 25% of Wyoming's GDP came from mining while 6% of Alaska's GDP came from mining. Not surprisingly, Wyoming and Alaska experienced slow growth from 1980 to 2000. In fact, Alaska and Wyoming experienced slower growth from 1980 to 2000 than all other states. However, in 1980 Wyoming had the fourth highest personal income per capita while Alaska had the highest personal income per capita in the country. By 2000, levels of personal income per capita in Wyoming and Alaska were just slightly above the country average. Eq. (1) describes growth at a moment in time. A similar identity describes growth over a period of time. Let ^ g equal growth in per capita GDP between time t and t + n
r
where ^ g and ^ g are growth rates in the composite and mining sectors from year t to year t + n.3 Eq. (3) is similar to Eq. (1) in that the resource curse exists when growth in the composite sector is greater than growth in the mining sector. For example, in Eq. (3) there is a negative rt 1 n^g c n^g r is negative. correlation between ^ g and r + c when − n e −e t t In the following empirical section of this paper we investigate the degree to which a slow growing mining sector explains the resource curse at the U.S. state-level. 3. Empirical Investigation Data from 49 U.S. states are used. Delaware is dropped because of missing data points. All data were downloaded from the U.S. Census Bureau.4 All prices are in 1980 dollars. The dependent variable is the annual growth in per capita personal income from 1980 to 2000. Mining dependence is measured as the percent of GDP earned from mining in 1980.
1 ct + n 3 c Growth rates in the two sectors are defined as ^ g = n ln ct 1 rt + n ln . n rt 4 http://www.census.gov/support/DataDownload.htm.
r and ^ g =
Table 1 OLS estimation of Eq. (4). Dependent variable: Gi Variable Intercept Resource share R2 Note. *** corresponds to 99% significance.
Coefficient/std. err. .0183*** (.0007) −.0530*** (.0141) .230
196
A.G. James, R.G. James / Economics Letters 111 (2011) 194–196
Table 2 OLS estimation of Eq. (5). Dependent variable: Gi −^ gi Variable
Constant Resources
Coefficient
Coefficient
Coefficient
Coefficient
Coefficient
Coefficient
Coefficient
Std. err.
Std. err.
Std. err.
Std. err.
Std. err.
Std. err.
Std. err.
.0002 (.000) −.007 (.014)
.102*** (.036) −.008 (.013) −.011*** (.004)
.192*** (.051) .001 (.013) −.022*** (.006) .005 (.025) .132** (.050)
.200*** (.060) .021* (.012) −.021*** (.006) −.023 (.027) .123*** (.043) −.179*** (.060) .024 (.038)
.272*** (.060) .023** (.011) −.026*** (.006) −.075** (.030) .077* (.042) −.198*** (.055) .033 (.035) −.075*** (.025)
.266*** (.061) .025** (.011) −.026*** (.006) −.069** (.031) .083* (.043) −.202*** (.055) .039 (.036) −.070** (.026) −.003 (.003)
.006
.150
.269
.495
.585
.592
.287*** (.058) .028** (.010) −.029*** (.006) −.043 (.031) .087** (.040) −.162*** (.054) .021 (.034) −.057** (.025) −.004 (.003) 6.28e−06** 2.55e−06 .647
ln(Y80) High school College Young Old Poverty White Pop/sq. mile R2
Note. ***, **, and * correspond to 99%, 95% and 90% significance, respectively. All controls represent 1980 values. Resources is the mining dependence, ln(Y80) is the natural log of personal income per capita, high school is the percent of the population having only a high school diploma, college is the percent of the population with at least a four year college degree, young is the percent of the population under 5 years old, old is the percent of the population that is over 65 years old, poverty is the percent of the population living in poverty, white is the percent of the population considered Caucasian and pop/sq. mile is the population per square mile.
rates of resource and non-resource sectors. A more formal analysis follows. Let g ^i be state i's growth assuming its two sectors grow at the national r 1 c r 1 n^g c i e −1 − en^g −en^g , where average rate, i.e., g ^ = i n n r + ci i c r ^ g = −.04 and n = 20. To test for the existence of a resource g = .0154, ^ curse at the U.S. state-level, while accounting for the relative slow growth in mining, we estimate Eq. (5) below ri i Gi −^ g i = α2 + β2 r + c + β3′ Xi + 2 i i
ð5Þ
where Xi is a matrix of control variables including the natural log of initial personal income per capita, population per square mile and a host of socio-economic variables including age, poverty, race and education attainment levels. See James and Aadland (2010) for theoretical justifications of the covariates. We estimate Eq. (5) using OLS. As a robustness check, we initially regress growth on constant and mining dependence and incrementally add controls. As shown in Table 2, β2 is insignificant in three of the regression specifications. β2 is positive and significant in a majority of specifications. Surprisingly, once the relative slow growth in the mining sector is accounted for, we find evidence that mining dependence acts as a catalyst for growth.
4. Conclusion Growth regressions often estimate a negative coefficient on resource dependence. Some argue that the negative coefficient reflects the fact that resource industries hinder economic growth by impeding growth in non-resource sectors. While the extraction of natural resources has not been condemned, the caveat in the literature is clear: consume natural resources at your own risk. Our findings suggest that this need not be the case. Resource extraction may increase income for both current and future generations. The negative relationship between growth and mining dependence in the United States is largely, if not completely, due to the relative slow growth of mining industries. References James, A., Aadland, D., 2010. The curse of natural resources: an empirical investigation of U.S. counties. Journal of Resource and Energy Economics. doi:10.1016/j.reseneeco. 2010.05.006. Matsuyama, K., 1992. Agricultural productivity, comparative advantage, and economic growth. Journal of Economic Theory 58, 317–334. Papyrakis, E., Gerlagh, R., 2007. Resource abundance and economic growth in the U.S. European Economic Review 51 (4), 1011–1039. Sachs, J.D., Warner, A.M., 1995. Natural resource abundance and economic growth. National Bureau of Economic Research Working Paper No. 5398, December. Sachs, J.D., Warner, A.M., 2001. The curse of natural resources. European Economic Review 45, 827–838. Van Wijnbergen, S., 1984. The Dutch disease: a disease after all? Economic Journal 111, 295–321.
Contents lists available at ScienceDirect
Economics Letters j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / e c o l e t
Do resource dependent regions grow slower than they should? Alexander G. James a,⁎, Robert G. James b a b
Department of Economics and Finance, University of Wyoming, Laramie, WY 82071-3985, United States Department of Economics, California State University, Chico, Chico, CA 95928, United States
a r t i c l e
i n f o
Article history: Received 10 April 2010 Received in revised form 19 January 2011 Accepted 31 January 2011 Available online 16 February 2011
a b s t r a c t A large literature documents a negative correlation between income growth and resource dependence. This correlation has been named the resource curse. We present evidence that suggests that the resource curse can be explained by a slow growing resource sector. Published by Elsevier B.V.
JEL classification: Q2 Q3 Keywords: Resource dependence Economic growth Resource curse
1. Introduction A large literature documents a negative relationship between resource dependence and economic growth (James and Aadland, forthcoming; Papyrakis and Gerlagh, 2007; Sachs and Warner, 2001, 1995). This negative relationship has been named the “resource curse.”1 Perhaps the most prominent explanation of the resource curse is the Dutch disease. The Dutch disease occurs when natural resource industries “crowd-out” other growth promoting industries by increasing wages, the exchange rate or decreasing the level of investment flowing to non-resource sectors (Sachs and Warner, 1995; Matsuyama, 1992; Van Wijnbergen, 1984). In this paper, we explore an alternative explanation of the resource curse: a slow growing resource sector. An implication of this is that although a resource discovery will decrease future growth, it will increase the level of income for current and future generations. When the relative slow growth in the resource sector is accounted for, we find evidence that resource dependence positively effects economic growth at the U.S. state-level. 2. A Growth Identity Assume an economy is composed of a mining and composite sector (the composite sector is the entire economy less mining). Output per capita (referred to as output hereafter) in the mining and composite ⁎ Corresponding author. E-mail address: [email protected] (A.G. James). 1 See Fig. 1 for a plot of 1980 U.S. state mining dependence on growth. 0165-1765/$ – see front matter. Published by Elsevier B.V. doi:10.1016/j.econlet.2011.01.029
sectors are r and c, respectively. Instantaneous growth in the economy (gT) is a weighted average of growth in the composite sector (gc) and the mining sector (gr)2 r
c
g r+g c r+c c c r = g − g −g
gT =
r : r+c
ð1Þ
From 1980 to 2000 composite and mining sectors among U.S. states grew at an annual average rate of 1.54% and −4%, respectively. Given the relative slow growth of the mining sector, −(gc − gr) is negative and there will be a negative correlation between growth (from 1980 to 2000) and 1980 mining dependence. To further understand growth implications of Eq. (1), consider an economy without any mining earnings prior to time t = 2. Between time t = 2 and t = 3, a resource discovery increases mining to 25% of total output. Assume growth rates in the composite and mining sectors are 1.54% and −4%, respectively. Fig. 2 plots the effect of the resource discovery on production and growth. Prior to the resource discovery growth is 1.54%. Immediately following the resource discovery growth is .75 ⁎ (.0154) + .25 ⁎ (−.04) = .155%. Eventually, resource earnings approach zero and income growth returns to 1.54%. Although the 2 Note that total production equals production in the resource industry plus production in the composite industry (i.e., T = r + c, where T is total production). Taking the total derivativeof T=r+ yields dT = ∂ r + ∂ c. Dividing by T yields c ∂r ∂c r+ c dT r c = ð∂r + ∂cÞ = ðr + cÞ = . T r+c
Growth in personal income per capita
A.G. James, R.G. James / Economics Letters 111 (2011) 194–196
0.03 0.025 0.02 0.015 0.01 0.005 0 -0.005
0
0.05
0.1
0.15
195
0.2
0.25
0.3
Mining Dependence (1980)
Fig. 1. Annual growth in personal income per capita (1980–2000) on 1980 mining dependence.
1 rt ^ = g n
+ n
ð2Þ
" n^ r # c 1 c r 1 rt e g + ct en^g −rt −ct 1 n^g c n^ n^ r t e −1 − e g −e g = n n n rt + ct rt + ct
ð3Þ c
15 10
GDP GDP net resource earnings Resource Earnings
5 0 1
6
11
16
21
26
31
36
Time (years) Fig. 2. GDP with a resource discovery.
The following regression is used to test for a negative correlation between growth and mining dependence
ri ð4Þ + i ri + ci Y00;i 1 ln where Gi = . Y00, i and Y80, i are levels of per capita 20 Y80;i ri personal income in 2000 and 1980 for state i, respectively. r i + ci measures state i's dependence on mining, where ri and ci are state i's earnings in the mining and composite sectors in 1980, respectively. A simple OLS regression was run to obtain estimates of β0 and β1. The regression results are reported in Table 1. The coefficient on mining dependence is negative (−.053) and significant at the 99% level. This result is consistent with the findings of Papyrakis and Gerlagh (2007) who test for and confirm the existence of the resource curse at the U.S. state-level. The estimation of Eq. (4) suggests that most of the resource curse can be explained by a relative slow growing mining sector. To see this, recall that −.04 and .0154 are the national average annual growth rates of the mining and composite sectors, respectively. c Substituting ^ ^r = −.04 and n = 20 into Eq. (3) yields a g = .0154, g coefficient on mining dependence equal to −.045. A t-test shows that −.045 and −.053 are not significantly different. This result is important because it suggests that common tests for the resource curse are measuring, to a degree, the difference between the growth Gi = β0 + β1
+ ct + n −rt −ct : r t + ct
Eq. (2) can be re-written as,
^ g =
20
Production
resource discovery lowers future growth it raises the level of current and future income. Alaska and Wyoming are excellent case studies for this analysis. In 1980 Wyoming was the most mining dependent state while Alaska was the seventh most mining dependent state. In 1980 nearly 25% of Wyoming's GDP came from mining while 6% of Alaska's GDP came from mining. Not surprisingly, Wyoming and Alaska experienced slow growth from 1980 to 2000. In fact, Alaska and Wyoming experienced slower growth from 1980 to 2000 than all other states. However, in 1980 Wyoming had the fourth highest personal income per capita while Alaska had the highest personal income per capita in the country. By 2000, levels of personal income per capita in Wyoming and Alaska were just slightly above the country average. Eq. (1) describes growth at a moment in time. A similar identity describes growth over a period of time. Let ^ g equal growth in per capita GDP between time t and t + n
r
where ^ g and ^ g are growth rates in the composite and mining sectors from year t to year t + n.3 Eq. (3) is similar to Eq. (1) in that the resource curse exists when growth in the composite sector is greater than growth in the mining sector. For example, in Eq. (3) there is a negative rt 1 n^g c n^g r is negative. correlation between ^ g and r + c when − n e −e t t In the following empirical section of this paper we investigate the degree to which a slow growing mining sector explains the resource curse at the U.S. state-level. 3. Empirical Investigation Data from 49 U.S. states are used. Delaware is dropped because of missing data points. All data were downloaded from the U.S. Census Bureau.4 All prices are in 1980 dollars. The dependent variable is the annual growth in per capita personal income from 1980 to 2000. Mining dependence is measured as the percent of GDP earned from mining in 1980.
1 ct + n 3 c Growth rates in the two sectors are defined as ^ g = n ln ct 1 rt + n ln . n rt 4 http://www.census.gov/support/DataDownload.htm.
r and ^ g =
Table 1 OLS estimation of Eq. (4). Dependent variable: Gi Variable Intercept Resource share R2 Note. *** corresponds to 99% significance.
Coefficient/std. err. .0183*** (.0007) −.0530*** (.0141) .230
196
A.G. James, R.G. James / Economics Letters 111 (2011) 194–196
Table 2 OLS estimation of Eq. (5). Dependent variable: Gi −^ gi Variable
Constant Resources
Coefficient
Coefficient
Coefficient
Coefficient
Coefficient
Coefficient
Coefficient
Std. err.
Std. err.
Std. err.
Std. err.
Std. err.
Std. err.
Std. err.
.0002 (.000) −.007 (.014)
.102*** (.036) −.008 (.013) −.011*** (.004)
.192*** (.051) .001 (.013) −.022*** (.006) .005 (.025) .132** (.050)
.200*** (.060) .021* (.012) −.021*** (.006) −.023 (.027) .123*** (.043) −.179*** (.060) .024 (.038)
.272*** (.060) .023** (.011) −.026*** (.006) −.075** (.030) .077* (.042) −.198*** (.055) .033 (.035) −.075*** (.025)
.266*** (.061) .025** (.011) −.026*** (.006) −.069** (.031) .083* (.043) −.202*** (.055) .039 (.036) −.070** (.026) −.003 (.003)
.006
.150
.269
.495
.585
.592
.287*** (.058) .028** (.010) −.029*** (.006) −.043 (.031) .087** (.040) −.162*** (.054) .021 (.034) −.057** (.025) −.004 (.003) 6.28e−06** 2.55e−06 .647
ln(Y80) High school College Young Old Poverty White Pop/sq. mile R2
Note. ***, **, and * correspond to 99%, 95% and 90% significance, respectively. All controls represent 1980 values. Resources is the mining dependence, ln(Y80) is the natural log of personal income per capita, high school is the percent of the population having only a high school diploma, college is the percent of the population with at least a four year college degree, young is the percent of the population under 5 years old, old is the percent of the population that is over 65 years old, poverty is the percent of the population living in poverty, white is the percent of the population considered Caucasian and pop/sq. mile is the population per square mile.
rates of resource and non-resource sectors. A more formal analysis follows. Let g ^i be state i's growth assuming its two sectors grow at the national r 1 c r 1 n^g c i e −1 − en^g −en^g , where average rate, i.e., g ^ = i n n r + ci i c r ^ g = −.04 and n = 20. To test for the existence of a resource g = .0154, ^ curse at the U.S. state-level, while accounting for the relative slow growth in mining, we estimate Eq. (5) below ri i Gi −^ g i = α2 + β2 r + c + β3′ Xi + 2 i i
ð5Þ
where Xi is a matrix of control variables including the natural log of initial personal income per capita, population per square mile and a host of socio-economic variables including age, poverty, race and education attainment levels. See James and Aadland (2010) for theoretical justifications of the covariates. We estimate Eq. (5) using OLS. As a robustness check, we initially regress growth on constant and mining dependence and incrementally add controls. As shown in Table 2, β2 is insignificant in three of the regression specifications. β2 is positive and significant in a majority of specifications. Surprisingly, once the relative slow growth in the mining sector is accounted for, we find evidence that mining dependence acts as a catalyst for growth.
4. Conclusion Growth regressions often estimate a negative coefficient on resource dependence. Some argue that the negative coefficient reflects the fact that resource industries hinder economic growth by impeding growth in non-resource sectors. While the extraction of natural resources has not been condemned, the caveat in the literature is clear: consume natural resources at your own risk. Our findings suggest that this need not be the case. Resource extraction may increase income for both current and future generations. The negative relationship between growth and mining dependence in the United States is largely, if not completely, due to the relative slow growth of mining industries. References James, A., Aadland, D., 2010. The curse of natural resources: an empirical investigation of U.S. counties. Journal of Resource and Energy Economics. doi:10.1016/j.reseneeco. 2010.05.006. Matsuyama, K., 1992. Agricultural productivity, comparative advantage, and economic growth. Journal of Economic Theory 58, 317–334. Papyrakis, E., Gerlagh, R., 2007. Resource abundance and economic growth in the U.S. European Economic Review 51 (4), 1011–1039. Sachs, J.D., Warner, A.M., 1995. Natural resource abundance and economic growth. National Bureau of Economic Research Working Paper No. 5398, December. Sachs, J.D., Warner, A.M., 2001. The curse of natural resources. European Economic Review 45, 827–838. Van Wijnbergen, S., 1984. The Dutch disease: a disease after all? Economic Journal 111, 295–321.