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Curbing corruption for higher growth: The importance of persistence
Economics Letters 116 (2012) 255–257
Contents lists available at SciVerse ScienceDirect
Economics Letters journal homepage: www.elsevier.com/locate/ecolet
Curbing corruption for higher growth: The importance of persistence Mushfiq Swaleheen ∗ Department of Economics and Finance, Lutgert College of Business, Florida Gulf Coast University, 10501 FGCU Boulevard South, Fort Myers, FL 33965-6565, USA
article
info
Article history: Received 24 November 2011 Received in revised form 6 March 2012 Accepted 11 March 2012 Available online 15 March 2012
abstract There is a view in the literature that curbing corruption is concurrently growth augmenting. We present evidence that such is not always the case: independent of its indirect effects, a drop in corruption is growth augmenting only if there has been a persistent decline in corruption in the past. © 2012 Elsevier B.V. All rights reserved.
JEL classification: H8 O4 Keywords: Corruption Growth Generalized method of moments (GMM)
1. Introduction It is widely believed that curbing corruption will spur a country’s economic growth concurrently (Mauro, 1995; Mo, 2001; Pelligrini and Gerlagh, 2004; Méon and Sekkat, 2005). We present evidence that it is not always true. A fall in corruption is good for growth only if it is part of a declining trend. The letter is organized as follows: the econometric model and the empirical strategy are motivated in Section 2. A brief discussion of the data is in Section 3. The empirical results are discussed in Section 4. Section 5 concludes. 2. The econometric model and empirical strategy 2.1. The econometric model The deterministic part of the model in Eq. (1) follows the existing literature, except for the inclusion of an interaction term. log GDPi,t = α0 + α1 log GDPi,t −1 + α2 Ci,t + α3 INVi,t
+ α4 Trendi × Ci,t + β Xi′,t + µi + vi,t ,
(1)
where GDP is real gross domestic product per capita, C the absence of corruption, INV investment-GDP ratio, and X is a matrix of control variables that includes the degree of openness
∗
Tel.: +1 239 590 7331; fax: +1 239 590 7330. E-mail address: [email protected].
0165-1765/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.econlet.2012.03.011
(OPEN), the ratio of general government expenditure to GDP (GE), primary education enrollment rate (PEDU), secondary education enrollment rate (SEDU), the growth rate of population (POP), political stability (POLSTAB). INV and C are endogenous i.e., E [INVit vis ] = / = 0 and E [Cit vis ] = / = 0 for all t, s = 1, . . . , T , and other explanatory variables are either exogenous or predetermined.1 µ represent unobserved country fixed effects which is correlated with INV and C , and possibly other explanatory variables. The coefficient of C measures the effect of corruption on the growth of per capita income in percent point terms, ceteris paribus. The problem posed by short term volatility of the rate of growth (which is essentially a long run process) is addressed by using five-yearly averages of the variables as single data points. Thus the time subscript t denotes one five year period. Subscript i indicates country. Trendi is the average of the annual changes in corruption in the ith country. Trendi is negative if a country experienced declining corruption during 1985–2006, and positive if corruption increased. Following Eq. (1), the partial effect of corruption on the growth rate is given by,
∂ log GDPi,t = α2 + α4 Trendi . ∂ Ci,t
(2)
A negative value of the above expression indicates that a drop in corruption is growth augmenting. The current literature focuses
1 A variable is predetermined when its current and lagged values are uncorrelated with the current error term.
256
M. Swaleheen / Economics Letters 116 (2012) 255–257
Table 1 Summary statistics. Variable
GDP per capita (2000 US $) Primary enrollment ratio (%) Secondary enrollment ratio (%) Population growth rate (%) Political stability (Years) Openness (Trade/GDP ratio, %) Government exp. as ratio of GDP (%) Investment/GDP ratio (%) Corruption
Symbol
GDP PEDU SEDU POP POLSTAB OPEN GE INV C
Standard deviation Mean
Within
Overall
5725 98.15 67.16 1.34 23.78 66.17 16.43 22.51 2.95
1592 9.33 8.96 0.81 8.10 19.57 2.91 8.10 0.65
8694 19.32 32.75 1.40 29.86 46.30 7.02 29.87 1.32
on the first right hand term in Eq. (2) and it is expected to be negative. With respect to the second term, if α4 is positive then a sustained reduction in corruption (Trendi is negative) adds to the negative value of the first term while a history of failure to rein in corruption (Trendi is positive) will lead to a smaller negative value. If α4 works out to be negative then a sustained reduction in corruption is harmful to economic growth. 2.2. Estimation options We make use of Arellano and Bover (1995); Blundell and Bond (1998) generalized method of moments (GMM) to estimate Eq. (1). The GMM estimator uses first differencing to eliminate µi from the estimating model and use several moment conditions involving lagged and level values of the explanatory variables for an instrumental variable estimation of the parameters. We also use robust standard errors in testing hypotheses in general. Finally, the Sargan test of over-identifying restrictions is used to test whether the model is identified and a test for the absence of a second order auto correlation in the error term is implemented to ensure that our estimates are consistent. 3. The data 3.1. Measuring corruption Several authors have used the Corruption in Government index from the International Country Risk Guide (ICRG) prepared by Political Risk Services (referred to as the ICRG index hereafter) to measure corruption. We do the same because it is available for the longest length of time. The index ranges from 0 to 6 with a higher number signifying lower corruption. For the ease of interpretation, we rescaled the index so that a higher number means a higher incidence of corruption.2 3.2. Definition and sources for data on other variables Political stability is proxied by Durable which is the number of years since the most recent regime change. The durable measure is obtained from the Polity IV data base.3 Readers are referred to the World Bank’s World Development Indicators database for detailed definition of the remaining variables. The summary statistics are presented in Table 1.
2 It is to be noted that corruption has persistence. Moreover, the perceptions based indices (e.g., the ICRG index) are constructed from multi country surveys that may be volatile. Changes in the index, therefore, may be small and may not necessarily reflect changes in corruption perceptions. I am grateful to an anonymous referee for pointing out this caveat. 3 Readers are referred to Marshall and Jaggers (2002) for a discussion of the Polity IV data base and the variable definitions.
Number of Countries
Obs.
166 168 167 172 150 168 169 170 144
802 537 507 856 722 789 776 788 872
4. Empirical results 4.1. The concurrent relationship between corruption and the growth rate Table 2 present the estimates obtained for different versions of the model in Eq. (1). These are two step estimates meaning that the variance–covariance weighting matrix for the estimator is obtained by using the residuals from a first step model that is estimated under an assumption of homoskedasticity. The two-step GMM estimates’ standard error is known to be biased downwards and we use Windmeijer’s (2005) bias-corrected robust standard errors to obtain the t-statistics for hypotheses testing. The GMM model in column 1 satisfies the Sargan test of overidentifying restrictions and the Arellano–Bond test for absence of autocorrelation in the differenced errors. The coefficient of Ci,t is negative and significant and its size indicates that a one standard deviation (1.322) drop in the incidence of corruption will increase the growth rate concurrently by close to a half percentage point. The coefficients for the control variables have the expected signs in most cases: the elasticity of current real per-capita GDP with respect to its own past value is quite high confirming that GDPi,t has persistence. A higher rate of investment, more education, political stability and a smaller government promotes growth and a high rate of growth of the population lowers growth. The coefficient for OPEN i,t is not statistically or economically significant. The use of robust standard errors does not change much of these results (column-2). 4.2. The role of a persistent decline in corruption The estimates reported in columns 3 and 4 are similar to the models in columns 1 and 2 respectively but with the interaction term Trendi × Ci,t added. A comparison of columns 3 and 4 indicate that the tests of significance of most of the variables stand up to the use of robust standard errors and the estimated coefficients have the expected signs. The coefficients of C and the interaction term have the expected signs and are statistically significant. Thus, if the trend in corruption is declining (Trendi < 0), a current fall in corruption is unambiguously growth enhancing. However, if Trendi is positive, then the effect of a current fall in corruption on growth is ambiguous. We further analyzed the data after splitting the sample by the historical trend in corruption and the results are presented in Table 3. In the case of the countries that have a history of declining corruption, the coefficient for the interaction term has the expected positive sign (columns 3–4). And, in the case of countries that do not have a history of falling corruption, the coefficient for the interaction term has the expected negative sign (columns 1–2). Even if we ignore the results in columns 3–4 owing to a small sample size; the results in columns 1–2 does establish that without a history of previous success, curbing corruption do not produce a concurrent growth effect.
M. Swaleheen / Economics Letters 116 (2012) 255–257
257
Table 2 Full sample estimates—dependent variable, log real per capita GDP. Explanatory variables
GMM estimates (1)
Robust estimates (2)
GMM estimates (3)
Robust estimates (4)
Log real GDP per capita (Lagged once) Primary enrollment rate
0.947 (50.04) 0.002 (5.65) 0.001 (3.58) – −0.050 (−6.10) −0.007 (−3.67) 0.001 (1.74) 0.011 (6.55) −0.037 (−4.46)
0.947 (27.92) 0.002 (2.82) 0.001 (1.86) – −0.050 (−3.25) −0.007 (−1.94) 0.001 (0.87) 0.011 (3.81) −0.037 (−2.24)
0.942 (52.29) 0.003 (8.68) 0.001 (3.04) – −0.029 (−3.01) −0.012 (−5.63) 0.002 (2.95) 0.012 (8.38) −0.038 (−3.92) 0.036 (3.26) 0.00 0.11 0.31 0.34 117 334
0.942 (25.63) 0.003 (3.64) 0.001 (1.40) – −0.029 (−1.52) −0.012 (−2.50) 0.002 (1.24) 0.012 (4.41) −0.038 (−1.71) 0.036 (2.18) 0.00 Not applicable 0.32 0.35 117 334
Secondary enrollment rate Trade-GDP ratio Population growth rate Government expenditure -GDP ratio Political stability Investment-GDP ratio -GDP ratio Corruption Trend × Corruption Wald test (p-value) Sargan test (p-value) H0 : absence of AR(1) (p-value) H0 : absence of AR(2) (p-value) Countries Observations
0.00 0.15 0.27 0.59 117 334
0.00 Not applicable 0.28 0.59 117 334
Note. Dashes indicate a value close to zero. t-statistics in parentheses. Table 3 Sub-sample estimates-dependent variable, log real per capita GDP. Explanatory variables
Countries with Trend > 0 GMM estimates (1)
Log real GDP per capita (lagged once) Primary enrollment rate Secondary enrollment rate Trade-GDP ratio Population growth rate Government expenditure -GDP ratio Political stability Investment-GDP ratio Corruption Trend × corruptiona Wald test (p-value) Sargan test (p-value) H0 : absence of AR(1) (p-value) H0 : absence of AR(2) (p-value) Countries Observations
Countries with Trend < 0 Robust estimates (2)
0.877 (40.95) 0.003 (9.19) 0.001 (4.54) –
0.877 (14.40) 0.003 (2.98) 0.001 (2.25) –
−0.046 (−5.63) −0.007 (−2.70)
−0.046 (−1.77) −0.007 (−1.08)
0.004 (5.06) 0.012 (6.82) −0.008 (−0.84) −0.016 (−0.01) 0.00 0.19 0.43 0.50 97 266
0.004 (1.77) 0.012 (3.10) −0.008 (−0.37) −0.016 (−0.00) 0.00 Not applicable 0.48 0.51 97 266
GMM estimates (3)
Robust estimates (4)
1.00 (38.90) 0.002 (3.36) –
1.00 (2.28) 0.002 (0.50) –
−0.001 (−0.73) −0.004 (−0.09)
−0.001 (−0.11) −0.004 (−0.01)
–
–
−0.008 (−2.79)
−0.008 (−0.46)
0.007 (1.19) −0.012 (−0.44) 21.034 (1.91) 0.00 1.00 0.32 0.51 20 58
0.007 (0.14) −0.012 (−0.12) 21.034 (0.28) 0.00 Not applicable 0.71 0.93 20 58
Note. Dashes indicate a value close to zero. t-statistics in parentheses. a Coefficient multiplied by 100.
5. Conclusion That corruption is harmful to economic growth is now widely accepted and curbing corruption is at the top of the policy agenda of governments and aid agencies worldwide. We present evidence that growth responds to lower corruption only if the fall in corruption is a part of a declining trend. Thus, persistence will matter in getting a growth dividend from curbing corruption. References Arellano, M., Bover, O., 1995. Another look at the instrumental variable estimation of error-component models. Journal of Econometrics 68, 29–51.
Blundell, R., Bond, S., 1998. Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics 87, 115–143. Marshall, M.G., Jaggers, K., 2002, Polity IV Project: Dataset and User’s Manual, University of Maryland, College Park. Mauro, P., 1995. Corruption and growth. Quarterly Journal of Economics 110, 681–712. Méon, P., Sekkat, K., 2005. Does corruption grease or sand the wheels of growth? Public Choice 122, 69–97. Mo, P.H., 2001. Corruption and economic growth. Journal of Comparative Economics 29, 66–79. Pelligrini, L., Gerlagh, R., 2004. Corruptions effect on growth and its transmission channels. Kyklos 57 (3), 429–456. Windmeijer, F., 2005. A finite sample correction for the variance of linear efficient two-step GMM estimators. Journal of Econometrics 126, 25–51.
Contents lists available at SciVerse ScienceDirect
Economics Letters journal homepage: www.elsevier.com/locate/ecolet
Curbing corruption for higher growth: The importance of persistence Mushfiq Swaleheen ∗ Department of Economics and Finance, Lutgert College of Business, Florida Gulf Coast University, 10501 FGCU Boulevard South, Fort Myers, FL 33965-6565, USA
article
info
Article history: Received 24 November 2011 Received in revised form 6 March 2012 Accepted 11 March 2012 Available online 15 March 2012
abstract There is a view in the literature that curbing corruption is concurrently growth augmenting. We present evidence that such is not always the case: independent of its indirect effects, a drop in corruption is growth augmenting only if there has been a persistent decline in corruption in the past. © 2012 Elsevier B.V. All rights reserved.
JEL classification: H8 O4 Keywords: Corruption Growth Generalized method of moments (GMM)
1. Introduction It is widely believed that curbing corruption will spur a country’s economic growth concurrently (Mauro, 1995; Mo, 2001; Pelligrini and Gerlagh, 2004; Méon and Sekkat, 2005). We present evidence that it is not always true. A fall in corruption is good for growth only if it is part of a declining trend. The letter is organized as follows: the econometric model and the empirical strategy are motivated in Section 2. A brief discussion of the data is in Section 3. The empirical results are discussed in Section 4. Section 5 concludes. 2. The econometric model and empirical strategy 2.1. The econometric model The deterministic part of the model in Eq. (1) follows the existing literature, except for the inclusion of an interaction term. log GDPi,t = α0 + α1 log GDPi,t −1 + α2 Ci,t + α3 INVi,t
+ α4 Trendi × Ci,t + β Xi′,t + µi + vi,t ,
(1)
where GDP is real gross domestic product per capita, C the absence of corruption, INV investment-GDP ratio, and X is a matrix of control variables that includes the degree of openness
∗
Tel.: +1 239 590 7331; fax: +1 239 590 7330. E-mail address: [email protected].
0165-1765/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.econlet.2012.03.011
(OPEN), the ratio of general government expenditure to GDP (GE), primary education enrollment rate (PEDU), secondary education enrollment rate (SEDU), the growth rate of population (POP), political stability (POLSTAB). INV and C are endogenous i.e., E [INVit vis ] = / = 0 and E [Cit vis ] = / = 0 for all t, s = 1, . . . , T , and other explanatory variables are either exogenous or predetermined.1 µ represent unobserved country fixed effects which is correlated with INV and C , and possibly other explanatory variables. The coefficient of C measures the effect of corruption on the growth of per capita income in percent point terms, ceteris paribus. The problem posed by short term volatility of the rate of growth (which is essentially a long run process) is addressed by using five-yearly averages of the variables as single data points. Thus the time subscript t denotes one five year period. Subscript i indicates country. Trendi is the average of the annual changes in corruption in the ith country. Trendi is negative if a country experienced declining corruption during 1985–2006, and positive if corruption increased. Following Eq. (1), the partial effect of corruption on the growth rate is given by,
∂ log GDPi,t = α2 + α4 Trendi . ∂ Ci,t
(2)
A negative value of the above expression indicates that a drop in corruption is growth augmenting. The current literature focuses
1 A variable is predetermined when its current and lagged values are uncorrelated with the current error term.
256
M. Swaleheen / Economics Letters 116 (2012) 255–257
Table 1 Summary statistics. Variable
GDP per capita (2000 US $) Primary enrollment ratio (%) Secondary enrollment ratio (%) Population growth rate (%) Political stability (Years) Openness (Trade/GDP ratio, %) Government exp. as ratio of GDP (%) Investment/GDP ratio (%) Corruption
Symbol
GDP PEDU SEDU POP POLSTAB OPEN GE INV C
Standard deviation Mean
Within
Overall
5725 98.15 67.16 1.34 23.78 66.17 16.43 22.51 2.95
1592 9.33 8.96 0.81 8.10 19.57 2.91 8.10 0.65
8694 19.32 32.75 1.40 29.86 46.30 7.02 29.87 1.32
on the first right hand term in Eq. (2) and it is expected to be negative. With respect to the second term, if α4 is positive then a sustained reduction in corruption (Trendi is negative) adds to the negative value of the first term while a history of failure to rein in corruption (Trendi is positive) will lead to a smaller negative value. If α4 works out to be negative then a sustained reduction in corruption is harmful to economic growth. 2.2. Estimation options We make use of Arellano and Bover (1995); Blundell and Bond (1998) generalized method of moments (GMM) to estimate Eq. (1). The GMM estimator uses first differencing to eliminate µi from the estimating model and use several moment conditions involving lagged and level values of the explanatory variables for an instrumental variable estimation of the parameters. We also use robust standard errors in testing hypotheses in general. Finally, the Sargan test of over-identifying restrictions is used to test whether the model is identified and a test for the absence of a second order auto correlation in the error term is implemented to ensure that our estimates are consistent. 3. The data 3.1. Measuring corruption Several authors have used the Corruption in Government index from the International Country Risk Guide (ICRG) prepared by Political Risk Services (referred to as the ICRG index hereafter) to measure corruption. We do the same because it is available for the longest length of time. The index ranges from 0 to 6 with a higher number signifying lower corruption. For the ease of interpretation, we rescaled the index so that a higher number means a higher incidence of corruption.2 3.2. Definition and sources for data on other variables Political stability is proxied by Durable which is the number of years since the most recent regime change. The durable measure is obtained from the Polity IV data base.3 Readers are referred to the World Bank’s World Development Indicators database for detailed definition of the remaining variables. The summary statistics are presented in Table 1.
2 It is to be noted that corruption has persistence. Moreover, the perceptions based indices (e.g., the ICRG index) are constructed from multi country surveys that may be volatile. Changes in the index, therefore, may be small and may not necessarily reflect changes in corruption perceptions. I am grateful to an anonymous referee for pointing out this caveat. 3 Readers are referred to Marshall and Jaggers (2002) for a discussion of the Polity IV data base and the variable definitions.
Number of Countries
Obs.
166 168 167 172 150 168 169 170 144
802 537 507 856 722 789 776 788 872
4. Empirical results 4.1. The concurrent relationship between corruption and the growth rate Table 2 present the estimates obtained for different versions of the model in Eq. (1). These are two step estimates meaning that the variance–covariance weighting matrix for the estimator is obtained by using the residuals from a first step model that is estimated under an assumption of homoskedasticity. The two-step GMM estimates’ standard error is known to be biased downwards and we use Windmeijer’s (2005) bias-corrected robust standard errors to obtain the t-statistics for hypotheses testing. The GMM model in column 1 satisfies the Sargan test of overidentifying restrictions and the Arellano–Bond test for absence of autocorrelation in the differenced errors. The coefficient of Ci,t is negative and significant and its size indicates that a one standard deviation (1.322) drop in the incidence of corruption will increase the growth rate concurrently by close to a half percentage point. The coefficients for the control variables have the expected signs in most cases: the elasticity of current real per-capita GDP with respect to its own past value is quite high confirming that GDPi,t has persistence. A higher rate of investment, more education, political stability and a smaller government promotes growth and a high rate of growth of the population lowers growth. The coefficient for OPEN i,t is not statistically or economically significant. The use of robust standard errors does not change much of these results (column-2). 4.2. The role of a persistent decline in corruption The estimates reported in columns 3 and 4 are similar to the models in columns 1 and 2 respectively but with the interaction term Trendi × Ci,t added. A comparison of columns 3 and 4 indicate that the tests of significance of most of the variables stand up to the use of robust standard errors and the estimated coefficients have the expected signs. The coefficients of C and the interaction term have the expected signs and are statistically significant. Thus, if the trend in corruption is declining (Trendi < 0), a current fall in corruption is unambiguously growth enhancing. However, if Trendi is positive, then the effect of a current fall in corruption on growth is ambiguous. We further analyzed the data after splitting the sample by the historical trend in corruption and the results are presented in Table 3. In the case of the countries that have a history of declining corruption, the coefficient for the interaction term has the expected positive sign (columns 3–4). And, in the case of countries that do not have a history of falling corruption, the coefficient for the interaction term has the expected negative sign (columns 1–2). Even if we ignore the results in columns 3–4 owing to a small sample size; the results in columns 1–2 does establish that without a history of previous success, curbing corruption do not produce a concurrent growth effect.
M. Swaleheen / Economics Letters 116 (2012) 255–257
257
Table 2 Full sample estimates—dependent variable, log real per capita GDP. Explanatory variables
GMM estimates (1)
Robust estimates (2)
GMM estimates (3)
Robust estimates (4)
Log real GDP per capita (Lagged once) Primary enrollment rate
0.947 (50.04) 0.002 (5.65) 0.001 (3.58) – −0.050 (−6.10) −0.007 (−3.67) 0.001 (1.74) 0.011 (6.55) −0.037 (−4.46)
0.947 (27.92) 0.002 (2.82) 0.001 (1.86) – −0.050 (−3.25) −0.007 (−1.94) 0.001 (0.87) 0.011 (3.81) −0.037 (−2.24)
0.942 (52.29) 0.003 (8.68) 0.001 (3.04) – −0.029 (−3.01) −0.012 (−5.63) 0.002 (2.95) 0.012 (8.38) −0.038 (−3.92) 0.036 (3.26) 0.00 0.11 0.31 0.34 117 334
0.942 (25.63) 0.003 (3.64) 0.001 (1.40) – −0.029 (−1.52) −0.012 (−2.50) 0.002 (1.24) 0.012 (4.41) −0.038 (−1.71) 0.036 (2.18) 0.00 Not applicable 0.32 0.35 117 334
Secondary enrollment rate Trade-GDP ratio Population growth rate Government expenditure -GDP ratio Political stability Investment-GDP ratio -GDP ratio Corruption Trend × Corruption Wald test (p-value) Sargan test (p-value) H0 : absence of AR(1) (p-value) H0 : absence of AR(2) (p-value) Countries Observations
0.00 0.15 0.27 0.59 117 334
0.00 Not applicable 0.28 0.59 117 334
Note. Dashes indicate a value close to zero. t-statistics in parentheses. Table 3 Sub-sample estimates-dependent variable, log real per capita GDP. Explanatory variables
Countries with Trend > 0 GMM estimates (1)
Log real GDP per capita (lagged once) Primary enrollment rate Secondary enrollment rate Trade-GDP ratio Population growth rate Government expenditure -GDP ratio Political stability Investment-GDP ratio Corruption Trend × corruptiona Wald test (p-value) Sargan test (p-value) H0 : absence of AR(1) (p-value) H0 : absence of AR(2) (p-value) Countries Observations
Countries with Trend < 0 Robust estimates (2)
0.877 (40.95) 0.003 (9.19) 0.001 (4.54) –
0.877 (14.40) 0.003 (2.98) 0.001 (2.25) –
−0.046 (−5.63) −0.007 (−2.70)
−0.046 (−1.77) −0.007 (−1.08)
0.004 (5.06) 0.012 (6.82) −0.008 (−0.84) −0.016 (−0.01) 0.00 0.19 0.43 0.50 97 266
0.004 (1.77) 0.012 (3.10) −0.008 (−0.37) −0.016 (−0.00) 0.00 Not applicable 0.48 0.51 97 266
GMM estimates (3)
Robust estimates (4)
1.00 (38.90) 0.002 (3.36) –
1.00 (2.28) 0.002 (0.50) –
−0.001 (−0.73) −0.004 (−0.09)
−0.001 (−0.11) −0.004 (−0.01)
–
–
−0.008 (−2.79)
−0.008 (−0.46)
0.007 (1.19) −0.012 (−0.44) 21.034 (1.91) 0.00 1.00 0.32 0.51 20 58
0.007 (0.14) −0.012 (−0.12) 21.034 (0.28) 0.00 Not applicable 0.71 0.93 20 58
Note. Dashes indicate a value close to zero. t-statistics in parentheses. a Coefficient multiplied by 100.
5. Conclusion That corruption is harmful to economic growth is now widely accepted and curbing corruption is at the top of the policy agenda of governments and aid agencies worldwide. We present evidence that growth responds to lower corruption only if the fall in corruption is a part of a declining trend. Thus, persistence will matter in getting a growth dividend from curbing corruption. References Arellano, M., Bover, O., 1995. Another look at the instrumental variable estimation of error-component models. Journal of Econometrics 68, 29–51.
Blundell, R., Bond, S., 1998. Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics 87, 115–143. Marshall, M.G., Jaggers, K., 2002, Polity IV Project: Dataset and User’s Manual, University of Maryland, College Park. Mauro, P., 1995. Corruption and growth. Quarterly Journal of Economics 110, 681–712. Méon, P., Sekkat, K., 2005. Does corruption grease or sand the wheels of growth? Public Choice 122, 69–97. Mo, P.H., 2001. Corruption and economic growth. Journal of Comparative Economics 29, 66–79. Pelligrini, L., Gerlagh, R., 2004. Corruptions effect on growth and its transmission channels. Kyklos 57 (3), 429–456. Windmeijer, F., 2005. A finite sample correction for the variance of linear efficient two-step GMM estimators. Journal of Econometrics 126, 25–51.