Soaring economic growth for the sake of environmental degradation? - PowerPoint PPT Presentation

Soaring economic growth for the sake of environmental degradation? Evidence using second-generation panel methods Lars Sorge and Anne Neumann Vienna, 05 September 2017 Lars Sorge and Anne Neumann Vienna, 05 September 2017 1 / 27

Motivation ’[...] there is clear evidence, that [...], in the end the best - and probably the only - way to attain a decent environment in most countries is to become rich.’ (Beckerman, 1992) Lars Sorge and Anne Neumann Vienna, 05 September 2017 2 / 27

Contribution Inclusion of CO 2 emissions in EKC framework Application of recently developed nonstationary panel data methods Consideration of international trade Transparent documentation of data and method Lars Sorge and Anne Neumann Vienna, 05 September 2017 3 / 27

Agenda Agenda 1 Literature review 2 Data and empirical strategy Empirical Specification Data Cross-section dependence test Second-generation panel unit root test Second-generation panel cointegration test Panel long-run estimation Panel causality test 3 Results 4 Conclusions, policy implications, further research Lars Sorge and Anne Neumann Vienna, 05 September 2017 4 / 27

Literature Review Development of the literature Threshold income (Environmental Kuznets Curve, EKC) Grossmann and Kruger, 1991 (QJE): ’We find no evidence that environmental quality deteriorates steadily with economic growth’ controversial evidence Causality energy consumption - economic growth higher development requires more energy consumption; more efficient energy use requires more economic development (Kraft and Kraft, 1978) conflicting evidence Energy consumption - economic growth - environmental pollutants impact of energy consumption and economic growth on CO 2 emissions (and considering trade) Lars Sorge and Anne Neumann Vienna, 05 September 2017 5 / 27

Empirical Specification Empirical Specification Model 1 CO 2 it = α i + β 1 i GDP it + β 2 i GDP 2 it + β 3 i E it + ǫ it CO 2 : CO 2 emissions per capita in metric tons per capita GDP : GDP per capita in constant 2010 USD E : energy consumption in kg of oil equivalents Lars Sorge and Anne Neumann Vienna, 05 September 2017 6 / 27

Empirical Specification Empirical Specification Model 2 CO 2 it = α i + β 1 i GDP it + β 2 i GDP 2 it + β 3 i E it + β 4 i T it + ǫ it T : trade-openness as sum of exports and imports of goods and services measured as share of gross domestic product Lars Sorge and Anne Neumann Vienna, 05 September 2017 7 / 27

Data Data Summary statistics by panel World Bank Development Indicator database WTO countries ( N = 70), 1971 - 2013 ( t = 43) clustered in three panels ( N high = 29, N middle = 17, N lower = 24) Lars Sorge and Anne Neumann Vienna, 05 September 2017 8 / 27

Data CO 2 , GDP, energy consumption, trade-openness (in logs) Lars Sorge and Anne Neumann Vienna, 05 September 2017 9 / 27

Empirical Strategy Empirical strategy 1 detect contemporaneous correlation among countries after controlling for individual characteristics (i.e. global shocks, local interactions) 2 test for unit roots in the presence of cross-section dependence from a single common factor 3 check for cointegration 4 dynamic long-run estimation of non-stationary cointegrated panels and calculation of turning point income 5 identify causal relationship among the variables Lars Sorge and Anne Neumann Vienna, 05 September 2017 10 / 27

Empirical Strategy Cross-section dependence test contemporaneous correlation among countries that is left over after controlling for individual characteristics (Moscone and Tosetti, 2009) first-generation panel methods assume cross-sectional independence Pesaran (2004) CD test is robust to the presence of nonstationary processes, parameter heterogeneity or structural breaks, ... and perfoms well in small samples. Lars Sorge and Anne Neumann Vienna, 05 September 2017 11 / 27

Empirical Strategy Second-generation panel unit root test using nonstationary variables can lead to apparently significant regression results although the data is unrelated Pesaran (2007) CIPS panel unit root test Cross-sectionally augmented Im-Pesaran-Shin (2003) (IPS) test J J � � ∆ y it = δ ‘ i d t + ρ i y i , t − 1 + c i y t − 1 + d ij ∆ y t − j + β ij ∆ y i , t − j + ǫ it j =0 j =1 H 0 : ρ i = 0 is tested against H 1 : ρ i < 0 and H 1 : ρ i = 0 Lars Sorge and Anne Neumann Vienna, 05 September 2017 12 / 27

Empirical Strategy Second-generation panel cointegration test Westerlund (2007) error-correction-based panel cointegration test Based on structural rather than residual dynamics J i J i ∆ y it = δ ‘ i d t + α i ( y i , t − 1 − β ‘ � � i X i , t − 1 )+ α ij ∆ y i , t − j + γ ij ∆ X i , t − j + ǫ it j =1 j =0 H 0 : α i = 0: no error-correction implies no cointegration H 1 : α i = α < 0 for panel statistics P τ and P α H 1 : α i < 0 for group mean statistics G τ and G α Bootstrapped critical values to account for cross-sectional dependence Lars Sorge and Anne Neumann Vienna, 05 September 2017 13 / 27

Empirical Strategy Panel long-run estimation validation of the EKC hypothesis and turning point income determination of the corresponding long-run elasticities Pedroni (2001) dynamic OLS (DOLS) estimator applicable to non-stationary cointegrated panels J i � y it = α i + β i X it + γ ij ∆ X i , t − j + ǫ it j = − j i slope coefficients β i are permitted to vary across panel members DOLS estimation technique adds lags and leads of differenced explanatory variables to control for endogeneity consistent and efficient estimates of the long-run relationship Lars Sorge and Anne Neumann Vienna, 05 September 2017 14 / 27

Empirical Strategy Panel causality test Pesaran et al. (1999) pooled mean group (PMG) estimator J J J � � � ∆ CO 2 it = α 1 i + β 11 ij ∆ CO 2 i , t − j + β 12 ij ∆ E i , t − j + β 13 ij ∆ GDP i , t − j j =1 j =1 j =1 J � β 14 ij ∆ GDP 2 + i , t − j + λ 1 i ECT i , t − 1 + ǫ 1 it j =1 i = countries, t = years, ∆ = first difference operator, j = lags, λ ki = speed of adjustment ECT t − 1 = CO 2 t − 1 − β 1 GDP t − 1 − β 2 GDP 2 t − 1 − β 3 E t − 1 Short-run causality: significance of the lagged dynamic terms Long-run causality: significance of the ECT Lars Sorge and Anne Neumann Vienna, 05 September 2017 15 / 27

Empirical Results Empirical Results I Peasaran (2004) CD test all series are highly dependent across all income groups Pesaran (2007) panel unit root test: strong evidence that a panel unit root in the variables exists Westerlund (2007) panel cointegration tests: test statistics with smallest size distortios when cross-sectional dependence is present indicate a long-run equilibrium relationship Implications First generation panel data methods are inappropriate Non-stationary variables that are found to be cointegrated are involved in EKC regression Lars Sorge and Anne Neumann Vienna, 05 September 2017 16 / 27

Empirical Results Empirical Results II: Pedroni (2001) DOLS estimates Empirical evidence of an inverted U-shape pattern Lars Sorge and Anne Neumann Vienna, 05 September 2017 17 / 27

Empirical Results Empirical Results III: Summary Lars Sorge and Anne Neumann Vienna, 05 September 2017 18 / 27

Conclusion Findings We find cross-section dependence within all panels. All series are I(1). We find evidence for cointegration. There is empirical support of an inverted U-shape pattern within all panels for model 1 and model 2. positive coefficient for energy consumption negative relation between trade-openness and per capita CO 2 emissions only for high- and middle-income panel Lars Sorge and Anne Neumann Vienna, 05 September 2017 19 / 27

Conclusion Conclusion Economic growth accompanied by environmental degradation in early stages of economic development. Policies supporting sustainable economic growth improve environmental quality. The simple existence of the EKC relationship does not guarantee that CO 2 -emissions will decrease automatically with economic growth. for high-income panel results support conversion hypothesis (economic growth leads energy consumption) for middle- and lower-income panels support neutrality hypothesis (no causal relationship between income and energy consumption) → a reduction in energy consumption might not affect economic growth negatively Lars Sorge and Anne Neumann Vienna, 05 September 2017 20 / 27

Conclusion Thank you. aneumann[at]diw.de Lars Sorge and Anne Neumann Vienna, 05 September 2017 21 / 27

Conclusion Causality analysis middle-income panel Lars Sorge and Anne Neumann Vienna, 05 September 2017 22 / 27

Conclusion Causality analysis lower-income panel Lars Sorge and Anne Neumann Vienna, 05 September 2017 23 / 27

Conclusion Results Pesaran (2004) CD test Lars Sorge and Anne Neumann Vienna, 05 September 2017 24 / 27

Conclusion Results Pesaran (2007) unit root test Lars Sorge and Anne Neumann Vienna, 05 September 2017 25 / 27

Conclusion Results Westerlund (2007) panel cointegration test Lars Sorge and Anne Neumann Vienna, 05 September 2017 26 / 27

Conclusion Empirical Results II: Causality analysis high-income panel unidirectional causality CO 2 → E and T → E bidriectional causality CO 2 ↔ T and GDP ↔ T Lars Sorge and Anne Neumann Vienna, 05 September 2017 27 / 27