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The Effect of Government Expenditure on the Environment

The Effect of Government Expenditure on the Environment. Prof. George Halkos & Mr Epameinondas Paizanos Lab of Operations Research, Dept of Economics University of Thessaly 6 th International Scientific Conference on Energy and Climate Change Athens, 10 October 2013.

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The Effect of Government Expenditure on the Environment

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  1. The Effect of Government Expenditure on the Environment • Prof. George Halkos & Mr Epameinondas Paizanos • Lab of Operations Research, Dept of Economics University of Thessaly • 6th International Scientific Conference on Energy and Climate Change • Athens, 10 October 2013 Prof. George E. Halkos & Epameinondas A. Paizanos 1

  2. Introduction • A large fraction of national GDP is spent by governments affecting a variety of economic variables and wealth in general. • Despite the important influence that public spending may have on the environment, this relationship has not been studied extensively in the literature. • The effect of government spending on the environment may be distinguished between direct and indirect effects. Prof. George E. Halkos & Epameinondas A. Paizanos 2

  3. Introduction (cont’d) • Four mechanisms by which level and composition of fiscal spending may affect pollution levels: • Scale effect (increased environmental pressures due to economic growth), • Composition effect (increased human capital intensive activities instead of physical capital intensive industries that harm environment more), • Technique effect (due to higher labor efficiency) • Income effect (increased income raises demand for improved environmental quality).

  4. Introduction (cont’d.) • The direct effect of government spending on pollution has been studied in Frederik and Lundstrom (2001), Bernauer and Koubi (2006), Lopez and Palacios (2010), Lopez et al. (2011), with contradicting empirical results. • In addition, government size has been found to influence prosperity (Bajo-Rubio, 2000; Folster and Henrekson, 2001; Bergh and Karlsson, 2010) which in turn may impose an indirect effect on pollution, depending on the shape of the Environmental Kuznets Curve (Grossman and Krueger, 1995). • Our paper is the first to distinguish and empirically estimate both direct and indirect effects of fiscal spending on the environment. Prof. George E. Halkos & Epameinondas A. Paizanos 4

  5. Data • Panel data for 77 countries with full set of SO2, CO2, share of GOVEXP, GDP/c and other explanatory variables for 1980-2000. 1617 observations per variable. • The two pollutants vary in geographical range of impact. Since SO2 moves away from the atmosphere within 10 days after its emission, impact is mainly local or regional, whereas CO2 emissions, whose atmospheric life is between 50 to 200 years, have a more global impact. • Based on sources of pollution, SO2 pollution characterized as production-generated, while CO2 emissions are mix between production and consumption–generated pollution. Prof. George E. Halkos & Epameinondas A. Paizanos 5

  6. Methodology • Model • Control variables in Eq. (1) include GDP/c (scale effect), household final consumption (income effect) and democracy level (proxy for environmental regulation). • In Eq. (1), coefficient of GOVSHARE (i.e. direct effect) mainly captures composition effect and part of technique effect. • Equation 2 is augmented Solow model: is a production function based formulation and expresses income as function of share of GOVEXP in GDP and other explanatory factors (investment, education, population growth, trade, inflation rate) 6 Prof. George E. Halkos & Epameinondas A. Paizanos

  7. Methodology (cont’d) • To consider correlation between cross-section specific error-component (e.g. country specific climate and geography) and explanatory variables, FE are used instead of RE. • Since our panel consists of large N and T dimensions, dynamics and non-stationarity are taken into account by employing the Dynamic Fixed Effects (DFE) estimator proposed by Pesaran and Smith. • To address bias occurring from potential endogeneity between GOVSPEN with pollution and income respectively, we apply A-B GMM which treats predetermined and exogenous variables as instruments in systematic way, while taking into account dynamics. • For equation (1) we set-up an initial general ADL. Nonlinearity in parameters requires models are estimated using ML. Prof. George E. Halkos & Epameinondas A. Paizanos 7

  8. Methodology (cont’d.) Prof. George E. Halkos & Epameinondas A. Paizanos 8

  9. Panel data methods • First method employed imposes same intercept and slope parameter for all countries (equivalent to OLS estimation). • Second method is the FE allowing each individual country to have a different intercept treating the δi and ζt as regression parameters.This practically means that the means of each variable for each country are subtracted from the data for that country and the mean for all countries in the sample in each individual time period is also deducted from the observations from that period. Then OLS is used to estimate the regression with the transformed data. • Third model is the RE in which individual effects are treated as random, with δi and ζt treated as components of the random disturbances. Residuals from an OLS estimate of the model with a single intercept are used to construct variances utilized in a GLS estimates. • To control for non-observable specific effects 2SLS was applied but the results were insignificant.

  10. GMM Arellano - Bond is adopted using the orthogonality conditions between the lags of the dependent and the disturbances as additional instruments. This is the GMM non-linear instrumental variable estimator t = q+1,…,Ti i = 1,2,...,N and where λt and ηi are specific and individual effects respectively, Xit is a vector of explanatory variables, β(L) is a vector of associated polynomials in the lag operator and q is the maximum lag length. Identification of the model requires restrictions on serial correlation of the error term vit and on the properties of the independent variables Xit allowing only for MA or white noise errors. If the error term was originally autoregressive, the model is transformed The (Ti –q) equations for individual unit i can be written as where δ is a parameter vector including the ακ's, the β's and the λ' s and wi is a data matrix containing the time series of the lagged endogenous variables, the x' s, and the time dummies. di is a (Ti­q) x1 vector of ones. Following Arellano and Bond (1998), linear GMM estimators of δ are computed by where wi* and Yi* denote some transformation of wi and Yi such as first differences, orthogonal deviations or levels. Zi is the matrix of instrumental variables and Hi is an individual specific weighting matrix.

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  12. Estimated effect of GOVEXP share on GDP negative and significant, regardless of the method used • Estimated coefficient of GOVEXP equal to -0.210, while DFE estimate of GOVSIZE effect on income -0.872 suggesting that consideration of dynamics increases estimated impact of GOVSHARE on income/c, even without accounting for endogeneity. • To account for autocorrelation and heteroskedasticity, standard errors reported are robust and for FE estimation we report Huber-White-Sandwich estimates of var-cov matrix.

  13. Estimated impact of GOVEXP on GDP even greater, suggesting that increase of 1% in share of GOVSPEN of GDP, ceteris paribus, reduces income/c by 1.809%. • Signs and significance of coefficients associated with other control variables are plausible and consistent with literature, apart from human capital proxy which although has the expected sign, is significant only in OLS estimates. • Impact of capital stock, represented by share of investment in GDP positive and significant across all estimation methods. • Population growth has consistent negative and significant effect, • Τrade-openness coefficient significant with expected positive sign.

  14. Applying AB two-step GMM estimator, dynamics are still taken into account but GOVSHARE is treated as endogenous. We use FD and OD GMM to control for fixed country effects. Significance of lagged dependent variable suggests dynamic specifications should be preferred. • Tests for 1st and 2nd order serial correlation related to residuals from the estimated equation are asymptotically-distributed as normal variables under Ho: no-serial correlation. Test for AR(1) rejected as expected, while there is no evidence that the assumption of serially uncorrelated errors is inappropriate. • For both equations we test validity of instruments with Hansen test, which failed to reject Ho: IVs are uncorrelated with residuals. We also report Difference Hansen test for exogenous IV subset which does not reject Ho that subset is valid.

  15. Dynamic Fixed Effects: DFE estimation assumes intercepts differ across countries but that LR coefficients are equal across countries. • Mean Group: Alternative estimation method that fits model for each country individually and calculates arithmetic average of the coefficients is the mean-group estimator (MG). This method is less restrictive than DFE since intercepts, slope coefficients and error variances are all allowed to differ across countries. • Pooled Mean Group: PMG estimator combines DFE and MG methods by allowing the intercept, short-run coefficients and error variances to differ across groups while assuming equality of LR coefficients. • Table 4a: Comparing the MG and PMG estimators, with the use of a Hausman test, we see that the PMG estimator, efficient estimator under H0, is preferred and thus, assuming LR coefficients to be equal across panels, is appropriate in our panel.

  16. Fisher-type Philips-Peron tests allowing heterogeneity of autoregressive parameters In all cases variables are I(1) Prof. George E. Halkos & Epameinondas A. Paizanos 16

  17. We reject Ho: no-cointegration in 4/7 cases for SO2 and in 5/7 in CO2. Evidence of cointegration. • Application of DFE requires variables cointegrated (LR relationship).DFE is applicable. Prof. George E. Halkos & Epameinondas A. Paizanos 17

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  20. Table 4 provides estimates of pollution/c utilizing GMM estimates of Eq.1. Based on FE estimates GOVSHARE of GDP has negative and significant direct effect on SO2/c and insignificant negative relationship on CO2/c. • Dynamics in columns 2 and 4 of Table 4. Comparing the MG and PMG estimators, with Hausman test, we see PMG estimator, the efficient estimator under Ho, is preferred and thus, assuming LR coefficients to be equal across panels, more appropriate. • Another application of Hausman test suggests that simultaneous equation bias between error term and lagged dependent variable minimal and DFE model is most appropriate. • DFE estimates suggest that GOVSHARE of income possesses a significant negative relationship with SO2/c and CO2/c. • Both pollutants have significant cubic relationship with income/c. Taking into account endogeneity in A-B GMM estimates produces turning points for CO2 well within sample.

  21. Household income effect is negative, although insignificant in all cases except for SO2 in FD GMM. • Share of investment increases pollution, but the effect is significant only for CO2. • Coefficient of trade-openness always negative, but mostly insignificant. • Effect of population density robustly positive • Democracy index insignificant in all specifications. • Negative direct effect of GOVSHARE of income on pollution is estimated by all models (Table 4) except in case of GMM (CO2). • In GMM results, increase of GOVEXP by 1%, results in 0.903% reduction of SO2/c. Direct effect on CO2 insignificant. Indirect effects negative at median income, leading to negative total effect for both pollutants.

  22. Direct effect is insignificant and considerably smaller for CO2, in absolute values. This result was expected, considering both pollutants’ impact on human health and technological capabilities of reducing their levels in the atmosphere. • In particular, SO2 emissions externalities are local and immediate while CO2 emissions externalities are global and occur mostly in the future. Moreover, local and instant environmental degradation, as in the case of SO2, increases demand for technological improvements to diminish that impact. 22 Prof. George E. Halkos & Epameinondas A. Paizanos

  23. The negative sign of indirect effect occurs from the positive relationship between income and pollution at the median income level. Explicitly, at this income level an increase in government spending leads to a reduction in income and, as a result, to a reduction in emissions. • The estimated indirect effects are notably larger than the direct effects. Prof. George E. Halkos & Epameinondas A. Paizanos 23

  24. The total effect of government share on SO2/c is negative for low levels of per capita income and then turns to positive (above $10,809), while the total effect on CO2/c is also negative but becomes positive only for very high income levels (above $16,438). • These patterns largely depend on the relationship between pollution and income levels described by the EKC. Prof. George E. Halkos & Epameinondas A. Paizanos 24

  25. Figures 1 and 2 present direct, indirect and total effects of GOVSHARE of income on emission levels against income/c. • For CO2 direct effect insignificant. Indirect effect increases with income/c, since =-1.809 • and falls from 1.27 to –7.17 for SO2/c and from 0.22 to -1.39 for CO2/c throughout the sample income range. These patterns depend on relationship between pollution and income levels (EKC).

  26. Total effect of GOVSHARE on SO2/c negative for low levels of income/c and then positive, while total effect on CO2/c also negative but becomes positive only for very high income levels. • Table 5 reports estimated income level at which total effect changes from negative to positive. GMM estimates indicate that this level is $10,809 for SO2/c and $16,438 for CO2/c, i.e. total effect of GOVSHARE of income on CO2/c negative through most of sample income range. • From figures pattern of total effect is determined by shape of indirect effect.

  27. Sensitivity analysis Relative tests indicate that the results are robust to : • Inclusion of a GOVEXP composition variable. • Omission of time-variant variables. • Inclusion of interactive terms like (GOVEXP x GDP/c). • Use of different model specifications. • Inclusion/exclusion of extreme observations. Prof. George E. Halkos & Epameinondas A. Paizanos 27

  28. Policy Implications • Policy implications, differ according to level of income in a country. • In general, reducing GOVSIZE enhances economic performance. • However, cutting GOVEXP should be undertaken with particular care for some levels of GDP. For SO2 and CO2 pollution, results suggest that reducing government size in countries with an income level less than $10,809 and $16,438 respectively, leads to deterioration of environmental quality. • Therefore, cutting government expenditure in these countries should be accompanied by appropriate environmental regulation along with the establishment of international environmental treaties. Prof. George E. Halkos & Epameinondas A. Paizanos 28

  29. Policy Implications (cont’d.) • Cutting GOVEXP in countries with higher income levels, leads to improvements in both income and environmental quality. • In particular, countries with income level at the decreasing area of the EKC are more likely to have already established the environmental legislation and to have undertaken public expenditures for improvement of environmental quality, hence they are susceptible to diminishing returns from a further increase in government size. • Combining our findings with the results from Lopez et al. (2011), cutting out public spending items that increase market failure will be the most beneficial in high income countries. Prof. George E. Halkos & Epameinondas A. Paizanos 29

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