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Academy of Economic Studies Doctoral School of Finance and Banking Economic Growth, Fiscal Size and Volatility: A Panel Assessment for EU Developing Economies MSc Student: Dan Matei Supervisor: PhD Professor Moisa Altar Dissertation paper outline Introduction Literature review Methodology Empirical Analysis - Data - Results and Discussion - Robustness Analysis Conclusions Bibliography Introduction In the EU area, the gradual loss of monetary policy as an instrument to offset country-specific disturbances naturally places the onus on fiscal policy. European countries would thus be facing a difficult trade-off between maintaining large governments to ensure sufficient automatic fiscal stabilization and leaner ones to ensure efficiency and growth (there could be a tension between the ‘Maastricht’ and the ‘Lisbon’ goals). Debate between the need to ensure adequate macroeconomic stabilization and the reduction in the size of governments that often accompanied efforts to boost market efficiency and promote long-term growth The importance of high-quality fiscal policies for economic growth, a firm control and, where appropriate, reduction in public spending have been brought to the forefront by a number of developments over the past decades With a view to understand how to limit government size and restrict fiscal policy volatility, it is quite relevant to assess which components of general government spending and revenue (both in terms of size and volatility) have a negative effect on growth. Although the effect of government expenditure volatility has been widely analyzed, the effect of volatility in the components of public spending and revenue has not so far been widely addressed in the literature By regressing economic growth on budgetary items and on a set of other relevant variables we evaluate whether the allocation of taxes and public expenditures has been useful to promote growth in a panel of European countries for the period 1996-2007. The outcome of the paper suggests that for several components of general government revenue and spending both size and volatility measures have a negative effect on growth, and that restrictions on these variables should be pursued. Literature review Wagner’s Law- the long-run tendency for government spending as a share of some national income aggregate such as GDP to grow in the course of economic development has become more or less a stylized fact in public finance Keynesian perspective- public expenditure should act as a stabilizing force and move in a countercyclical direction Barro (1990) constitutes one of the first attempts at endogenizing the relationship between growth and fiscal policies, distinguishing four categories of public finances: productive vs. non-productive expenditures and distortionary vs. non-distortionary taxation Levine-Renelt (1992) found that most results from earlier studies on the relationship between longrun growth and fiscal policy indicators are fragile to small changes in the conditioning set Easterly-Rebello (1993) public transportation, communication and educational investment are positively correlated with growth per capita and aggregate public investment is negatively correlated with growth per capita Poot (1999) in a survey of published articles in 1983-1998 did not find conclusive evidence for the relationship between government consumption and growth, still found empirical support for the negative effect of taxes on growth and reported definitive results on the positive link between growth and education spending Afonso and Tanzi (2005) finds that a well-defined institutional framework and ‘high quality’ public finances are important to support the long-run growth. Studying the efficiency of the public sectors of 23 industrialized OECD countries, they noted that countries with large public sectors show more equal income distribution, while countries with small public sectors report significantly higher indicators than countries with medium-sized or big public sectors. Literature review (contd.) Fiscal volatility - There is little consensus on the sign of the effects of government expenditure volatility on growth, restrictions on government expenditure volatility may have both positive and negative effects on long-run growth. A crucial variable to determine the sign of these effects is business-cycle volatility Ramey and Ramey (1995) find a negative relation between the business-cycle volatility and growth in cross-country data and this relation is robust to various controls Aghion (2005) find that the effect of volatility on growth survives when one controls for the level of financial development, leaving open the possibility that volatility has a causal effect on growth (the negative relation between volatility on growth tends to be stronger in countries with lower financial development ) On the mechanism through which fiscal policy can affect business cycles, Lane (2003) shows that restrictions on government expenditure, and thus lower government expenditure volatility, result in a slower adjustment of the economy to unexpected shocks In contrast, Fatas and Mihov (2003) present evidence that aggressive use of discretionary fiscal policy generates undesirable output volatility and leads to lower growth. Not only discretionary changes but also transitory (and cyclical) changes in fiscal policy may increase output volatility and thereby reduce output growth Ayagari, Christiano and Eichenbaum (1992) temporary changes in fiscal policy may have a significant impact on interest rate volatility and this, in turn, will reduce long-run growth. Furceri (2007b) analyzing a panel of 99 countries from 1970-2000, shows that a 1 percent increase in government expenditure business cycle volatility determines a decrease of 0.78 percentage points in the long-run rate of growth The survey of different empirical studies shows that an objective and unambiguous overall catalogue of “high quality”-expenditure items is not feasible. There is no cookbook for growth. Economics gives an idea of the major ingredients, but it does not clearly tell the recipe. Methodology The inclusion of particular control variables in a growth regression can wipe out the negative bivariate relationship between growth and the measure of government size (Easterly and Rebelo, 1993) Levine and Renelt (1992) found that robust cross-country growth correlates to the average investment share of GDP, the initial log of GDP per capita, initial human capital and the average growth rate of the population Initial income is often used to test the convergence hypothesis Opening to trade is beneficial to economic growth on average, allows the dissemination of knowledge and technological progress, still the aftermath of trade openness varies considerably across countries and depends on a variety of conditions related to the structure of the economy and its institutions Output volatility: tends to have negative effects on long-term economic growth, welfare, and income inequality, particularly in developing countries. As main justifications for short-run “stabilization” policies (policies aimed at reducing volatility, The World Bank and the IMF routinely advise governments to reduce fluctuations to achieve higher growth rates Methodology (contd.) Time span- cross-country growth regressions make use of large time spans (30- 40 years) and consider the average value of growth determinants over this time period. As argued by Afonso and Furceri (2008), this could raise problems such as endogeneity and significant simultaneity. Cross-section analysis over long time spans may fail to capture growth causality effects of taxation The analysis is focused on combined cross-section time-series regressions using three four-year periods from 1996 to 2007, and we use pooled country and fixed effects The model- two growth equations respectively for general government revenue and expenditure: gi,t = α1 + β1Ri,t +γ1 R2i,t +δ1σRi,t +φ1Xi,t +εi,t (1) gi,t = α2 + β2Ei,t +γ2 E2i,t +δ2σEi,t +φ2Xi,t +εi,t (2) where the index i (i=1, …, 10) denotes the country, the index t (t= 1996-1999, 2000-2003, 2004-2007) indicates the period, α1 and α2 stand for the individual effects to be estimated for each country i. g is the growth rate of real GDP per capita, R is the vector of general government revenue variables as percentage of GDP, E is the vector of general government expenditure variables as percentage of GDP, σR is the vector of revenue volatility variables, and σE is the vector of expenditure volatility variables, X is a vector of control variables (initial level of output per capita, output volatility, investment share, population growth and openness). Both regressions also include square terms for R and E with a view to test the possible effect on economic growth of different government sizes. Empirical analysis- Data Sources of data are European Commission AMECO (Annual Macro- Economic Data), supplemented by EUROSTAT database, covering the period 1995-2007 The panel consists in 10 EU members and emerging economies: Bulgaria, Czech Rep, Estonia, Hungary, Lithuania, Latvia, Poland, Romania, Slovenia and Slovak Rep Variable Growth (g) rate Initial output Population growth Investment share of GDP Openness Output volatility Fiscal variables Definition of the variable Abbreviation The four year average in the growth rate of GDP per head of population (PPS EU25=100)- AMECO The log of real GDP per head of population at the beginning of each time period - AMECO The average of the annual log difference of total population AMECO Share of total economy investment in real GDP- AMECO Share of exports and imports of goods and services at 2000 prices in real GDP- AMECO Standard deviation in each period of the cyclical component of real GDP- AMECO Total Revenue (R), Direct Taxes (RD), Indirect Taxes (RI), Social Contributions (RC), Total Expenditure (EX), Government Consumption (EY), Government Investment (EI), Transfers (ET), Subsidies (ES)-current prices as in ratios to current GDP GS GDP POP INV OPEN VOL R, E Empirical analysis- Data (contd.) Advantages homogeneity - all 10 EU countries are emerging market economies data quality and cross-country comparability are likely to be of a good standard for the EU members (fiscal variable in ESA 95) Drawbacks fiscal data availability for the studied economies is rather limited only 3 observations per country for each variable, employing 4 year growth periods Two measures for both government revenues and expenditures (the aggregates and components): the relative share of each variable as a percentage of GDP and the volatility of the cyclical component for each fiscal variable For volatility measures, all fiscal variables are converted into constant prices using the GDP deflator. To compute the cyclical component for each fiscal variable, Hodrick and Prescott Filter was set with the smoothness parameter (λ) equal to to 6.25. In this way, as pointed out by Ravn and Uhlig (2002), the Hodrick-Prescott filter produces cyclical components comparable to those obtained by the Band-Pass filter. The analysis excludes those fiscal variables that have a residual importance on the public budget or whose interpretation is not clear Empirical analysis- Data (contd.) 15 10 5 0 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 RO SI 2006 2007 -5 -10 -15 BG CZ EE LV LT HU PL SK Table B . Total public revenue and expenditure as of % in GDP Revenues Expendit Revenues Expendit Revenues Expendit Bulgaria 1996-1999 41.89 47.45 2000-2003 42.99 45.12 2004-2007 40.70 38.29 Czech R Estonia Hungary Latvia Lithuania Poland Romania Slovenia Slovak R 38.84 38.61 45.75 38.06 36.79 42.11 46.10 43.73 41.76 39.28 35.92 42.77 33.40 33.49 38.57 37.53 44.20 37.63 41.33 36.19 42.96 36.41 33.19 39.09 33.06 44.00 34.74 42.80 39.19 51.91 38.77 42.07 46.13 49.96 45.99 48.93 44.98 35.45 48.55 35.58 35.98 43.43 40.40 47.45 45.04 44.01 33.61 50.21 36.82 34.12 43.05 34.84 45.26 37.48 Revenues Expendit Change in pp -1.19 -9.16 2.49 -2.42 -2.79 -1.65 -3.60 -3.01 -13.04 0.27 -7.02 1.21 -5.59 -1.70 -1.95 -7.95 -3.08 -15.11 -0.74 -11.45 Results and Discussion Autor (s) Data period and coverage 43 developing countries, yearly data, 1970-1990 22 OECD countries, yearly data, 1970-1995 Estimation method / model Fixed-effects (Five-year forward moving average dep. Variable) Fixed-effects, random effects (Five-year averages) Bassanini and Scarpetta (2001) 21 OECD countries, 19711998 Pooled Mean Group Estimator Folster and Henrekson (2001) Bose, Haque and Osborn (2003) 23 OECD countries, 19701995 30 developing countries, decade averages, 19701990 Fixed-country and period effects (five-year averages) OLS (Decade average dep var.) Romero de Avila and Strauch (2007) 15 European countries, 19602001 Long-term coefficients estimated by variables in levels Afonso and Furceri (2008) EU 15 and OECD Fixed-country and period effects (five-year averages) Devarajan, Swaroop and Zou (1996) Kneller, Bleaney and Gemmell (1999) Main results Excess public capital expenditure for their data set. Negative effect distortionary taxation Negative impact non productive expenditures (social transfers) Negative effect deficit Positive impact of public investment Unclear effect of public current expenditure. Negative impact of taxation Significant negative effect for total government spending; negative effect of total taxes. Identify the importance of education and government spending for economic growth in their set of countries. Also find a significant correlation with capital expenditure. Negative impact of total expenditure on growth.Positive impact of direct taxation, indirect taxation and public investment. Negative effect of government consumption, transfers, and social security revenues. Negative impact of total revenue and expenditure (size and volatility) on growth. Total general gov revenue and Growth Total general gov expenditure and Growth Dependent Variable: ?GS Method: Pooled Least Squares Date: 07/01/08 Time: 12:05 Sample: 1 3 Included observations: 3 Cross-sections included: 10 Total pool (balanced) observations: 30 Dependent Variable: ?GS Method: Pooled Least Squares Date: 07/01/08 Time: 12:01 Sample: 1 3 Likelihood Ratio Test (LR) Included observations: 3 Cross-sections included: 10 Total pool (balanced) observations: 30 Variable Coefficient Std. Error t-Statistic Prob. Variable Coefficient Std. Error t-Statistic Prob. C ?GDP ?OPEN ?INV ?POP ?VOL ?EX ?EV Fixed Effects (Cross) BG_--C CZ_--C EE_--C LV_--C LT_--C HU_--C PL_--C RO_--C SI_--C SK_--C 72.35072 -7.452652 0.033865 0.287571 -0.645998 -3.420679 -0.206032 -35.90981 26.15442 2.940737 0.018252 0.106261 1.010027 20.46026 0.104064 12.73928 2.766290 -2.534280 1.855477 2.706262 -0.639585 -0.167186 -1.979866 -2.818825 0.0160 0.0249 0.0863 0.0180 0.5336 0.8698 0.0693 0.0145 C 79.62294 24.66883 3.227674 0.0066 ?GDP -8.576761 2.715306 -3.158672 0.0075 ?OPEN 0.043079 0.015015 2.869154 0.0132 ?INV 0.479516 0.086206 5.562420 0.0001 ?POP 0.101703 0.873380 0.116448 0.9091 ?VOL -2.015172 17.03405 -0.118303 0.9076 ?RT -0.252000 0.103690 -2.430307 0.0303 ?RV -53.13048 14.92202 -3.560543 0.0035 Fixed Effects (Cross) BG_--C 2.891983 CZ_--C -2.231262 EE_--C 1.752868 LV_--C 3.411685 LT_--C 1.977317 HU_--C -3.365424 PL_--C -0.976839 RO_--C 0.862814 SI_--C -0.740042 SK_--C -3.583100 Effects Specification 0.312628 -0.891112 0.331291 2.287829 1.400373 -1.315973 -0.813290 -1.118800 0.911902 -1.104848 Effects Specification Cross-section fixed (dummy variables) Cross-section fixed (dummy variables) R-squared 0.941045 Mean dependent var 2.965056 Adjusted R-squared 0.868486 S.D. dependent var 2.614630 S.E. of regression 0.948193 Akaike info criterion 3.028569 Sum squared resid 11.68792 Schwarz criterion 3.822581 F-statistic 12.96926 Prob(F-statistic) 0.000017 Log likelihood Durbin-Watson stat -28.42853 2.105775 R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat 0.921397 0.824654 1.094860 15.58333 -32.74322 2.247730 Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion F-statistic Prob(F-statistic) 2.965056 2.614630 3.316215 4.110227 9.524194 0.000099 Government revenue composition and Growth Government expenditure composition and Growth Dependent Variable: ?GS Method: Pooled Least Squares Date: 07/02/08 Time: 14:45 Sample: 1 3 Included observations: 3 Cross-sections included: 10 Total pool (balanced) observations: 30 Dependent Variable: ?GS Method: Pooled Least Squares Date: 07/01/08 Time: 12:14 Sample: 1 3 Included observations: 3 Cross-sections included: 10 Total pool (balanced) observations: 30 Likelihood Ratio Test (LR) Variable Coefficient Std. Error t-Statistic Prob. C ?GDP ?INV ?POP ?OPEN ?VOL ?RC ?RD ?RI ?RCV ?RDV ?RIV Fixed Effects (Cross) BG_--C CZ_--C EE_--C LV_--C LT_--C HU_--C PL_--C RO_--C SI_--C SK_--C 92.10316 -8.140363 0.226942 0.372617 0.029658 14.19782 -0.869911 -0.654178 -0.100484 -92.07420 -29.72830 -10.83928 22.21132 2.362437 0.096309 0.785986 0.013590 18.43361 0.424020 0.223529 0.347201 22.04242 15.88484 20.94634 4.146677 -3.445748 2.356399 0.474075 2.182393 0.770213 -2.051580 -2.926589 -0.289411 -4.177137 -1.871489 -0.517479 0.0025 0.0073 0.0429 0.6467 0.0570 0.4609 0.0704 0.0169 0.7788 0.0024 0.0941 0.6173 2.331263 -0.441158 1.477878 1.318199 -1.305672 -2.573520 -0.630894 -0.274016 1.136243 -1.038323 Variable Coefficient Std. Error t-Statistic Prob. C ?GDP ?INV ?OPEN ?POP ?VOL ?EI ?ES ?ET ?EY ?EIV ?ESV ?ETV ?EYV Fixed Effects (Cross) BG_--C CZ_--C EE_--C LV_--C LT_--C HU_--C PL_--C RO_--C SI_--C SK_--C 104.7285 -9.630068 0.134199 0.035115 -2.029919 -17.68561 0.280551 -1.495720 -0.373082 -0.603592 -9.096458 -18.69154 -19.74383 31.24314 27.79607 3.112627 0.168179 0.020162 1.148612 20.89954 0.299895 0.920913 0.417786 0.242555 3.031519 7.465719 6.184621 22.41553 3.767745 -3.093871 0.797953 1.741643 -1.767280 -0.846220 0.935500 -1.624170 -0.892999 -2.488475 -3.000627 -2.503650 -3.192407 1.393817 0.0070 0.0175 0.4511 0.1251 0.1205 0.4254 0.3807 0.1484 0.4015 0.0417 0.0199 0.0408 0.0152 0.2060 Effects Specification Effects Specification Cross-section fixed (dummy variables) Cross-section fixed (dummy variables) R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat 0.972638 0.911834 0.776354 5.424534 -16.91418 2.178442 1.978706 -1.374290 -0.261079 2.585113 -1.164567 -3.316032 -0.628892 -3.486577 5.340207 0.327410 Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion F-statistic Prob(F-statistic) 2.965056 2.614630 2.527612 3.508450 15.99631 0.000092 R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat 0.975975 0.900467 0.824887 4.763072 -14.96359 2.439251 Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion F-statistic Prob(F-statistic) 2.965056 2.614630 2.530906 3.605157 12.92544 0.000975 Total general revenue Size and Growth Dependent Variable: ?GS Method: Pooled Least Squares Date: 07/09/08 Time: 00:20 Sample: 1 3 Included observations: 3 Cross-sections included: 10 Total pool (balanced) observations: 30 Total general expenditure Size and Growth Dependent Variable: ?GS Method: Pooled Least Squares Date: 07/08/08 Time: 16:59 Sample: 1 3 Included observations: 3 Cross-sections included: 10 Total pool (balanced) observations: 30 Likelihood Ratio Test (LR) Variable Coefficient Std. Error t-Statistic Prob. Variable Coefficient Std. Error t-Statistic Prob. C ?GDP ?OPEN ?INV ?POP ?VOL ?RT^2 ?RV Fixed Effects (Cross) BG_--C CZ_--C EE_--C LV_--C LT_--C HU_--C PL_--C RO_--C SI_--C SK_--C 72.36091 -8.324898 0.042109 0.477871 0.156597 -3.133775 -0.003057 -53.35587 23.69199 2.729112 0.015231 0.087510 0.884737 17.19895 0.001322 15.14364 3.054235 -3.050406 2.764765 5.460738 0.176998 -0.182207 -2.312323 -3.523320 0.0092 0.0093 0.0161 0.0001 0.8622 0.8582 0.0378 0.0037 C ?GDP ?OPEN ?INV ?POP ?VOL ?EX^2 ?EV Fixed Effects (Cross) BG_--C CZ_--C EE_--C LV_--C LT_--C HU_--C PL_--C RO_--C SI_--C SK_--C 64.94703 -7.107881 0.032386 0.287045 -0.582702 -3.999283 -0.002335 -36.32117 25.33219 2.949936 0.018796 0.107623 1.016925 20.65448 0.001235 12.87477 2.563814 -2.409504 1.723011 2.667127 -0.573004 -0.193628 -1.890842 -2.821113 0.0236 0.0315 0.1086 0.0194 0.5764 0.8495 0.0811 0.0144 2.932885 -2.356132 1.798650 3.425212 2.099944 -3.371045 -1.040173 1.035649 -0.958997 -3.565992 Effects Specification 0.425986 -1.069837 0.469644 2.278679 1.503218 -1.243772 -0.927904 -0.936891 0.595419 -1.094542 Effects Specification Cross-section fixed (dummy variables) Cross-section fixed (dummy variables) R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat 0.939247 0.864475 0.962544 12.04439 -28.87918 2.076776 Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion F-statistic Prob(F-statistic) 2.965056 2.614630 3.058612 3.852624 12.56137 0.000021 R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat 0.919762 0.821008 1.106182 15.90729 -33.05186 2.222071 Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion F-statistic Prob(F-statistic) 2.965056 2.614630 3.336791 4.130803 9.313679 0.000112 Robustness analysis The inclusion of country specific effects has the advantage of controlling for unobserved country heterogeneity, it could lead to misleading conclusion in the analysis of the results Re-estimating the growth equations excluding country dummies, the results remain robust to the change To control for a possible endogeneity problem in the regression, the equations were reestimated using the initial level of government spending and revenue-to- GDP ratios Table C. Robustness control HP 6.25 HP 100 Difference -35.909** -23.922** -13.327* (-2.81) (-2.51) (-1.58) Average volatility 0.030 0.060 0.068 Effect -1.073 -1.435 -0.906 Notes: t-statistics are in parenthesis. *, **, *** - Statistically significant at the 10, 5 and 1 percent level respectively. Total general gov revenue and Growth Total general gov expenditure and Growth including only period dummies including only period dummies Dependent Variable: ?GS Method: Pooled Least Squares Date: 07/08/08 Time: 17:23 Sample: 1 3 Included observations: 3 Cross-sections included: 10 Total pool (balanced) observations: 30 Dependent Variable: ?GS Method: Pooled Least Squares Date: 07/08/08 Time: 17:22 Sample: 1 3 Included observations: 3 Cross-sections included: 10 Total pool (balanced) observations: 30 Likelihood Ratio Test (LR) Variable Coefficient Std. Error t-Statistic Prob. C ?GDP ?OPEN ?INV ?POP ?VOL ?RT ?RV Fixed Effects (Period) 1--C 2--C 3--C 37.92442 -3.036599 0.007881 0.207064 -2.655265 -22.85483 -0.286801 -26.93313 13.25092 1.523758 0.010071 0.112629 0.875854 24.14660 0.096315 21.53513 2.862021 -1.992836 0.782532 1.838455 -3.031629 -0.946503 -2.977731 -1.250660 0.0096 0.0601 0.4431 0.0809 0.0066 0.3552 0.0074 0.2255 -0.377782 -0.365811 0.743594 Variable Coefficient Std. Error t-Statistic Prob. C ?GDP ?OPEN ?INV ?POP ?VOL ?EX ?EV Fixed Effects (Period) 1--C 2--C 3--C 43.07583 -3.580702 0.016002 0.195564 -1.624522 -16.18842 -0.273875 -28.22993 9.965472 1.150954 0.007742 0.080012 0.716700 17.00960 0.054512 11.48521 4.322508 -3.111073 2.066900 2.444184 -2.266669 -0.951722 -5.024116 -2.457937 0.0003 0.0055 0.0519 0.0239 0.0347 0.3526 0.0001 0.0232 0.190278 -0.122914 -0.067364 Effects Specification Effects Specification Period fixed (dummy variables) R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat 0.767977 0.663567 1.516561 45.99913 -48.97953 1.673357 Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion F-statistic Prob(F-statistic) 2.965056 2.614630 3.931969 4.399035 7.355372 0.000108 Period fixed (dummy variables) R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat 0.866716 0.806738 1.149434 26.42396 -40.66426 1.818559 Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion F-statistic Prob(F-statistic) 2.965056 2.614630 3.377618 3.844683 14.45056 0.000001 Total general gov revenue and Growth initial share Total general gov expenditure and Growth initial share Dependent Variable: ?GS Method: Pooled Least Squares Date: 07/09/08 Time: 01:46 Sample: 1 3 Included observations: 3 Cross-sections included: 10 Total pool (balanced) observations: 30 Dependent Variable: ?GS Method: Pooled Least Squares Date: 07/09/08 Time: 01:31 Sample: 1 3 Included observations: 3 Cross-sections included: 10 Total pool (balanced) observations: 30 Likelihood Ratio Test (LR) Variable Coefficient Std. Error t-Statistic Prob. C ?GDP ?OPEN ?INV ?POP ?VOL ?RT0 ?RV Fixed Effects (Cross) BG_--C CZ_--C EE_--C LV_--C LT_--C HU_--C PL_--C RO_--C SI_--C SK_--C 84.97852 -9.322428 0.043165 0.488989 0.103089 -4.715752 -0.201735 -70.17708 32.86627 3.458115 0.016551 0.099072 0.966134 19.05833 0.125387 18.62933 2.585584 -2.695812 2.608043 4.935688 0.106702 -0.247438 -1.608905 -3.767021 0.0226 0.0183 0.0217 0.0003 0.9167 0.8084 0.1316 0.0024 3.535717 -2.477439 1.744033 3.411158 2.065010 -3.792842 -1.275051 1.594174 -1.123011 -3.681749 Variable Coefficient Std. Error t-Statistic Prob. C ?GDP ?OPEN ?INV ?POP ?VOL ?EX0 ?EV Fixed Effects (Cross) BG_--C CZ_--C EE_--C LV_--C LT_--C HU_--C PL_--C RO_--C SI_--C SK_--C 65.24022 -7.147506 0.036034 0.319227 -0.601574 -12.39607 -0.115351 -38.66577 26.81962 3.085459 0.019274 0.109559 1.064831 20.51973 0.076828 13.56090 2.432556 -2.316513 1.869524 2.913749 -0.564948 -0.604105 -1.501428 -2.851269 0.0302 0.0375 0.0842 0.0121 0.5817 0.5562 0.1571 0.0136 Effects Specification Effects Specification Cross-section fixed (dummy variables) R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood 0.928498 0.840495 1.044235 14.17554 -31.32296 Durbin-Watson stat 2.146637 0.716744 -1.495695 0.810049 2.743530 1.857939 -2.462766 -0.902785 0.039254 -0.256152 -1.050119 Cross-section fixed (dummy variables) Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion F-statistic 2.965056 2.614630 3.221531 4.015543 10.55074 Prob(F-statistic) 0.000056 R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat 0.912814 0.805508 1.153084 17.28484 -34.29764 2.119379 Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion F-statistic Prob(F-statistic) 2.965056 2.614630 3.419843 4.213855 8.506656 0.000184 Conclusion The overall results suggest that both fiscal size and volatility tend to hamper growth in EU developing economies A percentage point increase in the share of total revenue (expenditure) would reduce output growth by 0.25 and 0.21 percentage points respectively, for the EU developing countries Among total revenue the variables that are most detrimental to growth, both in terms of size and volatility, are direct taxes and social contributions Among government outlays, subsidies and government consumption have a significantly negative impact on growth, government investment and transfers does not significantly affect growth In terms of volatility, the government transfers and public subsidies volatility have the largest negative effect on growth in the sample, in addition the investment volatility have a negative and statistically significant effect on growth in the EU developing countries Conclusion (contd.) 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