THE EFFECTS OF PUBLIC SECTOR INVESTMENTS ON ECONOMIC GROWTH OF CROATIA Saša Drezgić, PhD University of Rijeka Faculty of Economics 14 th Dubrovnik Economic Conference June, 2008

2 CONTENTS: INTRODUCTION EMPIRICAL CONTRIBUTIONS OVERVIEW OF CROATIAN ECONOMY ECONOMIC FEATURES OF CROATIAN REGIONS DATASET CONSTRUCTION EMPIRICAL ANALYSIS CONCLUSION

3 INTRODUCTION REDUCTION OF PUBLIC INVESTMENTS LACK OF RESEARCH FEATURES OF CAPITAL ACCUMULATION IN CROATIA GREAT INFRASTRUCTURE NEEDS

4 EMPIRICAL CONTRIBUTIONS Abramowitz (1956) and Solow (1957) Mera (1973), Looney and Frederiksen (1981), Biehl (1986) Aschauer (1989, 1990), Munnel (1990), Holtz- Eakin (1994) Baltagi and Pinnoi (1995) Perreira (1999, 2000, 2001), Sturm (1998), Kamps (2004, 2005), Voss (2002), Mittnik, Neumman (2001)

5 ECONOMETRIC MODEL APPLIED PRODUCTION FUNCTION FRAMEWORK – TIME SERIES APPROACH SPATIAL ECONOMETRIC METHODS VECTOR-AUTOREGRESSION MODELS (VAR) PANEL DATA REGRESSION

6 OVERVIEW OF CROATIAN ECONOMY ( ) HIGH INFLATION TILL 1997 GDP GROWTH RATE STABILE FROM 1997 – AVERAGE 4% HIGH RATE OF UNEMPLOYMENT FIXED EXCHANGE RATE (APPRECIATION OF CURRENCY HIGH TAX BURDEN DETERIORATING TRADE BALANCE

7 CROATIAN REGIONS FORMATION OF REGIONS HIGH INCOME INEQUALITY DIVERGENCE OF GROWTH

8

9 GDP/NCS PER CAPITA IN CROATIAN COUNTIES

10 GDP PER CAPITA, BY COUNTIES (2006/1997)

11 AVERAGE GROWTH RATES ( )

12 GINI COEFFICIENTS ( )

13 DESCRIPTION OF DATA AND METHODOLOGY TIME SPAN annual GDP of the Croatian economy, annual investments (given by expenditure- based GDP accounting) labor of enterprises per counties (small entrepreneurs are excluded) average annual wage per counties average unemployment in the Croatian counties

14 DERIVATION OF GDP PER COUNTIES REVENUE-BASED ACCOUNTING OF GDP PROXY FOR GDP DISTRIBUTION: AVERAGE INCOME PER COUNTIES OBTAINED BY MULTIPLYING AVERAGE WAGES PER COUNTIES AND LABOR EMPLOYED HIGH CORRELATION WITH OFFICIAL DATA ( )

15 GDP COMPARISON 2001

16 GDP COMPARISON 2002

17 GDP COMPARISON 2003

18 GDP COMPARISON 2004

19 ESTIMATION OF CAPITAL STOCKS UNOFFICIAL ESTIMATES OF CBS PIM METHODOLOGY GEOMETRIC RATE OF DEPRECIATION APPLIED DEPRECIATION RATES VARY FOR EACH SECTOR! PUBLIC SECTOR: E, F, I, L, M, N

20 CAPITAL STOCK MEASURMENT PHYSICAL MEASURE OF CAPITAL STOCK MONETARY APPROACH: PERPETUAL INVENTORY METHOD (PIM)

21 APPLICATION OF PIM IN PRACTICE

22 FEATURES OF PIM FREQUENTLY USED – Jacob et al. (1997), Sturm and de Haan (1995), Sturm (1998), Munnel (1990), U.S. Bureau of Economic Analysis (1999), OECD (2001), Kamps (2004,2005) GENERAL FORMULA:

23 CROATIAN NCA

24 DEPRECIATION RATES

25 OTHER VARIABLES LABOR DATA – CBS OFFICIAL STATISTICS (PRIVATE ENTERPRENEURS ARE NOT INCLUDED) UNEMPLOYMENT DATA – UNRELIABLE DUMMY VARIABLE – FINANCIAL CRISES IN YEAR 1999

26 ESTIMATION RESULTS NUMEROUS MODELS USED – ROBUSTNESS OF RESULTS TIME SERIES APPROACH NOT POSSIBLE COMPARISON OF DIFFERENT SOURCES SPILLOVER EFFECTS

27 ESTIMATION EFFICIENCY TESTING POOLABILITY (Chow test) FIXED OR RANDOM EFFECTS? HAUSMAN TEST LM BREUCH-PAGAN TEST

28 ESTIMATION RESULTS (MODEL 1) MODEL 1 MODEL 2 MODEL 3

29 RESULTS FIXED EFFECTS ESTIMATOR IS CONSISTENT POSITIVE SHORT-TERM EFFECTS IN THE SECTOR OF CONSTRUCTION (2,8 %) AND TRANSPORT (7%) SHORT-TERM EFFECTS ON SOCIAL CAPITAL AMBIGUOUS LONG-TERM EFFECTS SHOW DIFFERENT RESULTS – POSITIVE EFFECTS OF PUBLIC PHYSICAL CAPITAL AND TRANSPORT

30 ASSUMPTION OF LINEARITY

31 COMPARISON OF DIFFERENT DATA SOURCES OFFICIAL DATA AUTHOR’S DATA AUTHOR’S DATA SIGNIFICANT DIFFERENCES

32 SUMMARY – DATASETS COMPARISON

33 RESULTS REASONS FOR COMPARISON ESTIMATED COEFFICIENTS FOLLOW THE SAME SIGN BUT DIFFERENT VALUE PHYSICAL SECTOR CAPITAL (13,6% - 27%) CONSTRUCTION SECTOR (6,9% - 13,7%) EXCEPTION: SOCIAL CAPITAL

34 MEASURMENT ERROR DIFFERENCING SCHEMES (Grilliches and Hausman, 1986, Baltagi and Pinnoi, 1995) CONSTRUCTION SECTOR - STABILE COEFFICIENTS VALUES CONCEQUENCES OF MIXING THE SHORT- TERM AND LONG TERM EFFECTS EXAMPLE OF LIČKO-SENJSKA COUNTY

35 PUBLIC INVESTMENT AND PUBLIC CAPITAL STOCKS DYNAMICS

36 LONG-DIFFERENCES ESTIMATION MODEL 1 MODEL 2 MODEL 3

37 LONG-DIFFERENCES ESTIMATION (CASE 1)

38 LONG-DIFFERENCES ESTIMATION (CASE 2)

39 INSTRUMENTAL VARIABLES ESTIMATION CIRCUMVENT THE ENDOGENEITY PROBLEM, MEASURMENT ERROR Holtz-Eakin, Newey and Rosen (1988) PROPOSE FIRST-DIFFERENCES AND IV ESTIMATOR INSTRUMENTS USED: FIRST AND SECOND DIFFERENCES OF THE CAPITAL VARIABLES USED IN PARTICULAR MODEL

40 INSTRUMENTAL VARIABLES ESTIMATION

41 SPILLOVER EFFETS ESTIMATION MODEL 1 MODEL 2 MODEL 3

42 RESULTS CONFIRMATION OF HIGH SPILLOVER EFFECTS (9,9 % PHYSICAL PUBLIC CAPITAL, 6,3% F SECTOR) EXAMPLES: ZAGREBAČKA, LIČKO- SENJSKA COUNTY HIGWAY INVESTMENT SPILLOVERS (Boarnet 1996) “POINT” AND “NETWORK” INFRASTRUCTURE EFFECTS IN CROATIA

43 DISCUSSION METHODOLOGY RELIES ON Baltagi and Pinnoi (1995), Boarnet (1996), Sturm (1998), Ligthart (2000), Kamps (2004,2005) DIFFERENT MODELS USED CONSIDERATION OF LONG-TERM AND SHORT TERM EFFECTS (Baxter-King, 1993) DIFFERENT DATASETS USED COMPARISON WITH OTHER RESEARCH POSITIVE AND SIGNIFICANT RESULTS FOR CONSTRUCTION (F) SECTOR SPILLOVER EFFECTS

44 CONCLUSION POTENTIAL CONTRIBUTION LIMITATIONS OF RESEARCH FUTURE RESEARCH

45 LIMITATIONS OF RESEARCH SEVERAL SOURCES: NO OFFICIAL DATASETS, PROXY, PIM METHODOLOGY, PANEL REGRESSION TECHNIQUE – AVERAGE COEFFICIENT, COBB-DOUGLAS FUNCTION HUMAN CAPITAL BROADER INSTITUTIONAL FRAMEWORK EFFECTS

46 FUTURE RESEARCH NEW DATASETS NONLINEAR TECHNIQUES, SPATIAL MODELS, DYNAMIC PANEL MODELS R&D VARIABLE OTHER INSTITUTIONAL FACTORS

47 THANK YOU!