THE EFFECTS OF PUBLIC SECTOR INVESTMENTS ON ECONOMIC GROWTH OF

CROATIA

Saša Drezgić, PhDUniversity of Rijeka

Faculty of Economics

14th Dubrovnik Economic ConferenceJune, 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 (1996-2006)

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

ZGKZ

SMKZVZ

KK

BB

PG

LS

VP

PS

BP

ZDOB

ŠKVSSD

IS

DN

ME

GZ

HR

20000 30000 40000 50000 60000 70000 80000 90000

GDPpc

0

50000

1E5

1,5E5

2E5

2,5E5

3E5

3,5E5

NC

Spc

10

GDP PER CAPITA, BY COUNTIES (2006/1997)

0,0

20,0

40,0

60,0

80,0

100,0

120,0

140,0

160,0

180,0

200,0

ZG KZ SM KZ VZ KK BB PG LS VP PS BP ZD OB ŠK VS SD IS DN ME GZ HR

11

AVERAGE GROWTH RATES (1997-2006)

6,8

1,3

2,1 2,1

2,6

2,1

1,2

3,0

7,4

-0,3

4,3

2,9

3,8

2,4

3,0

1,6

4,33,9

3,0

3,8

5,5

3,9

-1,0

0,0

1,0

2,0

3,0

4,0

5,0

6,0

7,0

8,0

ZG KZ SM KZ VZ KK BB PG LS VP PS BP ZD OB ŠK VS SD IS DN ME GZ HR

12

GINI COEFFICIENTS (1996-2006)

0,15

0,15

0,16

0,16

0,17

0,17

0,18

0,18

0,19

0,19

0,20

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

13

DESCRIPTION OF DATA AND METHODOLOGY

TIME SPAN 1997-2006 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 (2001-2004)

15

GDP COMPARISON 2001

0

10000

20000

30000

40000

50000

60000

CBS

Author

16

GDP COMPARISON 2002

0

10000

20000

30000

40000

50000

60000

70000

CBS

Author

17

GDP COMPARISON 2003

0

10000

20000

30000

40000

50000

60000

70000

CBS

Author

18

GDP COMPARISON 2004

0

10000

20000

30000

40000

50000

60000

70000

80000

CBS

Author

19

ESTIMATION OF CAPITAL STOCKS

UNOFFICIAL ESTIMATES OF CBS 1999-2003

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:

1

0 111 )2

1()1(t

i tit

t IKK

23

CROATIAN NCAA Agriculture, hunting and forestry B Fishing C Mining and quarrying D Manufacturing E Electricity, gas and water supply F Construction

G Wholesale and retail trade; repair of motor vehicles, motorcycles and personal and household goods

H Hotels and restaurants I Transport, storage and communication J Financial intermediation K Real estate, renting and business activities L Public administration and defense; compulsory social security M Education N Health and social work O Other community, social and personal service activities

24

DEPRECIATION RATES

0,00%

1,00%

2,00%

3,00%

4,00%

5,00%

6,00%

A B C D E F G H I J K L M N O

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)

ititititititit uDummyUnlKGKY 4321

itititititititit uDummyUnlKSGKPGKY 54321

itititit

itititititit

uDummyUnl

KSGKIGKFGKEGKY

765

4321

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

Dependent variable: ln (GDP) Number of observations: 210

Variables Pooled OLS Within Between Random GLS

Constant -1.537501* (-65.90)

-1.275704* (-21.89)

-1.56682* (-26.16)

-1.437645* (-34.04)

KG/K .0945963* (7.23)

.1070115* (5.46)

.0680138*** (1.85)

.1293281* (7.67)

l/K .8418266* (66.42)

.6324686* (16.70)

.8618347* (26.93)

.7559057* (30.59)

Dummy -.062205* (-4.03)

-.0492048* (-4.77)

-.0538595* (-5.01)

Un -.0013174* (-2.47)

-.0006351 (-0.70)

-.0020363 (-1.43)

-.0000995 (-0.13)

R-square 0.96 0.95 0.95 0.95

LM-test 35.80*

Hausman test 194.01*

31

COMPARISON OF DIFFERENT DATA SOURCES

OFFICIAL DATA 2001-2004 AUTHOR’S DATA 2001-2004 AUTHOR’S DATA 1997-2006 SIGNIFICANT DIFFERENCES

32

SUMMARY – DATASETS COMPARISON

table 9, within estimation

table 11, within estimation

table 12, within estimation

K 0,160 -0,011 1,027

KG 0,057 0,180 0,378

KPG 0,390 0,136 0,270

KSG 0,080 0,058 1,080

KEG -0,030 0,049 -0,010

KFG 0,028 0,069 0,137

KIG 0,070 -0,016 0,090

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

55,5

66,5

77,5

88,5

99,510

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

gdp

public ncs

pinv

private ncs

36

LONG-DIFFERENCES ESTIMATION

00302

0100

)()(

)()(

iitiitiit

iitiitiit

uuUnUnll

KGKGKKYY

004

0302

0100

)(

)()(

)()(

iitiit

iitiit

iitiitiit

uuUnUn

llKSGKSG

KPGKPGKKYY

0060504

0302

0100

)()()(

)()(

)()(

iitiitiitiit

iitiit

iitiitiit

uuUnUnllKSGKSG

KIGKIGKFGKFG

KEGKEGKKYY

MODEL 1

MODEL 2

MODEL 3

37

LONG-DIFFERENCES ESTIMATION (CASE 1)Dependent variable: ln (GDP) Number of observations: 21

Variables

Model 1 Model 2 Model 3

Constant -.2187182 (-0.16)

-.3703061 (-0.25)

.2710294 (0.16)

K -.4738724 (-0.89)

-.3869461 (-0.68)

-.5759531 (-0.88)

KG .5479519* (3.01)

.5405485 (0.85)

KPG .3016189* (2.44)

KSG .296399 (0.53)

KEG .3973549 (1.55)

KFG .055394*** (2.11)

KIG .1092291 (0.30)

L 1.417071* (5.10)

1.462543* (5.04)

1.318196* (4.03)

Un -.0076061 (-1.29)

-.0066301 (-1.05)

-.0080337 (-1.12)

Adj R-square 0.80 0.77 0.75

38

LONG-DIFFERENCES ESTIMATION (CASE 2)Dependent variable: ln (GDP) Number of observations: 126

Variables

Model 1 Model 2 Model 3

Constant .1714413* (11.76)

.1746567* (11.69)

K -.0911077 (-1.80)

-.0807545 (-1.42)

-.0211409 (-0.38)

KG .0215748 (1.24)

KPG -.0001609 (-0.01)

KSG .0366844 (0.62)

.0655073 (1.10)

KEG -.0536421* (-3.83)

KFG .0029721 (0.55)

KIG .0108967 (0.75)

L .9856202* (17.14)

1.012447* (16.14)

.9748848* (15.93)

Un -.0043819* (-3.68)

-.0041408* (-3.31)

-.0045973* (-3.87)

Adj R-square 0.86 0.86 0.88

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

Dependent variable: ln (GDP) Number of observations: 147

Variables

Model 1 Model 2 Model 3

Constant .0386188* (7.26)

.0398014* (7.13)

.0395066* (5.43)

K -.447122* (-3.55)

-.386687* (-2.96)

-.363371* (-2.77)

KG .0193463 (0.54)

KPG .0180656 (0.71)

KSG -.2207474 (-1.49)

-.2180282 (-1.42)

KEG -.0579711 (-0.87)

KFG .0244803** (2.42)

KIG .0021488 (0.04)

L .7004617* (8.70)

.6899084* (8.62)

.6774209* (8.59)

Un .0005886 (0.39)

.0004363 (0.29)

.0000677 (0.04)

Adj R-square 0.53 0.53 0.50

41

SPILLOVER EFFETS ESTIMATION

ititititititit uUnlsKGKGKY 321

itititititititit uUnlsKPGKSGKPGKY 43121

itititititit

itititititit

uUnlKSGsKEGsKIG

sKEGKIGKFGKEGKY

6542

1321

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!