Transcript THE INVESTMENT ACTIVITY OF THE PUBLIC SECTOR AND …

THE EFFECTS OF PUBLIC SECTOR
INVESTMENTS ON ECONOMIC GROWTH OF
CROATIA
Saša Drezgić, PhD
University of Rijeka
Faculty of Economics
14th Dubrovnik Economic Conference
June, 2008
CONTENTS:

INTRODUCTION

EMPIRICAL CONTRIBUTIONS

OVERVIEW OF CROATIAN ECONOMY

ECONOMIC FEATURES OF CROATIAN REGIONS

DATASET CONSTRUCTION

EMPIRICAL ANALYSIS

CONCLUSION
2
INTRODUCTION

REDUCTION OF PUBLIC INVESTMENTS

LACK OF RESEARCH

FEATURES OF CAPITAL ACCUMULATION IN
CROATIA

GREAT INFRASTRUCTURE NEEDS
3
EMPIRICAL CONTRIBUTIONS





Abramowitz (1956) and Solow (1957)
Mera (1973), Looney and Frederiksen (1981),
Biehl (1986)
Aschauer (1989, 1990), Munnel (1990), HoltzEakin (1994)
Baltagi and Pinnoi (1995)
Perreira (1999, 2000, 2001), Sturm (1998),
Kamps (2004, 2005), Voss (2002), Mittnik,
Neumman (2001)
4
ECONOMETRIC MODEL APPLIED




PRODUCTION FUNCTION
FRAMEWORK – TIME SERIES
APPROACH
SPATIAL ECONOMETRIC METHODS
VECTOR-AUTOREGRESSION
MODELS (VAR)
PANEL DATA REGRESSION
5
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
6
CROATIAN REGIONS

FORMATION OF REGIONS

HIGH INCOME INEQUALITY

DIVERGENCE OF GROWTH
7
8
GDP/NCS PER CAPITA IN
CROATIAN COUNTIES
3,5E5
GZ
LS
3E5
2,5E5
NCSpc
IS
2E5
PG
DN HR
1,5E5
ŠK ZD
OB
KK SD
SMKZ
VZ
KZZG
PS
ME
BP
VP BB
VS
1E5
50000
0
20000
30000
40000
50000
60000
70000
80000
90000
GDPpc
9
GDP PER CAPITA, BY COUNTIES
(2006/1997)
200,0
180,0
160,0
140,0
120,0
100,0
80,0
60,0
40,0
20,0
0,0
ZG
KZ
SM
KZ
VZ
KK
BB
PG
LS
VP
PS
BP
ZD
OB
ŠK
VS
SD
IS
DN
ME
GZ
10
HR
AVERAGE GROWTH RATES
(1997-2006)
8,0
7,4
7,0
6,8
6,0
5,5
5,0
4,3
4,3
4,0
3,9
3,8
3,0
3,0
2,1 2,1
2,0
3,0
2,9
2,6
3,9
3,8
3,0
2,4
2,1
1,6
1,3
1,2
1,0
0,0
-0,3
-1,0
ZG KZ SM KZ VZ KK BB PG LS VP PS BP ZD OB ŠK VS SD
IS DN ME GZ HR
11
GINI COEFFICIENTS (1996-2006)
0,20
0,19
0,19
0,18
0,18
0,17
0,17
0,16
0,16
0,15
0,15
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
12
DESCRIPTION OF DATA AND
METHODOLOGY






TIME SPAN 1997-2006
annual GDP of the Croatian economy,
annual investments (given by expenditurebased GDP accounting)
labor of enterprises per counties (small
entrepreneurs are excluded)
average annual wage per counties
average unemployment in the Croatian
counties
13
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)
14
GDP COMPARISON 2001
60000
50000
40000
30000
CBS
Author
20000
10000
0
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a reb
a
a
a
a
sk
čk
sk
sk
sk
čk
sk
sk
sk
čk
sk
sk
sk
sk
sk
sk
sk
sk
čk
sk
ba gor lava lova ždin eva gor ran senj rav von sav dar ranj knin ijem atin istar tvan mur Zag
e
i
f
s kar ara križ bilo -go
la
za -ba kood
gr
za
lm
po
sr
ore
eđ ity o
za sko- -mo
o- -da
o
v kos
čk ko-p ko-s skone
o- sko
m
i
k
l
k
n
k
c
s
o
o
č
š
o
s
or
itič ože
od
ije šibe var litsk
pin ačk
čk
or
nič
o
br
ov
os
va
riv elog prim
p
p
r
k
kra sis
i
o
p
s
u
v
v
br
bj
ko
du
15
GDP COMPARISON 2002
70000
60000
50000
40000
30000
CBS
Author
20000
10000
0
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a reb
a
a
a
a
sk
čk
sk
sk
sk
čk
sk
sk
sk
čk
sk
sk
sk
sk
sk
sk
sk
sk
čk
sk
ba gor lava lova ždin eva gor ran senj rav von sav dar ranj knin ijem atin istar tvan mur Zag
e
d
r
a križ bilo -go os
za -ba kogr
za
đi
of
lm
sr
re
sla -po
po
ka var
za sko- -mo
o- -da
o
s
ne
me city
ičk ko- ško- sko
o- sko- rsko
k
l
k
n
k
s
o
o
č
o
o
itič že rod
ije šibe var litsk
pin ačk
čk
or
nič
o
b
ov po
os
va
riv elog prim
p
r
k
kra sis
i
o
p
s
u
r
v
v
b
bj
ko
du
16
kr
ap z a
in gr
si sk eb
sa o ač
čk -za ka
o- go
m
os rsk
la a
v
ka ačk
ko
rlo a
pr
v
iv va ač
n
r
ka
a
bj ič
el ko žd
i
og -k n
or riž ska
s
pr ko eva
im -b č
or ilo ka
sk go
o- r s
go k a
vi
r
ro lič an
vi ko sk
tič -s a
e
k
po o- nj
že po ska
šk dra
br o-s vs
od l a k a
s k vo
o- ns
po ka
sa
v
os
ije za ska
čk d
o a
ši -b rsk
b
v u e ar a a
ko ns nj
va k o s k
-k a
sp rsk nin
o
lit
sk -sr ska
o- ije
da ms
du
lm ka
br
at
ov
in
ač
s
ko is ka
-n ta
er rs
et k a
v
m an
eđ sk
a
i
ci mu
ty
rs
of k a
Za
gr
eb
GDP COMPARISON 2003
70000
60000
50000
40000
30000
CBS
Author
20000
10000
0
17
GDP COMPARISON 2004
18
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
19
CAPITAL STOCK MEASURMENT


PHYSICAL MEASURE OF CAPITAL
STOCK
MONETARY APPROACH:
PERPETUAL INVENTORY METHOD
(PIM)
20
APPLICATION OF PIM IN
PRACTICE
21
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:

K t 1  (1   ) K1  i 0 (1  ) i I t 1
2
t
t 1
22
CROATIAN NCA
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
Agriculture, hunting and forestry
Fishing
Mining and quarrying
Manufacturing
Electricity, gas and water supply
Construction
Wholesale and retail trade; repair of motor vehicles, motorcycles and personal and
household goods
Hotels and restaurants
Transport, storage and communication
Financial intermediation
Real estate, renting and business activities
Public administration and defense; compulsory social security
Education
Health and social work
Other community, social and personal service activities
23
DEPRECIATION RATES
6,00%
5,00%
4,00%
3,00%
2,00%
1,00%
0,00%
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
24
OTHER VARIABLES



LABOR DATA – CBS OFFICIAL
STATISTICS (PRIVATE
ENTERPRENEURS ARE NOT
INCLUDED)
UNEMPLOYMENT DATA –
UNRELIABLE
DUMMY VARIABLE – FINANCIAL
CRISES IN YEAR 1999
25
ESTIMATION RESULTS




NUMEROUS MODELS USED –
ROBUSTNESS OF RESULTS
TIME SERIES APPROACH NOT
POSSIBLE
COMPARISON OF DIFFERENT
SOURCES
SPILLOVER EFFECTS
26
ESTIMATION EFFICIENCY
TESTING




POOLABILITY (Chow test)
FIXED OR RANDOM EFFECTS?
HAUSMAN TEST
LM BREUCH-PAGAN TEST
27
ESTIMATION RESULTS
(MODEL 1)
MODEL 1
Yit    K it  1 KGit   2lit   3Unit   4 Dummyit  uit
MODEL 2
Yit    K it  1 KPGit   2 KSGit   3lit   4Unit   5 Dummyit  uit
MODEL 3
Yit    K it  1 KEGit   2 KFGit   3 KIGit   4 KSGit
  5lit   6Unit   7 Dummyit  uit
28
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
29
ASSUMPTION OF LINEARITY
Number of observations: 210
Dependent variable: ln (GDP)
Variables
Pooled OLS
Within
Between
Random GLS
Constant
-1.537501*
(-65.90)
.0945963*
(7.23)
.8418266*
(66.42)
-.062205*
(-4.03)
-.0013174*
(-2.47)
0.96
-1.275704*
(-21.89)
.1070115*
(5.46)
.6324686*
(16.70)
-.0492048*
(-4.77)
-.0006351
(-0.70)
0.95
-1.56682*
(-26.16)
.0680138***
(1.85)
.8618347*
(26.93)
-1.437645*
(-34.04)
.1293281*
(7.67)
.7559057*
(30.59)
-.0538595*
(-5.01)
-.0000995
(-0.13)
0.95
KG/K
l/K
Dummy
Un
R-square
-.0020363
(-1.43)
0.95
LM-test
35.80*
Hausman test
194.01*
30
COMPARISON OF DIFFERENT
DATA SOURCES




OFFICIAL DATA 2001-2004
AUTHOR’S DATA 2001-2004
AUTHOR’S DATA 1997-2006
SIGNIFICANT DIFFERENCES
31
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
32
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
33
MEASURMENT ERROR




DIFFERENCING SCHEMES (Grilliches and
Hausman, 1986, Baltagi and Pinnoi, 1995)
CONSTRUCTION SECTOR - STABILE
COEFFICIENTS VALUES
CONCEQUENCES OF MIXING THE SHORTTERM AND LONG TERM EFFECTS
EXAMPLE OF LIČKO-SENJSKA COUNTY
34
PUBLIC INVESTMENT AND PUBLIC
CAPITAL STOCKS DYNAMICS
10
9,5
9
8,5
8
7,5
7
6,5
6
5,5
5
gdp
public ncs
pinv
private ncs
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
35
LONG-DIFFERENCES
ESTIMATION
MODEL 1
Yit  Yi 0     ( K it  K i 0 )  1 ( KGit  KGi 0 )
  2 (lit  li 0 )   3 (Unit  Uni 0 )  uit  ui 0
MODEL 2
Yit  Yi 0     ( K it  K i 0 )  1 ( KPGit  KPGi 0 )
  2 ( KSGit  KSGi 0 )   3 (lit  li 0 ) 
 4 (Unit  Uni 0 )  uit  ui 0
MODEL 3
Yit  Yi 0     ( K it  K i 0 )  1 ( KEGit  KEGi 0 )
  2 ( KFGit  KFGi 0 )   3 ( KIGit  KIGi 0 )
  4 ( KSGit  KSGi 0 )   5 (lit  li 0 )   6 (Unit  Uni 0 )  uit  ui 0
36
LONG-DIFFERENCES
ESTIMATION (CASE 1)
Dependent variable: ln (GDP)
Number of observations: 21
Variables
Model 1
Model 2
Model 3
Constant
-.2187182
(-0.16)
-.4738724
(-0.89)
.5479519*
(3.01)
-.3703061
(-0.25)
-.3869461
(-0.68)
.2710294
(0.16)
-.5759531
(-0.88)
.5405485
(0.85)
K
KG
KPG
.3016189*
(2.44)
.296399
(0.53)
KSG
KEG
KFG
KIG
L
Un
Adj R-square
1.417071*
(5.10)
-.0076061
(-1.29)
0.80
1.462543*
(5.04)
-.0066301
(-1.05)
0.77
.3973549
(1.55)
.055394***
(2.11)
.1092291
(0.30)
1.318196*
(4.03)
-.0080337
(-1.12)
0.75
37
LONG-DIFFERENCES
ESTIMATION (CASE 2)
Dependent variable: ln (GDP)
Number of observations: 126
Variables
Model 1
Model 2
Model 3
Constant
.1714413*
(11.76)
-.0911077
(-1.80)
.0215748
(1.24)
.1746567*
(11.69)
-.0807545
(-1.42)
-.0211409
(-0.38)
K
KG
KPG
-.0001609
(-0.01)
.0366844
(0.62)
KSG
KEG
KFG
KIG
L
Un
Adj R-square
.9856202*
(17.14)
-.0043819*
(-3.68)
0.86
1.012447*
(16.14)
-.0041408*
(-3.31)
0.86
.0655073
(1.10)
-.0536421*
(-3.83)
.0029721
(0.55)
.0108967
(0.75)
.9748848*
(15.93)
-.0045973*
(-3.87)
0.88
38
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
39
INSTRUMENTAL VARIABLES
ESTIMATION
Dependent variable: ln (GDP)
Number of observations: 147
Variables
Model 1
Model 2
Model 3
Constant
.0386188*
(7.26)
-.447122*
(-3.55)
.0193463
(0.54)
.0398014*
(7.13)
-.386687*
(-2.96)
.0395066*
(5.43)
-.363371*
(-2.77)
K
KG
KPG
.0180656
(0.71)
-.2207474
(-1.49)
KSG
KEG
KFG
KIG
L
Un
Adj R-square
.7004617*
(8.70)
.0005886
(0.39)
0.53
.6899084*
(8.62)
.0004363
(0.29)
0.53
-.2180282
(-1.42)
-.0579711
(-0.87)
.0244803**
(2.42)
.0021488
(0.04)
.6774209*
(8.59)
.0000677
(0.04)
0.50
40
SPILLOVER EFFETS
ESTIMATION
MODEL 1
Yit    K it  1 KGit  sKGit   2lit   3Unit  uit
MODEL 2
Yit    K it  1 KPGit   2 KSGit   1sKPGit   3lit   4Unit  uit
MODEL 3
Yit    K it  1 KEGit   2 KFGit   3 KIGit   1 sKEGit
  2 sKIGit  sKEGit   4 KSGit   5 lit   6Unit  uit
41
RESULTS




CONFIRMATION OF HIGH SPILLOVER
EFFECTS (9,9 % PHYSICAL PUBLIC
CAPITAL, 6,3% F SECTOR)
EXAMPLES: ZAGREBAČKA, LIČKOSENJSKA COUNTY
HIGWAY INVESTMENT SPILLOVERS
(Boarnet 1996)
“POINT” AND “NETWORK”
INFRASTRUCTURE EFFECTS IN CROATIA
42
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
43
CONCLUSION

POTENTIAL CONTRIBUTION

LIMITATIONS OF RESEARCH

FUTURE RESEARCH
44
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
45
FUTURE RESEARCH




NEW DATASETS
NONLINEAR TECHNIQUES, SPATIAL
MODELS, DYNAMIC PANEL MODELS
R&D VARIABLE
OTHER INSTITUTIONAL FACTORS
46
THANK YOU!
47