1. THE DRIVERS OF INTERREGIONAL POLICY CHOICES: EVIDENCE FROM ITALY
Fabio Padovano
DIPES, Università Roma Tre, Roma, Italy
and CREM-CNRS, Université de Rennes1, Rennes, France.

2. Introduction - 1
New theoretical developments in literature on the determinants of transfers from CG to LG
Bailing out expectations (Rodden, 2005; Bordignon & Turati, 2009; Josselin, Padovano and Rocaboy, 2009)
Alignment effects (Dasgupta et al., 2002)
‘Too big to fail’  effects (Wildasin, 1997)
Asymmetries in representation of local interests in national legislatures (Porto and Sanguinetti, 2001)
Common pool situations (Persson and Tabellini, 2001)
Soft budget constraints (Quian and Roland, 1998; Goodspeed, 2002)

3. Introduction - 2 …to be added to ‘traditional political determinants’
Political budget cycles
Local political capital (Grossman, 1994)
Interest groups
…and normative welfare economics theories
provision of differentiated public goods to heterogeneous populations
Common standards in basic services
Race to the bottom

4. 2 problems Degrees of freedom Institutional detail
Solution to the first  panel times series-cross section but:
If cross section composed by a variety of countries  loose on institutional precision
 Trade off between problem 1 and 2

5. How NOT to do it
Avoiding institutional complexity creates inconsistencies between results (Feld and Schaltegger, CesIFO 2007 vs. Feld and Schaltegger, PC 2005 vs. Feld and Kirchgassner, RSUE 2001)
Leviathan hypothesis literature another example (Oates, 1985; Rodden, 2003; Asworth, Galli and Padovano, 2008, 2009)
Capturing institutional changes through dummies does not produce very satisfactory empirical specifications (Bordignon and Turati, 2009)

6. Possible solutions
Development of theoretical constructs that identify relevant institutional details (e.g. political economy of debt creation)  more parsimonious specifications
More cross country institutional data in this domain (like DPI)
Selection of within country panels
Minimize institutional variance
Large data set

7. Merits of Italian data set
20 regions  15 RSO and 5 RSS only institutional difference (stable)
no major changes in transfer policy
No changes of expectations
No further institutional change dummy
210 observations  data set large enough to account for theoretical innovations
Transfer policy historically important
Bipartisan policy of progressive substitution of transfers with ‘own resources’
Health care main responsibility of regions (50% of regional expenditures, 70% net of administration)  but there is also more  worth looking at overall transfers

8. Income per family, Italian Regions 1995-2000 95% confidence intervals, Source: BdI

9. Age structure by Regions - 1
Population
density
(n/km2)
Population by age
0-15 (%)
>65 (%)
Piedmont
168
12,4
22,4
Valle d'Aosta 37
13,2
20,2
Lombardy
388
13,6
19,4
Trentino Alto Adige 71
16,1
17,7
Veneto
253
13,9
19,2
Friuli Venezia Giulia
153 12
22,6
Liguria
291
11,1
26,5
Emilia Romagna
184
12,5
22,7
Tuscany
155
12,1
23,2
Umbria
100
23,3
Italy
192
14,1
19,7

10. Age structure by regions - 2
Population
density (n/km2)
Population by age
0-15 (%)
> 65 (%)
Marche
155
13,1
22,6
Lazio
303
13,9
19,1
Abruzzo
119
13,4
21,3
Molise 72 22
Campania
424
17,5
15,3
Puglia
209
15,7
17,3
Basilicata 60
14,5
19,9
Calabria
133
18,3
Sicily
195
16,2 18
Sardinia 68
12,9
17,6
Italy
192
14,1
19,7

11. Transfers in Italian public sector (% of total expenditures, 2001)
Taxes
Soc. Sec.
contributions
Transfers from
Other
revenues
Deficit
(1)
(2)
(3)
(4)
(5)
(6)
Central government (1)
78,3
0,2
0,0
0,5
0,1
10,7
10,2
Social security institutions (2)
70,1
27,4
0,4
2,0
Regions (3)
40,9
53,0
0,3
4,9
0,8
Local Health Units (4)
90,2
Provinces and municipalities (5)
28,5
21,9
13,2
1,3
33,5
1,6
Other public institutions (6)
3,6
52,0
4,7
12,6
3,4
5,1
18,6
-0,2
Duplications
57,7
1,2
0,6
5,5
-0,1

12. Transfers vs. own resources

13. Type of transfers,

14. Model specification Specification Transfers per capita Total
Earmarked to current expenditures
Earmarked to capital expenditures
As a function of
Economic state variables
Political variables
Demographic indicators
Health care variables

15. Economic state variables
+ Ut-1 lagged unemployment
- DGDP/POP regional growth differential
- GDP/POP income per capita
+ TRN linear trend, incremental rule (spesa storica)
Padovano (2007), Perotti (2001) vs. closing income gap

16. Political variables - 1
+ ELN national elections dummy (Grossman, 1994; Rogoff, 1990)
+ ELR regional elections dummy (Grossman, 1994; Rogoff, 1990)
- NDIF vote margin in national elections
+ RDIF vote margin at regional elections (Cox and McCubbins, 1988)
+ RDIF, - RDIF2 (Dixit and Londregan, 1994)

17. Political variables - 2
+ SAME dummy for alignment effect (Dasgupta et al. 2002)
+ YEARS lobbying efficiency of region (Olson, 1982)
- RIGHT dummy for ideology (Hibbs, 1977, Alesina, 1997)

18. Demographic variables
POP + demand effect, too big to fail effect
– economies of scale
+ POP15 education, social security
+ POP65 health care, social security

19. Health care variables BED, number of hospital beds × 1000 inhabitants
+ demand induced effect, Niskanen effect
- economies of scale (Crivelli et al. 2000)
+ PUPHY Public sector doctors, demand induced effect, Niskanen effect
+ PRPHY private sector doctors, demand effect

20. Empirical strategy
Model 1: only economic state variables  welfare economics explanatory power
Model 2: full model, 20 regions
Model 3: 15 RSOs
Model 4: 5 RSSs
Model 5: current transfers
Model 6: capital transfers
Model 7: sample, check for expectations changes
Model 8: political economy vs. welfare economics interpretation of economic state variables

21. Estimates: economic variables
Model 1 2 3 4 5 6 7 8
Sample 20
regions 15
RSOs
RSSs
Dependent variable
TR/POP
TRCC/POP
TRCK/POP
Ut-1
3.121***
(0.303)
4.4644***
(0.38)
3.446***
(0.411)
2.845***
(0.592)
0.539***
(0.072)
4.122***
(0.48)
3.928***
(0.608)
DGGDP/POP
-1.22***
(0.314)
-2.516***
(0.265)
-2.69***
(0.472)
-1.645
(0.532)
-0.895***
(0.089)
-2.407***
(0.307)
-2.636***
(0.388)
GDP/POP
(8.259)
TREND
0.017**
(0.008)
0.038
(0.04)
-0.086***
(0.024)
0.006***
(0.002)
-0.016**
(0.007)
0.02*** C
0.276***
(0.023)
-2.349***
(0.326)
-0.161
(0.74)
-0.377***
(0.07)
-2.263***
(0426)
-1.865***
(0.408)

22. Estimates: political variables-1
Model 1 2 3 4 5 6 7 8
Sample 20
regions 15
RSOs
RSSs
Dependent variable
TR/POP
TRCC/POP
TRCK/POP
ELN
0.093***
(0.02)
0.103***
(0.025)
0.082***
(0.024)
0.032***
(0.004)
0.069***
(0.015)
(0.021)
ELR
0.128***
-0.119
(0.207)
0.071
(0.054)
0.02***
(0.007)
0.173***
(0.017)
(0.022)
YEARS
0.037***
(0.005)
-0.027***
(0.041)
0.05***
(0.01)
0.025**
(0.012)
0.001
(0.001)
0.043***
0.039***
(0.006)
SAME
0.035**
0.066***
(0.014)
0.058**
(0.027)
0.006***
(0.002)
0.023
0.032**

23. Estimates: political variables-2
Model 1 2 3 4 5 6 7 8
Sample 20
regions 15
RSOs
RSSs
Dependent variable
TR/POP
TRCC/POP
TRCK/POP
NDIF
-9.806***
(2.2)
-23.9*
(14.19)
***
(2.98)
3.16***
(0.489)
-7.971
(1.69) ***
***
(2.33)
RDIF
1.126***
(0.174)
0.253***
(0.069)
15.928***
(2.16)
0.234***
(0.1)
0.045***
(0.016)
0.64***
(0.155)
1.023***
(0.19)
(RDIF)2
-1.372***
(0.393)
***
(5.87)
-0.459
(0.38)
-0.896**
RIGHT
-0.067***
(0.017)
***
( )
-0.11
(0.03)
0.026***
(0.003)
-0.066***
(0.018)
-0.066

24. Estimates: demographic controls
Model 1 2 3 4 5 6 7 8
Sample 20
regions 15
RSOs
RSSs
Dependent variable
TR/POP
TRCC/POP
TRCK/POP
POP
***
(2-08)
***
( )
***
( )
( )
***
( )
***
(2.68)-08
***
( )
POP15
5.989***
(1.772)
0.582
(1.843)
1.569
(1.157)
0.236
(0.358)
9.492***
(2.27)
5.405***
(1.756)
POP65
7.178***
(0.805)
2.599**
(0.856)
28.852***
(11.01)
2.203***
(0.446)
0.707***
(0.19)
7.748***
(0.989)
6.818***
(0.84)

25. Estimates: health care variables
Model 1 2 3 4 5 6 7 8
Sample 20
regions 15
RSOs
RSSs
Dependent variable
TR/POP
TRCC/POP
TRCK/POP
BEDS
***
( )
***
(3.5-06)
***
( )
***
( )
***
( )
***
( )
PUPHY
0.0005**
(0.0002)
0.0006***
(0.0001)
0.0003***
(3.7-05)
0.0004** C
0.276***
(0.023)
-2.349***
(0.326)
-0.161
(0.74)
-0.377***
(0.07)
-2.263***
(0426)
-1.865***
(0.408)
AR(1)
Yes No
Adjusted R2
0.485
0.755
0.849
0.65
0.777
0.878
0.715
S.E.R.
0.374
0.408
0.451
0.16
0.23
0.104
0.409
0.404
F-statistic
50.92***
25.67***
30.5***
47.617***
17.96
42.75***
59.86***
23.405***
Durbin Watson
2.05
1.947
1.85
2.02
1.97
1.78
2.177
1.93
Obs.
210
165 55
150

26. RSOs fixed effects Region Model 3 ABR 7.26 UMB 6.41 MOL 7.06 LOM 6.38
CAL
6.98
ERO
6.31
VEN
6.71
MAR
6.29
CAM
6.67
TOS
6.26
PUG
6.66
PIE
6.25
BAS
6.49
LIG
6.06
LAZ
6.44

27. RSSs fixed effects Region Model 4 SIC -49.86 SAR -19.26 FVG -16.81 TAA
-14.05
VDA
-7.19

28. Main results: commentary-1
Inclusion of political, health care and economic variables increases model’s explanatory power by 33%
In RSOs electoral process prevails
PBC
Alignment effect
Grants reward local political success (Cox and McCubbins, 1988)
National political success lowers grants
In RSSs lobbying more important (different party system)
Grants targeted to swing regions (Dixit and Londregan, 1994)
More resistance to further grants in RSSs

29. Main results: commentary-2
Right wing governments
receive less grants in total and for current expenditures (partisan effect)
Receive more grants for capital expenditures
Health care variables reveal significant induced demand/Niskanen effects
No expectations turbulence (but more research is warranted)
Political economy explanations of interregional redistribution more supported than standard welfare economics ones