TERRITORIAL PUBLIC EXPENDITURE AND REVENUE: ECONOMIC IMPACT IN THE EUROPEAN REGIONAL GROWTH
GUISÁN, M.Carmen
[email protected] CANCELO, M. Teresa
[email protected] Faculty of Economics University of Santiago de Compostela (Spain)
EURO-AMERICAN ASSOCIATION OF ECONOMIC DEVELOPMENT Working paper nº 8 Serie: ECONOMIC DEVELOPMENT Disponible en/Available in: www.usc.es/economet/aea.htm http://ideas.uqam.ca/ideas/data/eaaecodev.html
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TERRITORIAL PUBLIC EXPENDITURE AND REVENUE: ECONOMIC IMPACT IN THE EUROPEAN REGIONAL GROWTH
GUISÁN, M.Carmen
[email protected] CANCELO, M. Teresa
[email protected] Faculty of Economics University of Santiago de Compostela (Spain) http://www.usc.es/economet
ABSTRACT:
The topic of fiscal federalism from the standpoint of the tax revenue and the expenditure distribution is extremely significant for the regional growth, both quantitatively(income and employment) and qualitatively (types of public disbursement and growth impact).In the present paper we will analyze the distribution between both aspects of public disbursement in the OECD European countries in the last years, in this connection we will examine it with econometric models with the aim of estimate the impacts mentioned bellow.
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1.- INTRODUCTION
We hold the public sector’s role as being of utmost importance in encouraging the required activity at a regional level, as we find ourselves at an important stage for implementing the idea of regional development in Europe. This, however, is not a simple task when we are faced by growing challenges such as: a single market, the integration of the Eastern countries, international competition in industry, etc.
In our opinion, fiscal federalism must unfold its responsibilities appropriately, without putting aside or forgetting regional development, such that the required and ideal channels to growth can come about in all the regions, for a harmonized growth.
If it is not done in this way, the economic crises existent in the most unfortunate regions could exercise pressure on the rest and provoke economic and social tensions, both in these regions and in the richer ones, that would have to cope with greater fiscal pressure to subsidize the poorer regions.
Here, we want to concentrate on the impact of Public Expenditure on regional VAB growth and on employment. However, we must highlight that other important effects on social well-being exist, that have been analyzed in other studies carried out (see for example GUISAN and FRIAS, 1996).
The percentage of population distribution in the regions for each country has seen very small changes, within the decade 1980-90. This has largely been due to the lack of job creation and the certain homogeneity of income per head within each country, encouraged by the policy of public spending leading to this result.
Many are in favor of greater population mobility and of greater internal competition between the regions, such that public spending favors those regions with greater economic dynamism and avoiding excessive protectionism with respect to the regions with lower VAB per head levels. 3
On the other hand, we have those who believe that protectionism would be favor, hence increasing government spending on these more disadvantaged regions. This would allow the expenditure to be re-allocated to less productive regions, ie. re- allocation of resources, on the condition that production levels per head remained low.
We are of the opinion that Europe should be inclined towards adopting a position lying between both views, such that the growth of VAB is enhanced in all regions. Public sector policy
favors a feasible equality in the distribution of the investment of the private
manufacturer which is a major contributor towards the growth of the majority of regions because of its effect on the other sectors in turn, hence avoiding excessive migratory movements.
2. IMPACT OF PUBLIC EXPENDITURE ON THE REGIONS
Public expenditure is a determining factor on growth, not only in relation to quantity but in relation to its functional distribution, as the effect varies between: public expenditure on infrastructure, expenditure on education and R&D, and general expenditure on current services. In some countries there has been an excessive persistence in giving priority to public investment, even if it is not a great use, before that needed current expenditure.
DALENBERG and PARTRIDGE (1995) use an econometric model to illustrate 28 US metropolitan areas over a 15-year period to analyze the impact of government spending, taxes and public expenditure on total employment and disaggregated employment, and they show that taxes are negatively related to total employment and expenditure on education is positively related to total employment. The results imply that greater spending on education or lower taxes can positively influence growth, while changes in infrastructure do not always appear to have a positive influence on metropolitan employment.
Public expenditure in general, expenditure on education and R&D in particular, have resulted in the growth of industry, hence proving their importance in showing similar results at regional or national level. In addition, the importance of the competitiveness is illustrated by 4
MAYES and BEGG (1994), who point out the need to make un effort to achieve reduction in disparities for the disadvantaged regions and to apply various aspects of industrial policy to help them to compete on an even footing within the single market. Among the instruments of decentralized industrial policy proposed by them, we emphasize there interesting analysis of the role of the Public Sector as a "broker" and the importance of the University industry links.
Here we present the results for the models we have estimated for the twelve countries of the EEC in 1990, classified in 98 regions that figure in the Table 2.
Data
Data corresponding to the 98 European Regions for years 1985 and 90 have been used, which can be found in the Table 2. Eurostat´s R6 Classification has been used for Value Added and Employment data. The main source of Data is SYR (Statistical Yearbook of Regions) from several years. Other sources are from Eurostat and OECD, detailed in the Bibliography. The problems encountered by missing data have brought an exhaustive amount of work and research, from which the elaboration and completion of some series have been our own, and we explain in the Annex some problems of missing data.
The main variables used are:
POP85 = Population for 1985 (in thousands) SYR. Eurostat. 1989 POP90 = Population for 1990 (in thousands). SYR. Eurostat. 1993. VABi 85 = Real Value added at market prices of 1985, in Mill.$ 85 acc to PPP of GDP in 85. SYR. Regions. Eurostat 1989 and own elaboration. VABi90 = Real Value added at market prices of 1990, en Mill.$ 85 acc to PPP of GDP in 90. SYR. Regions. Eurostat 1994. Li90 = Employment, in thousands. SYR. Regions. Eurostat 1994.
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RDGOB90 = R&D Regional Government Sector Expenditure 1990. Mill $ 85. Research and Development. Annual Statistics. Eurostat 1995. RDUNI90 = R&D Expenditure by region of the Higher Education Sector. Mill $ 85. Research and Development. Annual Statistics. Eurostat 1995. IWS = Index of well-being in socio-cultural advancement. Elaborated by Guisan and Frías 1996. RD90 = Sum of R&D Expenditure by region of the Higher Education Sector and Government Sector. 1990. Mill $ 85.
Data corresponding to Value-added, R&D Expenditure and all the other variables have been expressed in millions of dollars and in 1985 prices, using the Purchasing Power Parities (PPP) and prices from the OECD National Accounts.
Number of Sector i corresponds to R6 classification of Eurostat:
R1: Agriculture: agricultural, forestry and fishing products. R2: Energy: fuel and power products. R3: Industry: industrial products. R4: Construction: building and construction. R5: Market services: all services but those included in R6. R6: Non-market services: mainly services financed by public budgets like Public Administration, Public Health and Public Education. Eurostat also includes domestic services in this group.
The variables expressed in per head terms finish with letter H.
Intersectoral Impacts
The impact of Public Expenditure on regional development is produced mainly through the following variables: 6
EXP1:
Value added by sector R6
EXP2:
Oficial auctions and public works.
EXP3:
Family benefits, ie. pensions, subsidies, grants, etc.
EXP4:
R&D Expenditure.
An increase in each of these expenditures bring about the following effects:
Increase in:
Direct effect on
Indirect and other
variables:
effects:
EXP1
POP, FINC*, VAB5
VAB4, VAB
EXP2
VAB4
VAB5, POP,VAB
EXP3
FINC
VAB5, VAB4, POB, VAB
EXP4
VAB3, VAB5, POB
VAB4, POB, VAB
Note: * is Family Income
Econometric Models
Equation 1
The first equation reveals the resulting impact of increasing the regional share of VAB on the population.
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The corresponding shares are measured by the following ratios:
RPO90 = The ratio of the regional population for 1990 divided by the national population for that same year. RVAB90 = Ratio of regional VAB for 1985 divided by the national VAB for 1990. RVAB85 = Ratio of regional VAB for 1985 divided between the national VAB for that same year. IRVAB = RVAB90- RVAB85
Results of Estimation Equation 1:
LS // Dependent Variable is RPOP90 Sample: 1 98 Included observations: 98 Variable
Coefficient
Std. Error
t-Statistic
Prob.
RPOP85
0.999217
0.000863
1157.894
0.0000
IRVAB
0.089809
0.023531
3.816606
0.0002
R-squared
0.999899
Mean dependent var
0.122449
Adjusted R-squared
0.999898
S.D. dependent var
0.193122
S.E. of regression
0.001947
Akaike info criterion
-12.46304
Sum squared resid
0.000364
Schwarz criterion
-12.41028
Log likelihood
473.6329
F-statistic
954539.9
Durbin-Watson stat
2.597055
Prob(F-statistic)
0.000000
The estimated results offer very significant outcomes (high t- statistic values) and a good fitness (R2 adjusted is equal to 0.99), together with a low margin of error which lies between 0 and 2% for the nearly all of the regions. The residuals Graph shows a very good fitness, with the only exception being of two Greek regions.
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If we observe the Residuals graph, we can see that dummy variables could be included to ”pick-up” the pronounced errors corresponding to two regions in Greece, probably produced by the lack of statistical data used. If dummy variables are included, the resulting estimation would improve marginally.
Residuals Graph from Equation 1.
Equation 2
Equation 2 relates the variable VAB690H at regional level with the other sectors´ VAB (VABN6H) of the same region and year (1990), with the national VAB of the other sectors (XVABN690H) and its own lagged value, all expressed in terms `per head`.
The estimated results presented below, show the positive influence of the other sectors` VAB per head on the values added for regional sector 6, mainly at national level, value of XVABN690H is 0.044 (0.0089 + 0.0351), as well as the lagged value of the same sector at regional level, which has been included in VAB685H.
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So, for the growth of Public Expenditure 1, EXP1, not only is the growth of regional VAB for the other sectors needed, but also national VAB, because the results indicate a country´s growth has a direct positive influence on the regional growth of Sector 6. Another important variable is population as EXP1 is equal to VAB690H *POP90.
Results of the estimation Equation 2.
LS // Dependent Variable is VAB690H Sample: 1 98 Included observations: 98 Variable
Coefficient
Std. Error
t-Statistic
Prob.
VAB685H
0.835015
0.038702
21.57558
0.0000
VABN690H
0.008923
0.006568
1.358623
0.1775
XVABN690H
0.035111
0.008048
4.362530
0.0000
R-squared
0.917386
Mean dependent var
1869.605
Adjusted R-squared
0.915647
S.D. dependent var
570.3287
S.E. of regression
165.6442
Akaike info criterion
10.24982
Sum squared resid
2606612.
Schwarz criterion
10.32895
Log likelihood
-638.2971
F-statistic
527.4624
Durbin-Watson stat
1.376126
Prob(F-statistic)
0.000000
Dummy variables could be included in this equation to differentiate a country, or a region, that due to its own characteristics, present a different VAB6 growth to the rest of the regions and/or countries, as is the case for Luxembourg, as the Residual´s graph shows.
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Residuals Graph of Equation 2.
Equation 3
The third equation explains how research expenditure influences industrial VAB. In a first calculation, we include the industrial sector’s Value-added per head for 1990, VAB390H, in relation to its lagged values, VAB385H, and the variable IWS, Index for Social Well-being, which not only includes research investment alone, but also the regions social dynamism.
Furthermore, another series of dummy variables are included so as to reflect the growth differentials in industrial production in the European regions. Hence, we define three groups of regions according to value of the ratio between Industrial VAB in 1990 and 1985. These groups are as follows:
Group 1: Value of VAB390/VAB385 between 1.203 and 1.626 (17 regions). Group 2: Value of VAB390/VAB385 between 1.124 and 1.193 (41 regions). Group 3: Value of VAB390/VAB385 between 0.732 and 1.121 (40 regions).
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The regions that belong to group 1, in descending order, are: 70, 76, 58, 33, 10, 87, 34, 54, 17, 38, 56, 16, 55, 47, 62, 24 and 93.
In group 2 they are 31 regions with rate of increase above the mean of regions (13.9%) are the following: 57, 74, 64, 46, 92, 63, 44, 82, 72, 5, 25, 78, 61, 19, 94, 50, 8, 48, 32, 59, 85, 96, 66, 67, 42, 89, 30, 60, 22, 49 and 81. The other regions of this group, with rate bellow mean, are: 29, 90, 68, 23, 69, 26, 80, 86, 77 and 88.
The 40 regions in group 3, listed also in descending order of the rate of increase, are: 95, 39, 31, 7, 79, 43, 14, 75, 65, 21, 13, 37, 12, 45, 97, 84, 51, 83, 20, 41, 73, 91, 52, 11, 53, 35, 36, 28, 15, 18, 1, 9, 98, 27, 4, 40, 3, 2, 71 and 6.
Results of the estimation Equation 3:
LS // Dependent Variable is VAB390H Sample: 1 98 Included observations: 98 Variable
Coefficient
Std. Error
t-Statistic
Prob.
VAB385H
1.049875
0.031197
33.65253
0.0000
IWS
5.709263
1.795247
3.180210
0.0020
Z1IH
0.098818
0.040322
2.450710
0.0161
Z3IH
-0.148844
0.026224
-5.675754
0.0000
R-squared
0.931426
Mean dependent var
2859.933
Adjusted R-squared
0.929237
S.D. dependent var
1276.960
S.E. of regression
339.6871
Akaike info criterion
11.69601
Sum squared resid
10846410
Schwarz criterion
11.80152
Log likelihood
-708.1605
F-statistic
425.5938
Durbin-Watson stat
1.569793
Prob(F-statistic)
0.000000
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Where: Z1IH = D1 * GDP185H
D1 = Dummy for the first group.
Z3IH = D3 * GDP185H
D3 = Dummy for the third group.
First of all, the results displayed show that the influence of the 1985 industrial VAB over that of 1990 is different for each group of regions. The coefficient 1.1486 (1.0498 + 0.0988) corresponds to group 1, the coefficient 1.0498 corresponds to group 2, and 0.9010 (1.0498 0.1488) corresponds to group 3. All these coefficients are statistically significant.
Secondly, it can be seen that the IWS variable has a high and positive influence on industrial VAB, and we want to emphasize this result because this variable has several components that include Research Expenditure related with EXP4 and thus allows us to measure the important effect of this type of public expenditure. In the model where the estimate is done without dummy variables, the IWS coefficient is even higher (7.68) which indicates the importance of both Public Expenditure on Research, as well as the other components of the IWS indicator, over the regional growth of industrial VAB and, through that, indirectly over other sectors.
Residuals Graph of Equation 3.
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Equation 4
This equation explains the Market Services Sector’s VAB per head for 1990, VAB590H, in relation to the VAB per head of the other sectors (VABN590H), Non- Market Services Sector’s VAB per head (VAB690H), and R&D Expenditure per head by region (RD90H). This last variable has been included to consider that R&D Expenditure not only has it an effect as a component of VAB6 but also has a positive influence on the social environment that greatly expands the Market Services Sector.
Results of Estimation Equation 4. LS // Dependent Variable is VAB590H Sample: 1 98 Included observations: 94 Excluded observations: 4 Variable
Coefficient
Std. Error
t-Statistic
Prob.
VABN590H
0.626378
0.080543
7.776920
0.0000
VAB690H
0.645647
0.296340
2.178735
0.0321
RD90H
4.136312
2.042290
2.025330
0.0459
DR14
5155.923
1242.700
4.148967
0.0001
DR40
7382.367
1266.285
5.829944
0.0000
DR44
3848.169
1249.561
3.079616
0.0028
DR52
6428.289
1368.484
4.697380
0.0000
DR77
5025.030
1268.665
3.960879
0.0002
R-squared
0.732260
Mean dependent var
5938.608
Adjusted R-squared
0.710467
S.D. dependent var
2295.848
S.E. of regression
1235.355
Akaike info criterion
14.31949
Sum squared resid
1.31E+08
Schwarz criterion
14.53594
Log likelihood
-798.3964
F-statistic
33.60102
Durbin-Watson stat
1.225957
Prob(F-statistic)
0.000000
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The positive influence of the VAB of the other sectors on the Market Services VAB is clearly significant. What is more, we can see that the influence of the Non-Market Services over the R5 sector is superior to that of the rest of the sectors, as demonstrated by the value of its estimated coefficient which is the sum of 0.6263 and 0.6456. Also, expenditure on R&D have a positive influence on the Market Services VAB.
A series of Dummy Variables have been included in the model so as to ´pick-up´ the special effects, like those revealed in GUISÁN and FRÍAS (1995) amongst which tourism, harbor activities and the advantages encompassed in a capital city’s surroundings stand out.
The Dummy variables included in this model are the following:
DR14. Dummy for Region 14, Baleares. DR40. Dummy for Region 40, Hamburg. DR44. Dummy for Region 44, Hessen. DR52. Dummy for Region 52, Bruxelles. DR77. Dummy for Region 77, Ille-de-France.
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Residuals Graph of Equation 4.
3. - THE REGIONAL ALLOCATION OF PUBLIC FUNDS.
In some countries some controversies exist over the criteria that should preside over the regional distribution of public funds, especially in relation to the criteria on population and production.
PONSATI (1990) carries out an intesting international comparison, through the use of econometric models from the US, Germany, Switzerland, France and Canada, trying to evaluate the impact of the mentioned variables. The conclusion was that population is very important in all countries, that production measured by VAB has a positive and significant influence with a quantitative difference amongst countries. Another variable which has an influence, even though small, is the surface area in Switzerland and US.
In the following table we present our results for the following countries: Germany, France, Spain, Switzerland and US, based on National Statistical Sources, the same data used by PONSATI. The correlations calculated and the models estimated demonstrate the great
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importance the variable for population has and also the positive influence and general significance of the productive capacity measured by VAB.
TABLE 1. Relationship between Public Revenue and other variables, at regional level. Correlation Coefficients. Nº of Regions
Country
VAB
POP
SUP
21
France
0.9897
0.9941
-0.0453
11
Germany
0.9982
0.9955
0.7379
17
Spain
0.8898
0.8846
0.2621
26
Switzerland
0.9476
0.9326
0.3471
51
U.S.
0.9838
0.9708
0.1072
Note: This results are based on cross-sectional data for one year for each country, around 1985.
Significant Regression Coefficients. Country
VAB
POP
SUP
Adjust. R2
France
yes
yes
non
0.99
Germany
yes
yes
non
0.99
Spain
non
yes
non
0.95
Switzerland
non
yes
non
0.98
U.S.
yes
non
non
0.99
Taking into account these results together with the previous, our main conclusion is that to strengthen the harmonized growth of Europe, the countries´ regional policies should encourage the growth of VAB and maintain, or increase, population growth. In this manner there will be a balance between what the regions contribute to and receive from the national funds. The manner for encouraging a harmonized growth using the measures which lead to the growth of Industry and Market Services has been analyzed in this study.
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TABLE 2. POPULATION, PER HEAD VALUE-ADDED (TOTAL, INDUSTRY AND NON-MARKET SERVICES) AND R&D EXPENDITURE PER HEAD. (all variables are in dollars per head, except population expressed in thousands) Region 1. Galicia 2. Asturias 3. Cantabria 4. País Vasco 5. Navarra 6. Rioja 7. Aragón 8. Madrid 9. Castilla y León 10. Castilla-Mancha 11. Extremadura 12. Cataluña 13. Comunidad Valenciana 14. Baleares 15. Andalucía 16. Murcia 17. Canarias 18. Denmark 19. Piemonte 20. Valle d´Aosta 21. Liguria 22. Lombardía 23. Trentino-Alto Adige 24. Veneto 25. Friuli-Venezia Giulia 26. Emilia Romagna 27. Toscana 28. Umbría 29. Marche 30. Lazío 31. Campania 32. Abruzzi 33. Molise 34. Puglia 35. Basilicata 36. Calabria 37. Sicilia 38. Sardegna 39. Scheleswig-Holstein 40. Hamburg
POP90 VAB90H VAB390H VAB690H RD90H 2804 1126 527 2129 521 260 1213 4878 2626 1714 1128 6008 3787 682 6920 1027 1485 5140 4357 116 1723 8926 889 4392 1202 3925 3562 822 1433 5181 5831 1269 336 4076 624 2153 5185 1661 2615 1641
7678 8577 9546 11912 11995 12505 10640 12260 8448 8535 6317 12020 9994 12719 7618 9668 9692 13945 16039 16475 14976 17828 15664 15426 15676 16675 14449 12378 13949 15137 9000 11426 9704 9703 7866 7551 8601 9548 13118 25245
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1282 1663 2316 3656 4076 4703 2571 2127 1622 1518 427 3345 2316 972 1036 1392 664 2572 5489 2426 2456 5835 2985 4768 3478 4898 3878 2953 3762 1997 1375 2125 1702 1568 841 511 767 1199 2765 4050
1060 1169 1226 1094 1290 1324 1474 1835 1405 1139 1226 1022 1052 1405 1155 1325 1459 3110 1504 2383 2019 1359 2240 1548 2169 1668 1800 2021 1763 2723 1677 1731 1836 1663 1809 1718 1748 2027 2381 2613
16.14 26.21 20.53 21.25 49.09 8.07 30.01 116.97 19.85 4.59 15.70 28.49 20.00 11.54 23.17 29.69 23.19 110.12 41.89 1.72 123.58 57.08 42.77 37.66 68.01 73.62 76.32 18.50 17.25 510.41 32.93 26.97 5.66 19.59 60.93 7.95 19.43 48.07 87.49 184.58
Region
POP90 VAB90H VAB390H VAB690H RD90H
41. Niedersachsen 42. Bremen 43 Nordrhein-Westfalen 44. Hessen 45. Rheinland-Pfalz 46. Baden-Wüttenberg 47. Bayern 48. Saarland 49. Berlin 50. Vlaams Gewest 51. Region Wallomme 52. Bruxelles 53. Noord-Nederland 54. Ost-Nederland 55. West-Nederland 56. Zuid-Nederland 57. Luxembourg 58. Ireland 59. Norh U.K. 60. Yorkshire and H. 61. East Midlands 62. East Anglia 63. South-East 64. South-West 65. West-Midlands 66. NorthWest 67. Wales 68. Scotland 69. NorthernIreland 70. Norte Portugal 71. Centro Portugal 72. Lisboa e V. Tejo 73. Alentejo + Algarve 74. Voreia Ellada 75. Kentriki Ellada 76. Anatolika Kai Notia Nisia 77. Ille-de-France 78. Champagne-Ardenne 79. Picardie 80. Haute-Normandie 81. Centre 82. Basse-Normandie 83. Bourgogne
7343 679 17247 5719 3735 9729 11337 1071 2118 5754 3251 962 1596 3050 6996 3306 381 3503 3075 4952 4019 2059 17458 4667 5219 6389 2881 5102 1589 3453 1732 3305 888 3286 5859 978 10633 1341 1804 1731 2363 1385 1602
12816 19174 14326 18213 13148 16706 15666 13273 16938 13872 10829 22395 13063 11527 14574 12771 16660 9027 11850 12307 12206 12634 15739 12468 11782 13084 12010 12604 9630 6493 5100 9989 5043 6079 6326 5867 22491 14364 12278 14467 13345 12372 12758
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3281 5125 4324 4442 4320 5861 4647 3871 5482 3613 2268 2522 2032 2409 2208 3654 4270 2744 3350 3032 3985 3394 2994 3040 3783 3998 2600 3252 2489 2356 1654 2120 653 2865 2823 961 3972 3693 3448 3572 3239 2953 3108
2181 2510 1947 1962 1968 1890 1951 1912 3079 1539 1764 3978 1345 1429 1585 1275 2277 1399 2211 1938 1903 1997 2424 2308 1754 1887 2009 2381 2736 765 815 1455 656 843 1087 876 2847 2328 1988 2119 2300 2305 2209
103.34 123.38 95.19 105.08 81.96 127.35 93.22 132.38 267.00 90.06* 90.06* 90.06* 120.91* 120.91* 120.91* 120.91* NA 36.80 48.59 59.55 69.23 214.36 197.43 95.93 75.07 61.57 62.89 161.51 57.94 11.56 22.20 51.46 6.66 23.48 33.62 47.00 199.50 2.72 3.54 5.27 31.26 25.68 17.08
Region 84. Nord-Pas-de-Calais 85. Lorraine 86. Alsace 87. Franche-Comté 88. Pays de la Loire 89. Bretagne 90. Poitou-Charentes 91. Aquitaine 92. Midi-Pyrénées 93. Limousin 94. Rhöne-Alpes 95. Auvergne 96. Languedoc-Rousillon 97. Provence-Alpes-Côte d´Azur 98. Corse
POP90 VAB90H VAB390H VAB690H RD90H 3945 2293 1619 1092 3048 2784 1588 2787 2423 719 5338 1314 2113 4250 249
11540 12271 14542 13275 12409 11479 11544 12846 12320 11195 14516 11602 11296 13373 10080
2887 3202 4450 4877 2807 2071 2330 2112 2190 2421 3799 2833 1514 1683 604
2049 2421 2287 2237 1918 2409 2223 2337 2337 2300 2174 2349 2174 2508 2201
9.02 30.63 59.71 6.68 17.65 82.88 17.80 26.51 143.41 3.81 76.54 31.23 145.46 89.70 14.65
* Data correspondig to country average: Belgium and Netherland. SOURCE: Own elaboration on S.Y.R. Eurostat 1994, and N.A. OECD 1994.
ANNEX.
Missing data Germany: Berlin 1990 data corresponds to the west alone. Data is not available for east Germany. Government Sectors´ regional data for R&D Expenditure is not available. Belgium: Regional data for R&D Expenditure in not available. Netherlands: Data for 1990 employment is only available in total and data for R1 sector. Regional data for R&D Expenditure in not available. Italy: Regional data for 1990 R&D Expenditure is not available for the Higher Education sector. United Kingdom: Data for 1990 employment is not available for R5 and R6 sectors. Greece: Value added Regional data for 1990 is not available for R5 and R6 sectors. Regional employment in 1990 is also unavailable.
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5.- REFERENCES
DAJMEMBERG, Douglas R. and PARTRIDGE, Mark D. (1995): "The effects of taxes, expenditures and Public infrastructure on Metropolitan area employment". Journal of Regional Science, vol. 35, nº 4, pp. 617-640.
GUISÁN, M.Carmen (1991): "Financiación regional y local. Situación en España y comparación internacional". En: Estudios en Homenaje al Profesor C. Otero Díaz. Universidad de Santiago de Compostela, pp. 37-43.
GUISÁN, M.Carmen and FRIAS, Isidro (1995): "An interregional econometric model for market services employment in 120 EEC Regions". Documentos de Econometría, nº 1. Publications Office. University of Santiago de Compostela.
GUISÁN, M. Carmen and FRIAS, Isidro (1996): "Economic Growth and Social Welfare in the European Regions". European Regional Science Association. 36th European Congress.
EUROSTAT (1989, 1993, 1994) : Statistical Yearbook, Regions.
EUROSTAT (1995): Research and Development. Annual Statistics.
MAYES, D.G. y BEGG, Iain (1994): "Rethinking industrial policy in Europe: A decentralized Approach". Paper presented in: Fall Meeting of Project Link, en Salamanca (Spain). OECD (1994): National Accounts. Vol.I.
PONSATI, Clara (1990): "El finançament de les Comunitats Autònomes: Conparació Internacional". Institut d´Analisi Economica CSIC. Barcelona.
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