Differences between regional income and regional GDP in Hungary

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1 Differences between regional income and regional GDP in Hungary György Budaházy Paper presented to the 48 th Congress of the European Regional Science Association Liverpool, UK, August 2008 György Budaházy Tomori Pál College Kalocsa Hungary 1

2 Abstract There are two aims of this paper. On the one hand, it provides some critical reflections of the existing methods and used indicators of the examinations and interpretations of spatial income differences. While many papers use regional GDP data as a proxy for regional income, it will be demonstrated that GDP per capita is not an appropriate indicator of regional income differences because of two main reasons: difference of regions of workplaces and the regions where the employed live (it is most important in city regions and the neighbouring regions, like for example Inner London and Outer London); and the spatial differences of capital intensive activities. There are other problems with the localization of gross domestic product within countries. Moreover, the differences between GDP per capita and personal income is an important economic indicator of the local economies itself. On the other hand the paper demonstrates by Hungarian time series the differences between regional GDP and regional income. Keywords: Regional GDP, Regional income level, Relation between GDP and income, Hungary JEL Classification: N97, O18 2

3 Introduction One aim of this paper to show that while many papers use regional GDP data as a proxy for regional income, GDP per capita is not an appropriate indicator of regional income differences because of two main reasons: difference of regions of workplaces and the regions where the employed live (it is most important in city regions and the neighbouring regions, like for example Inner London and Outer London); and the spatial differences of capital intensive activities. There are other problems with the localization of gross domestic product within countries. Moreover, the differences between GDP per capita and personal income is an important economic indicator of the local economies itself. The second aim of the paper is to demonstrate by Hungarian time series the differences between regional GDP and regional income, and decomposes the various demographic and economic impacts which explain the regional income differences. About the usability of regional GDP Gross domestic product is one of the most popular economic and social indicator. However, the interpretation of GDP is not free from misunderstandings. For example, Wikipedia defines GDP in such way: The gross domestic product (GDP) or gross domestic income (GDI) is one of the measures of national income and output for a given country's economy. GDP is defined as the total market value of all final goods and services produced within the country in a given period of time (usually a calendar year). The mistake in this definition (there are elementary failures in later part of this Wikipedia article also) is the disturbing mixing the production and income side of the economic activity: GDP is not an indicator of incomes but an indicator of production. The country level GDP can be measured three different ways: expenditure method, output method (or production method, industry of origin method) and income method. The three different ways should give in principle the same results. In practice there are always differences because of not perfect matching of various statistical databases and data sources. Counting regional GDP the industry of origin method is applicable, because there is not any statistical data about the interregional flows of income and goods. It is available only on country level (with many potential sources of uncertainty and mistakes). 3

4 It is important to distinguish various types of localization problems. It is not an aim of my paper to give a detailed discussion about the various problems 1, I present only a list about it: 1. Indivisible activities under country level (foreign affairs, activities on seas). 2. The activity is linked to interregional movements (transport, electricity) or activity can be conducted only in a spatial network (banking system, post, non-profit organizations such as political parts and the Church). 3. Companies with more than one company seat Activities outside the company seat. 5. The accuracy of statistical data from the spatial point of view. The various problems mean that the results concerning the regional GDP must be treated as an estimation with much more methodology dependencies than occurred in the country level GDP results. However, if we were able to produce a correct and not methodology dependent regional GDP measure, during the interpretation of that result caution would be needed thanks to the at least the following three factors: 1. The uneven spatial distribution of natural resources. 2. The uneven spatial distribution of capital intensive activities. 3. The interregional labor movement (commuting). The first factor means that per capita GDP level of regions with significant extractive industry and low population can be significantly higher than the local income level. Nevertheless, the mining royalties are centralized and the income of mining company can leave the region very easily, in the mining region only the destruction of environment remains. This is the case in Western Siberian Khanti-Mansi Region or Cabinda Region in Angola. The per capita GDP of these regions is very high, however, it is attributable only to the oil extraction. The impact of the second factor is the same as that of the first factor. In a village with a nuclear power station the local per capita GDP is surely very impressive, but it can be hardly interpreted as a sign of real economic development, it is only a sign of differences in production structure. The third factor constitutes one of the basis for the high per capita GDP level of urban regions, such as Inner London, Hamburg, Budapest or Paris. GDP measures the production of an area and not the production of the inhabitant of that area. The interregional labor flows lead 1 See further details for example: Capron Thys-Clement (1992), Dusek-Kiss (2008), Nemes Nagy (1995), Sourrouille (1976) 2 On the practical importance of the chosen method of localization see the several examples in Edvardsen- Monnesland (1997). 4

5 to large differences between the production and income in country level also in the case of small countries like Luxembourg. Of course, at regional level the effect of commuting can be more important than at country level. These three factors became more and more important if the spatial division became more and more detailed. If we had more detailed spatial division, the regional GDP data could be treated less and less as the proxy of regional incomes. The real differences (and not only differences due to the methodological reasons or spatial scale) at regional GDP level can be originated from differences of general capital intensity of production (and not only sectoral differences) and from the differences of labor force. This latter factor has several distinct aspects: age distribution, rate of activity, quality of labor force and labor culture. The database The regional GDP data are available in Hungary from The income data do not cover the whole monetary personal incomes but are limited to the share of income which appears in the personal tax base. This is approximately 50% of total personal incomes, but the share can be estimated only. There are parts of income, which are left out officially: incomes from agricultural production, pension, welfare payments, unemployment aid, maternity allowance and so on, and of course there is a share of income which are hidden from the tax office. However, we can assume that the omitted part of incomes moves together spatially with the incomes appearing in personal tax base. The spatial income differences were investigated with this data in several papers and at many different spatial levels. 3 The structure of database can be seen in Figure 1: there are three dimensions, time, space and the third dimension is the different attribute data: income, GDP, number of inhabitant, number of economically active inhabitants, number of employed, number of person who made tax return. The time dimension covers 13 years, the space dimension the 20 Hungarian counties and the country. The aim of my investigation was not to analyze the spatial distribution of income and GDP, this question was investigated also with this data by several papers before. My investigations focused only on the connection between income and GDP data and the effect of changing the base of comparison from the number of inhabitant to the number of other possible bases. 3 See for example Bódi et al (1999), Dusek (2004), Dusek (2006), Major Nemes Nagy (1999), Nemes Nagy et al. (2001). See Túry (2006) for an examination with several other variables beside income level. 5

6 Insert Figure 1 here The analysis The first analysis concerns the correlation between relative GDP and relative income, from year to year, altogether 13 distinct years (Figure 2). The GDP and income was compared to the four distinct bases: number of inhabitants, number of economically active inhabitants, number of employed, number of person who made tax return. It can be seen that the correlation between GDP and income is the highest with the base of number of inhabitants and this is the case in every year. The temporal stability of results is quite large. It means that the change of GDP level and the change of income level move more or less to similar direction and with similar magnitude from year to year. One interesting exception is 2002: in this election year the 50% wage increase had a significant impact to the change of spatial distribution of incomes also. Insert Figure 2 here The correlations with the base of economically active inhabitants are lower and its variance is larger than the previous case. This can be due to the fact that the number of economically active people changes from year to year according to the labor regulation and its spatial variability is significantly larger than the variation of the number of inhabitants. However, the most surprising result is that the lowest correlation and largest variation of correlations were produced by the base of the number of employed. This means that the GDP and income compared to employed changes in most hectic way. The reason behind this fact can be found first of all in the regulation and redistribution system of state, but maybe there is also an other factor: the number of employed is based on an estimation and the estimation is sensitive to the differences in economic structure of counties. If we assume that the regulation and redistribution role of state is the most important factor, then we could say that this role was decreasing over the period because the correlation was increasing. As regards the results with fourth base, that is, the number of person who made tax return, the correlations are higher in this case compared to the previous two cases. One explanation behind this fact is that this number is based not on estimation like the number of employed. The correlations are slightly lower than the correlation with the number of inhabitant. This reflects that some parts of GDP are disaggregated to county level according to the number of inhabitants. 6

7 We can summarize our results in the following way. The order of the size of correlation (apart from 2005) between GDP and personal income: where r 1 r2 r3 r1 - correlation with the base of inhabitants, and r1 r4 r2 - correlation with the base of economically active inhabitants, r3 - correlation with the base of employed, r4 - correlation with the base of persons who made tax return, We now turn to the other dimension, to the temporal change of data in each county. The correlation between GDP and income was counted with four different bases to the 20 counties and to the country. This means 21*4=84 different correlation coefficients. The results can be seen in Figure 3-4. Insert Figure 3-4 here The correlation for the whole country shows that the relation is very strong and almost independent from the base. (Table 1) This is a strong evidence for the common movement of GDP and personal income on country level. However, the case is different at county level. Table 1 Correlation between GDP and income at country level, Base economically persons who inhabitant active inhabitant employed made tax return Correlation 0, , , ,99808 We can summarize the results in the same way as in the previous case. The order of the size of correlation between GDP and personal income at country level: where r 1 r2 r3 r1 - correlation with the base of inhabitants, and r1 r4 r2 - correlation with the base of economically active inhabitants, 7

8 r3 - correlation with the base of employed, r4 - correlation with the base of persons who made tax return, The similar situation can be seen in the following three counties: Somogy, Hajdú-Bihar and Heves. (Table 2) The competitiveness of these counties is similar: the static competitiveness is low, the dynamic competitiveness is low in Hajdú-Bihar and medium in Somogy and Heves. (Lengyel, 2003, p. 376) Table 2 Correlation between GDP and income, Base County economically persons who inhabitant active inhabitant employed made tax return Somogy 0, , , ,99815 Hajdú-Bihar 0, , , ,99649 Heves 0, , , ,99777 In the second interesting group of counties the correlations are highly dependent from the base and the size of correlations are low. These counties are the following ones: Fejér, Borsod-Abaúj-Zemplén, Nógrád, Tolna. (Table 3) These four counties have an interesting common characteristic: in each county dominant industrial companies can be found, while in other counties this is not typical. However, as regards the competitiveness, the four different counties are different characteristics. Table 3 Correlation between GDP and income, Base economically County persons who active inhabitant Inhabitant employed made tax return Fejér 0, , , ,97875 Borsod-Abaúj- Zemplén 0, , , ,98543 Nógrád 0, , , ,98643 Tolna 0, , , ,98729 Up to this point we analyzed the two correlations between variables (GDP and income) according to the four different bases. Now we examine in the case of the previous four 8

9 Deviation of GDP level from county average, % County counties and the whole country that how much was the difference from year to year between the country average of the whole period and the averages of counties. As we can see in Table 4, the county averages change significantly but this change mainly did not move together with the country average. However, the structure of change is different form county to county. This would be not a problem, if the difference between county averages was the same for GDP and income data. In Figure 5-9 we depicted the change of GDP and income level compared to country average in each county. The differences are summarized in Table 4. The Table shows vividly the huge differences between the amount of deviations of counties and the whole country. Thus, we are able to detect a significant not co-movements of the two important economic variables in these four counties. Table 4 Deviation of income level and GDP level from county average, % Deviation of income level from county average, % County Fejér Tolna Borsod-Abaúj- Zemplén Nógrád Country total Fejér Tolna Borsod-Abaúj- Zemplén Heves Country total Summary In this paper we presented the simple, but often overlooked issue about the differences between regional GDP and regional income level. There are connections between these two 9

10 important economic indicators, but using GDP as a proxy of incomes is a not justifiable practice. As regards the concrete reasons between the differences of two variables, the exploration of these reasons was not an aim of this paper. However, this would be an interesting task of a descriptive-historical research. References Bódi F. Obádovics Cs. Mokos B. (1999) Adózás, jövedelemkülönbségek Magyarországon. Területi Statisztika, 39, pp Capron, H. Thys-Clement, F. (1992) Regional accounts in action: concerning methodological problems from a Belgian perspective. The Review of Income and Wealth, 38, pp Dusek T. (2004) Spatially aggregated data and variables in empirical analysis and model building for economics. In: Cybergeo: Revue européenne de géographie, 2004, No. 285, p. 21. Dusek T. (2006) Regional income differences in Hungary: a multi-level spatio-temporal analysis. Paper presented to 46 th Congress of the European Regional Science Association, Volos Greece, 30 August- 3 September, 2006 Dusek T. Kiss J. P. (2008) A regionális GDP értelmezésének és használatának problémái. In: Területi Statisztika, pp Edvardsen, H. M. Monnesland, J. (1997): Regional distribution of value added. Paper presented to 37 th Congress of the European Regional Science Association, Rome, August Kiss J. P. (2003): A kistérségek évi GDP-jének becslése. In: Nemes Nagy József (szerk.): Kistérségi mozaik. (Regionális tudományi tanulmányok 8.) ELTE Regionális Földrajzi Tanszék MTA ELTE Regionális Tudományi Kutatócsoport, Bp. pp Lengyel I. (2003) Verseny és területi fejlődés: térségek versenyképessége Magyarországon. JatePress, Szeged Major K. Nemes Nagy J. (1999) Területi jövedelemegyenlőtlenségek a kilencvenes években. Statisztikai Szemle, 77. pp Nemes Nagy J. (1995): A GDP regionális számbavétele. In: Probáld Ferenc (szerk): Pro Geographia Humana. ELTE Eötvös Kiadó, Bp. pp

11 Nemes Nagy, J. Jakobi, Á. Németh, N. (2001) A jövedelemegyenlőtlenségek térségi és településszerkezeti összetevői. Statisztikai Szemle, Sourrouille, J. V. (1976) Regional accounts: theoretical and practical problems encountered in the recent experience of Argentina. The Review of Income and Wealth, 22, pp Túry G. (2006) Regional Disparities during the Transition: The New Spatial Structure in Hungary. Ikonomicseszkij Isledvanija, pp Enlarging EU: Macroeconomic and Firm Level Experience and Expectations Bilateral Meeting 27 October 2006 Sofia. 11

12 Figure 1 The structure of database Figure 2 Correlation between GDP and income at county level, ,98 0,96 0,94 Korreláció (r) 0,92 0,9 0,88 0,86 0,84 0,82 0, inhabitants Ec. Act employed pers m tax r Idő 12

13 Figure 3 Correlation between time series of GDP and income, inhabitant economically active inhabitant employed persons who made tax returns 0,996-0,998 0,993-0,995 0,991-0,992 0,986-0,990 0,973-0,985 Figure 4 Correlation between time series of GDP and income, inhabitants Ec. Act employed pers m tax r 0,995 0,99 Korreláció (r) 0,985 0,98 0,975 0,97 Budapest Pest Fejér Komárom-Esztergom Veszprém Győr-Moson-Sopron Vas Zala Baranya Somogy Megyék Tolna Bács-Kiskun Békés Csongrád Hajdú-Bihar Szabolcs-Szatmár-Bereg Jász-Nagykun-Szolnok Borsod-Abaúj-Zemplén Heves Nógrád össz 13

14 Figure 5 Deviation of income level and GDP level from county average, %, Fejér county SZJA GDP 20 % Figure 6 Deviation of income level and GDP level from county average, %, Tolna county SZJA GDP 20 %

15 Figure 7 Deviation of income level and GDP level from county average, %, Borsod-Abaúj- Zemplén county SZJA GDP 20 % Figure 8 Deviation of income level and GDP level from county average, %, Nógrád county SZJA GDP %

16 Figure 9 Deviation of income level and GDP level from county average, %, Country SZJA GDP %

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