Territorial Distribution of Insurances under the Influence of Nongovernmental Credit

Available online at www.sciencedirect.com ScienceDirect Procedia Economics and Finance 22 (2015) 222 – 231 2nd International Conference ‘Economic Scientific Research - Theoretical, Empirical and Practical Approaches’, ESPERA 2014, 13-14 November 2014, Bucharest, Romania Territorial distribution of insurances under the influence of nongovernmental credit Dan Constantinescua* a Ecological University of Bucharest, 1st G, G-ral Vasile Milea Av, 061341, Bucharest, Romania Abstract Usually, the influence factors that determine the territorial structure of insurances are included in indicators’ regional profile, indicators that define the demand and supply of such products, such as gross domestic product, the nominal average wage and the population – on one hand and own or associate distribution network (brokerage firms, banc assurance systems), on the other hand. Starting with the fact that many products of the insurance industry are purchased in the same “package” with credit institutions products, the present study aims to add up the usual benchmarks of insurance products territorial distribution analysis with elements that concern nongovernmental loan’s regional structure. In this aspect we take in consideration a series of correspondences common in the practice of selling financial products, such as those between consumer credit and life insurance, between mortgage or real estate loans and property and goods insurances, between loans for car purchase and MTPL and CASCO insurances and, last but not least, between business loans and credit and surety ship insurances. © 2015 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license © 2015 The Authors. Published by Elsevier B.V. (http://creativecommons.org/licenses/by-nc-nd/4.0/). Selection and/or peer-review under responsibility of the Scientific Committee of ESPERA 2014. Selection and/or peer-review under responsibility of the Scientific Committee of ESPERA 2014 Keywords: territorial distribution; insurance; disparities; credits; correlations 1. Premises The Romanian development regions correspond to NUTS-II divisions in the EU. Although they become more and more significant in the regional development area, these regions do not have an administrative status by not having a legislative council or an executive body. * Corresponding author. Tel.: +40-721-280-966; fax: +40-21-316-63-24. E-mail address: dan.constantinescu@ueb.ro 2212-5671 © 2015 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Selection and/or peer-review under responsibility of the Scientific Committee of ESPERA 2014 doi:10.1016/S2212-5671(15)00267-1 Dan Constantinescu / Procedia Economics and Finance 22 (2015) 222 – 231 The Regional Development Council is a deliberative areal organism, without juridical personality, constituted and operating on partnership principles in each development region in order to coordinate the monitoring and elaborating activities arising from regional development policies. It is composed of chairmen of district councils of the development regions and of one representative of the local municipal, town and communal councils, in each district in the region. Districts correspond to NUTS-III divisions in the EU. Development regions are not administrative units, don’t have juridical personality, but are the result of an agreement between district councils and the local ones. Their function is to allocate funds received from the EU for regional development and to interpret and research regional statistics (Jula și Jula, 2000). Also, development regions coordinate regional infrastructure projects and have become members of the Committee of Regions once Romania entered the EU in 2007. Romania is administratively organized in eight development regions (each having 2 to 7 districts), named after their geographical position: Nord-East, South-East, South Muntenia, South-West Oltenia, West, Nord-West, Center, Bucharest-Ilfov. 2. Analysis of territorial distribution of insurances Gross written premiums distribution on the eight development regions in 2013 for insurances (overall and for the two categories - life and non-life) is shown in figure 1. We see an acute concentration of premiums for both types of activities in the Bucharest-Ilfov region, which includes the capital, where are located almost half of the non-life insurances and less than 75% of life insurances. It can also be seen that non-life insurances, which is about 4/5 of the total, are the ones who put their mark on the regional distribution of gross written premiums. Source: 2013, ASF, Annual Report Fig. 1. Development regions’ percentage contribution to gross written premiums in 2013. By excluding the Bucharest-Ilfov region we can say that data become comparable, which does not exclude significant variations in the levels recorded for the seven remaining regions, the spread between the minimum and maximum share being 1,83 times for total insurance, 1,77 times for non-life insurance and 2,40 times for life insurance. The same phenomenon may be revealed if we analyze de dispersion of individual values from the average share. (fig. 2) The above mentioned dispersion is explicable considering that the seven regions have significant differences in the level of socio-economic development and, somewhat smaller differences, in the population number, both representing decisive factors in the level of accessing insurance products. In this context, to deepen the analysis requires the use of indicators expressing the intensity of the saving phenomenon in these products, respectively the penetration degree of insurances in the GDP (does not include the GDP from extra-regions) and their density, relative to the population number. (table 1). 223 224 Dan Constantinescu / Procedia Economics and Finance 22 (2015) 222 – 231 7,60 6,82 3,89 Fig. 2. Regional distribution of gross written premiums in 2013 (%), without the Bucharest-Ilfov region. Table 1. Insurance intensive indicators from a regional perspective in 2013. Regions Penetration degree in GDP (%) Density (lei/pers.) North-East 0,78 141,20 South-East 0,91 217,96 South - Muntenia 0,72 178,02 South-West - Oltenia 0,68 163,32 West 0,66 222,51 North-West 0,98 242,54 Center 0,83 240,92 Bucharest - Ilfov 2,61 1.797,90 Total 1,25 368,15 Source: CNP, Forecast in territorial profile - Autumn 2013 version; INS, TEMPO database Disparity indices of the two intensive indicators, calculated by dividing the regional level to the national level (Krugman, 1993), highlight the already known significant deviations from the average of the Bucharest-Ilfov region and a lower dispersion of values corresponding to the penetration degree compared to insurance density. (fig.3) Under these circumstances, regional differences (Aiginger et al., 1999) were determined by comparing the analyzed indicators to the levels recorded in the North-West region, located in the second place in their hierarchy, after the Bucharest-Ilfov region which, given its specificity, was eliminated from the calculation. (fig. 4) It is much easier to note the slightly higher disparity in the insurance density, but also that the gaps in the two territorial indicators are practically determined by figures for non-life insurances, obviously due to the share they have in total insurances, despite a significantly more pronounced dispersion in the case of life insurances. Thorough the analysis up to the insurance class structure, we observe that territorial profiles corresponding to motor car insurances and property and assets insurances are quite similar, and credit and surety-ship insurances present a fairly uniform territorial distribution for the analyzed indicators, excepting the North-West region (fig. 5). Dan Constantinescu / Procedia Economics and Finance 22 (2015) 222 – 231 500,00 400,00 % 300,00 200,00 100,00 0,00 Penetration degree Density Fig. 3. Disparity indices of insurance intensive indicators. Density 110,00 110,00 90,00 90,00 70,00 70,00 % % Penetration degree 50,00 50,00 30,00 30,00 Non life Life Total Non life Life Total Fig. 4. Regional gaps in the penetration degree and insurance density. Although this region does not occupy the top position in all six indicators anymore, for comparability reasons I preferred to keep it as a reference. As assumed, even developing regions are not homogeneous in terms of penetration degree and insurance density, as can be seen from the following example in which I have selected two regions at opposite poles of insurance intensity, respectively South West Oltenia and North West. (fig. 6) We note that districts’ polarization is more pronounced in the South West Oltenia region, the indicators’ dispersion in the other region being within more reasonable limits. The regional analysis of the volume of written premiums – overall and by type of activity – based on the Herfindahl indicator and Gini-Struck ratio (Isaic-Maniu et al., 2005) shows a higher degree of concentration compared to the average for life insurances, significantly dependent on companies’ sales policy, while the mandatory nature of MTPL (motor third party liability) insurances, a significant component of non-life insurances, leads to a higher distribution uniformity for the latter on these regions. (table 2) 225 226 Dan Constantinescu / Procedia Economics and Finance 22 (2015) 222 – 231 Penetration degree Density 110,00 110,00 90,00 90,00 % 130,00 % 130,00 70,00 70,00 50,00 50,00 Auto Prop. Credits gar. Auto Prop. Credits gar. Fig. 5. Regional disparities in the density and penetration degree of the main non-life insurance classes. North-West 120 120 100 100 % % South-West 80 80 60 60 DJ GJ Penetration degree MH BH OT BN VL Penetration degree Density CJ MM SM SJ Density Fig. 6. Intra-regional variations of disparity indicators for penetration degree and insurance density. Table 2. Concentration levels of gross written premiums, GDP and population. Indicatori Total insurances of which: Non life GWP Population Life Herfindahl 0,3066 0,2611 0,5409 0,1440 0,1303 Gini-Strück 0,4556 0,3944 0,6895 0,1473 0,0778 0,1478 0,1480 0,1540 0,1447 0,1490 0,0763 0,0772 0,1142 0,0464 0,0849 Herfindahl a Gini-Strück a a without the Bucharest-Ilfov region A significant contribution to the concentration level previously determined has the Bucharest-Ilfov region. If we don’t take this region in consideration, concentration indicators show a more uniform spreading of gross written premiums in the other seven development regions. In addition, we can observe that after recalculation, the concentration level of overall subscriptions approaches that of the population, but remains higher than the one for the GDP. 227 Dan Constantinescu / Procedia Economics and Finance 22 (2015) 222 – 231 3. Analysis of territorial distribution of nongovernmental credit A similar analysis can be done for territorial distribution of nongovernmental credit (fig. 7). We note the high percentage of Bucharest-Ilfov region – approximately 40%, though somewhat lower than the insurance distribution and a significant variation of other regions, from 6% to 12%. We can also see a very high similarity of distribution on regions for the two nongovernmental credit beneficiary categories, although the concentration on Bucharest-Ilfov region is somewhat higher for credits granted to economic agents. Companies Individuals 10% 9% 8% 42 % 7% Total loans 9% 9% 10% 37% 8% 8% 40% 6% 12 % 9% North - East West 6% 7% 7% 9% 11% South - East North - West 8% 9% South Center 7% 12% South-West Bucharest-If. Source: 2013, BNR, Territorial structure of loans granted to non-banking clients, nongovernmental Fig. 7. The percentage contribution of development regions to the dimension of nongovernmental credit in 2013. Regarding intensive indicators of accessing the nongovernmental credit (share of GDP and indebtness) we see that the Bucharest-Ilfov region is situated on a higher level than the ones registered for other regions concerning the share of credit in the GDP, but exceeds almost four times the average of indebtness (table 3). Table 3. Intensive indicators of accessing the nongovernmental credit in 2013. Regions Weight of loans in GDP (%) Indebtness (lei/pers.) North-West 29,97 5.453,92 South-East 29,90 7.146,23 South - Muntenia 20,61 5.096,64 South-West - Oltenia 25,88 6.176,85 West 24,26 8.184,66 North-West 39,12 9.653,78 Center 27,52 7.955,40 Bucharest - Ilfov 55,83 38.401,64 Total 35,02 10.292,68 Source: CNP, Territorial forecasting – autumn version 2013; INS, TEMPO Data Base The same aspects can be highlighted if we look at the regional distribution of disparity indices of the above mentioned indicators. Regarding the calculation of regional disparities (fig. 8) the reporting base used was the North-West region, both to allow a graphical comparison of the indicators in the insurance sector with the nongovernmental loans and for the fact that the region in question presents the highest level of analyzed indicators. 228 Dan Constantinescu / Procedia Economics and Finance 22 (2015) 222 – 231 Indebtness 110 110 90 90 70 70 % % Loans weight in GDP 50 50 30 30 Companies Individuals Companies Total Individuals Total Fig. 8. Regional disparity of loans weight in GDP and indebtness degree. Moreover, we note that for most developing regions, loans to economic agents show more pronounced disparities than those for individuals, except the North-West and Central regions. If we analyze the structure of credits for individuals, we see not only significant regional differences in the share of mortgage loans in the GDP but also in the indebtness degree of this type of credit (fig. 9). Indebtness 110 110 90 90 70 70 % % Loans weight in GDP 50 50 30 30 Consumer Mortgage Others Consumer Mortgage Others Fig. 9. Regional disparity of loan weight in GDP and individuals’ indebtness degree. The above mentioned variations don’t have, however, but a limited impact on the disparity registered in overall individual loans, whose profile is largely determined by consumer loans due to their higher slare (51,77%, compared to 39,55%). The regional analysis of nongovernmental loans – overall, on the main beneficiaries and loan categories – based on the Herfindahl indices and Gini-Struck ratio, shows a rather low degree of concentration, closer to the one associated to population. Unlike insurances the Bucharest-Ilfov region has a lesser influence on loans’ concentration degree, but nevertheless presents significant influences, especially in the Gini-Struck ratio. 229 Dan Constantinescu / Procedia Economics and Finance 22 (2015) 222 – 231 Table 4. Concentration degree of nongovernmental loans, GDP and population. Indicators Total loans of which: Companies Herfindahl 0,2130 Gini-Strück 0,3172 Herfindahl a Gini-Strück a Herfindahl a Gini-Strück a a 0,0833 Consumer loans 0,1681 Gini-Strück Herfindahl 0,1488 0,2274 0,3421 0,1518 0,1020 Mortgage loans 0,2385 GDP Population Individuals 0,1941 0,1440 0,1303 0,2810 0,1466 0,1473 0,0778 0,1447 0,1490 0,0663 Other loans 0,0464 GDP 0,0849 Population 0,2116 0,1440 0,1303 0,0778 0,1717 0,3340 0,2832 0,1473 0,1459 0,1507 0,1449 0,1447 0,1490 0,0490 0,0464 0,0849 0,0594 without Bucharest-Ilfov region 0,0955 In relation to the average concentration level (in overall loans), slightly lower values are recording the loans for population, consumer loans and other loans, whose level of territorial concentration approaches rather the values of GDP. 4. Factors of influence in the insurances territorial distribution The main factors that cause the differences between development regions in the volume of gross written premiums - GWP, overall, on insurance categories (non-life – NL, life – L) and on the main classes (auto – Au, property and goods – Pr, credits and guarantees – Cg) can be identified and quantified for their contribution by the well-known statistical methods of analysis of correlation and regression (Nijkamp and Reggiani, 2006). To increase the number of freedom degrees, we used a NUTS-3 data structure in estimating the regression equations. As statistics on the volume of loans include Ilfov district in the figures for Bucharest, for comparability reasons we have further used this convention for all other indicators. The demand of insurance products was estimated through gross domestic product – GDP, monthly net average earnings – NAE and the number of population – P. The correlation coefficients of the mentioned variables (0.82; 0.94 and 0.79) highlight the existence of the multi-collinearity phenomenon, which doesn’t allow a multifactor approach of the influence of demand’s territorial distribution on the regional disparities of the insurance industry. Individual relationships with a high level of significance can be though, highlighted, as follows (t-Statistic): GWP 206,848  0,026 GDP, R 2 0,9584 (1) [-8,5573] [29,9594] GDP 2717,298  2,115 NAE, R 2 0,5370 (2) [-6,2097] [6,7261] GDP 691,282  0,0017 P, R 2 0,7907 (3) [-8,0727] [12,1383] If we detail the analysis on categories and insurance classes, we’ll see a higher level of significance (R2) for nonlife insurance. In addition, for motorcar insurance, the territorial distribution of GDP and population are more significant, while credit and suretyship insurances are more influenced by the territorial distribution of monthly net average earnings. The insurance industry’s offer was estimated based on the sales force, manifested by: the number of insurance agents – Ag, the insurance companies’ territorial network (branches, agencies, operating points) – Bn (Branch Network) and insurance brokerage firms – Bk. The policy of territorial sales reveals a common strategy for the three sales channels mentioned before, which induces the multi-colinearity phenomenon between the mentioned variables, all three correlation coefficients being 0.97 and does not allow us a multifactor approach of the influence of offer indicators’ territorial distribution. 230 Dan Constantinescu / Procedia Economics and Finance 22 (2015) 222 – 231 Regression equations that shape the relationship between territorial distribution of gross written premiums and the mentioned indicators, for 2013, are presented below: (4) GWP 312,892  0,3182 Ag , R 2 0,9315 [-9,2282] [23,0307] (5) GWP 459,691  14,2412 Bn, R 2 0,9336 [-12,185] [23,4106] (6) GWP 4,446  13,644 Bk , R 2 0,9901 [-0,4295] [62,3356] In the analysis on categories and insurance classes, insurance agents distribution is a more significant factor for nonlife insurance and especially, for credit and guarantees insurances, while territorial configuration of the branches network of insurance companies puts its mark especially on the regional distribution of auto insurances. Returning to the influence of insurance products demand variables, we see a much more closer connection between territorial distribution of gross written premiums amount and nongovernmental loan - Tl (Total loans): GWP 58,164  0,0467 Tl, R 2 0,9880 (7) [-4,9737] [56,6959] This phenomenon seems natural if we take into consideration that the procedure of granting consumer loans - Cl usually involves having a life insurance. Hence the equation: (8) GWPL 52,575  0,0707 Cl, R 2 0,9661 [-8,7144] [33,3311] Moreover, mortgage loans - Ml are conditioned by the existence of a property and goods insurance: (9) GWPPr 0,586  0,029 Ml , R 2 0,9831 [0,3286] [47,6926] Not to mention that other loans for individuals - Ol are mostly intended for the purchase of cars and from all auto insurances, motorcar third party liability is mandatory and the CASCO insurance although optional it is imposed by banks as an instrument of conserving the suretyship’s value. (10) GWPAu 7,674  0,2745 Ol , R 2 0,9909 [3,6040] [65,2253] In addition, we can highlight a significant relationship between the territorial distribution of credit and guarantees insurances and that of company loans (economic agents) - Cl (Company Loans): GWPCg 2,852  0,0023 Cl, R 2 0,9804 (11) [-7,1001] [44,1531] It is true that, in turn, territorial distribution of nongovernmental loan is determined by the regional profile of indicators regarding the GDP, the monthly average net income and population, and regression equations that shape the connection between the above mentioned indicators for 2013 are presented below: Tl 3239,96  0,5628 GDP, R 2 0,9823 (12) [-9,6502] [46,5126] Tl 1259,601  0,0076 NAE, R 2 0,6736 (13) [53,7101] [8,9707] Tl 1105,112  0,0005 P, R 2 0,6170 (14) [27,5312] [7,9264] We appreciate that territorial distribution of demand for insurance products can be estimated much better by the territorial profile of nongovernmental loans, on the whole and by type of loans, as prov ed both by the significance level of the connection between the analyzed variables and the highlighted ties between the structural components of the supply of goods in the two financial sectors mentioned in the present study. 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