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Non-life insurance consumption in Italy: a sub-regional panel data analysis

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Abstract

We analyze the consumption of non-life insurance across 103 Italian provinces in 1998–2002 in order to assess its determinants, in the light of the empirical literature. Using sub-regional data, we overcome an important limitation of cross-country analyses, i.e. the systemic heterogeneity due to country-specific characteristics. Individual heterogeneity is accounted for through panel data techniques. However, considering spatial units within a single market raises issues of cross-sectional or spatial dependence, either due to common nationwide and/or regional factors or to spatial proximity. We carefully assess spatial dependence, employing recent diagnostic tests, finding out that the regressors included in our specification successfully account for spatial dependence. Insurance turns out to depend on income, wealth and some demographics, as already established, but also on trust, judicial efficiency and borrowing conditions. These findings help in explaining the gap between Central-Northern Italy and the south of the country.

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Notes

  1. Data from Sigma, Swiss RE, relative to 2007.

  2. For a survey of insurance in emerging markets, see Outreville (1990, 1996, 2000).

  3. The penetration of non-life covers related to so-called personal insurance, like accident and health, is also much higher in countries, such as Germany, where some citizens are allowed to opt out of the public health insurance scheme.

  4. Due to data unavailability, in these international comparisons we separate motor from non-motor, instead of mandatory motor-TPL from the rest (including motor own damage). In the latter case the comparisons would be even less favourable, given the relatively low importance of non-mandatory motor in Italy.

  5. See Esho et al. (2004) for a survey.

  6. Surveys of the existing empirical literature can be found in Browne et al. (2000) (property-casualty) and Focarelli et al. (2004) (health).

  7. Their results are quite similar when regressing the amounts insured, but the role of age, macroregional dummies and education is weaker.

  8. They also estimate, but do not report, a random effects regression with results similar to the fixed effects one.

  9. The authors are ostensibly assuming all these factors to be time-invariant.

  10. One perhaps more convincing proxy for price is the (inverse) loss ratio, i.e. the ratio between premiums earned and claims paid for a given period, used by Outreville (1990) and Esho et al. (2004), which can be seen as an ex-post estimate for the average price paid for the services of the insurer per each unit of claims paid. Yet the reasons for inclusion of a proxy for price in a reduced-form consumption equation are unclear (see the formalization by Beenstock et al. 1988).

  11. Having been forced to omit these two time-invariant variables from the fixed effects analysis, these two claims are based only on pooled models, so that the above-mentioned inconsistencies between the two specifications may cast doubt on this finding.

  12. Hoy and Robson (1981) examined conditions for insurance to even be a Giffen good, a possibility later dismissed by Borch (1986) while the Hoy and Robson (1981) model has been generalized by Briys et al. (1989) and recently by Hau (2008).

  13. In general, the tendency apparent from the various specifications reported in the literature is for income to be positively correlated with most other regressors, so that the more omitted variables there are, the greater the coefficient of income will result. It is therefore crucial to this end to specify a reasonably complete model.

  14. For an example in the field of insurance development, see Beck and Webb (2003), where the RE specification is accepted only on the subgroup of developing countries.

  15. Guiso et al. (2004a), analizing the causal link between financial development and growth from a regional perspective, observe that the level of political, regulatory and financial integration reached within Italy can be considered an upper bound for that a set of countries will ever be able to attain.

  16. Provinces correspond to level 3 in the NUTS (Nomenclature of Territorial Units for Statistics) classification by Eurostat.

  17. Notice that the model with insurance penetration as the dependent variable is a simple reparameterization of our model.

  18. Data on insurance premiums are collected on a provincial basis by ISVAP, the Italian Insurance authority.

  19. In other words, at a first glance heterogeneity in insurance consumption does not seem to be only due to differences in available resources, since the average propensity to buy insurance out of one’s income is almost as differentiated.

  20. Tests and diagnostic plots for spatial correlation as well as spatial models are based on a spatial weights matrix constructed according to the principle of queen contiguity (that is, provinces are considered neighbours if they share a common border or vertex; see LeSage 1999). According to common practice, the matrix has been row-standardized. Reggio Calabria and Messina, divided by the Messina Strait, have been considered contiguous.

  21. We verified the appropriateness of deposits as a proxy for wealth drawing on a new database from the Bank of Italy (see Albareto et al. 2008), comparing our data on bank deposits with their estimates of household wealth for the year 1998. On a per capita, cross-region basis, the correlation between bank deposits and real assets was 0.92, with financial wealth 0.80, both significantly positive at the 1% level. Province-level data are not available.

  22. This is collected at regional level.

  23. This indicator comes from the database of Guiso et al. (2004b) and is relative to 1999, thus it is included as a time-invariant variable; the variability over time should be almost negligible with respect to the high cross-sectional variance.

  24. This (like the previous one) is included as a time-invariant variable both due to unavailability of other waves and in the belief that such attitudes would show scant variability over a 6- or 7-year horizon.

  25. Of the 20 Italian regions, two (Valle d’Aosta and Molise) contain only one province.

  26. The results for a specification where 18 regional fixed effects are included instead of the 5 macroregional ones, not reported, are quite similar and can be obtained from the authors upon request. The fact that the behaviour of the model does not change substantially between the two cases is taken as evidence in favour of the robustness of our specification.

  27. We are therefore assuming that the inclusion of (macro)-regional and time effects does make the residual individual effect to be uncorrelated with the regressors. One further restrictive hypothesis our model rests upon is that all regressors be strictly exogenous, which is needed for consistency of the RE estimator.

  28. A structural a-priori reason to suspect the presence of serial correlation is the existence of pluriannual contracts.

  29. For the RE-HC model a robust Wald test is done as discussed in Wooldridge (2002, Ch. 10).

  30. It must be noted that this test has power against the presence of random effects as well as against serial error correlation.

  31. The results for the model with regional dummies are also generally consistent with the former, the main difference being that the coefficient for the share of the agricultural sector on GDP, while retaining the same sign, is not statistically significant any more.

  32. We have also tested for differences in wealth elasticity across the two macroareas, without finding statistical evidence in favour of it.

  33. The (lagged) loss ratio of the property sector, included in an alternative specification as an ex-post estimate of risk conditions, proved not significant. See also the discussion in Sect. 3.2.

  34. While fixed effects estimators are inconsistent for fixed T in dynamic panel data models, Pesaran (2004, 6) shows that the CD test continues to hold also in this context, as long as the disturbances are symmetrically distributed.

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Acknowledgments

We would like to thank for useful comments and suggestions at various stages of this work Giacomo Pasini, Annapaola Lenzi, Elisa Tosetti, Roberto Cannata and participants to the following conferences: Spatial Econometrics Association, Cambridge 2007; European Regional Science Association, Liverpool 2008; Applied Statistics Conference, Bled 2008; Italian Congress of Econometrics and Empirical Economics, Ancona 2009; and to internal seminars at DEAMS, University of Trieste.

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  1. Generali Research & Development, Generali S.p.A., via Machiavelli 4, 34131, Trieste, Italy

    Giovanni Millo

  2. Dipartimento di Scienze Economiche, Aziendali, Matematiche e Statistiche “B. de Finetti” (DEAMS), University of Trieste, Piazzale Europa 1, 34127, Trieste, Italy

    Giovanni Millo & Gaetano Carmeci

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Correspondence to Giovanni Millo.

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Millo, G., Carmeci, G. Non-life insurance consumption in Italy: a sub-regional panel data analysis. J Geogr Syst 13, 273–298 (2011). https://doi.org/10.1007/s10109-010-0125-5

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