Análisis de la Solvencia de las Mutualidades de Previsión Social

Resumen:Analizamos la solvencia de las mutualidades de previsión social cuyas actividades superan el territorio de una Comunidad Autónoma durante el período 2010-2012. Se muestra que el ratio regulatorio de solvencia es un buen predictor de la fortaleza financiera futura de estas entidades y que los modelos clasifican adecuadamente a una amplia mayoría. Del análisis de los factores que influyen en el futuro nivel de solvencia de las mismas se desprende que la rentabilidad económica y el crecimiento de las primas afectan de manera positiva, mientras que el uso del reaseguro y el apalancamiento de suscripción lo hacen de manera negativa.Abstract:This article analyzes solvency of social benefit institutions for the period 2010-2012 using the regulatory solvency ratio as a measure of financial strength. Two basic objectives are pursued: (1) to test whether the lagged regulatory solvency ratio is a strong predictor of the future regulatory solvency ratio and develop a prediction model to classify social benefit institutions regarding their financial strength; and (2) to know the firm characteristics that affect the probability of insolvency of social benefit institutions. This paper contributes to literature by: (1) being the first analyzing solvency of social benefit institutions; (2) applying ordered logit models that have not previously been applied to insurers of the Spanish insurance market although these models have been used in the study of solvency of other markets; (3) being one of the few studies on Spanish insurers using non-financial variables in the analysis. Following previous literature analyzing solvency of insurers we first apply Ordinary Least Square (OLS) regression with robust standard errors to correct for heterocedasticity and run a separate analysis for each year of the sample period. We use standardized coefficients to compare the magnitude of the effects of the determinants on the insurer’s regulatory solvency ratio. The objective is to provide evidence of whether or not the lagged regulatory solvency ratio is the key variable to predict future financial strength of social benefit institutions. We also include other firm specific factors that previous literature has shown that could affect insurers’ financial strength as control variables in the model. Additionally, we verify if our prediction model classifies insurers according to their financial strength correctly. In doing so, we follow previous studies (see, e.g. Kramer, 1996) and use ordered logistic regression for financial strength prediction models of insurers. We distinguish three levels of solvency in our ordered logistic model, which are given by the corresponding values of percentiles 33rd and 66th of the regulatory solvency ratios in every year. That is, the dependent variable takes 1 if the social benefit institution shows a regulatory solvency ratio below the value given by percentile 33rd; it takes 2 if the regulatory solvency ratio takes a value between the values corresponding to percentiles 33rd and 66th; and it takes 3 if the regulatory solvency ratio is higher than the value corresponding to percentile 66th. Therefore, social benefit institutions are classified into one of the three possible groups, ordered from a lower to a higher level of solvency. As independent variables, we use the lagged regulatory solvency ratio as well as the same firm-specific variables used in the Ordinary Least Square regressions. All the independent variables are lagged two years. To know the firm characteristics that affect the probability of insolvency of social benefit institutions we conducted an analysis for the whole sample period 2010-2012 using an ordered logistic model where the three values that the dependent variable can take are based on the levels corresponding to percentiles 33rd and 66th of the regulatory solvency ratio taking into account all the observations of the sample period in this analysis. As firm characteristics we include size, profitability, investment risk, underwriting leverage, premium growth, liquidity, capital structure, age and the diversification/specialization status of the firm which are the factors used as control variables in previous models. We also include year dummy variables being 2010 the omitted year to avoid singularity. Results show that, as a whole, only 2 social benefit institutions in the period 2008-2012 had a total regulatory solvency ratio of less than 1 and these cases were due to the fact of not having the minimum required regulatory solvency ratio in the life insurance segment. The average total regulatory solvency ratio for the period 2008-2012 was 16.84, indicating that on average the insurer solvency margin is more than 16 times the required total minimum solvency margin. In general, the regulatory solvency ratio for the period 2008-2012 corresponding to the life segment (with values for the mean and median of 20.06 and 6.44, respectively) was higher than the regulatory solvency ratio corresponding to the non-life segment (with values for the mean and median of 11.72 and 4.04, respectively) and these differences were statistically significant. There are on average important differences among the three groups of social benefit institutions representing life specialists, non-life specialists and composite social benefit institutions operating in both segments, life and non-life. Composite social benefit institutions are the biggest firms in the sample, followed by life specialists and these by non-life specialists. The use of reinsurance is higher for life specialists than for non-life specialists. However, non-life specialists show a higher investment risk than life specialists. Composite social benefit institutions show higher premium growth than non-life specialists, but non-life specialists show on average the highest liquidity ratio, followed by life specialists and these by composite social benefit institutions. These differences in the average values of the main variables corresponding to these three groups of social benefit institutions provide additional support to the need to control the specialization/diversification strategy of the firm in the regression analyses. Regarding the first objective of this article, results show that the two-year lag regulatory solvency ratio is the strongest predictor for the future regulatory solvency ratio of social benefit institutions and that the model fits in very well with adjusted R2 ranging from 0.704 to 0.857. We also develop prediction quality models to classify social benefit institutions according to their financial strength by estimating ordered logit models for every year of the sample period. Results show that the models classify the vast majority of social benefit institutions according to their financial situation with levels ranging from 83.33% in 2010 to 90.41% in 2011. Additionally in this article we evaluate firm factors affecting the probability of insolvency of social benefit institutions by applying ordered logit models to all observations of the sample. The results indicate that profitability and premium growth are the variables that affect positively and statistically significant to the probability of social benefit institutions having higher levels of solvency in the future. However, the use of reinsurance and underwriting leverage affect the probability of social benefit institutions having higher levels of solvency in the future negatively. A positive relationship between profitability and the probability of future financial strength could be due to the fact that more profitability is associated with more efficient management and lower risk and is consistent with previous studies (e.g. Barniv and McDonald, 1992). Higher premium growth is an indicator of higher market penetration that could be associated with higher financial strength. On the other hand, the negative association between the use of reinsurance and the probability of future financial strength is explained because a less solvent insurer tends to use more reinsurance because of its inability to raise needed capital in the financial market (see Chen et al., 2001). And the negative relationship between underwriting leverage and the probability of future financial strength could be associated with the fact that higher underwriting leverage could make it too challenging to fulfil claim obligation in the future (see Pottier and Sommer, 2005).