Recursos: Humanos, Materiales, Financieros (Diagrama de Gantt)

This research on factors influencing the demand for micro life insurance continues in the tradition of the research on regular life insurance. Consumers are expected to make use of financial markets to level their lifetime consumption by means of savings and credit (Ando & Modigliani, 1963). Basically, life insurance contracts can be considered as a means of saving (Beck & Webb, 2003). Therefore, it is no surprise that the first widely recognized model for life insurance demand by Yaari (1965) takes the works of Marshall (1920) and Fisher (1930) on the utility of savings as a starting point. Life insurance provides an instrument to reduce uncertainty in a household’s income stream due to the death of the breadwinner (Browne & Kim, 1993). Hence, Yaari (1965) extends the previous saving models with a provision for income uncertainty, due to lifetime uncertainty, in order to explain the demand for life insurance. He argues that for a proper evaluation of life insurance demand the context of a consumer’s lifetime allocation process must be considered. In his adapted life-cycle model the utility of an arbitrary consumption plan U(c) is a function of the consumption c at any time t valued by g and discounted by α plus the value of any bequest S at random time of death T weighted by β and φ for time and size of bequest respectively.

(1)

In a world where life insurance is available, the consumer is able to separate the consumption decision from the bequest decision and the consumption of life insurance can be beneficial to an individual who is interested in leaving any bequest (Yaari, 1965). The bequest motive is not further expounded in Yaari’s (1965) model. From a philosophic, utilitarian view, however, the virtuous behavior is on which increase the welfare of all affected individuals (Hume, 1751); and thus an individual can increase his

own expected utility by buying a life insurance policy to protect dependents. In Yaari’s (1965) model the expected utility of the life insurance purchaser depends on the discounted present utility of consumption and the discounted utility of any bequest. Lewis (1989) advances this model by incorporating the preferences of dependents in the model. He assumes that the breadwinner in a family conducts regular income transfers to his dependents. Therefore, the dependents have their own utility function on the breadwinner’s uncertain income. Formerly exogenous explanation factors are made endogenous and the demand for life insurance can be analyzed according to the preferences of dependents. A utility maximizing dependent will prefer that some income is allocated to insure the ability of the breadwinner to generate non-capital income (Lewis, 1989). In the maximization problem (Equation 2), F is the face value of all insurance contracts written on the breadwinner’s life. The probability of the breadwinner’s death is written as p. The loading factor of insurance is noted as l. And the dependents’ risk aversion is accounted for in δ. TC stands for the present value of dependents’ total consumption in case the breadwinner survives and, finally, W is the household’s net wealth.

(2)

In this model five factors explain the demand for life insurance: (i) policy loading factor

l, (ii) probability of breadwinner’s death p, (iii) household’s risk aversion δ, (iv) net wealth W, and (v) total transfer of wealth on dependents TC. While the policy loading factor is subject to actuarial calculations, the other factors can be inferred from socio- economic characteristics of households (Lewis, 1989).

More recently and adequately for the microfinance context, Ginè et al. (2008) provided a framework regarding the demand for rainfall index insurance in rural India. The determining factors here are: (i) risk aversion, (ii) size of risk exposure, (iii) correlation between risk insured and insurance payout, (iv) high actuarial value of insurance, and (v) financial constraints of household. Their one-period model is based on the assumption of symmetric information and thus neglects effects of moral hazard and adverse selection. The concepts of moral hazard and adverse selection from new institutional economics play a crucial role in the insurance market. Various

contributions (e.g. Rothschild & Stiglitz, 1976; Cawley & Philipson, 1996) assign them a hampering effect for the development of insurance markets. Bendig and Arun (2011) consider adverse selection and moral hazard problematic in the context of micro life insurance since the insurer is disadvantaged in assessing the individual’s death probability at reasonable costs ex ante and the insured’s risk taking behavior might changes ex post. Murdoch (1995), however, argues that life insurance is particularly suited to explore new markets because of low adverse selection and moral hazard effects and easy verification of claim legitimacy. Therefore, adverse selection and moral hazard considerations are excluded from the scope of this analysis.

The theoretical model applied in this thesis follows the approach by Bendig and Arun (2011) which is rooted in the described models of Lewis (1989) and Giné et al. (2008). Bendig and Arun (2011) argue that a household’s participation in a microinsurance scheme is conditional on its wealth status (w), other household characteristics (Z), personal characteristics of the household’s decision maker (H), regional characteristics (R), and an uncovariant error term (u). Thus the probability that a household participates in an offered insurance scheme is described with the following equation:

(3)

The dependent variable in this econometric analysis is of binary form: either the respondent is participating (1) or is not participating (0) in the offered insurance scheme. Therefore, the common ordinary least square (OLS) regression method cannot produce the best linear unbiased estimator and is not applied. In fact, a maximum likelihood estimation method is apt for the analysis at hand. The binary probit function used is:

(4)

with if the respondent is participating in the insurance scheme and if the respondent is not participating.

The previous chapter reviewed the literature on life insurance demand and reported a set of standard explanatory variables which have become widely accepted and repeatedly

tested in empirical investigations. Therefore, they will be included in the econometric analysis in this thesis. In addition, this paper wants to explore a set of variables which are expected to influence the factors of the above model for insurance participation, related to the household characteristics (Z) and the characteristics of the decision-maker (H). The additional areas under investigation can be clustered into four categories: (i) life-cycle effects and (ii) economic capacity of the household Z, (iii) product understanding and (iv) trust of the respondent H. In the following chapter, firstly, the collected data is described and, secondly, a marginal effect probit regression analysis is conducted.

6

Empirical Results and Analysis

In this chapter, the data sample collected is analyzed and tested for the hypotheses stated above. To begin with, a statistical description of the collected data provides an overview and understanding for the sample. Subsequently, the data set is regressed on insurance participation of the interviewed sample. A marginal effect probit regression is reported to allow for evaluation of the individual variables’ effect.