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EXTENDING SOCIAL HEALTH INSURANCE TO THE INFORMAL SECTOR IN KENYA: A CASE OF GIKOMBA MARKET
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ABSTRACT

Social health insurance schemes are generally understood as health insurance schemes provided by government to its citizens, especially to low and middle income populations. Recently, apart from governments, several non-government organizations at the community level provide social health insurance in developing countries   (Churchil   2006,   Dror   et al 2002).   Social   health insurance pools both the health risks of its members, on the one hand. and the contributions of,  households and  government, on  the other,  and  is generally organized   by  national governments (Carrin   2002,  WHO  2004). Social   health   insurance can  bring  about   welfare improvement through  improved  health status and maintenance of non-health consumption goods through ensuring that health expenditures are smoothed  over time and that there is no significant decline  in household  labor supply (Varian  1994, Townsend 1994).

Health Insurance is a mechanism for spreading the risks of incurring health care costs over a group of individuals or households constitutes. This definition is not dependent on the nature of the administrative arrangements employed, but on the outcome of risk sharing and subsequent cross-subsidization of health care expenditures among the participants. An arrangement designed to provide  risk  sharing  for  illness  related  events,  and  which  is accessible  to households in the informal   sectors  in  low-income  countries,  is  a  health   insurance   scheme   regardless  of  the orthodoxy of its operational modalities. In such an arrangement, an insured individual acquires a state-contingent income  claim"  before  the  state  of  the  world  is  known  and  is entitled  to resources and/or  income to address the event for which he or she is insured  if the event occurs.

 

Grossman   (1972) developed   a theoretical   model based on the neoclassical   framework. This model assumed the existence of certainty in demand for health.  In his theoretical  formulation, demand  for health  is considered to have consumption elements  (utility  is derived  from  feeling healthy)  and investment elements  (sound  health enables  an individual  to participate in economic activities  and  earn  income).   In this model, the consumer maximizes an inter-temporal utility under conditions of certainty. Health care services enter the utility function indirectly through. The  budget  constraint  in  the  model  is  the  discounted lifetime  full  income.  A  consumer will therefore demand  for  health  care,  hence  increase  health  stock  as  long  a marginal cost  of investment in health is lower than the marginal rate of return. Consumption will continue until equilibrium (where the marginal cost of the investment is equal to the marginal rate of return) point is attained.  In  this  model,  when  the  health  stock  declines  beyond  a  certain  positive minimum,  death  results.  The  assumption  of  certainty  is a major shortfall  since  it  is  hard to calculate the marginal rate of return (in  terms of extra healthy days) against  marginal cost (in terms  of  extra  expenditure  on  health  care).  With such certainty, rational individual would evaluate the extra resources to be spent to obtain extra healthy days against the extra resources to be gained as a result of the extra healthy days and choose when it is economical to die.

Data collection is a term used to describe a process of preparing and collecting data (Freeman & Haddow, 2008).  A formal data collection process is necessary as it ensures that data gathered is both defined and accurate and that subsequent decisions based on arguments embodied in the findings are valid. The process provides both a baseline from which to measure from and in certain cases a target on what to improve. Data collection is an important aspect of any type of research study. Inaccurate data collection can impact the results of a study and ultimately lead to invalid results.   The   data collection   tools   that will   be used   in this study are questionnaires and key informant interviews.

This study will generate both qualitative (open-ended questions) and quantitative data (open­ ended questions). Quantitative data will be coded and entered into Statistical Packages for Social Scientists (SPSS Version 17.0) and analyzed using descriptive statistics. Qualitative data will be analyzed  based on the content  matter of the responses  as responses  with common  themes  or patterns will  be grouped  together  into coherent  categories.    Only the relevant non-redundant content will be presented.

http://erepository.uonbi.ac.ke/handle/11295/9229