Int. J. Environ. Res. Public Health 2011, 8 916 To carry out this study we will examine malaria incidence in 25 African countries for which data were available (Algeria, Benin, Botswana, Burkina, Burundi, Central African Republic, Chad, Cote D’Ivoire, Djibouti, Egypt, Ethiopia, Ghana, Guinea, Malawi, Mali, Mauritania, Morocco, Niger, Rwanda, South Africa, Sudan, Togo, Uganda, Tanzania, Zimbabwe).To do this we use the World Health Organization (WHO) data on disease incidence from 1990 to 2000 plus associated temperature and precipitation data while controlling for socio-economic confounding factors. Several African countries do not regularly report their national malaria incidence data to the WHO and those with missing data and outliers were not included. 3. Model Specifications The basic data set is in the form of a panel with repeated observations at multiple sites. To carry out the statistical analysis a semi-parametric panel model is specified and estimated [28-30]. Such a model allows us to simultaneously handle nonlinearity in the relationships along with the effects of countries and time (years). Pooling the data across countries and years allows us to capture the likely impact that we might expect to see in a longer, but unavailable, time series for the individual countries. The estimated coefficients give information on the relationship between observed malaria cases, temperature and precipitation. The theoretical model follows the analysis in Li and Racine as well as Tseng et al. [28,31] and is specified as follows: , and (1) where is the natural log of the number of reported malaria cases per 1,000 people in country at time period is a vector of climate variables that includes temperature, precipitation and a measure of climate variability; is a vector of socio-economic control variables that includes population density, per capita gross domestic product, inequality index, per capita healthcare expenditure and number of hospital beds per 1,000 people; are unobserved individual country effects and is an idiosyncratic error term. The function , the coefficients β and the unobserved country effects are all parameters to be estimated. Note that climate variables are assumed to affect the number of malaria cases per 1,000 people through an unknown function to be estimated while the socio-economic variables affect malaria cases linearly. 4. Model Estimation The estimation method follows Robinson’s [32] two step estimation procedure and is as follows: Step1: Express the conditional expectation of malaria cases per 1,000 people the climate variables ( ) as follows: with respect to (2) where the nonlinear variables are assumed to be uncorrelated with the individual country effects and the idiosyncratic error terms. That is, climatic factors are uncorrelated to unobservable items that affect a particular country’s disease prevalence (e.g., poor environmental conditions, drug resistance) and the statistical error in fitting the model. This assumption allows consistent estimation of the linear parameters of the model. Note also that a measurement error on the number of cases is inevitable as