Governometrics: A Quasi-Quantitative Policy Syntax for Optimal Governance

Introduction

The government is instrumental in mobilizing various components of effective governance to deliver the services. There is no referral evidence of this term hence it adds the newer dimension of solving complex conundrum in a more logical way. This precisely means the application of mathematics and statistical methods to interpret the governmental data. The prime focus is to help the policy makers to organize data in the condensed and understandable form by applying statistical tools to draw inferences regarding specific decisions by interpreting data patterns. This incorporates the empirical content to examine the relationship between government and public as the important stakeholders of governance. Hence it refers to the Quasi-quantitative analysis of actual governmental phenomena based on the synchronized development of theory of governance and observation to draw appropriate inferences. In order to estimate the theoretical demands of service relationship with the capacity of functionaries the observations in the dataset can be in quality and morality pair that is collected along the demand schedule that is stable. Thus a fair amount of predictability is possible. From the pair mentioned above monthly delivery of services to public by the institutions of public governance the equation can be constituted as

Where, Y_t-i ’s are the difference between Estimated Number of Service Deliveries(E_t-i) to be executed and actual number of service deliveries executed (O_t-i) in previous r months(i=0,1,2,3,…..r);

For the instance,

The task of the Governometrician is to apply the technique of Regression Analysis (Fox, 1997; Kleinbaum et al., 1997) to obtain estimates of parameters a₀, a₁,a₂……..a_r. These parameters are in fact the regression coefficients which are prone to measure the service efficiency and can be obtained by using the Statistical Technique of Principle of Least Square (Croxton & Cowden, 1949). Simultaneously, the significance of the model fitted can be tested using the technique of Analysis of Variance (ANOVA) (Croxton & Cowden, 1949). The observed data may be categorized into multiple numbers of factors, affecting the response of service delivery in a month. These factors may be administrative, political, organizational etc.

If all the regression coefficients are found statistically insignificant the one can conclude that the service efficiency of the institutions of public governance is almost constant (a₀). In different aspects of policy governance the use of statistical tools is increasingly in use, for instance role of stipends as an institutional facilitator in volunteer civic services has been explored statistically (McBride, Gonzales, Morrow-Howell, & McCray, 2011). This study has inferred that there are statistically significant differences among some sites in terms of the outcome variable, though the relationship of stipend status and perceived benefits did not change (t= 4.06, p less than .0001). In the arena of studying impact of certain elements which relates to moral and governance where all variables identified are transformed using natural logarithms, the least squares regression model was estimated (Calabrese, 2011). In all estimations including robustness checks, the variance inflation factors were calculated to determine whether multicollinearity was influencing the errors. There are plethora of studies which can be quoted in the present times that advocate the usage of this application.