Specification of Models

Time Series and Panel Data Analysis

6.3 Specification of Models

It is dealt widely in literature that different classes of crimes may be result of different factors whose impacts are felt differently as short and long term determinants with varied magnitudes. Total crimes consist of elements of both violent and property crimes along with other crimes. But it is seen that violent crime are unique case of crime activities which can be influenced by variety of motives and it may not occur in a predicted direction in the long-run. However this is normally not so to some extent with property crimes. It is argued that violent crime might be influenced more by short-term influences rather than by the long-term determinants (Field 1990).Therefore it is felt that there is a need to test the model separately for different categories of crimes (Hamzah and Lau 2013). Accordingly two models have been looked into. The total crime model is based on the total IPC crimes in the state. The second model deals with burglary and theft crimes taken together. From the more inclusive definition of property crimes, viz. dacoity, robbery, burglary and theft, the present study has used only the last two kinds of crimes since it may be presumed that these two types may represent property crimes with least or nil violence components(Seals and Nunley,2007).

Since the present study aims at examining the dynamic properties of the relationship between crime rate and its determinants, the functional form involves a multiple regression equation. . However, as the time series data structure has limited sample size it is difficult to include a wide range of explanatory variables into the model of the present study.

As there is a limited degree of freedom in time series data with small sample size, it may not be appropriate to include too many variables in the study (Masih and Masih, 1996, Kelaher and Sarafidis, 2011). Therefore the possibility of bias that results from omitted variable bias cannot be avoided in such studies (Ehrlich, 1977).

While trying to capture the essence of this study as well as depending on the findings of previous studies, the model is represented in the following functional form :

In the above model both economic and deterrence determinants of crimes are considered.

In a linear function these can be represented as below:

Model I : (Total crime)

crt = α+β1 pcyt + β2 cpit + β3 arrestt + β4 policet +

ε

t

Model II : (Burglary and theft crime)

btcrt = α+β1 pcyt + β2 cpit + β3 arrestt + β4 policet +

ε

t

Where, cr is the total IPC crime rate per 100,000 population, btcr is the burglary plus theft crime rate per 100,000 population, pcy is the per capita net state domestic product.

cpi is the consumer price index,

arrest is the number of persons arrested per 100,000 population, police is the number of civil policemen per 100,000 population α is the constant,

β is the estimation parameter.

ε

t is error term

Models are transformed into log linear model. All the data are converted into log form.

Model I : (Total crime)

lcrt = α+β1lpcyt + β2 lcpit + β3 larrestt + β4 lpolicet +

ε

t

Model II : (Burglary and theft crime)

lbtcrt = α+β1 lpcyt + β2 lcpit + β3 larrestt + β4 lpolicet +

ε

t

It is to be noted that the first two explanatory variables, viz, pcy and cpi relate to economic factors while other two viz. police and arrest represent the deterrence factors.

However one may like to mention that variable like economic prosperity is said to have conflicting effects on crime rate as discussed by several studies (Fleisher 1963, 1966, Ehrlich 1973, Bennett 1991, Denney et al,2004). During the time when economy is better off, and there is more economic prosperity, some criminals may desist from committing crime since it makes the opportunity cost of crime higher. Another conflicting situation might be generated where possibility of crimes is higher since there are now more goods resulting in more reward. There may be another possibility where additional income accrued to the people through general macro economic prosperity leads to more allocation of resources to ensure security through burglary alarms and other costly gadgets for prevention of property crimes.

Therefore, the economic prosperity is taken as one of the explanatory variables in analysis of crime determinants so as to look at the ‗net effect‘ of these conflicting factors.

The Consumer Price Index (CPI) is used as a measure of inflation in the economy. It is to be noted that price stability is regarded as an important determinant of criminal behavior. This is due to the fact that rise in prices leads to decrease in real income of an individual, especially in the lower income group, thereby resulting in reduction of purchasing power. Because of this, it is very often argued that individuals belonging to these low income groups have to raise their income for maintenance of existing living standards through legitimate or illegitimate activities which may result in more crimes (Gillani et.al 2009, Tang 2007).

Amongst the deterrence variables, it is expected that the increase in police presence would lead to an increase in probability of apprehension and hence, to a reduction in

crime. Furthermore, a higher number of policemen would lead to proper investigation and disposal of cases thereby influencing severity of punishment through criminal justice system.

Similarly, arrests would have deterrence effect through incapacitation and fear of apprehension, leading to increase in opportunity cost of committing a crime.