Analysing the Gravity Model of Trade In the context of the B-School Selection Decision

Objective

To analyse the patterns of the Gravity Model of Trade and assess its application on the B-school Selection decision in the context of India.

Description of the problem

The gravity model of international trade was developed independently by Tinbergen (1962) and Pöyhönen (1963). In its basic form, the amount of trade between two countries is assumed to be increasing in their sizes, as measured by their national incomes, and decreasing in the cost of transport between them, as measured by the distance between their economic centers.1

Various studies thereon has used the model to determine the impact of various flows on stock factors. In a UK based small residential hospitals in North East London applied gravity model for the flows of patients, corresponding to removing a destination or adding a new one considering factors like number of beds, number of emergency facilities and time taken for the movement of patients.

Applying the same theory in the case of B-School selection decision in India, wherein a person’ decision to join a B-School or not depends on the ROI(Return on Investment as provided by College), PS (Profile Score), calculated according to formula provided by college and the distance between the candidate’ residence city/town and B-School’ city.

Motivation

The aspect which specifies the bent towards adoption of this topic is the need to know if the factors like ROI, profile score and the distance from hometown are the determinant factors for the choice of a business school.

The Proposition

The proposition of the paper is to analyse the relevance of the Gravity theory of Selection in the present B-Schools in India. The Gravity theory of Selection is defined with the following equation

,where Fij represents Chances of student i taking B-School jG is a constantMi is the Profile score of student iMj is the Return of Investment of B-School jDij is the distance between student city and city of B-School i and j

Taking logs on both sides gives us a linear log function for statistical analysis.

Hypotheses

Null Hypothesis: Stream of under graduation has un significant impact on selection of B-School.

Hypothesis 2: There is a Positive effect of Return of Investment of B-School in selection.

1 For examples see McCallum (1995), Helliwell (1996), and Boisso and Ferrantino (1997).

Proposed Methodology

The methodology involves the log-linear function. A null hypothesis testing for a set of B-schools a particular student can interested in. It also consider chances by considering profile score of student.

A coefficients in the log-linear function would be tested for a significance level of 5% and results would be tabulated.

Expected outcome of the problem

The results of the hypotheses should give a positive and significant value to the coefficients of the log-linear equations. This would display the relevance of the model for the Selection model in selections of B-School by student.

Contribution to the problem

The problem at present resides in the ambiguity in the relevance of a gravity selection model. The contribution from our side would be to check the relevance of the context in terms of the Indian B-Schools. A similar could also be used to see different types of institutions with some changes in variables. For example, we can use same model in job selection by changing Return of Investment with salary and Profile score with job profile offered. Several variables other than the ROI and distance would be significant determinants of the Selection of particular institute. Taking cues from the Selection data, we could present a set of the different variables we think apply to different B-School Selection.

1 For examples see McCallum (1995), Helliwell (1996), and Boisso and Ferrantino (1997).