Dripto Mukhopadhyay

Evidences suggest that rapid economic growth is often associated with lopsided regional development. This has raised concerns from various quarters, including policy makers, that how to avoid intensifying of spatial inequalities with development efforts which are actually been aimed towards reducing the same. However, apart from being socially and politically de-stabilizing, this divergence in economic welfare has immense impact on business decision making. In simple terms, spatial inequality is the net result of the balance of forces of concentration and dispersion of economic activities.

In regional economics, two different models exist to address regional inequality. The first one is based on the standard neoclassical assumptions of constant returns to scale and perfect competition. Within this concept, the role of government involvement is relatively limited to infrastructure investments, which affects mobility of goods, labor and other factors. Governments may have little ability to influence the centripetal forces that are based on comparative advantage stemming from technology or resources. But Government may increase regional specialization or inequality by lowering the mobility of goods or may decrease extent of spatial inequality by lowering the mobility of factors.

On the other hand, the “new economic geography” models, commonly associated with Paul Krugman, contain five essential ingredients: increasing returns to scale that are internal to the firm, imperfect competition, trade costs,  endogenous firm location and most importantly, endogenous location of demand. The first four ingredients give rise to the agglomeration economies of home market effects, but the last ingredient, endogenous location of demand, creates the well-known process of circular causation which causes core-periphery regions to arise from initially symmetric regions.

The primary purpose of this article is to understand the likely implications of spatial inequality on business instead of understanding the genesis of spatial inequality. Several studies on India suggest that spatial inequality in India in increasing over time, as opposite to the Government’s efforts to reduce it. However, for you and me, this may be disturbing from social welfare point of view. But for business this understanding spatial inequality has immense bearing on business growth. Let me give you a simple example. As we know, Indian market is quite heterogeneous across geography as well as within geography. Assume a car manufacturer, producing small car, sedan, SUV and luxury cars wants to expand its presence in the country. The manufacturer is already an established player in small car market. Their current focus is to go beyond the small car market and to capture the sedan and SUV market. It is a big challenge for him to decide on how to expand his dealership as well as services in various parts of the country so that he can reach out and cater to the right segment of people for the sedan and SUV categories.

The car market is experiencing a transition from lower segment to higher segment since last few years. It is critical for the manufacturer to know where this trend of upgradation will be prominent in next 5 years or so. Only then he will be able to chalk out a plan to plan to cater demand at the right location. So, what all the manufacturer needs to know, as basic information? Following are a few critical ones as examples.

1. How this upgradation trend has been recorded across geographies?
2. What all are the factors that are driving this upgradation or transition of customers from lower segment to higher segment of cars?
3. Has it been similar across the states of the country? Has it been similar within various cities or districts of the state?
4. What will be scenario in next 5 years or 10 years from now regarding this upgradation?
5. Based on the future scenario of upgradation across geographies, what will be the optimal way for creating new dealership development and service delivery plan?

 

All these can be answered through analyzing the dynamics of upgradation in passenger car with the help of spatial economics. Following is the flow of analysis that provides the answers to all critical questions that are important to this manufacturer.

1. A through study of past trend of car sales for a significant period of time. This should be able to capture data as granular as possible. In majority of the cases, the data may be available at the state level and may be for selected cities.
2. The analysis has to capture how this transition from one segment of car to others, reflecting upgradation, is happening by every state as well as from overall perspective.
3. This needs to capture the demand drivers including economic parameters like changes in various components of domestic income (GDP), employment, household income, policy and access to finance, availability of various models across geographies and the similar ones
4. An econometric model to be developed to measure elasticities and probabilities of upgradation and their nuances across states/cities.
5. A demand forecast scenario by type of car to develop based on the econometric modelling and considering the likely changes going to happen on economic front across geographies in next 5 to 10 years.
6. Mapping forecasted demand across states/cities to portray futuristic possibilities of upgradation and identify the clusters of higher demand of sedan and SUVs that are their focus segment to push in the market in next 5 to 10 years
7. Prioritize geographies where the aggregate demand for the clusters located in closer proximity to each other from the point of view of supply chain point of view.

This can enable the manufacturer to take a firm decision based on a scientifically obtained results and insights to expand his dealership and services activities. This model is applicable to most of the sectors of economy including FMCG, Durables and similar ones. The rate of returns obtained through understanding spatial economics can be much higher compared to decisions taken through other means. Also, this reduces the probability for wrong investment decisions to a large extent.