MAPm - Macro Analytical Projections (MODEL)
February 4, 2017 09:00 AM
VOTE SHARE PROJECTIONS 2019
Our MAPm projections are based on a macro-analytical model that runs more than 2000 simulations every week currently from 5 different data point parameters (which will be expanded later) to arrive at final numbers based on the national election data history of past electoral trends. Different data points have been assigned different weight scales structured on our electoral research of many years by analyzing the impact of each of those parameters on mass behavioral patterns in an election environment. These projections will be regularly updated as and when the underlying data parameters keep changing. The following 6 data points formulate our MAPm projections for now;
Indian elections are now extremely presidential in nature, especially in the national context. A powerfully charismatic leader at the top ensures that both the narrative and public opinion remains firmly in favour of his/her party. Even more importantly, a popular leader covers up for many short comings at the local level in a national election. 5Forty3 popularity ratings are based on aggregates of various polls and surveys conducted in different parts of India at different times along with the special weightage given to the "trust" factor by analyzing public reactions to various pronouncements and policy measures. This is by far the biggest factorial of our MAP model and has a weightage equivalent of 27% on our final projections.
The second most important determinant of any national political projection is the depth of opposition unity. It is a well-known fact that in Indian politics, the dominant political party is best challenged when all the opposition parties coming together in an election platform (the most recent example of this was Bihar in 2015). Index of Opposition Unity as a statistical tool still remains the best mathematical model to measure the impact of opposition on elections. Historically, IOU (Index of Opposition Unity) is broadly categorized into 3 main bands with political implications for each segment;
- Band 1 is the "safe band" when IOU score averages anywhere between 60 to 75. This 60 to 75 range is where the main ruling party remains virtually unchallenged due to weak opposition obstacle. Examples of previous election years include those of 1962, 1971, 1984 or the most recent one of 2014. Within this band, the 70 to 74 range is even more potent because here the ruling party prowess is at its peak
- Band 2 is the "opposition unity band" which is defined as any score above 75. Once the IOU score crosses 75, it just indicates the coming together of opposition forces to defeat the dominant ruling party and as it goes higher, the score indicates the weakening of the main party. Two big past electoral examples of this band are 1977 (when the score was a whopping 90 and Indira Gandhi not only lost the national election but many state polls too) and 1989 (when Rajiv Gandhi was defeated by opposition coalition of Left, Right and the Centre).
- Band 3 is essentially the "coalition band" which is any IOU score of less than 60. In this range, there is political uncertainty with the dominant ruling party ceding space to disparate opposition groups, enabling an environment for coalition governments. The coalition era starting from 1996, all the way up to 2009 provide ample examples for this score. It must be noted here that in 1999, the IOU score briefly went above 60 to indicate the first clear majority coalition government headed by Vajpayee.
IOU gets a priced position in our MAP model and a relative weightage of 24% due to its inherent ability to determine the strength and weakness of the dominant ruling party through a prism of opposition's ability. Our 2019 projection range is derived by the current vote positions of different political parties whereas all the historic IOU scores are based on actual votes. The IOU formula we use is a slight variation of the standard formula of adding up all the opposition votes and dividing the sum total with the total votes secured by the main opposition party/coalition.
Inflation v/s Growth
Beyond politics, the one aspect that affects India's voting patterns is economics. Recent electoral history of the last 2 decades clearly indicates the importance of economic factors in determining political outcomes. For instance, the 2009 national elections produced a stunning 200+ seats to the Congress party partly because of unprecedented growth achieved between 2002 and 2008 largely due to the lag effect of many reform measure carried out by the Vajpayee administration. Currently we employ only 2 main indicators of inflation and GDP growth as economic factorials which would soon be expanded to add more sensitivity to our model. (A study of past electoral data in relation to inflation and GDP growth of the preceding years shows a pattern of pro and anti incumbency being indirectly proportional to the former and directly proportional to the latter). Macro Economic Indicators enjoy a relative weightage of 18% in our MAP model.
Every phase of Indian election in every geography has a certain amount of bearing on the national political trajectory, so we constantly monitor electoral data to analyze the implications for 2019 elections. The general model we have been following is built on a 4 tier system - local body elections (less than 15% impact nationally), assembly by elections (22% impact nationally) state general elections (45% impact nationally), Parliamentary by elections (50% impact nationally). This model is tweaked to particular states based on the importance that geography has to national outcome - for instance a state like Uttar Pradesh has a much higher importance than a state like Tamil Nadu or Kerala etc. Beyond this 4 tier system, we also analyze election data at a much lower polling booth level to discern voting trajectories. Overall electoral data is assigned a relative weightage of 15% in our MAP model.
By analyzing booth level swings and through our poll surveys conducted in different geographies at different times, we gauge the demographic shift patterns of different sub-groups like castes, age-brackets and economic strata of population. The relative movement of these sub-groups with 2014 as the base year gives us a handle on the political trajectory of India's diverse demographics. Although this statistical research system has an error margin of 3%, it still gives us enough valuable data points for a more rounded projection scale that can withstand rigorous subaltern changes despite all other indicators remaining static. The demographics element gets a weightage of 11% in our MAP model.
This is a relatively minor factorial enjoying a mere 5% weightage in our MAP model which adds robustness to our projections by constantly monitoring India's mass markets in terms of keywords that are in circulation. We use a host of in-house tools to monitor how Indian masses are reacting in real time which gives us the ability to react quickly to sudden changes as and when they happen.