Tools of Economic Management

The then prevailing medieval cocktail of moral vigilantism and the rising specter of Marxism prompted Thomas Carlyle, the Scottish philosopher to call economics a “dismal science”. Most people agreed with the philosopher, particularly in the wake of, monk turned economist, Thomas Malthus predicting that humanity is doomed in a world where population growth would always outstrip natural resources and bring wide spread misery. For a time, science and technology seemed to relegate Malthusian prediction as just some irrelevant croakings of doom. But the climate crisis has put Malthus right back on centre stage. Dismal though it might be, economics is one science that impinges everyday life of every human, from prince to the pauper. Hence there is greater urgency today than ever before, to tear through the thickening veil hoisted by a plethora of jargon, to appreciate what tools are used by the powers that be to manage the economy. Our budget, which will be presented in about two months, is arguably the most potent tool of economic management.

It may be a little known fact. But Thomas Aquinas, the Italian Theologian was also an early economic thinker who in his treatise Summa Theologica has dealt with the concept of “just price”. Since then however, economics and along with it budget making have changed substantially, mainly because of using insights  provided by mathematical logic when applied to empirical evidence. It was John Meynard Keynes who first suggested that it is more important to balance the economy than just the budget. Since then deficit financing has become more a norm than exception. Although an ace mathematician himself, Keynes was able to get the attention of opinion makers and political leaders across the globe, by explaining his ideas in simple language. It might be of interest to get a peak into some of budget making tools employed in India right from the days of “command and control” economy of Nehruvian era.

Linear Programming(LP) & Input-output (IO) Ananlysis: LP is an optimization tool. Firms as also economies for instance, face the problem of resource allocation between competing uses/sectors. IO analysis is a special case of LP. IO tables are the foundation of input-output analysis, depicting rows and columns of data that quantify the supply chain for all of the important sectors of an economy. Three types of impacts are modeled in IO analysis. They are direct impact, indirect impact, and induced impact. These impacts on the economy are determined when certain input levels are changed.  I-O economic analysis was originally developed by Wassily Leontief (1906–1999), who later won the Nobel Prize in Economic Sciences for his work in this area. In 1953 Prof. P. C. Mahalnobis adopted a variant of Liontief’s model to determine the optimal allocation of investment among productive sectors to prioritise public sector development and rapid industrialization.

More recently, the budgets have been using modern management techniques to improve decision making in the areas of raising and investing resources and risk management as also to deal with what are called “tail” (black swan events) such as subprime crisis of 2008 and the recent crisis caused by the Covid pandemic.

Barbell Strategy: In his book Black Swan, Nassim Nicholas Taleb, introduced the Barbell strategy. It consists of taking both defensive and excessively aggressive approach at the same time. The aim is to protect assets or achieve desired outcomes by assigning appropriate weights to the two approaches based on probabilities after subjecting the pertinent  data/assumptions to “what if” analysis. Essentially it would mean deploying major part of the resources to a low risk strategy at one end and a small part to the high risk strategy at the other end, thereby balancing the overall strategy. Application of this was seen during the pandemic when substantial resources were devoted to provide an extensive safety anchor to vulnerable sections, while providing targeted support to industry, particularly to MSMEs. 

Bayesian Updating: Bayesian approach assesses the strength of new evidence to upgrade probability estimates to tweak policy response. A simple clue as to how Bayesian approach works is provided by the following example.

There is a prize of Rs 1000 for correctly guessing if it will rain tomorrow. The prize goes to the person paying the highest amount for the participating ticket and gets the prediction right. Information in the public domain predicts a 50 per cent chance of rain. Based on this info most would be willing to pay up to Rs 500 for a participating ticket. But someone with additional information is sure that there is 75 per cent chance of rain. He would then be willing to pay Rs 750 for the ticket. He uses his expertise to improve his probability of winning.

Bayesian model provides a step by step approach to allocating weights to different evidences. By doing so, the Bayesian approach reduces effects of cognitive biases. The Bayesian model has given birth to a new class of super forecasters. A famous example of this is how Britain used this method to crack the Enigma Code during World War II.

Today, smart governance across the globe employs a host quantitative techniques like Agile Approach, Inventory Management based on simulation to arrive at Economic Order Quantity (EOQ),  Queueing Theory (which is essentially the inverse of Inventory Management), PERT/CPM for scheduling and reviewing projects and many others. Alas! Frontiers of knowledge ever keep expanding and there is no end to one’s search for truth!