Ranking and Indexation: Concerns on Reality Check

By Dripto Mukhopadhyay

Yesterday I read a nice and enlightening article on “ranking” by my ex-colleague and friend Dr. Sanjib Pohit (Article link – https://www.dailypioneer.com/2019/columnists/it—s-not-just-about-the-ranking.html). Sanjib’s article is very timely as well as relevant from methodology practice point of view, especially in India’s context. In recent years ranking exercise has become extremely common in every domain of activities. Rankings are applied to announce awards for various state governments, departments, different initiatives and what not.

I have worked on more than 60/70 studies that involved certain indexation and ranking based on certain purpose-based parameters. This involved studies for Government departments to top end media. Ranges from ranking banks to cities to states to districts to specific locations or initiatives for awards like “Safaigiri” where the Prime Minister gives award to stakeholders. Also these studies span from IT domain to entrepreneurship.

Why I found Sanjib’s article relevant is because of the fact that in many cases I see that neither the researcher nor other stakeholders are well aware of how to address the problem and implications of the methodology. That is the reason I thought of extending Sanjib’s though a bit further and address a few other issues that he did not touch upon in the mentioned article.

Before going into good bad ugly, lets understand why a ranking exercise is important from academic as well as from action point of view. As we do understand and have experienced in our day to day life directly or indirectly, ranking is done to compare more than one entity from any particular reference purpose. That purpose can vary from comparing students appeared in an examination to understand quality of services hotels of a destination to ranking states on how they are performing in terms of income generation. In fact, the purposes can be numerous since human activities have expanded many folds and ranking helps us to understand relative positions among entities to compare.

Here I introduce the concept of relative position. This is extremely critical to understand rest of the argument I am going to put forward. In view of ranking what to be done and how to be done depends completely on how we want to view this relative position. Keeping in mind that the only constant in the world is “change”, this becomes even more important. Any ranking exercise, from professional perspective, should be actionable. This means unless we are able to use the ranking exercise meaningfully for a specific productive purpose, the entire effort is pointless.

It may not always be true that we require a benchmark while working on any ranking exercise. In fact, a benchmark should be used prior to ranking exercise to filter out irrelevant entities from purpose point of view. But, if the purpose is defined precisely and with complete clarity, we may overcome the need for filtering the irrelevant outlier. However, need for Apple to Apple comparison is no denying.

Now, its time that we think of how to go for a ranking and why indexation is used for this. Indexation helps in bringing information or data in a more understandable platform within a comparable scale. Its easier to comprehend and to interpretable and actionable. Indexation can be done in many ways. It can be with the help of a single variable or including multiple relevant variables. In general, unless in a very unique situation, no unobserved phenomenon can be captured truly and realistically with a single variable. That’s the reason, most of the time ranking through indexation involves multiple variable.

When multiple variables come into picture, to arrive at a composite index, the most important question is to whether to involve any weighting scheme or not. This depends on the problem in hand and its nature. Based on this it is decided whether to go for a statistical model like factor analysis or the similar one or to go for a non-model based indexation. Many a times arbitrary weights are used basis perception of the researcher. The robustness of indexation depends immensely on choice of variables, method used for indexation and also researchers understanding of the model.

In most of the cases if the indexation methodology chose right variables and right method, it automatically takes care of irrelevancy of entities that are not fir into a particular scheme of problem. Here comes the relevance of “relative position” concept. One can use two ways to create distinctions among entities. One is through ranking, which is more of a crude way. The reason being even for an insignificant different between entities, their positions can be significantly different. This does not serve the purpose realistically.

Therefore, more realistic and actionable way is to categorise entities based on certain principles, be it statistical or simple difference in values. with Most of the cases a static scenario is used. More precisely, entities are compared for a specific time point and their relation position through ranking or categorisation. This is to me a half-cooked meal. The entities should be compared to other entities in one time point, but also should be seen how it has changed compared to itself in past years. Very few studies do this, but this can play critical role in actionable decision making.

To sum up, ranking and usage of indexation methodology can and should play important role in decision making at any front. However, the impact expected out of such action completely depends on methodological robustness used for such purpose. Though apparently it looks simple, but the applicability and actionability can die down completely unless some key concerns are taken care of.

Inclusive Growth in India – A Perspective

By Dripto Mukhopadhyay

India is an independent country for more than 70 years. The country’s economic performance in recent years had been phenomenal whether we compare it with its own historical economic performance or with other countries. For instance, during the period 2010 to 2016 the annual average real GDP growth in India was about 6.7% in comparison to that of the global economy at only 2.7%. However, when we are one of the fastest growing nation in the world, did this growth touch entire population of the country? In other words, did all sections of population benefitted and participated during this income growth of the country? The answer is no. we are still struggling to reach the benefit of this growth to more than one-third of Indian population, as per conservative measure……………………………………………………………….

TO READ THE FULL ARTICLE PLS CLICK THE LINK BELOW

Inclusive growth in India_120219

TURNING WASTE INTO ENERGY – CASE STUDY OF UTTARAKHAND CITIES

Uttarakhand was carved out of Uttar Pradesh on 9th November 2000. Major portion of Uttar Pradesh (especially western part) became Uttarakhand. Uttarakhand is also known as the “Land of Lord” or “Dev Bhumi”. Uttarakhand is one of the most beautiful places which attracts people from India as well as from Foreign. Due to high rate of tourist activities the government decided to maintain its beauty as natural as possible and therefore, government formed the Municipal Solid Waste (Management and Handling) Rules in 2000. There were many projects launched by the government to manage the solid waste in major cities of Uttarakhand which includes Dehradun, Rishikesh, Haridwar, Tehri, Haldwani, and Nainital and as of now these ventures are in different phases of culmination.

Please click the link below to read/download the complete paper.

TURNING WASTE INTO ENERGY – CASE STUDY OF UTTARAKHAND CITIES 

Electric Vehicles in India – Current Scenario

Electric Vehicles in India – Current Scenario

By Satyam Saxena[1]

This is the era when every individual is concerned about the pace of climate change. One of the prime reasons is emission of harmful gases due to human activities. Another big challenge is depletion of natural resources. Countries like India, who are highly dependent on imports oil and oil products for production process and consumption, are looking for alternative sources of energy. India is the third largest importer of crude oil in the world which shows oil dependency on their economic activity.Petrol and diesel are the major refined crude oil products which are supplied by the oil industries to the ultimate consumers for running vehicles. Among various sectors that depend on diesel, transportation sector is the largest in terms of consumption of diesel/ petrol. Continue reading Electric Vehicles in India – Current Scenario

Tourism data to be available from January 2019 beginning

Tourism is one of the most important sectors in terms of income and employment generation in India as well as the world. However, data driven research is yet to be taken serious attention from researchers. Ascension Centre for Research and Analytics wants to promote tourism research by providing long time series data collated from government sources so that one can save enormous time and effort in collating data from different sources and volumes. Following are the collated tourism data in Indian context we are making available in our website from January beginning of 2019.

  • Foreign tourist arrivals to India – 2003 to 2017
  • Foreign tourist arrivals from different countries to India – 2003 to 2017
  • Month-wise Foreign tourist arrivals to India – 2003 to 2017
  • Age-wise foreign tourist arrivals – 2003 to 2017
  • Gender-wise foreign tourist arrivals – 2003 to 2017
  • Nationality-wise foreign tourist arrivals to India by purpose – 2003 to 2017
  • Port of disembarkment of foreign tourists – 2003 to 2017
  • Number of domestic tourists in India – 2003 to 2017
  • Number of domestic tourists in India by destination state – 2003 to 2017
  • Month-wise foreign exchange earning in Rs. And in US Dollars – 2003 to 2017

Economic data to be available from January beginning 2019

One of the biggest challenge for researchers is to collate data from different sources, especially if any time series analysis is intended. Our endeavour is to promote and facilitate research, for students, institution researchers, teachers, corporate and others, through providing collated time series data on India’s macro economic scenario from different data government data sources. All current data is provided for free to anyone who wants to download the data and work. The time series data that can save significant time on part of a researcher, is priced at a low price to cover the cost of that data collation. The following data will be available in our website from January beginning 2019.

Macro Economic Variables Years
Bank Rate ( Monthly) 2012 -2018
Cash Reserve Ratio ( Monthly) 2012 -2018
Cash-Deposit Ratio ( Monthly) 2012 -2018
Consumer Price Index ( Commodity Wise) 2011 -2018
Consumer Price Index (Annual) 2012 -2018
Consumer price index – Industrial worker wise 2012 -2018
Consumer Price Index – Industrial Worker- city wise 1998 -2012
Bank Credit ( Monthly) 2005 -2018
Credit-Deposit Ratio ( Monthly) 2012 -2018
Bank Deposits ( Monthly) 2005 -2018
Exports (Monthly) 2005 -2018
Foreign Exchange 2012 -2018
Foreign Trade (Monthly ) 1998 -2018
Gross Domestic Product annual ( Current and constant) 1961 -2018
Gross Domestic Product – Growth Rate 1961 -2017
Gross Domestic Product by sector and state    1990 – 2018
Gross Value Added and it’s components (Quarterly) 2011 -2018
Gross Value Added Sector wise ( Current and Constant)  2011 – 2018
Gross Value AddedState wise 1990 – 2015
Imports ( Monthly) 2005 -2018
Incremental Credit-Deposit Ratio ( Monthly) 2012 -2018
Incremental Investment-Deposit Ratio ( Monthly) 2012 -2018
Index of Industrial Production ( Sector Wise) 2005 -2018
Index of Industrial Production (Annual) 2005 -2018
Investment in Govt. Securities ( Monthly) 2005 -2018
Investment-Deposit Ratio ( Monthly) 2012 -2018
MCLR ( Monthly) 2012 -2018
Private final consumption annual ( current and constant) 1961 -2018
Private final consumption Expenditure ( product wise) 1999 – 2017
Whole sale price Index ( monthly- commodity wise) 1994 – 2018
Wholesale Price Index ( Annual) 2012 -2018

 

November 29th

  1. Global economy may be slowing more than expected, warns IMF https://mybs.in/2VrcVST
  1. India needs more reforms to breach 8% growth ceiling: NITI Aayog VC https://mybs.in/2Vrcx2R
  2. How Can We Use Artificial Intelligence To Prevent Crime? https://www.forbes.com/sites/nikitamalik/2018/11/26/how-can-we-use-artificial-intelligence-to-prevent-crime/?ss=ai-big-data#41425f07498c
  3. Here’s Why Data Scientists Should Embrace Graph Analyticshttps://www.analyticsindiamag.com/heres-why-data-scientists-should-embrace-graph-analytics/
  4. Emissions Gap Report 2018 https://www.unenvironment.org/resources/emissions-gap-report-2018

Understanding Spatial Economics can Help Business Decision Immensely

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.