The Consumption of Time

Time posits interesting possibilities. On the one hand it implies a forward-accelerating reality. On the other hand it exemplifies the cumulative burden of the past. All entities, living or not, appear to pass through and experience time. In the process of our experience of time, we consume it. We allocate it. 

It so happens that this allocation is undertaken across our desires and demands. The purification of reality that we undertake so as to realize time is succinct. Production of time is unknown. Throughout epistemological inquiries, knowledge about how and why time is produced is yet unclear and probably unknown. Yet, we humans are accustomed to a near-alcoholic addiction to believing in “time”. Emotions, evolution and energy flow so crushingly throughout our intellectual veins that we forget to realize that past, present and future might merely be an illusion. 

This illusion ceases to be a truth, when we consume it. Even though production of time is yet unknown, consumption of time is a critical element in economic theorizing. The so-called dynamism of economics emerges from the evolution of stock variables across time, thus generating flows. Time is a scarce resource and hence requires preferential allocation across our desires so as to produce demands.

(This is a partial work and more will be added. Please share your comments on the above content so as to further improve it.)

Economics as Technology: Rough notes

Economics as Technology

Economic ideas can evolve over space and over time. This evolution is evidently non-linear. The paths of spatial and temporal evolution taken by economic ideas are diverse and there exists no single unified theory to explain this dynamic behavior of economic theories. 

Ideas are many and some of them produce meaningful insights into the workings of the chunks of reality one is trying to understand. Some don’t. One may adhere to a certain idea of how the world works because of several motivations, particularly because one feels that that’s the way things should work and a given idea gives hopes of such a world. A normative outlook is evident in this argument. One may also believe in a particular idea because she feels that it explains how the world actually works. This is a positivist belief in an existing or a potential economic idea. 

Such an argument can most correctly be applied to not only social sciences but to the yet-to-be-discovered sections of perceived physical reality. There too one may have strong notions of how the unknown reality would be once it is discovered. Of course, this would mount to metaphysical speculations but nonetheless they hold some degree of authenticity at least for the believer.

Economic speculations become more refined as time passes by. Subjecting the ideas to more and more “prediction tests” as stated by several writers such as Friedman (See for example Milton Friedman’s “Essays on Positive Economics”) results into a body of knowledge that we now call Economics. 

These scientific collections of interrelated notions, theorems and behavioural realizations about the economic sphere of existence culminate into an exotic tool that can shift the ability and efficiency of production of commodities across households, firms and their macro aggregates. Economics becomes a technology.

The idea that ideas are technologies is perhaps not new on the intellectual landscape of human curiosity. Among many, J .S. Mill was probably one of the first theorists who could capture the benefits of economies of scale and scope that trading of ideas could produce. We now see the remnants of such theorizing in the large scale transfers of technologies across nations not only in their physical forms, but also in the form of technical knowledge flowing across borders.  Knowledge is now produced, distributed, exchanged and consumed at scales never imagined before. 

The embodiment, in our shared experiences, of the fact that Economics as an organic body of ways of thinking about the world, helps shift our production capabilities to larger levels than before despite our efforts remaining the same, may elicit the role of economic thinking in increasing welfares of people who utilize it as well those who are reap the gains from positive externalities of knowledge.

The core of this theme is to point out that by learning and applying the kind of behaviour that Economics teaches, all economic agents can improve their capacity to extract maximum possible benefits from the given means that they possess in order to achieve their various ends.

As an illustration, the understanding that an equal distribution of production time devoted between market and home goods among the male and female members of a household can help it shift its production possibility frontier to larger levels emerges from a careful analysis of social/institutional economic ideas about household behaviour. Here, the total production time devoted remains the same as before, only the change in distribution of total work time induces such gains.

At an aggregate level, the policy makers can increase their output (in terms of achieving successful and socially useful macro and sub-macro outcomes) with a given amount of input (time resource devoted in policy making and public funds utilized in the process) by better appreciating the insights derived from Macroeconomics and its various branches. Monetary ideas for illustration, that are embedded in major theories and empirical works, can and do help policy-makers to shift the production function of their output to higher levels with inputs constrained to the previous levels.

It thus emerges from our elementary approximations that Economics as an amalgamation of complex and heterogeneous ideas is not merely a discipline meant for consumption to participate and survive in the labour market. It is an organic entity, in all its diverse forms, that helps us make better consumption of our scarce means to produce more means and also to achieve more ends.

It might appear that the same argument can be applied to other branches of the social sciences such as Sociology, Political science, etcetera. Yet, it remains open to debate as to what advantages do these other brilliant disciplines directly supply to help choice-making entities increase their capabilities in achieving more outputs in terms measurable in an economic sense. It is to be borne in our understandings that the concept of technology depends critically on the concept of productivity and to be able to relate the benefits from ideas of other social sciences to this economic concept of productivity will require further detailed scholarly analysis. 

These modest and rough speculations presented in this article can only hope to motivate further analysis of such issues and nothing more. This is a rough version and further changes will be made with time.

- Bhagirath Baria.

Microeconomics, Macroeconomics and Consciousness.

Please Note:

The author of this article takes no credit for the ideas mentioned though there are his own ideas involved too. The exact work where the fundamental idea behind this article was present, is not in possession of the author, it shall be duly acknowledged as and when the author finds it or recalls about it.

Economic theorizing (except for some heterodox schools), follows the empirical method of investigation, primarily subjected to the principles of logical consistency and empirical falsification. In such a methodological set up, it’s imperative to understand the definitional categories of Economics so as to understand the subject and the ongoing research properly.

The definitions of Macroeconomics and Microeconomics are one such area. Historical developments and the contribution of numerous Economists have helped develop a particular definition of these terms. It is possible to understand them in one more way- as the results of conscious and unconscious individual decision making.

In Microeconomic analysis, an economy is essentially disaggregated into individual units, at the most being grouped into a non-aggregate group- such as industry, market, etc. The individuals are assumed to follow certain behavioural assumptions such as rationality, transitivity, etc. and are expected to do so under certain environmental settings such as under a particular market structure, under particular macroeconomic environment.

Methodological individualism thus considers an individual consumer or a producer as the fundamental unit of analysis. The results of this rationalized individual’s decision making through her interaction with other similar individuals produces Microeconomic outcomes. These outcomes are mainly in terms of Price, Demand and Supply generated in a market (whether labour, product or factor). What is to be noted here is that these economic outcomes are the result of conscious decision making by these individuals. The individuals consciously act and react in a particular manner which gives rise to their anticipated results. For e.g. when buyers and sellers negotiate over price, the outcome is something that is a result of their conscious efforts to reach that result- otherwise they wouldn’t get into it in the first place.

Contrast this with aggregated outcomes- as analyzed by Macroeconomics. Numerous individuals are aggregated into broad units of analyses- such as Households, Firms, Government and Foreign sector or in terms of markets- such as labour, goods, money and assets markets. The interaction of ‘aggregated’ individuals in these markets cuts across a hugely heterogeneous set of objective functions of individuals- all undertaking conscious rationalized decision making to achieve their desired economic outcomes.

Yet, what is surprising is that the macroeconomic outcomes produced by individual decision making units through their interactions in these aggregated markets are not at all produced by any conscious efforts to achieve these outcomes. Macroeconomic outcomes are primarily in terms of Output, inflation, unemployment, interest rate, exchange rate, price level, etc. None of these macroeconomic outcomes (produced by the macroeconomy) is a part of a microeconomic unit’s objective function, yet the diverse and theoretically infinite objective functions (due to subjective, non-comparable preference rankings of individuals) somehow fulfil the macroeconomic objective function- viz. of price and output stability and other such macro-policy objectives.

Macroeconomic outcomes are thus an unconscious result of conscious individual decision making and economic activity. This difference of the type of human consciousness that is associated with macroeconomics and microeconomics may open up new doors of understanding the role that Analytical Philosophy (and especially ontology) can play in appreciating better the beauty and breadth of the discipline of economics.

'Output per Worker' as the Opportunity cost of Unemployment: Rough notes

ALP (Average Labour Productivity) or OPW (Output Per Worker) as an Opportunity cost of Unemployment to society

It is very much plausible to look at Output Per Worker (OPW) or Average Labour Productivity (ALP) as a measure of Opportunity Cost of Unemployment to society. Unemployment theories and the theory of natural rate of unemployment [probably the Classical theory of Labour markets], talk about Unemployment and its related costs (in long-run & very long-run). So does the Keynesian theory of Output determination (in short-run).

Opportunity Cost means:

The cost of the next best alternative forgone is the Opportunity cost of the chosen alternative.

In Microeconomic theory, Consumer Behaviour theories like indifference preference theory, revealed preference theory; Production theories like Optimization behaviour of firms regarding their output under various market structures, etc.; Cost theories like Optimization behaviour by firms regarding their costs, etc. utilize this concept extensively.

In Macroeconomic theory too, Opportunity cost concept is utilized in various theories- for e.g. the Aggregate Labour Supply theory uses it in deriving the Aggregate Labour Supply curve from Individual labour supply curves. This is done by the means of the Microeconomic theory of Leisure-Income trade-off. Many other applications of this concept exist in Macroeconomics such as Production Possibilities frontiers, etc.

A simple model for Output Per Worker:

If the Production function is:
Y = A*f[K, N]
then OPW = Y/N.

This is but per labourer output in the economy.

OPW as such a measure:

OPW as above [at macroeconomic level] can be thought of as one such cost that the society has to forgo on account of there being people who cannot find work/do not want to work. Were they employed, we would have gained at least that much amount of output per labourer as produced by the current labour force of an economy [country]

Some Assumptions required here are:

Indeed, many qualifications arise here as below:

1. We need to assume that the Production function [Y = A*f(K, N)] remains the same even if the currently unemployed labourers become employed. It means even if N increases on account of increased labour force, Y changes in such a manner so as to keep ALP constant as before the unemployed became employed.
2. Technological parameter [A] remains constant. 
3. Production function continues to exhibit the same returns to scale as it did before the unemployed become employed. Probably a restatement of Assumption 1.
4. No change in Marginal Productivity of the inputs including labour [as well as others].  This means that the slopes of Total Product curves of Labour and Capital remain constant. In other words the partial derivatives of Total Product curves of Labour and Capital remain the same as they were before the unemployed become employed. Some other assumptions may be needed too.

Empirical research possibilities:

If above assumptions are satisfied, then OPW can be a proxy measure of the Output per unemployed labourer forgone by society on account of their unemployment. Data on Aggregate Output and Labour statistics, at least in the case of the U.S., where Labour statistics are available to some extent can be extensively used. As for India, proxy variables can be used. More on this later.

P.S.: Couldn't add mathematical equations I wanted to as such a feature isn't available here.

-Regards.

An Introduction to Multiplicity in Economics

Dear readers, I have prepared a paper on an issue that I consider of primordial importance for theoretical economics. Especially important is its implication for young students of Economics who are introduced to the subject of Economics in a particular manner that systematically blurs the existence of alternative methods and schools. It juxtaposes neo-classical and Keynesian as Economics per se. Moreover, the textbooks of G.S.E.B. and C.B.S.E. do not even admit that they're teaching a particular type of Economic theory and not Economics. Ignorance of such a simple fact leads to what I call 'confusing an epistemological fact for an ontological fact'. Though this paper does not delve in detail on this issue, it still touches upon it to sensitize our young students about the controversial and pluralistic character of the subject.

Kindly find it for download here:

Click on this: Academia.edu page
If that doesn't work, click here:
https://www.academia.edu/6480773/An_Introduction_to_Multiplicity_in_Economics
Critical commentaries, Constructive criticisms, suggestions and feedbacks are open-heartedly welcomed. Do write me about your understanding- whether supportive or critical. Do provide me at least 4 days to reply.

- Warm Regards,
- Bhagirath Baria.

Simple Regression, 'I' and 'A' in the IPAT model, Indian economy

This was a regression analysis of two variables of the IPAT model of effects of human activity on environment. It was submitted as a short empirical study, as apart of my assignment in an online course offered by The University of Edinburgh in critical reasoning[informal logic]. I'll put online all the regression output in Excel soon. This short analysis showed some interesting facts about the effects of Affluence factor on Environmental degradation. I had to use proxy variables as data availability was a critical issue here. Anyway, have a look. Let me know if more detailed study interests you on this topic.

Submitted material:

Hello. The homework section gives following instructions:

"For this week's homework, we would like you to do some research for yourselves and find one or more examples, in your own global context, where the environmental impact is being (or can be) mitigated through changes in A or T. Post a brief summary of what you have found, with references (e.g. links to websites), on the Homework/Population/Case Studies forum thread. Once you have done that, read and comment on the other submissions from fellow students, and upvote the examples that you think illustrate this principle best."

I’ve tried to do the following in context of Indian Economy[my nation of residency]. Hope I got it right. I understood it as giving one's own example. A simple empirical illustration too shall be admissible is what I've assumed here.

1.    Find a numerical value of the degree of relationship between the variables I[Environmental  degradation] and A[Affluence factor].

2.    Fit a regression line through the available data and predict the future behaviour of I with reference to a particular value of ‘A’.

3.    Also find out how much part of the variation in I is explained by A with the given data set. In short calculate the r-squared measure[coefficient of determination].

4.    All this using Microsoft Excel only. No use of advanced statistical packages has been done.

Proxy variables used:

1.    For ‘I’ [Environmental degradation], I used the Annual carbon dioxide emissions in tonnes per capita as a proxy variable.

2.    For ‘A’ [Affluence factor], I used GDP per capita in India at constant price [base year 2004-05]

3.    Isolated the data analysis to a statistical relation between ‘I’ and ‘A’ because the data was readily available.

Data used:

1.    For the variable ‘I’ {year 1990 to 2010}-http://en.wikipedia.org/wiki/List_of_countries_by_carbon_dioxide_emissions_per_capita

Please look at the row showing India’s data.

2.    For the variable ‘A’ {year 1990 to 2010}, in INR-

Handbook of Statistics on Indian Economy, 2012-2013, published by RBI. The section on ‘Macroeconomic aggregates at constant prices’ includes data on GDP per capital at constant price, base year being 2004-2005. Available here: http://rbidocs.rbi.org.in/rdocs/Publications/PDFs/002T_BST130913.pdf

3.    Data series used: Year 1990-91 to 2010-2011 for both I and A as explained above.

Results:

1. Correlation between 'I' and 'A' as per the data series is 0.978224 or 0.98[approx.]. This shows a strong degree of linear relation between both the variables in context of India.

2. Regression line produced: I = 0.612619153 + 1.98157E-05[A] 

3. r-squared comes out to be 0.9569 or 0.96[approx.]. Approximately 95% of the variation in I is explained by A.'I' being Environmental degradation measured by CO2 emissions, tonnes per capita in India between 1990-91 upto 2010-2011. And 'A' being GDP per capita at constant price, base year- 2004-05, from 1990-91 to 2010-2011.2.

4. Using the above Regression line, I found the following expected level of CO2 emissions[representing 'I'] for the year 2011-12 with GDP per capital being 55054.37 INR to be 1.70355 tonnes per capita per year

Data series:

The units have been stated above already.

Regression Output has been stated already above.

> Thus, it can be seen that in India, between 1990 to 2010, there has been a high correlation between I and A as stated here. The expected amount of environmental degradation[proxy- CO2 emissions in India] based on the data available for 2011 come out to be 1.70 tonnes per capita per year in India. More interpretations can be found out here.

Limitations of this analysis:

1. Proxy variables chosen might not be sufficient to explain all that needs to be explained. Also the isolated effect of one of the variable in the IPAT model, on 'I' has been explained. This was mainly due to data inavailability.

2. p-value, standard error, etc. has been ignored here. There's no autocorrelation[Error terms lacked linear correlation].

3. Data series might not be sufficient to explain the phenomenon satisfactorily.

4. High correlation does not imply causation. Hence this remains to be explained here.
5. Y variable has repetitive[same] values. Might be an issue.

Please let me know if this short analysis gave some insights to you. Your suggestions/criticisms/feedback are always welcomed.

- Thank you.

- Bhagirath Baria, Surat, Gujarat, India.