Aggregate Economic Activity: Measuring the Unmeasurable
March 2023 | Anurag Vinaykiran Bavaria
I Introduction
The Business Cycle Theory (BCT) is one of the earliest and most vibrant areas of macroeconomic research. The existence of vast and rich literature is a testimony to this fact. The recurrence of economic instability has a long-documented history. Zarnowitz (1991: 1-2) has quoted instances from the literature mentioning economic fluctuations, the earliest one being from Adam Smith’s The Wealth of Nations which was published in 1776. The existence of multi-stage cycles as we know it today was mentioned long back in 1837 by Lord Overstone (Overstone, 1837), an approach for which he is credited (Zarnowitz 1991: 2). Over two centuries have passed since the earliest documentation of the business cycle phenomenon, and still it is the cause of these fluctuations which has persistently been a subject matter of debate. Countless theories have been developed till date to explain the economic fluctuations, yet the economists have not agreed upon any specific nature of cause behind the said phenomenon.
To pinpoint the cause of this disagreement, the process of how an economic theory evolves needs to be looked at. The process of economic theory development progresses sequentially through three broad phases. In the first phase, a phenomenon is observed which concerns the economy. In the second phase, a theory is conceptualised to explain the observed phenomenon. To conceptualise the theory, there are two commonly adopted approaches. The first one is the Anurag Vinaykiran Bavaria, Department of Commerce, G. S. College of Commerce and Economics, Nagpur 440001, Maharashtra (Affiliated to RTM Nagpur University), Email: anuragvbavaria@gmail.com
deductive approach where a generalised theory is conceptualised by proposing a plausible explanation for the occurrence of the observed phenomenon. The second approach is the inductive approach in which data collection takes place, which acts as an input for subsequent analysis to conceptualise the theory. Lastly, the theory is validated using the subsequently observed data. A theory is valid and accepted only if it stands the test of time and works across time frames. A noteworthy conclusion that can be deduced from both approaches is that the observed data plays a vital role in theory conceptualisation as well as in its validation.
Colloquially, business cycles are defined as fluctuations in the Aggregate Economic Activity (AEA). The problem with the business cycle phenomenon is that it is an unobserved macroeconomic variable. There is no well-defined, widely accepted macroeconomic time series with the help of which the state of the aggregate economy can be gauged. Numerous time series have been deployed as a proxy for AEA by the researchers. Every such attempt has been criticised either for being too narrow or too wide to be a representative indicator of the state of the economy. Therefore, since business cycles are directly unobservable, it is difficult to identify and measure them. In BCT, it is at the very first step of theory development where the disagreement originates. Every school of economic thought observes the same phenomenon from a different perspective. As a result, there exists no definitive answer as to what constitutes the AEA. Observing an unobserved variable is indeed a tricky task and finding the cause for such a variable is even trickier. Once the methodology to observe the AEA is established, we can move to the subsequent phases of theory conceptualisation and validation with relatively fewer disagreements. The BCT has progressed to the second and third phases, disregarding the first phase of theory development. As a consequence, the theories explaining the business cycle phenomenon are based on a wide variety of time series that are treated as proxies for AEA. It is thus quite obvious to disagree on the source of economic fluctuations.
In order to resolve the theoretical dispute over the cause of fluctuations, firstly there is a need for resolution of the controversy concerned with identification and measurement of AEA. This paper is a step towards the resolution of the intellectual debate by defining AEA and accordingly suggesting theoretically consistent changes to empirical business cycle measurement. Appropriately defining and measuring the AEA will make the theoretical justification for its fluctuation clearer. In this paper, I begin by discussing the origin of business cycle measurement literature and how it has influenced the current business cycle measurement practice. Next, in the second part, the novel contribution of this paper is discussed whereby a theoretical framework is developed to explain the concept of AEA. This theoretical idea is finally translated into empirical terms in the fourth part, by identifying and measuring the time series that closely represents the aggregate state of the economy. The fifth part summarises the proposition.
II The Inception of the Idea and Its Influence on Current Research
The idea of the business cycles as fluctuation in AEA was popularised by a seminal monograph of Burns and Mitchell (1946) where they gave the following working definition of business cycles which became a norm in BCT. “Business cycles are a type of fluctuation found in the aggregate economic activity of nations that organize their work mainly in business enterprises: a cycle consists of expansions occurring at about the same time in many economic activities, followed by similarly general recessions, contractions, and revivals which merge into the expansion phase of the next cycle; this sequence of changes is recurrent but not periodic; in duration business cycles vary from more than one year to ten or twelve years; they are not divisible into shorter cycles of similar character with amplitudes approximating their own.” (Burns and Mitchell 1946: 3) (emphasis added).
In the definition, business cycles are defined to be fluctuation in the AEA. However, AEA has not been defined on purpose, the primary cause for which is that the objective of Burns and Mitchell’s (B-M) research was the determination of the cyclical features for the purpose of theory development. For identifying the features of the cyclic fluctuation, analysing individual time series was indispensable. They point out that, had the multiple time series been aggregated as a proxy measurement of AEA, the cyclical movement of the specific series would have been obscured because of the netting-off effect, making the analysis of its causality even tougher (Burns and Mitchell 1946: 6). Another reason for not using an aggregate measure was the dynamic nature of business cycles. The proponents of this approach observed that the economy, being a complex and evolving entity, is not bound by deterministic laws and hence, seldom acts uniformly across the cyclic episodes. Therefore, no specific macroeconomic time series can be used as a representative proxy series. Zarnowitz unequivocally makes this observation, “What matters is that many diverse activities tend to expand and contract together; also, it should be added, that they evolve over time and cannot be reduced to any single aggregate (Moore and Zarnowitz 1986, p. 737). Hence the question of what precisely constitutes the aggregate economic activity’ is purposely and properly left open. The nature of business cycles depends on, and changes with, the major characteristics of the economy, society and polity. The most common and salient feature of business cycles is their pervasiveness and persistence (the high cyclical conformity or coherence of numerous variables and their pronounced serial correlation). It is not the fluctuation of any single aggregate, however important.” (Zarnowitz 1991: 8-9).
The research of B-M was subsequently carried forward at the National Bureau of Economic Research (NBER). In 1978, the Business Cycle Dating Committee was set up at the NBER to date the turning points in the United States economy. Since then, it has been keeping a record of peaks and troughs by adopting the non- parametric approach derived from B-M’s research. The NBER’s dating committee blended the two different areas of research, viz., identification of cycle
characteristics and dating of the cyclic turning points, into one. The business cycle dating procedure was an offshoot to the B-M’s project. Their methodology was replicated by the NBER to be used for the empirical exercise of detecting the peaks and troughs of business cycles. As a result, the process of identification of cyclical peaks and troughs is now labelled as a complex statistical exercise that requires analysis of hundreds of economic time series to ensure the existence of co- movement, a feature mentioned in the B-M’s definition. The proponents of this approach claim that using multiple time series as a representative of AEA endows robustness to the business cycle dating process as it helps in differentiating the business cycle fluctuation from other types of fluctuations like seasonal and stochastic fluctuations. Banerji (1999), on the lines of B-M, argued that the movement in the aggregate economy must be pronounced, pervasive, and persistent (the three Ps) to separate the cyclic movement from the rest of the noise. To measure the three Ps, he prescribes indicators and statistical techniques which may aid early identification of turning points. This idea was a partial modification of the three Ds, viz., duration, depth, and diffusion, used as criteria to differentiate recessions from slowdowns by the NBER (Fabricant 1972). However, the multiple time series approach is often criticised for being a theoretical and for its overuse of empirics involving substantial subjectivity.
Two schools of thought evolved simultaneously out of the NBER’s business cycle measurement approach. The first one believes that a single comprehensive time series is sufficient to capture the state of AEA. The proponents of this approach usually treat Gross Domestic Product (GDP) or Index of Industrial Production (IIP) as an appropriate reference series to track the economic fluctuation. Several studies have been conducted in India using the single time series approach to date the cyclical phases. Gangopadhyay and Wadhwa (1997), Mall (1999), Patnaik and Sharma (2002), Mohanty, Singh and Jain (2003), Reserve Bank of India (2006), Nandi (2011), and Pandey, Patnaik and Shah (2017, 2018) identified the business cycle phases in the Indian economy using a single time series as a representative of AEA in India. The single indicator approach invited criticism on several grounds. The foremost criticism is that a single time series fails to capture all the dimensions of the economic activity, hence cannot be used as a representative measurement of the aggregate activity (Moore 1982). Other common problems pointed out were the existence of measurement errors, significant lag in publication of data and its frequent revision after publishing. Layton and Banerji (2001) discuss in detail, the perils of using output as a measure of AEA. They reiterate the arguments of Burns and Mitchell (1946), Burns (1952), Moore (1982), Stock and Watson (1988), Crone (2000), and the NBER to justify the use of multiple indicators to determine aggregate fluctuations in the economy as the issues with the approach diminish the credibility of business cycle measurement process. Though all the criticisms of this approach are derived from B-M’s work, they agreed to the fact that Gross National Product (GNP) was a suitable candidate to act as a proxy for AEA. Although, GNP was not used by them, the reason for it was the non-availability of substantial data for analysis and
not its unidimensional nature, for which it is often criticised (Burns and Mitchell 1946: 72-73).
With time, the researchers at the NBER progressed a step further in business cycle measurement and developed the composite indicator approach. They were the proponents of the second school of thought who followed the NBER’s approach of multiple time series, but with a slightly different objective. Instead of merely dating the historical cycles, the composite indicator approach was developed with the aim of predicting the turning points. From the empirical analysis of numerous time series at the NBER, it was observed that every time series possesses leading, lagging or coincident attribute in context to a reference series. Time series possessing similar traits can be filtered and aggregated together to form composite leading, lagging or coincident indicators which can be used to predict the turning points by using leading indicators or to confirm it by using lagging indicators. Coincident indicators are developed with an intention to act as a proxy measurement for aggregate activity since aggregate macroeconomic data gets published with a significant lag. The composite indicator approach has been well received in India. Abundant literature is available where the Indian business cycle has been dated using the composite indicator approach. Chitre (1982) pioneered the turning point dating procedure using the composite indicator approach in India. This study was followed by Dua and Banerji (2000, 2001, 2007, 2012), Patnaik and Sharma (2002), and Pandey, Patnaik and Shah (2019). Although, this approach is also not free from defects. Like its predecessor, the composite indicator approach is also criticised for its subjectivity in developing the indicators. From the initial stage of selection of constituent time series to the final stage of their aggregation, judgement is involved at every step. With judgement comes biases, diminishing the credibility of the results obtained. Another defect lies in its methodology of developing the composite indicators. For developing an indicator, a reference time series needs to be identified. It is this reference series, based on which the leading, coincident and lagging nature of the constituent time series is determined. By identifying a single time series as a reference series, this approach invites the same criticisms as those of the single indicator approach like measurement error and lag in data availability. It is imperative to have defects in the composite indicators if it is based on a defective reference series.
The reference series used as a proxy indicator of AEA is an important step in dating the turning points. A common feature amongst all the derivative approaches of B-M’s research is that the theory behind using a specific time series or composite indicator as representative of AEA is seldom discussed. Taking into consideration a faulty time series and detecting the peaks and troughs in such series will be a futile exercise. Further, the above-discussed conventional business cycle measurement processes have failed to capture the critical aspect of the dynamic nature of the cycles. The very fact that more than two centuries have passed and yet the phenomenon of business cycles is eluding us inductively indicates the dynamic nature of business cycles. As discussed previously, the research by B-M
was conducted with the objective of the identification of quantitative aspects or stylised facts of business cycles that were to be subsequently used for theory development. Although to the contrary, Zarnowitz makes a tenable point that inadvertently raises doubts over the utility of the B-M’s research. He points out that had there been any statistical regularity, they would have been identified by now, considering a long history of repeated occurrences of cyclical fluctuations (Zarnowitz 1991: 16-17). Further, the prevailing approaches give the impression that the business cycle phenomenon is separate from the agents’ economic activities and that their economic decisions have no impact on business cycles as none of the approaches talk of the role of the agents in influencing business cycles. In the next part, a definition of AEA from the agents’ perspective is proposed while incorporating the behavioural attributes to BCT.
III The Theoretical Proposition
Since the fluctuation in AEA is referred to as business cycles, defining AEA would fulfil the objective of identifying and measuring business cycles empirically. AEA has not yet been defined in a way that may aid its empirical identification and measurement. Therefore, a definition is proposed here that is theoretically robust and would make it empirically measurable. The AEA can be defined as the spending by the domestic households and firms in an economy during a specified period.
There are two implicit theoretical ideas in this definition that require elaboration. The first idea is that of treating spending as a measure of AEA. Conventionally, the concept of AEA is treated as notional, similar to that of the concepts of the natural rate of unemployment and interest and the likes. It is because of their unobservable nature that such concepts invite more disputes. The prevailing state of the economy is experienced by the agents which cannot be measured by any macroeconomic variable directly. I propose to measure the same by inference. Even though the prevailing state of the economy cannot be measured, the response of agents to it in the form of economic decisions they make can be measured. The agents take into consideration all the available information to formulate their economic decision. Such economic decisions are quantifiable and hence measurable. The economic decisions of the agents manifest in the form of the monetary transactions conducted by them. A monetary transaction can be measured from three different perspectives viz., production, income, and expenditure. Economic activity measured from the production perspective will capture only the firms’ opinion on the state of the economy, ignoring the households. The income of the agents is beyond their control and cannot be altered solely by the agents’ decision. Hence, expenditure is the most plausible perspective to capture the state of aggregate economy, as the decision to spend is controlled by the agents themselves, after factoring in all the variables. Therefore, expenditure is identified to be the most plausible measure of the three.
Expenditure by the agents represents the dollar votes which they cast as an expression of their economic opinion. The decision of conducting expenditure by the agents, encompasses within itself the effect of changes in all the macroeconomic variables. After taking into consideration the prevailing macroeconomic phenomena, the agent spends money. For instance, income precedes expenditure for any household. Any effect on the income will reflect on their decision to spend. Also, employment is a key source of livelihood for households. A decline in employment in an economy is bound to have consequences on their expenditure at the aggregate level. Similarly, for firms, sales for a specified period will influence their capital expenditure in the subsequent period. Labour is an indispensable input in the production process. A firm reduces its labour only under such circumstances where its profitability and sustainability are expected to be adversely impacted. When revenue declines, firms tend to curtail their expenditure on labour. Any negative shock which impacts any of the macroeconomic variables will be reflected in the decision of the agents to spend. If the case is not so, then the agents believe that the shock is temporary in nature and may not possess the potential to cause a structural shift in the long term.
The second theoretical aspect of the definition of the AEA pertains to the agents whose economic activity is to be considered. In the proposed definition, I took into consideration only the expenditure of households and firms. A four- sector model of an open economy comprises households, firms, regulators, and the external sector. It is contended that, of all the types of agents, it is only the households and firms who perform the economic activity with the capitalistic objective of profit maximisation that is relevant for AEA measurement. It is their participation in the economy which results in the economic activity that is intended to be measured. Therefore, the households and firms are hereinafter collectively referred to as the Participating Agents for ease of reference. The government too conducts the economic activities albeit not with the profit maximisation objective, but for the collective welfare of its citizens. It also performs the function of developing and implementing the regulatory framework for the economy to minimise the effects of negative externalities that causes market failure. Similarly, a central bank performs the regulatory function of the oversight of money supply in the economy. The government and central bank regulate the economy with the help of fiscal policy and monetary policy tools respectively, to steer the economy towards the stability and sustainability of economic growth. The government and the central bank are henceforth collectively termed as the Regulating Agents. The economic activities performed by the regulating agents are intuitively meant to be counter-cyclical since their objective is to induce economic stability by intervention. Including the economic activity of regulating agents in the AEA will diminish the effect of fluctuations in the economic activity of participating agents, muting the cyclicality and making the identification of business cycles tougher. Thus, the economic activity performed only by the participating agents in aggregation is relevant for the business cycle measurement. From the above exposition, it can be concluded that the participating agents are explicitly responsible for conducting economic activities, the aggregation of which results in AEA.
The economic activity of the external sector is more representative of the state of the external economy than that of the domestic economy; thus, a part of it requires to be excluded from the definition of AEA. The transactions of the external sector comprise exports and imports in an economy. Exports form part of domestic production because the economic activity concerning these goods and services is carried out within the domestic territory of an economy, although the same gets consumed by the external economies. While, imports are excluded from domestic production because the imported goods and services are not produced within the domestic territory, although they are consumed within the economy. To determine the AEA, we require only the spending of participating agents. Since exports consist of the domestic goods and services demanded by the external agents, it does not form part of AEA. Any changes in exports will depict the economic decisions of the external agents. However, imports represent expenditure by domestic agents on foreign goods and services, hence required to be included in AEA. Though imports do not originate within the domestic economy, they do represent the manifestation of expenditure decisions carried out by the domestic agents, which is intended to be measured.
The AEA possesses a crucial behavioural attribute that makes it a dynamic phenomenon. The agents use the currently available information about the state of the aggregate economy to take economic decisions for the subsequent period. This creates a feedback loop resulting in the persistence of business cycle phases. Hence, the agents expand their respective economic activities when the aggregate economy is in the expansion phase and when the economy is contracting, the agents curtail their activities to minimise the negative effects of contraction. Such behaviour thus accentuates the prevailing state of the economy unless interrupted by shocks. The agents require information about the state of the economy at a localised level as well as the aggregate economy level, to take any decision for the future course of their economic activity. For the information to be used by the agents, it should possess three key attributes viz., availability, affordability, and acceptability. The information must be available to all the agents as a public good without any restriction of access, which limits its use only to a few eligible agents. Secondly, access to information ought to be economically feasible for the agents. Restricted availability of information at a high cost of access deters its use. Finally, the acceptability of information is determined by the reliability of its source. Information from a credible source is accepted and used by a greater number of agents as compared to that from an unreliable source. Information about the local state of the economy is available to the agents from sources like the local grapevine network and reports of local trade bodies and associations. Information from informal sources like grapevine networks has a negligible cost but possesses questionable reliability, while access to reports from reliable and legitimate sources involves the cost of acquiring information. To analyse the trade-off between availability, affordability, and acceptability and to take appropriate action is a prerogative of the agents. The agents form an opinion about the prevailing state of the economy by taking into consideration the available information based on the above-mentioned trade-off. Economic decisions taken are based on such opinion. Hence the time series that is required to be measured should possess the three attributes of availability, affordability, and acceptability. Only then would we be measuring the actual AEA. Measuring the AEA using a time series lacking any of the three attributes will yield inconsistent results, since agents may not be using that specific time series to take decisions. A true measure of the aggregate economy would be a time series that is available to the agents for observation, so as to form an opinion about the economy and take decisions accordingly.
IV The Empirics
The objective here is to measure the spending of the participating agents, for which a time series that consists of all the three attributes discussed above is required. The National Accounts is the most suitable data which confirms our criteria since it is published by the governments, which is a reliable source which is also made available to all, at practically zero cost as a public good. Further, the real GDP for National Accounts is computed using the expenditure approach, as required by our theory. The real GDP requires minor tweaking to arrive at our theoretically proposed AEA. There are four constituents of real GDP (Y) as per the expenditure approach. These are private final consumption expenditure (C), gross fixed capital formation (I), government final consumption expenditure (G) and net exports (X − M). The aggregation of all the constituents results in what is referred to as the national income identity, denoted by the below equation.
Y = C + I + G + (X − M)
In measuring the GDP by expenditure approach, the spending of all the agents across the economy is aggregated to arrive at the aggregate production. However, the objective here is not to precisely measure the production but to gauge the prevailing state of the economy. In my theoretical exposition, I proposed the exclusion of economic activities of the regulating agents and exports from AEA. In the national income identity, C and I indicate the economic activity of households and firms respectively, i.e., participating agents. It is the only economic activity we are concerned with while computing the AEA. G indicates the economic activity of the regulating agents, hence, to be excluded. Similarly, export transactions of an economy, represented by X are also not concerned with AEA. The imports (M), comprise of demand for foreign goods and services by households, firms and the government, which may be represented by the following equation:
M = MC + MI + MG
In the earlier section, it was proposed that the imports should form part of the AEA. Imports indicate the expenditure by domestic agents on foreign goods and services. The prevailing state of the economy impacts the import demand of the agents, making it a relevant metric for cyclical measurement. Expenditure by the participating agents on domestic as well as imported production indicate the prevailing state of the economy. Thus, after eliminating the unwarranted components of GDP, we are left with private final consumption expenditure (C) and gross fixed capital formation (I) including imports as a measure for AEA. The change in AEA will represent business cycles. Hence, the AEA can be represented by the following equation:
AEA = C + I
Where C = CD + CM and I = ID + IM
The subscript D in the above equations denote expenditure on domestic goods and services and M refers to expenditure on imports. The above equation of AEA is the final product of our exposition. It does not require any statistical adjustment or filtering. Any modification in this indicator will diminish its efficacy. This is so because the participating agents directly use the information as disseminated, since they possess limited computational capabilities. This assumption does not imply that the agents lack intelligence. But the aggregate level of the computational competence of the agents cannot be assumed to be the same as that of a statistician or an econometrician. The agents are adept in their respective economic activities, and they require a benchmark aggregate level of economic activity, to compare their individual-level activity. The publicly available information about the aggregate economy acts as a benchmark. So, they view the information about the aggregate economy as is disseminated. It is unlikely that the majority of the agents would perform any statistical procedure to further refine the data, as is done by the economists. It was proposed that the agents use the information about AEA to conduct economic activity, which becomes the economic activity for the subsequent period. To capture this feedback loop, the same indicator must be used as a measure of AEA as is made available to the participating agents.
The derived equation is the closest representation of AEA. It is the quantified embodiment of the economic decisions taken by the agents who perform economic activities for profit-motive. Such decisions are taken in accordance with the prevailing state of the economy, hence acting as an inferential indicator of the AEA. It is derived from the publicly available national accounts data without conducting any complicated statistical modifications, as used by the agents. Thus, the derived AEA possesses the three attributes of availability, affordability, and acceptability.
V Conclusion
The business cycle measurement exercise has been over-reliant on the working definition given by B-M, disregarding the context in which it was developed. The problem of insufficient data and its poor quality compelled them to corroborate their work by using numerous time series. The B-M’s approach portrayed the business cycle as an extremely complex phenomenon requiring an enormous volume of data for its analysis. Their research led to the creation of two popular non-parametric business cycle measurement approaches. Although, these approaches have a dichotomous view on the concept of AEA. Either the idea of AEA is oversimplified, where it is represented by a single time series, which lacks substantial theoretical backing or it is overcomplicated and is depicted as an incomprehensible phenomenon that is difficult to explain theoretically. Both these approaches fail in theoretically describing the AEA. Hence, a definition is proposed that plausibly explains it and can be conveniently measured empirically. More importantly, the proposed definition of AEA makes it implicitly dynamic in nature as it is defined to be the manifestation of agents’ economic decisions after taking into consideration the prevailing state of the economy. The dynamic nature is also enforced by the feedback loop of information, whereby the agents use the information about the AEA to make decisions for the future, thereby influencing the future value of AEA. Precisely measuring the economic activity will aid in macroeconomic stabilisation policy decisions. When the regulating agents will track the economic activities of participating agents and identify the causes for its unfavourable trajectory, more effective policy measures can be formulated to stabilise the business cycle in the economy.
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