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Linear Regression

Linear regression belongs to the econometric methods of empirical research, which are applied in almost all sciences. Linear regression is a set of econometric methods of estimating statistical causality between two or more factors (variables of interest). A central assumption of linear regression is the ceteris paribus condition, which means nothing other than “if the conditions are the same” or “if other factors are kept constant“. The literature on linear regression can be found in almost every textbook on statistics, textbooks on econometrics, and research literature on methods of empirical economic research.

Simple Linear Model

In a simple linear model, it is assumed that only one variable $x_{1i}$ has significant impact on variable $y_i$ and all unexlained variance is explained by the error term $e_i$.

y_i=\beta_0 + \beta_1 \cdot x_{1i} + e_i \, \text{or} \, y_i=\beta_0+\sum_{j=1}^{k=1} \beta_i \cdot x_{ji} + e_i

Multi-Regression model

y_i=\beta_0 + \beta_1 \cdot x_{1i} + \beta_2 \cdot x_{2i} + ... + \beta_{k-1} \cdot x_{(k-1)i} + \beta_k \cdot x_{ki} + e_i
y_i=\beta_0+\sum_{j=1}^{k} \beta_i \cdot x_{ji} + e_i

Why is the linear regression method used in science?

Scientists use the regression method to explain the statistical causality between two or more factors so that they can identify the potential statistical correlations in their research question. However, a researcher also wants to test whether statistical estimates reflect reality, or at least whether the observation in the sample of his/her observations reflects reality in the population. Regression methods can be used to calculate or analyze relevant factors in a research question. While regression calculation aims to estimate the relevant coefficients (causality estimators), regression analysis aims to test relevant empirical hypotheses (inferential statistics).

Where is linear regression applied in economics and business administration?

Consider the following example. As an economics student, you are used to reading the following statements in almost all economics and business administration textbooks: “The law of demand says that when prices rise, the demand for a normal good falls”. Where does the statement of the law of demand come from? Can this assertion be proven empirically? When is a good a normal good? Although the answers to these questions can be found in any textbook, the background to their justifications and sometimes incomplete explanations are more likely to be found in empirical research using econometric methods.

Economists work with theoretical models that can be empirically tested to determine the extent to which they reflect your research question in reality. In the case of the law of demand, the Cobb-Douglas model can be applied, which assumes constant elasticity of demand. Here is where the first problem arises. This Cobb-Douglas theoretical model is not linear, as required by linear regression methods, but a non-linear (multiplicative) model. Utilizing the logarithm, however, the Cobb-Douglas model can be transformed into a (log-to-log) linear model (linear transformation). With a sufficient sample, a regression model can be estimated to statistically verify the claims. This example is one of many other applications of empirical analysis to test economic theories.

Simple and multi-regression analysis

In econometric regression analysis, a distinction is made between simple regression analysis and multi-regression analysis. In simple regression analysis, two factors are examined, e.g. a macroeconomic hypothesis could be that domestic consumption (C) has a positive influence on domestic income (Y). The propagated causality is that domestic income depends on domestic consumption – Y(C).

Econometric models start with a simple regression between two variables.

The aim of econometrics is to estimate empirical models that confirm or even refute the propagated causality between domestic consumption and domestic income of a country in the given population. The reverse causality, however, is also possible that domestic consumption tends to depend on domestic income – C(Y). Now, such an analysis, Y(C) and C(Y) takes place under the assumption of the ceteris paribus condition.

The simple regression is then extended by further variables – forming the multi-regression model.

Due to the ceteris paribus condition, the explanatory power of the simple model is limited to explaining the potential causality between domestic consumption and domestic income of a country, but without reference to other potential causalities between other (non-) observable factors, e.g. domestic and foreign investment, exports, imports, government expenditure, savings, taxes, etc. For this reason, the simple regression model is extended. If we now extend the propagated causality to other potential causalities, this results in the multi-regression model, e.g. domestic consumption (C) is influenced by disposable income (income (Y) minus taxes (T)) and other factors, the classical macroeconomic theory of consumption according to Keynes.

C(Y_V)=c_1Y_v+c_0

Health and Economic Risk: Social Distancing as a Health Risk Management Tool

Social distancing measures propagated by health experts and belatedly by governments worldwide as a means of health risk minimizer, are yet to unfold their full impact on health risk management caused by the COVID-19. Nevertheless, social distancing poses a high risk to the economic and social life of affected communities globally. Fears of a global economic slowdown have dominated discussions amongst the public, experts of different disciplines, politicians, entrepreneurs, and others.

What is at stake while implementing social distancing?

Social distancing has slowed income generation among the majority of economic sectors. Consumption and production of certain goods have dropped to the lowest level, while online business models have prevailed to be a necessity for entrepreneurs and employees. Minimal social interaction reduced to digital communication via Skype, Zoom, Facetime, and other social media outlets. The minimum of 1.5 Meters of social distance between individuals in public space puts the society on a psychological test. This amount of economic, social, and psychological pressure seems to cause an explosion of both optimism and pessimism toward the social distancing measures. Differences in reactions are visible amongst different interest groups. Some interest groups are also pleading for ease of social distancing measures. Contrary to those special interest groups, health experts, practitioners, nurses, caretakers, and the health sector, in general, pleads for tight implementation of the social distancing measures.

Fears about the macroeconomic implications additionally add more pressure to efforts of minimizing unemployment, inflation, and global trade dependencies while maximizing investment, private, as well as public spending. Several pandemic-simulation-models oppose the ideal of seeking a quick opening of economic and social life at the cost of unprecedented health risks.

What is riskier? Health Risk or Economic Risk?

Before making a judgment on whether the health risk or economic risk is riskier than the other, we should seek prudent wisdom. Decisions to be made will need high ethical standards and a balance between exposure to health risk and economic risk. Measures proposed to open economic and social life around the world are disregarding the current health optimization goals at the cost of uncertain short-term economic and social gains. The long-term economic and social impact should be the driving force behind any adjustments to the social distancing measures. Nevertheless, society should not neglect the importance of a democratic decision mechanism. Acceptance of social distancing may depend on the decision mechanisms put in place to safeguard the social, economic, political, technological, ethical, and ecological interests of all members of the society.

We are in a learning process of how globalization works and could work.

The global lockdown of borders, trade, tourism, and mobility of people, reveals the vulnerability of our conjoint societies in different aspects. Dependencies on global supply chains, consumption, and mobility have become visible. Large developed economies depending on emerging economies to facilitate their health centers with respiratory machines. Member countries of the European Union closing their borders amid the Corona-Pandemic, which is contrary to the Schengen treaty of 14 June 1985. Developed countries chartered airlines to bring back their citizens from emerging and developing countries. These issues, among many, pose global risks to world democracy, if not dealt with on due time. After the pandemic, the world will have a lot to repair, adjust, and negotiate. Does a global democracy exist?

Synergy between Health and Economics: Oil-Price-Crash or Corona-Crash

Health shocks can also cause economic shocks (ceteris-paribus). The coronavirus pandemic is one of the health shocks that might unravel another global economic shock. Why is that the case? Fact number one, health is a basic human need. Consequently, the satisfaction the health needs requires human decision-making in all economies. Decision-makers face the challenge of managing the synergies between health and economics amid a health shock. How is the Scenario? Simultaneously, economic shocks unveil themselves amid health shocks as the pattern of decision-making changes with time. The time-lagged impact of change in decision-making is revealed through market reactions and is observable. Markets not only react to consumer choice and entrepreneurial decisions but also react to government decisions.

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Macroeconomic objectives of Economic Policy

All macroeconomic objectives could be affected by health and economic shocks. The potential reactions of markets are; the increase in market price volatility, the demand and supply shocks, among others. Economists of different schools of thought may differ in their economic forecasts, but the assumptions of any approach should be tested and empirically evident. The objective of the scientific approach should be to explain the global economic impact of the coronavirus. In the coming days, we will have the chance to observe; how strong or weak the global impact of the health and economic shocks will be in different economies around the world.

What is Health Economics?

Health Economics is the scientific branch of economics that deals with healthcare provision (supply) and healthcare utilization (demand) and how society makes coordination choices to meet the healthcare needs of its population. Economics as behavioral science influences health economics as a subbranch through the contribution of economic thought to healthcare issues. For example, we may see the solution to the following question: what are the most important behavioral choices of society that would help find the first-best allocation of healthcare goods and services in a world of scarce resources. The Coronavirus-Pandemic is the current health issue that is stress-testing our healthcare systems on a global scale. How we will deal with the pandemic depends on the behavioral incentives and contingencies to be resolved. In summary, health economics is the branch of economics that deals with the decision-making of how to allocate resources to health provision in economies. European countries have compulsory social welfare systems that coordinate health risks, while some economies have a voluntary system of insurance or no backup system in place.

How did the world economies react to the coronavirus pandemic outbreak?

It all started with the coronavirus pandemic outbreak somedays before 31.12.2019 in the Wuhan province in China. The first cases of the COVID-19 were reported to the World Health Organization by the Chinese government officials. Days after the World Health Organization reported the decease outbreak to the public on 05.01.2020. Globally, the reactions remained moderate and optimistic towards the ability to resolve the emerging disease. As reality became more prevalent and the cases wouldn’t be contained in China but spread around the world, the world Governments reactions as decision-makers were divergent and showed less global coordination. China put a whole province under quarantine, while most countries continued with life, as usual, giving out mild health alerts to the public.

Belated reaction of Governments

Maybe, the governments wanted to buy time to set up contingency plans, consult with experts on how to manage the unavoidable risks. The belated plan of actions by governments might have accelerated the spread of the coronavirus.

Economic slowdown, hoard purchases and travel bans

Simultaneously, the staggering impact of economic activities due to production stops in China channeled a global slowdown on the delivery of goods, services and the global supply (value) chain. The imposed travel bans and quarantines could heavily hurt the mobility of the people, the global flow of migration and tourism around some hotspots. Public life (e.g. traveling, local transport, retailing, etc.) in affected regions had been slowed down due to quarantines imposed by Governments, leading to hoarding behavior, the closing of industries and production, travel bans and other limitations to public life.

Global oil crash or Corona-Crash in Financial Markets

As expected, the impact was still to become visible in public life. The economic pressure has slowly become visible through reactions in financial markets. Financial markets are volatile markets, which adjust their valuation of assets in very short periods.

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The oil-price-crash on 09.03.2020 agitated the global financial markets three months down the path. The reason for the price downfall was caused by the OPEC negotiations, which ended without a compromise. The over-production of oil, coupled with low demand for oil, led to the downfall of the oil price in the stock exchanges around the world.

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