THE KEYNESIAN STIMULUS MODEL STIMULATING ECONOMIC ACTIVITIES WITH STIMULUS PAYMENTS

CHAPTER ONE: INTRODUCTION

Overview

When the Coronavirus disease (COVID-19) first emerged in Wuhan, Hubei Province of

China, the global concern was focused on its public health impact and the threat to human life.

Governments worldwide implemented many measures to contain the spread of the virus. Apart

from the daily increase in deaths and hospitalizations, these restrictive measures and recommended

health protocols culminated in an economic shock. The U.S. government responded with a fiscal

stimulus package to prevent the economy from sliding into another Great Depression after the

painful recession experienced a decade earlier. This study analyzes the Keynesian model to

ascertain how fiscal transfers stimulate economic activities. The study seeks to illuminate how

such transfers could boost economic activities to address the economic impacts of COVID-19. The

pandemic caused unemployment, panic, and a decline in consumer confidence. Keynes (1957),

Baker et al. (2020), and Baqaee and Farhi (2021) noted that increased liquidity sustains personal

consumption expenditure (PCE) and disposable personal income (DPI) because of the multipliers

it produces.

However, monetarists believe that expansionary monetary policy could produce the desired

outcome of stimulating economic activities in the long run. Milton Friedman (1968) argued that

monetary policy could offset severe economic disturbances from other sources in the long run.

Mankiw and Reis (2018) disagreed with Friedman’s classical long-run theme and its centrality on

expectation. In their words, “The long run is a misleading guide to current affairs” (Mankiw &

Reis, 2018, p. 87). Keynes argued that “in the long run, we are all dead” (Keynes, 1937). This

study analyzes the fiscal stimulus applied during the ongoing pandemic compared with an

alternative policy to illuminate the efficacy of Keynesianism.

Background

The origin of COVID-19 is traceable to Wuhan, Hubei Province in China. Park et al. (2020)

and Ren et al. (2020) explained that COVID-19, caused by severe acute respiratory syndrome

coronavirus 2 (SARS-CoV-2), first appeared in Wuhan in December 2019. The early COVID-19

cases were diagnosed in December 2019; by February 2020, the virus had spread to an alarming

proportion of the world and quickly became a global pandemic. In March 2020, COVID-19 began

ravaging New York, spreading rapidly to other big cities in the United States, including Chicago,

Seattle, and Miami, and becoming an unprecedented public health crisis. Baker et al. (2020) and

Benzeval et al. (2020) documented many measures that federal and state governments used to

contain the spread of the virus, including travel restrictions, shelter-in-place orders, and the closure

of non-essential businesses. These measures caused many economic hardships, such as massive

job losses, declines in consumer confidence, and a significant strain on national output. Ren et al.

(2020) noted that fear and misinformation about the virus caused panic and disrupted the free flow

of people, goods, and services. They emphasized the psychological effect of the fear of a deadly

infectious disease on society and insisted that people feel unsafe, uneasy, and anxious (Ren et al.,

2020). Ordinarily, when such fear and uncertainty grip society, people are compelled to be cautious

of others in order to protect themselves. However, such precautionary health safety measures have

severe economic consequences and distort the market dynamics for non-essential products and

services. Literature, including that of Baker et al. (2020), Chetty et al. (2020), and Baqaee and

Farhi (2021), supported the notion that the adverse effects of fear or stigma constrain consumer

confidence and severely depress demand.

The U.S. government responded with fiscal stimulus, a Keynesian approach that President

Franklin D. Roosevelt (FDR) used to implement the New Deal to revive consumer confidence and

stabilize the economy during the protracted Great Depression of the 1930s. Gravelle, Hungerford,

and Labonte (2009) explained that fiscal policy temporarily stimulates the economy but increases

the budget deficit, raising government spending through direct or consumption spending by the

recipients of tax cuts or cash transfers. The justification of the stimulus proponents is that direct

cash transfers are a quick method to sustain household consumption and stimulate aggregate

demand (AD). Dender, O'Reilly, and Perret (2020) argued that policies that provided liquidity

support to vulnerable small businesses and families relieved them of the economic hardship caused

by the containment measures. However, the stimulus transfers aimed to alleviate the pandemic's

severe effects on social outcomes and stimulate AD through the multiplier effect of government

transfers. Baker et al. (2020) discussed the rationale for the stimulus extensively. They offered

valid evidence that "the effect of these payments relies on the household's marginal propensity to

consume (MPC), out of the stimulus transfers" (Baker et al., 2020, p. 2). Their findings showed

that MPCs are essential to public policy and economic theory because the MPCs from transfers

produce multipliers in many policy models (Baker et al., 2020). Therefore, if the economy is

expected to be hit by a severe shock, proactive policies become indispensable to insulate the

economy from that shock. The significant rise of PCE after each round of the transfers supports

Keynes’s view on government intervention.

The trend shown in Figure 1 on page 17 reemphasizes the Keynesian theory, suggesting

that the main barrier to economic activities is contracting income due to the COVID-19 threat,

leading to inadequate personal income and declining personal consumer spending; hence, the $600

payment of the second fiscal stimulus in January 2021 caused a rise in both DPI and PCE. Casado

et al.(2020), Chetty et al. (2020), and Fornaro and Wolf (2020) found a link between the Keynesian

theory and their empirical results. Moreover, they constructed various indices from different data

sources to establish relations with the theoretical foundations.

As shown in Figure 2 on page 17, the lowering of the interest rate in February 2020 caused

a significant decline in the inflation rate; thus, inflation neared 0% in April 2020 and then

maintained an upward trajectory. The inflation rate reached a 40-year high of 7.9% in February

2022 and has continued to rise since. While interest rates remained at their lowest from April 2020

until March 2022, the Federal Reserve Bank (Fed) raised interest rates from 0.20% in March 2022

to 1.21% in June 2022. The changes in GDP fluctuated significantly between March and October

2020. The GDP growth was unstable from June to October 2021 despite the Fed maintaining stable

interest rates. The unemployment rate consistently declined from April 2020 to January 2022.

Figure 2 suggests that starting from January 2022, the Taylor rule could not hold, thus reinforcing

Guerrieri et al.'s (2020) view on monetary policy; therefore, the interest rates were adjusted in

tandem with the rise in the inflation rate without considering the decline in GDP.

Carvalho and Rezai (2016) argued that changes in income distribution affect AD, which

supports Keynes's theory that increased liquidity leads to higher output and a greater multiplier

from consumer spending. Keynesians, including Chetty et al. (2020) and Baqaee and Farhi (2021),

admit that the COVID-19 shock requires a stimulus to maintain household consumption and

provide a higher MPC to restore macroeconomic equilibrium. However, critics of the Keynesian

stimulus solution to shocks expounded the Hayekian free-market idea to reemphasize Freidman’s

claim that the money supply has an enormous effect on the national output. Krugman and Wells

(2017) and Mankiw and Reis (2018) argued that fiscal stimulus was effective in confronting the

Great Depression of the 1930s and the last Great Recession in 2008 despite the debate in the policy

arena that trailed the outcomes of the New Deal policy and the American Recovery and

Reinvestment Act (ARRA). The belief that fiscal stimulus was successful in those instances

emboldened the federal government to proactively pass the legislation to ameliorate the hardship

and contain the economic effects of the pandemic.

On March 25, 2020, the CARES Act was passed as a comprehensive policy response to

the economic hardship caused by the pandemic. The evidence from Baker et al. (2020) and Chetty

et al. (2020) suggests that the $2 trillion stimulus program under the CARES Act, including the

cash transfer of $1,200 per adult, an additional $500 per child under the age of 17 years, and the

payroll protection assistance to small businesses, stimulated AD. The U.S. government provided

two more rounds of the stimulus in 2021, with $600 and $1400 transfers, respectively. The

argument that dominated the policy arena among mainstream economists is whether the size of the

transfers was large enough to generate the expected multipliers and if the timings were consistent

with the desired policy outcome. Although the pandemic continues, looking at the time horizon of

the analysis of the policy measures is critical, as observed by Mankiw and Reis (2018). They

argued that "Milton Friedman viewed the long run as the timeframe under which we should apply

principles of classical economics, especially monetary neutrality" (p. 84). Their view was that

regardless of the actions taken by the Fed, unemployment would, over time, reach its natural rate,

which implies that the time horizon is a misleading guide to current affairs. Hence, this research

focuses on the short run. The policy assessment of the past pandemics in the last century, including

the 1918 Influenza, the 1957 H2N2 virus, the 1968 H3N2, and the 2009 H1N1 Swine Flu, shows

that the time horizon shapes the dominant reactionary policies.

Problem Statement

As the Coronavirus continues to ravage economies worldwide, its impacts are profoundly

felt. Economists have a near consensus that the pandemic is the most severe global health crisis

since the 1918 Influenza that claimed 50 million lives worldwide. Keynes (1936) and free-market

economists, including Fredrich Hayek (1944) and Milton Freidman (1968), agreed that shocks

have severe impacts on the economy, hence the use of economic policy to manage them and

stimulate the economy. To address the 2008 Great Recession, the Fed complemented the U.S.

government’s fiscal measures with expansionary monetary policy. The combination of both

approaches constrained the ability to ascertain each policy's efficacy empirically. Consequently,

this constraint created a gap in the existing literature due to the lack of proper instrumentations to

compare the effects of the distinct policy measures using the two models. Therefore, this research

proposes using the Keynesian and monetarist models to investigate the outcome of the policy

interventions applied to manage the impact of COVID-19.

This study measures the effects of the explanatory variables, including the percentage

changes in the DPI, PCE, and the unemployment rate (UR), as proxies of the dependent variable,

the percentage change in GDP in the Keynesian model. In contrast, the monetarist model uses the

federal funds rate, inflation rate, and UR as independent variables to predict the changes in the

GDP. Both models used the UR as a variable because the Keynesian and monetarist models aim

to stimulate the economy, close the recessionary gap, and restore full employment to reduce the

UR. Therefore, the UR measures the outcome of fiscal as well as monetary policies. Baker et al.

(2020), Chetty et al. (2020), and Baqaee and Farhi (2021) found that consumption (C) and

investment (I) are additive components of spending. However, Fiebiger and Lavoie (2018) and

Pollitt et al. (2021) found that the Taylor rule provides intertemporal coordination to stabilize AD

in the long run. This study compares the two models' outcomes to ascertain whether the Keynesian

stimulus offers a better policy solution to the COVID-19 shock or whether monetary expansion is

a more practical alternative.

Purpose Statement

The purpose of this research is to examine the direct stimulus payments by the U.S.

government using the Keynesian model and the variations in the interest rates by the Fed using the

monetarist model to determine their efficacy in stimulating economic activities during the COVID-

19 pandemic. This study identifies relevant variables and employs similar procedures as those

employed by Casado et al. (2020), Chetty et al. (2020), Fornaro and Wolf (2020), and Baqaee and

Farhi (2021) to gather time series data from the beginning of the pandemic from primary sources.

Monthly time-series data were collected from the Bureau of Economic Analysis (BEA), Bureau of

Labor Statistics (BLS), and the Fed from July 2019 to the most recently available data of the

percentage changes in GDP, DPI, and PCE, as well as the UR, federal funds interest rate, and

inflation rate. The percentage changes in the DPI, PCE, and UR are the explanatory variables in

the Keynesian Model. In the alternative monetarist model, the independent variables are the federal

funds rate, inflation rate, and UR. In both models, the explanatory variables predict the percentage

change in GDP as the dependent variable. The study uses 120 data points from the data collected

to provide insight into the problem and answer the research questions.

Significance of the Study

The study is significant in that it addresses the concerns about the efficacy of the Keynesian

stimulus policy in the aftermath of the Covid-19 pandemic. Apart from contributing to the growing

body of literature on stimulus transfers during shocks such as the Covid-19 pandemic, this study

is of practical and theoretical significance by filling the gaps in the literature and providing new

evidence in the Keynesian and monetarist models using actual data. From a theoretical perspective,

it provides evidence that the Keynesian model's new variants are consistent with the classical

Keynesian theory. Keynes (1936) noted that "the transition from lower to a larger scale of activity

involves an increased demand for liquid resources" (p. 668). Studies have shown that intervention

increases demand, which induces firms to increase their investment, thus sustaining the consumers'

expectation for future income with the resultant effect of unleashing the animal spirit that drives

consumption (Baker et al., 2020; Baqaee & Farhi, 2021; Bernanke et al., 2005; Casado et al., 2020;

Fornaro & Wolf, 2020). However, these studies did not compare the evidence under the fiscal and

monetary models to provide clear evidence of the comparative outcomes. This study tests the

theory with new data.

The evidence from Fornaro and Wolf (2020) suggests that monetary intervention to sustain

demand produces a multiplier that reverses the supply-demand loop. Altig et al. (2020) contested

this view by finding that a high level of uncertainty does not bode well with monetary policy for

rapid recovery because firms and consumers are cautious; it retards investments, hiring, and

consumer spending on durable goods. This study seeks to provide valuable analytical and

empirical evidence to help policymakers and analysts evaluate the effectiveness of economic

stimulus and decide on the appropriate stimulus size to deliver the desired outcome. Fornaro and

Wolf (2020) argued that to restore full employment, "the Fed needs to inject further monetary

stimulus" (p. 4). The size and number of rounds of the stimulus have been the subject of policy

debate. The proponents of the stimulus argued that more rounds of stimulus yield a desirable result,

whereas the critics insisted that more stimuli will increase the national debt. The existing literature

did not indicate whether the policy measures should target households' and firms' incomes. This

study seeks to prove whether fiscal measures divert spending from consumer goods to savings.

Research Questions

To fill the gaps in the literature, this study aims to gain insight into the efficacy of the

stimulus model. The study investigates the outcomes of the measures implemented by the U.S.

government to manage the impact of the Covid-19 pandemic to ascertain whether the Keynesian

stimulus or the monetarist approach delivers a more desirable outcome in the short run. The

desirable outcome is the increase in economic activities measured as the percentage change in the

real GDP. The Keynesian theory postulates that fiscal stimulus provides liquidity to support

consumers' spending on goods and services (Keynes, 1936). Keynes argued that declining

consumption and investment spending depress the economy and lead to a recessionary gap,

underemployment of labor, and disequilibrium in potential output. The research question of this

study is whether the fiscal measures under the CARES Act and the subsequent stimulus yielded

the desired result of stimulating economic activities than monetary expansion. Therefore, to

address this question, it is pertinent to investigate the policy response to the COVID-19 pandemic

beyond the findings of Baker et al. (2020), Chetty et al. (2020), Casodo et al. (2020), Fornaro and

Wolf (2020), and Baqaee and Farhi (2021) to choose the best. Thus, the following sub-questions

provide the conceptual and empirical imperative to evaluate using the Keynesian model to address

the Covid-19 shock.

RQ1: How much, if at all, does the change in DPI from direct government transfer

payments raise the GDP?

RQ2: To what extent does the increase in PCE correlate with the changes in economic

activities?

RQ3: To what extent does the unemployment rate correlate with GDP change?

RQ4: To what extent does the absence of government intervention lead to increased

economic activity?

Monetarists, including Milton Freidman (1968) and Fiebiger and Lavoie (2018), argued

that reducing interest rates to increase the money supply makes borrowing attractive and stimulates

consumption and investment spending. Freidman's monetary expansion idea is to "restate

theoretically, apply empirically and enunciate the policy implications of the quantity theory of

money" (White, 2012, p. 320). Freidman (1968) and Feibiger and Lavoie (2018) explained that the

essence of monetary expansion is to induce a long-term AD by varying lending rates, injecting

reserves, and altering the reserve requirements to boost the stock of money growth rate. In addition,

"the Fed needs to be open to a rational discussion of alternative monetary rules in attempting to

improve the monetary regime" (Dorn, 2019, p. 592). These views make it imperative to empirically

examine the monetarist theory to provide further insight by analyzing these research questions that

pertain solely to the monetarist model.

RQ1: To what extent does the interest rate (INT) reduction induce growth in the GDP?

RQ2: To what extent does the UR resulting from money injection and altering the reserve

requirements correlate with a change in GDP?

RQ3: To what extent does the inflation rate correlate with GDP change?

RQ4: To what extent does the absence of monetary policy intervention or free-market

approach in the short run lead to increased economic activities?

Definitions

1. Cash Transfers – Cash payments to vulnerable households or individuals to reduce

economic hardship, alleviate poverty, improve health conditions, and achieve other

socioeconomic outcomes (Heise, Lutz, & Ranganathan, 2013).

2. Coronavirus – Coronavirus or COVID-19 is a highly contagious disease caused by the

severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that was first

diagnosed in Wuhan, China in December 2019 (Park et al., 2020; Ren et al., 2020).

3. Fiscal Policy – The use of government spending and tax to influence macroeconomic

conditions (Keynes, 1936; Mankiw, 2020).

4. Keynesian Model – John Maynard Keynes’s theory that fiscal spending effectively

manages AD to stimulate a depressed economy by raising consumer confidence (1936).

5. Monetary Policy – The Fed’s interest rates and money supply variations help steer the

economy in the desired direction (Krugman & Wells, 2017).

6. Monetarist model – Milton Freidman's theory that reducing the interest rates and

reserve requirements increases the money supply and liquidity (Freidman, 1969).

7. Shock – Economic contagion that severely disrupts the flow of goods and people and

stalls economic activities, causing recession (Carlsson-Szlezak, Reeves, & Swartz,

2020).

CHAPTER TWO: LITERATURE REVIEW

Overview

Economic shocks are critical events that impact the economy. Shocks disrupt demand and

supply in a complex and unpredictable manner, thus destabilizing the macroeconomy. Therefore,

there are two types of shocks: demand and supply shocks. Severe weather, war, and labor strikes

that disrupt the supply of goods and services are factors that cause a supply shock. Carlsson-

Szlezak, Reeves, and Swartz (2020) noted that supply shocks damage the supply side of the

economy and thus, disrupt credit mediation, stunt capital formation, and slow recovery, which

forces workers to leave the workforce and leads to the loss of vital skills and a decline in economic

activities. However, increasing prices, loss of income, and an extensive drop in consumer and

investor confidence constrain the animal spirit and cause demand shock. The impacts of shocks on

the economy could be drastic. They can affect micro-level demand and supply changes,

culminating in significant changes in AD and aggregate supply (AS). The COVID-19 pandemic is

a severe health crisis of global magnitude. Andolfatto (2021) argued that this pandemic fits the bill

of a supply shock because as the pandemic surges, it severely affects society's ability to produce

goods and services. Barret et al. (2020) observed that economists see COVID-19 as a supply shock

but reasoned that a supply shock could create a demand shock. Additionally, considering the nature

of the economic effects of the COVID-19 shock, it is appropriate to place it within the Keynesian

context as a demand shock because the demand aspect appears more dominant than the supply

aspect. Keynes (1936) identified the 1930s Great Depression as a demand-side problem caused by

liquidity constraints that weakened consumer confidence and depressed AD. A supply shock is

easier to correct by boosting suppliers' production ability.

The Coronavirus precipitated the economic contagion that continues to spread as the virus

mutates into different variants. Baker et al.'s (2020) view was consistent with Chetty et al.'s view

that the preventive health protocols to contain the spread of the virus severed market dynamics,

stalled economic growth, and posed the potential threat of an epic recession on a global scale.

Their views resonate with that of Carlsson-Szlezak, Reeves, and Swartz (2020), who argued that

the pandemic's trajectory imposes a higher economic cost, making the prediction of its path nearly

impossible as multiple dimensions of the pandemic are unprecedented and unknowable. Moreover,

health authorities contend that the virus has been mutating into many variants and has now reached

an endemic stage. Therefore, developing an appropriate policy response to mitigate the severity of

its economic impacts is necessary. Recently, relevant works have examined the economic effects

of the pandemic and policy interventions to reduce them. Notable among the literature are the

studies by Baker et al. (2020), Chetty et al. (2020), and Baqaee and Farhi (2021), which used the

Keynesian fiscal policy approach to analyze the pandemic's impact, and the studies by Dorn

(2019), Fornaro and Wolf (2020), Casado et al. (2020), and Pollitt et al. (2020), which used the

monetarist approach. Data constraints were a significant limitation in these studies that created

gaps due to the ongoing nature of the pandemic and its antecedent economic impacts. For example,

Dorn (2019) used the federal fund's rates to test the modern monetary theory (MMT) to investigate

the efficacy of monetary theory. Fornaro and Wolf (2020) estimated the intertemporal effect of

interest rate. In contrast, Baqaee and Farhi (2021) calibrated their model and data in a quantitative

input-output model to test the impact of the stimulus on output, employment, and inflation.

New data will uncover fresh evidence and trends that may support or refute the findings in

the earlier literature. However, the evidence available in the literature is valuable in developing

the theoretical and empirical framework for this study. First, the information available in the

related literature helps contextualize the problem, identify the research gaps, and develop an

appropriate research design to fill the gaps. For example, Chetty et al. (2020) suggested that

stimulus transfers rely on the MPC and the multipliers they produce, reinforcing the Keynesian

classical theory that liquidity unleashes the propensity to consume. In addition, this study

conceptualizes the research inquiry under the Keynesian and monetarist models to address the

policy problems identified in the research questions by reviewing the methods and exploring the

gaps in the related literature. For example, Baker et al. (2020) identified PCE and DPI as variables

to measure the relationship between the $1200 cash stimulus and MPC. Also, Fornaro and Wolf

(2020) estimated the effect of nominal interest rate based on spending to stabilize output around

its potential level and manage Friedman's temporary trade-off between inflation and employment.

Second, the theoretical constructs of this study help address the research questions with the

policymakers and researchers as the target audience to provide the rationale for addressing severe

shocks such as COVID-19 with these policy approaches.

Benzeval et al. (2020) found that the pandemic's shock affected different individuals and

households disproportionately. As Nakamura and Steinsson (2018) identified, the choice of policy

approach depends on the exogenous variations, such as structural shocks and their causal effects.

Chetty et al. (2020) isolated monetary expansion and focused on the Keynesian model. Fornaro &

Wolf (2020) and Casado et al. (2020) argued that comparing both models helps to determine a

better policy approach. However, data limitations constrained Baqaee and Farhi (2021) in their

attempt to address this problem. Therefore, it is essential to compare the outcomes of the

Keynesian fiscal stimulus and monetary expansion to rigorously evaluate the application of the

Keynesian model to the COVID-19 shock.

The Conceptual Framework

The Keynesian Model

John Maynard Keynes's fiscal multiplier is the conceptual foundation of economic stimulus

policy. Keynes (1936) claimed to discover the practical flaws of the free-market approach to

shocks during the Great Depression and posited that increased government spending to stimulate

the economy is critical in reviving the depressed demand. The idea behind this is that fiscal

spending effectively manages the AD to stimulate a depressed economy by raising consumer

confidence. In Dender, O'Reilly, and Perret's (2020) view, while fiscal measures could stimulate

demand, fiscal policy could incentivize behavior congruent with the desired public health goals.

Therefore, fiscal policy should target the macroeconomy and not individual consumers' financial

burdens, which can divert income to non-consumable spending. The classical Keynesian goal of

the stimulus is to reinvigorate economic activities, restore consumer confidence, and close the

output gap to bring employment to its full potential. The positive shift in consumer expectations

drove the recovery from the 1930s Great Depression and the 2008 Great Recession. Even

Eggertsson's (2005) evaluation of FDR's fiscal approach to the Great Depression in a dynamic

stochastic general equilibrium (DSGE) model showed that expanded government actual and deficit

spending caused a significant shift in consumers' expectations. He argued that "the key to the

recovery was the successful management of expectations about future policy" (Eggertsson, 2005,

p. 4). However, Sergent (1983) and Temin and Wigmore (1990) offered a counternarrative: FDR's

removal of the policy dogma to combine fiscal and monetary policies increased demand during

the Great Depression.

FDR heeded Keynes's advice in designing the New Deal policy, thus rejecting the classical

economists' orthodoxy and opening a new frontier for economists to manage shock. Keynes (1936)

refuted Friedrich Hayek's free-market theory and argued that the free market is incapable of self-

correction. Keynes (1937) further argued that without intervention, it is difficult for the market to

adjust itself in the long run during persistent contraction of AD; instead, the economy would reach

a new equilibrium characterized by slow growth, a high unemployment rate, and a recessionary

gap. Thus, the central idea of classical Keynesian theory is that direct stimulus transfers provide

liquidity that increases consumption spending. Keynes's approach has been validated in recent

literature. The evidence in Baker et al. (2020), Chetty et al. (2020), and Baqaee and Farhi (2021)

validated the classical Keynesian theory because the cash transfers to households and small

businesses increased consumption spending among low-income families, nearly restoring pre-

COVID-19 consumer spending. However, the results from Fornaro and Wolf (2020), Casado et al.

(2020), and Pollitt et al. (2020) provided incomplete evidence of cash transfers increasing DPI.

Fornaro and Wolf’s (2020) estimate of the intertemporal substitution effect of nominal interest

following the new standard Keynesian model proposed by Gali (2009) showed a demand-driven

slump and supply-demand doom loop that may last longer than the pandemic and can cause a

pessimistic animal spirit, the unwillingness of consumers to spend. These findings support the

view that the cash transfers stimulated consumer spending, alleviated the pandemic's hardship, and

stimulated the economy.

The long debate among economists on the role of government tends to draw the line at the

choice of interventionist policy to manage the economy. Adam Smith (1776) made a clear case for

limited government involvement in his thesis, "The inquiry into the causes of the wealth of

nations." He laid out the free-market principle that profit motive and competition align private

interest with the public interest. Smith (1776) brought to bear the idea that government should not

control the private sector. However, more than a century later, Adolph Wagner (1883) found a

positive correlation between fiscal spending by the government and economic growth. Hence, the

increased public demand for government intervention for regulatory and protective purposes,

particularly during shocks. Keynes (1937) argued that the free market fails to achieve the optimal

allocation of resources; hence, the government's role is to disrupt the competitive market process

to alter the distribution between individual consumers with intervention measures. Keynes’s idea

is that the government's increase in fiscal spending and the tax cuts will increase disposable

income, drive consumption, and boost private sector investment. Hayek's view, which reflects the

monetarist concern, is that fiscal stimulus has the long-term consequence of a higher inflation rate.

Keynesian theorists believe that stimulus spending increases disposable income and output,

thus causing an increased demand for money. However, Mankiw (2011) showed that the effect of

the stimulus on production and employment depends on the investment-savings (IS) and liquidity

preference-money supply (LM) curves. He explained that the IS curve, which represents

equilibrium in the goods market, and the LM curve, which represent equilibrium in the money

market, jointly determine interest rates and national income in the short run (Mankiw, 2011). Thus,

the consumption function assumes the following form:

C = C + c (Y+TR – tY) c>o (Eq.1)

where 𝐶 is the autonomous consumption, c is the marginal propensity to consume, Y is income,

𝑇𝑅 is transfer payment (stimulus), t is the tax rate, and c>o shows the direct relationship between

consumption and disposable income. Assuming that the private investment function is:

I = 𝐼 – bi b>0. (Eq.2)

where b measures the interest rate elasticity of investment, 𝐼 is the autonomous investment, which

is not dependent on the federal funds rate and consumers’ income. However, Baker et al. (2020),

Chetty et al. (2020), and Baqaee and Farhi (2021) noted that transfer payment could cause a

substantial shift in the AD curve. Hence, Cheng (2015) derived the AD curve as follows:

AD = C + I + G + NX = [ 𝐶 + c𝑇𝑅 + c (1– t) Y] + (𝐼 – bi) + 𝐺 + 𝑁𝑋

= 𝐴 + c(1-t) Y-bi (Eq.3)

where 𝐴= 𝐶 + c𝑇𝑅 + 𝐼 +𝐺 + 𝑁𝑋 represents the level of autonomous spending needed to maintain

equilibrium in the goods market. Therefore, Eq.3 can be modified to meet the requirement for the

market to clear as:

Y = AD = 𝐴 + c (1- t) Y – bi (Eq.4)

However, from Keynes's (1937) idea of government-increased fiscal spending, the IS curve can be

derived as the function of the multiplier fiscal spending produces. Thus,

i = 𝐴

𝑏 – [1−𝑐(1−𝑡)]𝑌

𝑏 = 𝐴

𝑏 – 𝑌

𝑎𝐺𝑏 (Eq.5)

where 1

𝑎𝐺𝑏 is the slope of the IS curve and 𝛼𝐺=1

1−𝑐(1−𝑡) is the multiplier of fiscal spending. This

implies that the IS curve is determined by the multiplier of fiscal spending 𝛼g and the interest

elasticity of private investment b.

Fadul's (2021) finding reemphasized the power of the government spending (G) multipliers

discussed in Blanchard and Perotti (2002) as a post-estimation transformation that helps to appraise

the ratio of response of economic activity to fiscal spending. Blanchard and Perotti (2002)

estimated the spending multiplier as the ratio of the GDP response at the time horizon k to the

initial variation of government expenditure at horizon 0, dividing it by the average share of

government spending in GDP to stimulate economic activities (Fadul, 2021). Thus, the multiplier

is derived as follows:

impact (k) = y0 1 (Eq.6)

g0 g/y

where k = 0 denotes the impact multiplier. This implies that for any 1% increase in government

spending, the GDP will rise by the percentage calculated in Eq.6. However, Montford and Uhlig

(2009) proposed a cumulative multiplier of fiscal measures to estimate the changes in GDP and g

using their discounted present values. They modified Blanchard and Perotti's (2002) multiplier as

a summation of responses in output y and the summation of the current value of the changes in g

for both time horizons t from 0 to T as follows:

cumulative (T) = Tt=0 (1 + i)-t yt 1 (Eq.7)

Tt=0 (1 + i)-t gt. g/y

The shortcomings of this measure, as noted by Gordon and Krenn (2010) and later by

Ramey (2019), are two-dimensional. First, the variation in GDP is calculated as a marginal effect

of g on y relative to the marginal impact of g on itself, which is contradictory in the absence of

innovation generated by g. Second, the equation assumes that the fiscal spending to GDP ratio

(g/y) is constant, which Ramsey (2019) argued makes the multipliers counter-cyclical compared

to the actual pattern. This study estimates the impacts of the stimulus using the Keynesian approach

and compares it to the outcome from the monetarist model to avoid this problem in measuring the

effect on GDP.

Considering GDP as a national accounting system, all the components of its equation rely

on liquidity to expand. Thus:

GDP = C + I + G + NX (Eq.8)

where C is consumption spending, I is investment spending, G is government spending, and NX is

net export, NX = (EX-IM). This implies that the GDP growth depends on the increase in these

component variables. The Keynesian theorists believe that liquidity drives AD and AS; hence,

fiscal intervention effectively stimulates the economy during shock. Regardless of the source of

liquidity, an increase in liquidity raises households' and individuals' income and stimulates

consumption spending. Gwartney et al. (2018) explained that "an increase in the supply of money

will lead to a proportionate increase in the price level" (p. 285), which means that price (P) will

drive output (Y), catalyzing higher private-sector productivity measured in the real GDP. The

multiplier of consumption spending (M) will induce a higher velocity (V) of money that places the

economy on a faster recovery path in the short run. Thus, operationalizing the quantity theory of

money to demonstrate the effectiveness of the money transmission mechanism expressed as PY =

GDP = MV in Gwartney et al. (2018) shows the relationship between monetary policy and

increased private sector spending. From the Keynesian perspective, fiscal intervention after the

CARES Act of 2020 was expected to significantly increase households’ income. Indeed, the effect

produced a significant rise in DPI and PCE, as shown in Figure 1.

However, the critical policy question remains unanswered: To what extent does fiscal

transfer stimulate demand compared to monetary expansion? Thus, this crucial question touches

on the fundamental assumption that raising household consumption spending translates into

increased economic activities, as Keynes (1936) emphasized and Baker et al. (2020) reemphasized.

The premise of these submissions provides the Keynesian stimulus model's conceptual framework

and this research's theoretical foundation. The new Keynesians have modified the classical

Keynesian construct of liquidity to show that the efficacy of transfer payments relies on the

household's MPCs that influence the consumers' behavior (Baker et al., 2020; Baqaee & Farhi,

2021). The CARES Act of 2020 was passed based on the envisioned liquidity it would provide

consumers and the multipliers arising from the spending. Parker et al. (2013) explained that the

limitations of traditional monetary policy were the rationale for the Obama Administration's

preference for fiscal policy to stabilize the economy during the 2008 Great Recession. Stilwell and

Primrose (2010) held a similar view, namely, that social spending during a severe economic shock

reestablishes investor and consumer confidence to boost AD in the short run. The theoretical

quagmire was recently raised by Andolfatto (2021) in his study of the problem of monetary-fiscal

policy coordination using three policy parameters: nominal interest rate, budget deficit, and money

growth rate. Andolfatto (2021) noted that the money growth rate determines inflation in a steady

state. Still, during shocks, the fiscal authority decides the money supply, making long-run inflation

a fiscal phenomenon (Andolfatto, 2021). For a pragmatic solution in dire situations, such as the

COVID-19 shock, policymakers often turn to relevant policy models. Keynes (1937) recognized

the link liquidity provides between demand and consumption and posited that "the transition from

a lower to a larger scale activity involves an increased demand of liquid resources" (p. 668). The

main criticism against Keynesianism is its enormous budget deficit consequences.

The pandemic challenged researchers to channel considerable efforts to identify the policy

impact of fiscal transfers on economic aggregates—specifically, the effect of government spending

during a severe shock. Opinions are still divided on whether increased government spending

during the pandemic helped boost the U.S. economy and avert significant contraction. Keynesian

theorists, including Baker et al. (2020), reemphasized Barro's (1981) position that temporary

spending affects GDP more than a permanent purchase. In contrast, Dender, O'Reilly, and Perret

(2020) contended, from a neoclassical perspective, that permanent purchases provide more

remarkable results than temporary purchases. It implies that there is a considerable distinction

between temporary and permanent changes in government spending. Hence, this study focuses on

the short-run impact of the fiscal stimulus rather than the long-run effect. However, Baqaee and

Farhi (2021) looked at fiscal spending multipliers differently. They grouped the fiscal multipliers

into local and national types using cross-sectional panel data and econometric methods to estimate

the impact (Baqaee & Farhi, 2021). The approach did not consider the possible implications of

monetary policy. This study finds it critical to evaluate the direct effects of fiscal stimulus using

multiple regression (MR) to seek a causal relationship between the aggregates of fiscal spending

and changes in GDP and compare it to the outcome of monetary expansion.

The Monetarist Model

The conceptual foundation of the monetarist model is that reducing interest rates and

reserve requirements increase the money supply and liquidity. Bordo and Rockoff (2013) argued

that "lowering interest rates is more effective in managing the aggregate demand and supply to

stimulate private-sector spending" (p. 8). The idea is that varying interest rates and money supply

help steer the economy in the desired direction. Krugman and Wells (2017) noted that the broad

definition of money comprises cash in circulation, current account balances, savings account

balances, other near monies such as travelers' checks, and certificates of deposits used in regulating

the economy. Like fiscal policy, "monetary policy can serve as contractionary and expansionary

measures in the short run" (Krugman & Wells, 2017, p. 552). Expansionary monetary policy

manages shocks such as the Coronavirus by lowering interest rates to make borrowing attractive

and expand the aggregate money supply through the open market operation (OMO). Cochran et

al. (2015) explained that the central bank could buy or sell government bonds to regulate the

economy by injecting or withdrawing money from circulation to contract or expand liquidity.

Seidman and Lewis (2015) explained that monetary policy could stimulate the economy during a

severe recession without significantly increasing the budget deficit, which implies that the

monetarist model does not affect the budget deficit; thus, stimulus-without-debt is preferable.

Hamilton and Herrera (2001) noted that OMO directly regulates liquidity because of the shorter

time lag of added liquidity, estimated at seven months. In Hamilton and Herrera's (2001) view,

"for a modest and unanticipated expansion in aggregate demand, the liquidity effect of monetary

policy dominates" (p. 7). The time lag found in Bernanke, Gentler, and Watson (1997) raised

concern about whether lowering interest rates and increasing the money supply can raise the GDP

growth rate. If it can, to what extent does it stimulate economic activities?

The policy lag found in Bernanke, Gentler, and Watson (1997) brings to bear the concern

about the effectiveness of monetary expansion in managing shocks such as the COVID-19

pandemic. Recall that during the 2008 Great Recession, Bernanke applied quantitative easing (QE)

in the trenches to maintain a steady money supply growth. On the other hand, the monetarists

argued that fiscal measures alone could not address the severe shocks sufficiently. This argument

resonates with the policy question posed by Fornaro and Wolf (2020) about what constitutes the

optimal economic policy. Moreover, such a policy concern resonates with the research questions

and reinforces the study's purpose of finding an appropriate metric for gauging the optimality of

the Keynesian and monetarist models in addressing the Coronavirus shock. In a severe crisis like

the COVID-19 pandemic, determining optimal endogenous variables such as inflation rate,

unemployment, and GDP often presents a public policy challenge, making trade-offs between

policy goals critical. For example, the CARES Act of 2020 and other relief policies prioritized

liquidity over fiscal deficit because of limited government resources and competing spending

demands. Schmitt-Grohe and Uribe (2005) considered this and proposed that interest rates and

OMO are the best tools to regulate the economy. In their words, "the central goal of optimal

monetary policy is price stability, and an optimal inflation rate of 0.5 percent with the volatility of

1.1 percent" (Schmitt-Grohe & Uribe, 2005, p. 393). Their position raised a new challenge of

policy phases the government must undergo beyond the initial response to COVID-19 to relax

measures, enact economic support recovery, and restore public finances after the pandemic

(Dender, O'Reilly, & Perret, 2020). However, if these views are correct, how can monetary

expansion maintain a stable price level and simultaneously stimulate AD without significant

changes in the DPI and PCE during the pandemic?

There have been concerns about the utilization of government monetary expansion during

shock and the potential antecedent inflation. Alpanda (2019) and McLeay and Tenreyo (2020)

noted that the Phillips curve describes the trade-off between monetary policy utilization and

inflation. Watson (2007) and Coibion and Gorodnickenko (2015) argued that the Phillips curve

had maintained a flatter trend in the short run, implying that inflation has become significantly

insensitive to standard measures of monetary policy utilization such as unemployment. In recent

studies, including that of Kan (2021), the slope of the Phillips curve and the Fed's welfare loss

function jointly determined optimal monetary policy. Therefore, it is vital to understand the slope

of the Phillips curve and the factors that cause it to flatten. Policymakers must correctly interpret

the dynamics of the Phillips curve and its application to address the COVID-19 shock to reach

policy optimality. The rationale is that shocks, particularly cost-push shocks resulting from the

pandemic, should capture the trade-off between inflation and GDP growth in policymaking.

Guerrieri et al. (2020) observed that the shock represents the labor market conditions in the

ongoing pandemic. If monetary expansion causes the inflation rate to rise, then monetary policy is

optimal in the short term. Thus, Guerrieri et al. (2020) modified the Phillips curve to:

𝜋A,t = t{

𝜋A,t + 1}+ k yt + t + t (Eq.9)

where t is the cost-push shock from the pandemic, it becomes imperative to determine the optimal

monetary policy. The optimal monetary policy optimizes the Fed's inflation rate, output gap, and

federal lending rate at different stages of the shock. Thus, the shock moves the inflation rate and

output gap in the opposite direction, representing the trade-off that faces the Fed. It implies that

the optimal inflation rate, output gap, and federal funds rate are a function of the shock t. The

shock modifies the Taylor rule as:

it = 𝜋t + yyt + u t. + (1- h)ht (Eq.10)

Taylor's rule shows that the optimal inflation rate will rise. The argument posed by Baker

et al. (2020), Baqaee and Farhi (2021), and Guerrieri et al. (2020) is that the Phillips curve would

generate a smaller rise in inflation and more loss in output. Guerrieri et al. (2020) argued that

inflation is less responsive to changes in interest rates when the slope of the Phillips curve is low,

making the monetary policy less effective. The combination of the cost-push shock and weak

personal consumption expenditure due to the COVID-19 containment measures depressed

consumers' confidence in a manner that variation in interest rate could not stimulate economic

activities enough to cause changes in the level of economic activities. Baker et al. (2020) and

Guerrieri et al. (2020) explained that the variations in the expectation of inflation have a feedback

effect on the output gap, which dampens AD.

Gravelle, Hungerford, and Labonte (2009) argued that fiscal stimulus is effective if it drives

AD. On the one hand, monetarists argue that fiscal stimulus diverts transfers from domestic

consumption spending to savings or leakages in the form of remittances to recipients abroad, which

reduces the spending multipliers. On the other hand, Jomo and Chowdhury (2020) argued that the

unprecedented nature of the Coronavirus pandemic generated uncertainties and discouraged

household spending and business investment, as apprehension compels the holding of cash savings

for future exigencies. Thus, they argued that "resources made available by the government in rich

countries were spent because of the uncertainty about the future and reduced spending options,

resulting in a situation similar to a Keynesian liquidity trap" (Jomo & Chowdhury, 2020, p. 232).

However, Fornaro and Wolf (2020) offered a counternarrative that monetary policy could only do

little because the policy rate is constrained by the zero lower bound; hence, aggressive fiscal

intervention can avert stagnation and expand AD. These divergent views and research gaps

inspired the undertaking of this study to empirically analyze the COVID-19 stimulus using the

Keynesian and monetarist models to compare their outcomes.

Related Literature

The results in recent literature, including those of Baker et al. (2020), Chetty et al. (2020),

and Casado et al. (2020), support the classical Keynesian theory. However, the full effects of the

stimulus on economic activities remain inconclusive because of the ongoing nature of the

pandemic. The authors’ evidence suggests that the automatic stabilizers, countercyclical policies,

and Ricardian equivalence are not effective short-term panacea to the COVID-19 shock. Hence,

this study empirically explores these research gaps and ascertains the link between the transfer

programs and DPI, PCE, and UR to determine if the free-market approach is a viable alternative

to the Keynesian model. A plethora of evidence of the successful application of the Keynesian

theory to manage shocks exists in the literature.

Nevertheless, it is appropriate to check if the new dataset supports the outcomes of the

ideas in the literature. In addition, the study also checks if the stimulus amount and number of

rounds are adequate to yield a similar effect to past experiences. Zacharias, Masterson, and Kim

(2008) and Gravelle, Hungerford, and Labonte (2009) discussed the rationale extensively, noting

that the Obama Administration used ARRA as a fiscal measure to address the 2008 Great

Recession, combine transfers and tax cuts to provide relief to low-income families, and bailed out

distressed firms to stimulate aggregate demand. For example, Parker et al.’s (2013) evidence

indicates that the 2008 stimulus program lasted three months and raised PCE by 2.3%, shifting the

partial equilibrium of demand for nondurable goods from $33 billion to $80 billion in the second

quarter of 2008.

Keynes's (1936) evidence shows that stimulus models rely on fiscal authorities, whereas

that of Friedman (1968, 1982) indicates that monetary expansion depends on variations in interest

rates, money supply, and debt management. Keynes (1936, 1937) posited that liquidity unleashes

the animal spirit, the propensity that drives personal consumption expenditure, thus forming the

Keynesian theory's conceptual foundation. Alternatively, Freidman reinforced Hayek's free-

market view that lowering interest rates to increase money supply will increase liquidity

(Freidman, 1982). Bordo and Rockoff (2013) found that "lowering the interest was more effective

in managing the aggregate demand and supply shock to boost private-sector demand" (p. 8). Thus,

they buttressed the monetarists' view that fiscal measures cannot provide the desired panacea for

the COVID-19 shock. For example, Fornaro and Wolf (2020) found that the monetarist model

could sustain demand and generates multipliers that reverse the supply-demand loop. A near

consensus among monetarists is that changing nominal interest variables increases higher demand

for liquidity which stimulates investment spending, sustains consumers ' expectations for future

income, and boosts consumers' confidence (Baker et al., 2020; Baqaee & Farhi, 2021; Bernanke

et al., 2005; Casado et al., 2020; Fornaro & Wolf 2020). Altig et al. (2020) contested these views

by finding that a high level of uncertainty does not bode well with monetary policy for rapid

recovery because firms and consumers are cautious and curtail investments, hiring, and spending

on durable goods.

Suppose the economic impact of COVID-19 is unequal among economies and groups, as

found in Susskind and Vines (2020), Elgin, Basbug, and Yalaman (2020), and Savalanli (2021).

In that case, it is imperative to consider factors that affect these groups before choosing a policy

approach. Fornaro and Wolf (2020) explained that keeping money cheap would attract more

investment, increase AD, and incrementally increase economic activities. They reinforced

Freidman's (1982) position on monetarist restraints to maintain fiscal discipline (Fornaro and

Wolf, 2020). The monetarist model hinges on the premise that "an increase in the supply of money

will cause a proportionate increase in the price level" (Gwartney et al., 2018, p. 285). It implies

that price (p) drives output (y), stimulating higher private-sector activity. In addition, the higher

multiplier of consumer spending (M) produces a higher velocity (V) in the monetary transmission

mechanism, thus pushing the economy to the path of fast recovery in the short run. However,

"money becomes a veil, and monetary policy is neutral in the long run" (White, 2012, p. 315;

Krugman, 1997). Thus, the monetary transmission mechanism assumes the form MV = PT.

Keynes (1936, 1937), Stilwell and Primrose (2010), and Baker et al. (2020) used various

quantitative methods to show that recession creates recessionary gaps in the short run. Eggertsson

(2011), Pedrosa and Farhi (2015), Krugman and Wells (2017), and Mankiw and Reis (2018)

empirically demonstrated that expansionary fiscal policy shifts the AD curve rightward to cover

the recessionary gap faster than the monetary policy to restore the economy to full potential.

Therefore, the Keynesian model could provide a quick growth stimulant for a depressed economy

struggling with the COVID-19 shock. This evidence holds in the recent literature. Chetty et al.

(2020) and Casado et al. (2020) found significant aggregate effects of the COVID-19 stimulus on

spending and economic activities. For example, spending increased significantly among low-

income households in May 2020 after April's first round of stimulus payments.

A careful review of various works on Keynesian and monetarist models and their

applications in managing shocks, including the COVID-19 pandemic, provides a broad and

valuable theoretical grounding for this study. In addition, the related literature offers the

framework for the research design and methods of analysis to address the research questions under

the two models. The Keynesian perspective on managing the COVID-19 pandemic is founded on

Keynes's (1936) theory that fiscal stimulus provides liquidity to stimulate demand. The Keynesian

idea is that cash transfers and the injection of money into the economy boost AD and consumer

confidence, as expected from the CARES Act. Parker et al. (2013) noted that "in the winter of

2007–2008, facing an increasingly severe financial crisis and limitations of traditional monetary

policy, Congress and the Administration turned to fiscal policy to stabilize the U.S. economy" (p.

2530). Stilwell and Primrose (2010) explained that increased spending during an economic crisis

reestablishes investor and consumer confidence and stimulates AD in the short run. Figure 3 in the

Appendix illustrates that the pandemic needed a stimulus to increase DPI and PCE to generate

higher MPCs to increase AD significantly. Baker et al. (2020) explained that the U.S. government

projected the $2 trillion CARES Act of 2020 to produce a double multiplier effect.

In contrast, the monetarist model is rooted in Freidman's theory that reducing interest rates

and reserve requirements increase the money supply and liquidity. These controversies between

the Keynesians and monetarists could be solved empirically using the finite difference method

from Chen, Liu, and Burrage (2008) to solve the boundary difference between the two models.

Mankiw and Reis (2018) and Nakamura and Steinsson (2018) suggested using the structural

shocks and their causal effects to investigate how the multipliers translate into economic activities

and identify plausible exogenous variations. This study used the relevant research questions to

measure the causal impact of the explanatory variables on the dependent variable.

Summary

The COVID-19 pandemic, a severe health crisis, continues to cause micro-level demand

and supply changes, culminating in significant changes in AD and AS. The pandemic exhibits the

combined features of both demand-side and supply-side shocks. It severely affects society's ability

to produce goods and services (Andolfatto, 2021). The pandemic disrupted the global supply chain

operations, an explicit feature of a supply shock. Barret et al. (2020) described the nature of the

COVID-19 shock as a supply shock that created a demand shock. Regardless of the theoretical

purview, there seems to be a consensus among analysts that the pandemic constrained liquidity,

weakened consumer confidence, depressed AD, and affected suppliers' productivity. Thus, it

imposed higher economic costs on vulnerable individuals, households, and firms (Baker et al.,

2020; Baqaee & Farhi, 2021; Carlsson-Szlezak, Reeves, & Swartz, 2020; Chetty et al., 2020).

Therefore, it is expedient to use appropriate policy responses to mitigate the severity of the

pandemic's economic impacts. The Keynesian idea is that fiscal spending can stimulate the

economy by raising consumer confidence. Still, the findings of O'Reilly and Perret (2020) suggest

that fiscal measures could encourage demand and incentivize behavior congruent with the desired

policy goal. The monetarists believe that varying interest rates and money supply help steer the

economy in the desired direction (Krugman & Wells, 2017). The monetarist argument is that

monetary policy can serve as a contractionary and expansionary measure in the short run. Yet the

evidence in Kan (2021) shows that the COVID-19 shock is a cost-push shock with a trade-off

between inflation and output in policymaking. The monetarists are skeptical that fiscal measures

alone can sufficiently address severe shocks. Their argument resonates with Fornaro and Wolf’s

(2020) policy question of what constitutes the optimal economic policy. This study seeks to use

appropriate metrics for gauging the optimality of the Keynesian and monetarist models for

addressing the issues raised in the research question.

CHAPTER THREE: METHODS

Overview

This study used a mixed method research design under the positivist paradigm to test the

Keynesian and monetarist models' COVID-19 response outcomes. In Tolley et al.'s (2016) words,

"positivist paradigms provide researchers with a set of unified principles and rules in conducting

research" (p. 18). Denver and Frankel (2000) described the positivist paradigm as the researcher's

rough sketch as the inquiry proceeds. Tolley et al. (2016) noted that positivists believe that reliable

knowledge comes from direct observation or manipulation of natural phenomena through

empirical or experimental means. Therefore, the positivist paradigm uses quantitative models to

introduce the principles of objectivity, explanation, verification, and prediction to analyze

observations. The quantitative analysis utilized secondary data from relevant agencies to analyze

the economic effects of the COVID-19 pandemic. A mixed method research design in a positivist

paradigm makes it possible to scientifically investigate the research questions and explore the gaps

in the literature. Mitchell and Jolley (2010) identified three critical criteria that help infer that a

variable or set of variables causes a change in another variable: "specifically, you must establish

covariation, temporal precedence and changes are not due to something other than the suspected

cause" (p. 505).

A mixed method research design satisfies these criteria by combining quantitative and

qualitative methods in a self-supporting way, as Specht (2019) described. Reinforcing Creswell's

(1999) view, Specht (2019) noted that "combining quantitative and qualitative methods in a way

in which they support each other help you reach a more concrete conclusion" (p. 138). A mixed

method design avoids the problem of obscuring the conceptual distinction between the scientific

investigation tool and the principles that determine how to deploy and interpret it. Creswell (1999)

explained that triangulation, a unique feature of the mixed method study, "uncovers some unique

variance neglected by a single method" (p. 467). Like most policy research, this study categorized

variables of interest, then collected and analyzed relevant data. In addition, this research used a

similar investigation procedure to that used by Meier, Brudney, and Bohte (2015), as modified by

Baker et al. (2020), Chetty et al. (2020), and Baqaee and Farhi (2021), to develop the research

design and operationalize the data to test the causal relationship between the dependent and

explanatory variables. As Tolley et al. (2016) and Baker et al. (2020) suggested, a mixed-method

design is valuable in combining two or more methods using triangulation and drawing a conclusion

from the synthesis of the results. The mixed techniques used by Baqaee and Farhi (2021) improved

the internal validity of the research process and findings.

Research Questions

The research questions aimed to provide insight into the stimulus model's efficacy in filling

the research gaps in the pandemic-related literature. The study investigated the outcomes of the

measures used by the U.S. government to manage the impact of the Coronavirus to ascertain if the

Keynesian stimulus or the monetarist approach delivers a more desirable result. The research

question driving this study is: Did the fiscal measures under the CARES Act and subsequent

stimulus measures produce the desired outcome? Therefore, to address this question, the sub-

questions RQ1, RQ2, RQ3, and RQ4 discussed under the Keynesian model in Chapter 1 provided

the conceptual and empirical imperative to evaluate the use of the Keynesian model to address the

Coronavirus shock. Monetarists, including Milton Freidman (1968) and Fiebiger and Lavoie

(2018), argued that reducing interest rates to increase money supply makes borrowing attractive

and stimulates consumption and investment spending. White (2012) noted that the monetarists'

idea is to "restate theoretically, apply empirically and explain the policy implications of the

quantity theory of money" (p. 320). Thus, it is imperative to empirically examine the monetarist

theory to further elucidate its argument by analyzing the sub-questions RQ1, RQ2, RQ3, and RQ4

discussed under the monetarist model in Chapter 1.

Hypotheses

The research questions were tested with a set of hypotheses to predict the relationships

between the dependent variable (percentage change in GDP) and the explanatory variables to test

the consistency of the theoretical conception with the empirical data in the two models. Creswell

(1999), Mitchell and Jolley (2010), and Meier, Brudney, and Bohte (2015) suggested the use of an

alternative or research hypothesis (Ha) to predict the relationships, while the null hypothesis (H0)

tests and indicates no effect between the dependent and explanatory variables. Therefore, the

alternative or research hypothesis contradicts the null hypothesis to predict the relationships in

each model to address the research questions. The research questions and related hypotheses for

the Keynesian model are:

RQ1: How much, if at all, does the change in DPI from direct government transfer

payments raise the GDP?

H01: There is no statistically significant relationship between DPI and the percentage

change in GDP.

Ha1: There is a statistically significant relationship between DPI and the percentage

change in GDP.

RQ2: To what extent does the increase in PCE correlate with the changes in economic

activities?

H02: There is no statistically significant relationship between PCE and the percentage

change in GDP.

Ha2: There is a statistically significant relationship between PCE and the percentage

change in GDP.

RQ3: To what extent does the change in the UR due to COVID-19 correlate with economic

activities?

H03: There is no statistically significant correlation between the UR due to COVID-

19 and the GDP percentage change.

Ha3: There is a statistically significant correlation between the UR due to COVID-

19 and the GDP percentage change.

RQ4: To what extent does the absence of government intervention lead to increased

economic activity?

H04: There are no statistically significant relationships between DPI, PCE, and UR

due to COVID-19 jointly and the percentage change in GDP.

Ha4: There is a statistically significant relationship between DPI, PCE, and UR due

to COVID-19 jointly and the percentage change in GDP.

The hypotheses for the monetarist theory attempt to empirically examine the consistency

of the expansionary monetary policy theory with data to analyze the research questions and further

investigate the monetarist model. The hypotheses for the monetarist model are:

RQ1: To what extent does the interest rate (INT) reduction raise the GDP growth rate?

H01: There is no statistically significant relationship between the INT and the

percentage change in GDP.

Ha1: There is a statistically significant relationship between the INT and the

percentage change in GDP.

RQ2: To what extent does the UR correlate with GDP change?

H02: There is no statistically significant relationship between the UR and the

percentage change in GDP.

Ha2: There is a statistically significant relationship between the UR and the

percentage change in GDP.

RQ3: To what extent does the inflation rate (IFL) correlate with GDP change?

H03: There is no statistically significant relationship between the IFL and the

percentage change in GDP.

Ha3: There is a statistically significant relationship between the IFL and the

percentage change in GDP.

RQ4: To what extent does the absence of monetary policy intervention or free-market

approach in the short run lead to increased economic activities?

H04: There is no statistically significant relationship between INT, UR, and IFL

jointly and the percentage change in GDP.

Ha4: There is a statistically significant relationship between INT, UR, and IFL

jointly and the percentage change in GDP.

Participants and Setting

Participants

Creswell (2013), Meier, Brudney, and Bohte (2015), and Tolley et al. (2016) noted that the

research design and methodology of a study determine the setting and participants in the data

collection, encoding, analysis, and interpretations. Following these procedures, this study relied

mainly on a quantitative approach to obtain reliable data with sufficient validity to test the

consistency of the theories, then employed qualitative data collection techniques, mainly

interviews, to validate the empirical results. In addition, Tolley et al. (2016) and Wilkinson et al.

(2021) explained that the involvement of other participants in mixed method research is to assess

expert views and current practices to deepen the understanding of the data through the triangulation

of results from data collected from various sources.

Similarly, the study interviewed participants, including economic and policy analysis

experts, about their views on the efficacy of the Keynesian and monetarist approach to the COVID-

19 shock. The study maintained a high level of objectivity in collecting and analyzing the data but

followed the recommendation of Creswell (2013) to integrate the phenomenological approach in

selecting the five participants. Creswell (2013) recommended that the ideal sample size in

phenomenology ranges from three to fifteen. Wilkinson et al. (2021) suggested that participants

be purposefully selected to reflect their knowledge and strategic contribution to the field. The

researcher used purposive sampling, a non-probability sampling method like that used by Baqaee

and Farhi (2021) and Wilkinson et al. (2021), to select five participants. All five participants earned

Ph.Ds. in economics but varied in terms of their years of experience, area of specialization,

location, and practice. The reason for this choice of variety was to obtain a diverse expert opinion

on the two approaches to managing the COVID-19 shock.

Three participants were interviewed in person, while the other two were interviewed

through telephone calls. They were asked questions similar to the research questions under the

Keynesian and monetarist models to obtain their objective assessment of the U.S. government's

approach to managing the economic impact of COVID-19. Detailed notes were taken of each

participant's responses to the questions, while codes were assigned to the participants to protect

their identity, as indicated in Table 1.

Table 1: Description of Participants

Pseudonym

Degree

Area of Economics

Geographic

Location

Years of

Experience

Practice

Ph.D.

Applied Economics

New Mexico

> 38

Prof. / Dir. of

Policy Research

Ph.D.

Macroeconomics

Texas

>22

Professor / City

Mayor

Ph.D.

Financial Economics

Texas

>24

Professor

Ph.D.

Development

Economics

North

Carolina

Director of City

Economic Dev.

Ph.D.

Economics and

Agricultural Policy

California

Professor / Dir.

of Ag. Policy

Setting

Expert opinion is a critical element of qualitative analysis. Following the customary

practice in mixed method studies, the researcher interviewed experts knowledgeable in the diverse

fields of economics and policy analysis. This research relied mainly on a quantitative approach to

obtain reliable data with sufficient validity to test the consistency of the theories. The involvement

of other participants is valuable and helps to compare expert views and current practices with the

deductions from the data. The study maintained a high level of objectivity in collecting and

analyzing the data but followed the recommendation of Creswell (2013) to integrate the

phenomenological approach to select the five participants. Consistent with Creswell’s (2013)

recommendation, the ideal sample size in phenomenology ranges from three to fifteen. The

participants were purposefully selected to reflect their knowledge and strategic contributions to

the field of economic policy analysis.

Instrumentation

As mixed-methods research, this study relied significantly on a quantitative approach to

analyze the critical policy concern. The study used a similar approach to Tolley et al. (2016) to

collect data on the variables of interest. In addition, this research employed the procedures of

Casado et al. (2020), Chetty et al. (2020), Fornaro and Wolf (2020), and Baqaee and Farhi (2021)

to gather time series data from the pre-pandemic period to the currently available data from

primary sources. Monthly time-series data were gathered from the BEA, the BLS, and the Fed

from July 2019 to May 2022 of the percentage changes in GDP, DPI, and PCE as well as the UR,

INT, and IFL. The percentage changes in the DPI, PCE, and UR are the explanatory variables in

the Keynesian Model.

In contrast, the monetarists model's independent variables are INT, IFL, and UR. The

percentage change in GDP is the dependent variable in both models. As in Creswell (2013) and

Meier, Brudney, and Bohte (2015), the data was collected, coded, and run using MR as a

parametric test. The interview responses of the participants selected through a purposive non-

probability sample were analyzed and compared with the empirical results under the two models.

Procedures

The study followed the critical steps suggested by Mitchell and Jolley (2010) and Creswell

(2013) to develop empirical models to test Keynesian and monetarist theories. First, the identified

variables were defined and operationalized before testing the empirical models' consistency with

the data since correlation does not imply causation, and data without theory is treacherous. Thus,

the empirical model provided the quantitative imperative to establish the link between the theory

and the data. Like the approach of Dorn (2019), Baker et al. (2020), and Fornaro and Wolf (2020),

the variables' conceptual and operational definitions helped to explain the predictability of the

change in economic activities caused by these explanatory variables. The conceptual and

operational definitions helped to establish consistency between the theory and the data.

Moreover, MR helped determine the causal relationship between the monthly percentage

change in GDP and the explanatory variables in both models. MR describes the linear relationship

between multiple predictor variables and the dependent variable to explain their causal effects on

the changes in the single dependent variable. For example, in Shine et al. (2018), fiscal variables

such as tax cuts and increased government spending significantly impacted the GDP growth rate.

In contrast, the evidence in Benzeval et al. (2020) suggests that fiscal measures had a significant

effect in 2020 compared to the pre-COVID-19 baseline. The primary regression equation (Eq.11)

is the empirical framework used to test the Keynesian and monetarist theories. The basic MR

equations took the form expressed below:

Y = c+b1*x1+ b2*x2……. + ei (Eq.11)

where Y is the estimated dependent variable, c is the intercept, b is the regression coefficient of the

predictors x (independent variables), and e is the error or stochastic term. Therefore, Eq.11

provides the standard theoretical form to fit the MR model's specification for the two theories.

Next, a normality test was carried out to ensure that the data were normally distributed and had no

outliers. No outliers were detected; hence, using any quantitative measure for corrections was

unnecessary. After completing the normality test, the Keynesian Model was modified and

augmented as:

Per

△

GPD = C + b1 *Per

△

DPI1+ b2*Per

△

PCE2 + b3* UR3 + Ei (Eq.12)

where Per

△

GPD is the percentage change in GDP, C is the intercept, per

△

DPI is the percentage

change in the DPI, Per

△

PCE is the percentage change in PCE, UR is the unemployment rate, and

Ei is the random or stochastic error.

Augmentation of the basic empirical model is necessary to avoid methodological issues

associated with policy evaluation. Taylor (2011) noted that "estimated macroeconomic models

used for policy evaluation have basic mechanism built in them" (p. 687). The reason for

augmenting Eq.11 is that empirical models differ significantly in their predictions of policy

outcomes due to different assumptions about MPC, expectations, the extent of consumption, and

the unintended effects such as the speed of price adjustment and the crowding-out impact of

government spending. For example, augmenting the historical Keynes-Hansen-Samuelson

multiplier-accelerator model by incorporating a life-cycle savings model reduced the stagnation

effect quantitatively. However, it did not negate the quantitative intuitions of Hansen and

Keynes (Samuelson, 1988). Recent literature, including research by Baker et al. (2020) and Chetty

et al. (2020), considered the conceptual idea of the Keynesian countercyclical fiscal policy that a

depressed AD caused by a decline in investment can be offset by increasing government spending

or temporary transfer stimulus. Like the Keynesian model, the alternative monetarist model was

modified and augmented as:

Per

△

GPD = C + b1 *INT1+ b2*IFL2 + b3* UR3 + Ei (Eq.13)

where INT1 is the monthly federal fund interest rate, IFL2 is the monthly inflation rate, UR3 is the

monthly unemployment rate, and Ei is the random or stochastic error.

These modified empirical models provided the quantitative framework to evaluate the

Keynesian argument that stimulus payments raise DPI and stimulate consumption to prevent a

recession or fast-track recovery. In addition, the modified models helped to examine the

monetarists' counterarguments arising from doubts about the reliability and stability of fiscal

measures when the stimulus is temporal. Furthermore, the research design provided the appropriate

methodology to test the Keynesian and monetarist hypotheses. Finally, the empirical results helped

to articulate and interpret the economic impact of the COVID-19 pandemic. The null hypothesis

for the Keynesian model was that there is no statistically significant predictive relationship

between the percentage change in GDP and the percentage changes in DPI, PCE, and UR. The

alternative hypothesis was that there is a statistically significant predictive relationship between

the percentage change in GDP and the percentage changes in DPI, PCE, and UR. The null

hypothesis for the monetarist model was that there is no statistically significant relationship

between the percentage change in GDP and INT, IFL, and UR. The alternative hypothesis was that

a statistically significant predictive relationship exists between the percentage change in GDP and

INT, IFL, and UR.

Data Analysis

Definition, Description, and Sources

This section of the study focuses on the data of the percentage change in the real GDP and

the percentage changes in DPI and PCE, UR, INT, and IFL. The theoretical foundations of the

Keynesian and monetarist models, as well as evidence in the literature, including the studies by

Baker et al. (2020), Chetty et al. (2020), and Baqaee and Farhi (2021), influenced the choice of the

six variables selected for this study. The variables are the percentage change in the real GDP,

percentage change in DPI, percentage change in PCE, the unemployment rate, the federal fund

interest rate, and the inflation rate. In addition, secondary data was collected from different primary

sources, including the BEA, the BLS, the Fed, and the Coin News U.S. inflation calculator. The

electronic retrieval of the primary data made the fieldwork associated with quantitative data

collection a reliable and straightforward process. Quantitative datasets were downloaded from the

relevant internet web pages of the primary sources. The data of the variables had a population size

(N) of 31, totaling 124 pooled data points for each model. The data collected were monthly time

series data from July 2019 to January 2022, the most recent, for the analysis of the Keynesian and

monetarist models.

Table 2: Variable, Definition, Form, Description, Units, and Sources of Data

Variable

Form

Description

Units

Source

Per△GPD

percent

The average of the

percentage change in GPD

from previous quarter

($’Billion)

BEA

Per△DPI

percent

The percentage change in

DPI from previous month.

US ($)

BEA

Per△PCE

percent

The percentage change in

PCE from previous month.

US ($)

BEA

percent

The monthly rate of

unemployment

Scale of 0 to

100 percent

BLS

INT

percent

The monthly federal funds

interest rate

Scale of 0 to

100 percent

The Federal

Reserve Bank

(Fed)

IFL

percent

The monthly inflation

rate

Percentage

The Fed and the

Coin News U.S.

inflation calculator

Table 2 provides detailed information about the variables, including the data description,

forms, the units used to measure the data, and the data sources. The study used the percentage

change in the monthly real GDP to measure economic activity changes after the first COVID-19

case was diagnosed in the U.S. and the interventions to mitigate its economic impact. The study

used percentage change in GDP as the dependent variable in both the Keynesian and monetarist

models. The explanatory variables in the Keynesian model were the percentage change in DPI, the

percentage change in PCE, and the UR. Their data exist in standard forms that explain how fiscal

measures caused economic activity changes. The independent variables for the monetarist model

are the UR, the federal fund interest rate, and the inflation rate. These datasets exist in the same

standard form and do not require transformation. The data enabled the researcher to determine the

impact of monetary expansion on economic activity.

The data for these variables were collected directly in their standard forms from reliable

primary sources, tabulated, and organized as a pooled time series dataset. The dataset was

organized correctly and visualized, as shown in Tables 3 and 4 in the Appendix and Figures 1 and

3. The data was used to measure the two policy approaches' direct effect on the Keynesian fiscal

measures and monetary expansion. Using the dataset of variables unique to the Keynesian and

monetarist models helped test both models' conceptual frameworks with actual data. For example,

Nakamura and Steinsson (2008) showed that using model-specific variables can help isolate the

effect of the outcome of one model compared to the result of the other. A similar approach was

used in Taylor (2011) and Benzeval et al. (2020) to avoid using dummy variables to explain the

behavior of two variables under different methods. The datasets appropriately measured the

outcomes of the two policy approaches and compared them to assess their relative efficacy.

Moreover, evaluating the Keynesian model's unique features made it possible to compare the

results with those of an alternative model.

Data Collection and Analysis Procedures

The study collected the monthly time series data from July 2019 to the most recently

available data in May 2022 to measure the effects of fiscal transfers and monetary policy on

economic activities during the ongoing COVID-19 pandemic. The data are presented in Tables 2

and 3 in the Appendix and visualized in Figures 1 and 2. The percentage change in

GDP (Per

△

GPD) was computed by comparing the size of GDP to the previous quarter (BEA,

2021). However, the average for the three months was taken as the monthly percentage change.

BEA used this method to measure the federal recovery program because it reports the quarterly

real GDP data. The percentage change in GDP measures the policy impact on economic activities.

Therefore, change in economic activities is measured as the percentage change in real GDP. Baker

et al. (2020) used the average quarterly reported data to avoid calibrating demand and supply

shocks. Baqaee and Farhi (2021) noted that the intertemporal and intersectoral elasticities of

substitution from the realized short-term spending from BEA statistics fit well in models of the

demand shock.

The percentage change in DPI measured the effect of the multipliers from the stimulus that

households and small businesses received. This study identified the payments under the CARES

Act and subsequent stimulus programs, including tax refunds, deposits, government income, and

credits, to measure their effect on the DPI of people and small businesses. Pollitt et al. (2020) and

Baqaee and Farhi (2021) acknowledged that the percentage change in DPI estimates the extent of

the liquidity fiscal and monetary policies provide to stimulate the economy. Therefore, the changes

were measured on a scale of 1%–100%. In addition, BEA calculated the percentage change in DPI

to identify the changes that occurred in DPI the preceding month.

The unemployment rate (UR), which measures the number of unemployed civilian workers

aged 16 years and above, was generated from the monthly data reported by the BLS. The BLS

computes national unemployment statistics from its Current Population Survey (CPS), which

surveys households monthly and uses jobless insurance benefit claims to produce monthly

unemployment data (BLS, 2021). The BLS measures the unemployment rate as the percentage

change in the unemployed workers compared to the labor force. However, the BLS considers those

actively seeking jobs as a criterion for measuring employment and excludes discouraged

workers. The UR shows the relationship between the policy interventions and employment in both

models. This study relied on the BLS employment data to avoid confounding problems and self-

selection bias. The study strictly employed the BLS data for an objective assessment.

Interest rate (INT) measured the effect of the Fed's monthly federal funds interest rate

variations on monetary expansion. The dataset was downloaded from the Federal Reserve Bank of

St. Louis website. The federal funds rate is the interest rate at which depository financial

institutions trade their balances held by the Fed (Federal Reserve Bank of St. Louis, 2021). The

federal fund's interest rate determines the rate at which banks with excess liquidity lend to other

banks that need to raise liquidity quickly. The Fed measures the interest rate on a scale of 1%–

100%. Thus, the interest rate measures monetary policy's effect on the liquidity supply for

investment and consumption spending.

The inflation rate (IFL) is a variable that gauges the increase in the general prices of goods

and services over time (The Federal Reserve Bank of St. Louis, 2021). The Fed uses price indexes

to measure price changes in a group of goods and services. The inflation rates were downloaded

from the Federal Reserve Economic Data (FRED) and the Coin News U.S. inflation calculator

websites. The Fed reports the inflation rate monthly using a well-known indicator, the CPI, which

measures the percentage change in the price of the basket of goods and services households

consume. In the monetarist model, the inflation rate measures the extent to which monetary

expansion causes a rise in the general price level.

CHAPTER FOUR: FINDINGS

Overview

The results of the MR were interpreted and used to address the research questions and test

the hypotheses for the two models. The data analysis in the Appendix suggests that the two models'

outcomes are consistent with their conceptual framework. The findings of the Keynesian model

are compatible with the results in Baker et al. (2020) and Chetty et al. (2020) but provide new

evidence that suggests a potential leakage in the transfer that could divert spending from consumer

goods and services to forced savings. The result shows that the percentage change in DPI declined

a month after the round of stimulus payments, whereas the percentage change in PCE significantly

rose a month afterward. From December 2020 to June 2021, after two rounds of stimulus transfers,

the percentage changes in DPI and PCE showed a positive correlation. In the monetarist model,

interest rate and inflation showed a stronger negative correlation after November 2020; inflation

rose significantly while the interest rate remained stable. In March 2021, the inflation rate and

change in GDP diverged and maintained that trend until December 2021, when they seemed to

begin to converge.

The empirical results of the Keynesian model are consistent with the theoretical

framework; consistency between the theory and empirical statements strengthens a policy

research's validity. Further data from July 2021 to January 2022 provided insight into the need for

proper targeting of stimulus, thus raising the concern of whether the transfer's effects on DPI have

minimal behavioral effects. The COVID-19 shock is not yet over; the economic impact is still

ongoing, new data are becoming available, and the time lag of policy reveals new outcomes. As

more data becomes available, trends from observations will provide a qualitative imperative to

complement the recent empirical evidence to provide a broad perspective to look at the efficacy of

the Keynesian model stimulus and monetary expansion from a multidimensional spectrum. The

quantitative result is expected to reinforce the qualitative evidence and support the relationships

among the variables. Therefore, analyzing the empirical results and the participants’ responses will

help to assess the efficacy of the transfers in the Keynesian model and help to evaluate the impact

of the increase in the money supply in the monetarist model to improve the study's internal validity.

Descriptive Statistics

Descriptive statistics summarize the dataset used in the estimations in both models. Meier,

Brudney, and Bohte (2015) described descriptive statistics as the numbers used to summarize a

group of data. They noted that descriptive statistics help restructure data to understand the mean

in a frequency distribution (Meier, Brudney, & Bohte, 2015). George and Mallery (2018)

explained that descriptive statistics provide information about the distributions of the variables.

The descriptive statistics of the Keynesian model in Table 3 provide detailed information about

the percentage changes in GDP, DPI, PCE, and the UR. In the monetarist model, the descriptive

statistics provide detailed information about the percentage change in GDP and the explanatory

variables, including the INT, IFL, and UR. Table 3 shows that the variables in both the Keynesian

and monetarist models have positive mean, standard deviations, and variance values. However,

the minimum values of the variables in both models were negative except for the positive

unemployment rate, implying that during economic shocks such as COVID-19, percentage

changes in GDP, DPI, and PCE experience broad fluctuations, creating a recessionary gap in the

national output. Baqaee and Farhi (2021) explained that the volatility in the values of these

variables rationalizes the need for pragmatic policy intervention to stabilize the economy.

Table 3: Descriptive Statistics of the Variables

Variables

N Stat.

Minimum

Maximum

Mean

Std. Deviation

Variance

Std. Error

The Keynesian Model

Per GDP

-10.40

11.27

.9965

4.9791

24.791

.421

Per DPI

-15.30

23.60

.5581

6.4522

41.631

.421

Per PCE

-12.90

8.50

.4455

3.4808

12.116

.421

3.50

14.80

6.042

2.8912

8.359

.421

Valid N

The Monetarist Model

Per GDP

-10.40

11.27

.9965

4.97909

24.791

.421

INT

-15.30

23.60

.5581

6.45222

41.631

.421

IFL

-12.90

8.50

.4455

3.48077

12.116

.421

3.50

14.80

6.042

2.89123

8.359

.421

Valid N

Source: Author’s Multiple Regression Estimation in IBM SPSS with data from BEA – GDP,

DPI, and PCE Outlays July 2019 – Jan. 2022. FRED, and Coin News, USA.

The coefficients in Table 3 provide a brief description and summary of the data used in the

MR estimation. Table 3 shows that the variables were approximately normally distributed.

However, the regressions' standardized residuals plots in the Appendix show that the variables'

datasets were approximately normally distributed, satisfying the normality test. A normal

distribution has a sample size of not less than 30 (Creswell, 2013; George & Mallery, 2018;

Nakamura & Steinsson, 2008).

Results

The results of the MR analysis provided the empirical evidence to test the relevant

hypotheses for the Keynesian and monetarist models. The MR results were analyzed to address

each research question. Creswell (2013), George and Mallery (2018), and Benzeval et al. (2020)

noted that the linear MR models assume (a) the independence of the residuals; (b) the normality

of the residuals; and (c) the constant variance of the residuals or homoscedasticity. These three

conditions were the main assumptions of the MR analysis in the Keynesian and monetarist models.

The Mahalanobis test helped to identify potential outliers.

In contrast, Cooks's test helped identify more substantial undue influence in the regression

model to ensure that each predictor variable contributed similarly to the predicted output to avoid

one variable from dominating the MR results. As suggested by Meier, Brudney, and Bohte (2015),

Tolley et al. (2016), and Wilkinson et al. (2021), the zero-order correlation was used to predict the

change in GDP. The zero-order correlation measures the relationship between variables from 0 to

1, with values close to 0 indicating a weak relationship, whereas values relative to 1 show a strong

relationship. It implies that the percentage change in GDP (dependent variable) is measured by the

change in the explanatory variables, including changes in DPI, PCE, and UR.

Thus, 𝑦, or fitted 𝑦, as the change in GDP was measured as changes in the predictor

variables X1, X2, X3, where X1 = DPI, X2 = PCE, and X3 = UR. Therefore,

y = X1 + X2 + X3 + Ei (Eq. 14)

Table 4 presents the empirical evidence of the variables that influenced the percentage

change in the real GDP of the U.S. during the period selected for this study.

Table 4: The Linear Multiple Regression Results

Dependent Variable: Per△GPD

Explanatory

Variables

The Keynesian Model

Explanatory

Variables

The Monetarist Model

Coef.

Std.

error

Beta

T. stat

Coef.

Std.

error

Beta

T. stat

Constant

4.36**

2.088

2.08 **

Constant

11.918**

4.987

2.39**

Per△DPI

-.029

.146

-.036

-.198

INT

-3.361**

1.655

-.533

-2.031*

Per△PCE

.104

.290

.065

.360

IFL

-.611

.594

-.254

-1.029

-.56**

.314

-.327

-1.80**

-1.223**

.476

-.710

-2.57**

N Statistics

Observations

124

R Square

.113

.229

F Stat

1.142**

2.679

Note: (1) Std. error is the standard error. (2) ***, ** and * means significant at 99%, 95% and

90% levels, respectively.

Source: Author’s Multiple Regression Estimation in IBM SPSS with data from BEA – GDP,

DPI, and PCE Outlays July 2019 – May 2022. FRED, and Coin News, USA.

Under the Keynesian model, the result provided new evidence that stimulus diverts

disposable income from consumption to forced savings. In contrast, the PCE and UR were

consistent with the fiscal stimulus theory. The result of the MR analysis of the monetarist model

was marginally insignificant. However, INT and UR were significantly correlated with the

percentage change in GDP.

The evidence presented in Table 4 indicates that the UR correlated with the percentage

change in GDP. All the predictors in the Keynesian model were unbiased and approximately

normally distributed. The coefficient of the UR was -0.563 and significant at a 95% confidence

level. However, the correlation between the percentage change in GDP and UR was negative. The

R Square as the coefficient of correlation was used because multiple independent variables in the

model jointly explain the dependent variable's percentage change. The standardized coefficients

of beta for the independent variables in Table 4 on page 65 are the percentage change in DPI (-

0.036), the percentage change in PCE (0.065), and the UR (-0.327). Creswell (2013) and Meier,

Brudney, and Bohte (2015) explained that beta is used to describe how much change in the

dependent variable is caused by the difference in each predictor variable. Therefore, the beta

coefficient of the UR is consistent with the Keynesian theory.

The MR result of the Keynesian model had an R Square value of 0.113, F statistics of

1.142, and a significant p-value (< .05). The R Square shows how multiple independent variables

in the model jointly explain the percentage change in the dependent variable. The standardized

coefficients of beta for the independent variables in Table 4 are the percentage change in DPI (-

0.036), the percentage change in PCE (0.065), and the UR (-0.327). Beta describes the change in

the dependent variable that emanates from the changes in each predictor variable. The beta

coefficient of the unemployment rate is consistent with the Keynesian theory. The empirical results

in Table 4 on page 65 indicate that the three predictive variables in the Keynesian model correlated

with the percentage change in GDP; they are unbiased and approximately normally distributed.

The UR and INT coefficients are -1.223 and -3.361, respectively. Both were significant at a 95%

confidence level. However, the correlations between them and the percentage change in GDP were

negative. The values of the Pearson correlation coefficient in Table 8 of the Appendix on page 109

are INT (0.315), IFL (0.141), and UR (0.036). The residual plots in Figure 11 through Figure 14

on pages 118 and 119 in the Appendix show a moderate correlation between INT and percentage

change in GDP. In contrast, they show weak correlations between the dependent variable and IFL

and UR, respectively. The empirical results also indicate that no variable was removed. In addition,

no multicollinearity and heteroscedasticity were present. The MR result of the monetarist model

in Table 4 on page 65 has an R Square value of 0.229, F statistics of 2.679, and a p-value > .05.

Thus, this study examined the relationship between the predictor variables and the dependent

variable in the monetarist model with their beta coefficients. The standardized coefficients of beta

for the independent variables for the monetarists model in Table 4 on page 65 are INT (-0.533),

IFL (-0.254), and UR (-0.710). The beta coefficients attribute the substantial change in the

percentage change in GDP to the decreasing UR and INT in the monetarist model. However, the

five participants' responses gathered through face-to-face and telephone interviews provided a

qualitative imperative to gauge the empirical results and help analyze the hypotheses.

Participants' Responses

The research questions were used to frame the interview questions for the participants.

Accordingly, the participants were asked 10 questions, including five in each model. The four

research questions in each model helped obtain the participants' expert opinions on the efficacy of

the policy outcomes under the Keynesian and monetarist models. The fifth question probed their

assessment of the results of the fiscal and monetary policy measures in managing the COVID-19

shock. This is a practical demonstration of triangulation to obtain expert assessments from the field

to validate the empirical result. Wilson (2014) noted that "triangulation refers to using more than

one particular approach when researching to get richer, fuller data or help to confirm the results of

the research" (p.74). Flick (2002) was specific in describing the triangulation approach. He

explained that approaching the research data with multiple theories and scholarly perspectives

helps extend the possibilities for producing knowledge (Flick, 2002).

Therefore, following the triangulation approach, the participants were asked the following

questions under the Keynesian model:

I. Do you think the changes in households' disposable personal income (DPI) and support to small

businesses from direct government transfer payments helped raise the GDP during the COVID-19

pandemic?

II. To what extent do you think increasing personal consumption expenditure (PCE) helps boost

economic activities during the COVID-19 pandemic?

III. Do you think the changes in the unemployment rate due to COVID-19 correlate with the

changes in economic activities?

IV. Do you think the absence of government intervention could increase economic activity?

V. What is your overall assessment of the government's use of fiscal intervention to address the

COVID-19 shock?

Finally, the participants were asked five questions in the monetarist model to obtain their

views and gauge whether their perspectives align with the empirical results. The questions are as

follows:

I. To what extent do you think the interest rate (INT) reduction raises the GDP growth rate?

II. Do you think the falling unemployment rate (URat) resulting from money injection and altering

the reserve requirements leads to GDP growth?

III. Do you think changes in the inflation rate (IFL) could be associated with the change in GDP

growth?

IV. To what extent could the absence of monetary policy intervention lead to increased economic

activities?

V. What is your overall assessment of the government's use of monetary policy measures to

address the COVID-19 shock?

This research articulated the participants' responses to these interview questions into a

practical qualitative measure to objectively assess the consistency of the data analysis.

Additionally, these responses helped in analyzing the null hypothesis tests.

The Null Hypotheses

The null hypotheses analyze each research question to provide solutions to the problem

under the empirical investigation. The standard practice in statistical analysis is to base the null

hypotheses test on the p-value. Aczel et al. (2018) explained that under the null hypothesis

significance testing (NHST) logic, one could reject the null hypothesis when the p-value is less

than or equal to the predefined threshold set at 0.05. This implies that the researcher is expected to

withhold judgment at any p-value above 0.05. Cheng et al. (2019) noted that a p-value is the

probability of obtaining an effect at least as extreme as the one in the sample data, which assumes

the truth of the hypothesis. However, Aczel et al. (2018) argued that "the p-value does not entitle

one to claim support in favor of the null hypothesis" (p. 257). However, Creswell (2013) and

Harrison et al. (2020) noted that the p-values are interpreted as the error rate estimate in rejecting

the null hypothesis, implying that a p-value of 0.05 indicates the type I error probability of 5%.

Harrison et al. (2020) argued that the actual error rate might range from 23 to 50% when p = 0.05

(p. 561) in isolated studies. Therefore, this study will apply the NHST to each variable tested in

the hypotheses.

Assumption Tests

The null hypotheses test assumes that three main conditions, as in Creswell (2013), George

and Mallery (2018), and Benzeval et al. (2020), are critical in testing the empirical validity of the

answers to the research questions. The assumptions of the null hypotheses in both the Keynesian

and monetarist models are, I. The independence of the residuals, II. The normality of the residuals,

III. The constant variance of the residuals or homoscedasticity.

These assumptions will be the basis of testing the hypotheses to evaluate each research

question. Some limitations of the null hypotheses identified by Harrison et al. (2020) include a

simplistic dichotomous interpretation of the p-value as either significant or insignificant; thus, the

incorrect interpretation of p > 0.05, meaning no effect, and performing multiple tests without

adjusting the criterion for the p-value. As such, this study took cognizance of these limitations

when testing the model's variables using the research questions. Hence, the null hypothesis of each

variable will be used to predict its effect on the dependent variable.

Hypotheses for the Keynesian Model

The linear MR results are subjected to an empirical test to evaluate the research question,

address the problem statement, and satisfy the purpose of this research. The factorial ANOVA

with three levels will help assess each variable's effect by calculating their student t-statistics.

Cronk (2018) explained that "factorial ANOVA is valuable because it allows us to assess the

effects of each independent variable, plus the effect of the interaction" (p. 82). The one-way

ANOVA relying on the three main assumptions stipulated above will help identify the differences

among the variables. Creswell (2013) and Cronk (2018) noted that the ANOVA is subject to

posthoc analysis to determine the nature of the differences among the variables. The null

hypotheses for the Keynesian model are as follows:

RQ1: How much, if any, does a change in disposable personal income (DPI) from direct

government transfer payments raise the GDP?

H01: There is no statistically significant relationship between disposable personal income

(DPI) and the percentage change in GDP.

Ha1: There is a statistically significant relationship between disposable personal income

(DPI) and the percentage change in GDP.

An independent-sample test and the linear multiple regression results demonstrate that the

coefficient of DPI (-.029) negatively correlated to the Per△GPD. No significant difference was

found (t (3) = -.198, p > .05). We fail to reject the null hypothesis H01: there is no statistically

significant relationship between DPI and the percentage change in GDP. Therefore, we reject the

alternative hypothesis Ha1 instead and conclude that there is no statistically significant predictive

relationship between DPI and the Per△GPD. This implies that a change in disposable income from

direct stimulus does not significantly raise the GDP.

RQ2: To what extent does the increase in personal consumption expenditure (PCE)

correlate with the changes in economic activities?

H02: There is no statistically significant relationship between consumption expenditure

(PCE) and the percentage change in GDP.

Ha2: There is a statistically significant relationship between consumption expenditure

(PCE) and the percentage change in GDP.

The results of the independent-sample test of the MR on page 65 show that the beta

coefficient of PCE (.104) positively correlates to Per△GPD. No significant difference was found

(t (3) = .360, p > .05). We fail to reject the null hypothesis (H02): no statistically significant

relationship exists between consumption expenditure (PCE) and the percentage change in GDP.

Therefore, we reject the alternative hypothesis Ha2 and conclude that there is no statistically

significant predictive relationship between PCE and the percentage change in GDP.

RQ3: To what extent does the change in the unemployment rate due to COVID-19 correlate

with economic activities?

H03: There is no statistically significant correlation between the unemployment rate (UR)

due to COVID-19 and the GDP percentage change.

Ha3: There is a statistically significant correlation between the unemployment rate (UR)

due to COVID-19 and the GDP percentage change.

The MR results on page 65 demonstrate that the UR’s beta coefficient (-.563) has a strong

negative correlation with the Per△GPD. We found a significant difference (t (3) = -1.796, p < .05).

Therefore, we reject the null hypothesis (H03) and accept the alternative hypothesis (Ha3),

concluding that there is a statistically significant correlation between the UR due to COVID-19

with the Per△GPD. Hence, this implies that a 0.56 % decrease in UR leads to a 1% increase in the

GDP holding other variables constant.

RQ4: To what extent does the absence of government intervention lead to increased

economic activity?

H04: There are no statistically significant relationships between DPI, PCE, and UR due to

COVID-19 and the percentage of change in GDP.

Ha4: There is a statistically significant relationship between DPI, PCE, and UR due to

COVID-19 and the percentage of change in GDP.

This hypothesis tests the overall significance of the Keynesian model. The MR model was

statistically significant and predicted the percentage change in real GDP significantly (F (3,27) =

1.14, p < .05). Moreover, the model has a mean score of .9965 (sd = 2.09) and an R square of .113.

Therefore, we reject the null hypothesis H04: there is no statistically significant predictive

relationship between changes in real GDP and changes in DPI, PCE, and UR. We accept the

alternative hypothesis Ha4: a statistically significant relationship exists between DPI, PCE, and

UR due to COVID-19 and the percentage change in GDP. Hence, an 11.30% variation in real GDP

is attributed to changes in the DPI, PCE, and UR. Ultimately, the hypothesis test addresses the

fundamental research question and confirms that the Keynesian stimulus policy helps to replace

the loss of income, reduces unemployment, significantly impacts economic activities, and

alleviates the hardship of the COVID-19 shock.

Hypotheses for the Monetarist Model

Regarding the monetarist model, the hypotheses examined the consistency of the

expansionary monetary policy with data to analyze the research questions and further investigate

the model. The three main assumptions used in testing the null hypotheses in the Keynesian model

apply to the monetarist model. The hypotheses for the monetarist model are as follows:

RQ1: To what extent does the interest rate (INT) reduction raise the GDP growth rate?

H01: There is no statistically significant relationship between the interest rate (INT) and

the percentage change in GDP.

Ha1: There is a statistically significant relationship between the interest rate (INT) and the

percentage change in GDP.

An independent-sample test was conducted. Table 4 on page 65 shows that the standardized

coefficient of INT (-.533) is negative and moderately correlated with Per△GPD. No significant

difference was found (t (3) = -2.031, p = .05). We fail to reject the null hypothesis H01: no

statistically significant relationship exists between INT and the percentage change in GDP.

Therefore, we reject the alternative hypothesis Ha1 and conclude that there is no statistically

significant predictive relationship between the INT and the percentage change in GDP, implying

that varying INT and money supply do not significantly raise the GDP during the pandemic.

RQ2: To what extent does the unemployment rate (UR) resulting from money injection and

altering the reserve requirements correlate with the change in GDP?

H02: There is no statistically significant relationship between the unemployment rate (UR)

and the percentage change in GDP.

Ha2: There is a statistically significant relationship between the unemployment rate (URat)

and the percentage change in GDP.

The MR results in Table 4 on page 65 show that the standardized beta coefficient of the

UR (-.710) has a strong negative correlation with the Per△GPD. A significant difference was

found (t (3) = -2.570, p < .05). We reject the null hypothesis H02: no statistically significant

relationship exists between the UR and the percentage change in GDP. Therefore, we accept the

alternative hypothesis (Ha2) and conclude that there is a statistically significant relationship

between the UR and the percentage change in GDP. Hence, a 0.71% decrease in the UR leads to a

1% increase in the GDP holding other variables constant.

RQ3: To what extent does the inflation rate (IFL) correlate with GDP change?

H03: There is no statistically significant relationship between the inflation rate (IFL) and

the percentage change in GDP.

Ha3: There is a statistically significant relationship between the inflation rate (IFL) and

the percentage change in GDP.

The independent-sample test of the MR shows that the beta coefficient of IFL (-.254) is

negative and has a weak correlation with the Per△GPD. No significant difference was found (t (3)

= -1.029, p >.05). We fail to reject the null hypothesis H03: No statistically significant relationship

exists between the IFL and the percentage change in GDP. Thus, variation in the INT and money

supply has an insignificant impact on GDP during the pandemic.

RQ4: To what extent does the absence of monetary policy intervention lead to increased

economic activities?

H04: There is no statistically significant relationship between INT, URat, and IFL jointly

and the percentage change in GDP.

Ha4: There is a statistically significant relationship between INT, URat, and IFL jointly

and the percentage change in GDP.

This hypothesis tests the overall significance of the monetarist model to empirically ascertain

its efficacy during economic shocks such as COVID-19. The comprehensive MR model was not

statistically significant and could not predict the percentage change in real GDP significantly (F

(3,27) = 2.679, p > .05). The model has a mean score of .9965 (sd = 4.98) and an R square of .229.

Therefore, we accept the null hypothesis H04: there is no statistically significant relationship

between INT, UR, and IFL jointly and the percentage change in GDP. Instead, we reject the

alternative hypothesis Ha4: there is a statistically significant relationship between INT, UR, and

IFL jointly and the percentage change in GDP. Overall, the empirical evidence suggests that the

monetary policy alone is ineffective in managing severe shocks such as COVID-19. Ultimately,

the hypothesis test addresses the fundamental research question about the absence of monetary

intervention and confirms that varying interest rates and money supply during severe shocks affect

changes in the GDP but do not significantly impact economic activities or alleviate hardship.

Analysis of Participants' Responses

As a mixed-method research design study, this analysis addresses a critical policy issue

and gauges the empirical results using expert opinions. Flick (2002) and Wilson (2014) discussed

the rationale for this investigative approach. Triangulation aims to obtain an overall specialist

opinion on the issue under investigation using multiple methods to validate empirical results with

qualitative information to advanced knowledge. Additionally, the analyses of the participant's

responses to the interview questions will provide the qualitative imperative to analyze the research

questions in the two models.

The Keynesian Model

The study used the following questions in the Keynesian model to obtain an objective

policy assessment of fiscal measures employed by the government to address the economic impact

caused by the COVID-19 pandemic. A concise response from each participant was articulated into

a summary to address each interview question.

Question 1: Do you think the changes in households' disposable personal income (DPI)

and support to small businesses from direct government transfer payments help raise the GDP

during the COVID-19 pandemic?

The participants' responses were as follows:

R1: No, only if the households spent the increase in DPI on consumption expenditure, but support

for small businesses tends to preserve a substantial number of jobs and help reduce the

unemployment rate.

R2: Households' disposable income and thriving small businesses are the engines that run the U.S.

economy. An increase in DPI of households and support for small businesses help sustain

consumer confidence and help to keep the local and national economies virile to prevent another

severe recession.

R3: No, a misdirected stimulus will not produce a significant multiplier from increased DPI that

could impact economic activities positively. Instead, it is a trade-off between inflation and

increased liquidity in the short run; it is a zero-sum game in the long run.

R4: Yes, the multiplier effect from increased DPI and the Paycheck Protection Program (PPP) has

helped stabilize the economy from the COVID-19 shock.

R5: Yes, it will help address economic hardship, particularly among low-income households and

struggling small businesses. It will help sustain agricultural production, address increasing rural

poverty from the pandemic, and prevent small businesses from shutting down.

Three participants believed that the changes in the DPI of households and support to small

businesses from direct government transfer payments help to raise the GDP during severe shocks

such as the COVID-19 pandemic. Two participants disagreed with the government's policy of

achieving a high DPI. Their rationale resonates with the policy question in Fornaro and Wolf

(2020) of what constitutes the optimal economic policy. Hence, optimal interventions should target

transfers appropriately to achieve the desired policy outcome. Their responses reinforce O'Reilly

and Perret's (2020) view that fiscal measures could stimulate demand, but their implementation

incentivizes inconsistent behavior with the desired public health goals. This view validates the

outcome of the hypotheses test for RQ1 under the Keynesian model. However, the other three

participants' views align with Keynes's (1936) original idea that stimulus spending increases

disposable income and output, thus causing an increased demand for money. However, the efficacy

of transfer payments relies on the household's MPCs that influence consumer behavior (Baker et

al., 2020; Baqaee & Farhi, 2021).

Question 2: To what extent do you think increased personal consumption expenditure

(PCE) helps boost economic activities during the COVID-19 pandemic?

The participants' responses were as follows:

R1: The multiplier from PCE stimulates aggregate demand to a great extent, which could preserve

or create new jobs. An increase in PCE unleashes the animal spirit, as economists would think.

R2: Chris, the importance of high PCE is the multipliers it produces. Increased consumer spending

will result in more jobs if the MPC is high enough, but when forced savings or other leakages

constrain PCE, it undermines PCE and national output.

R3: In theory, I would agree with the view that high PCE would induce economic activities, but

the dilemma is real. If households spend on durable goods and settlement of old debts, it will divert

PCE from small businesses to big corporations and from current expenditure to past debt.

R4: One of the programs the city council adopted to alleviate the economic impact of the pandemic

is to assist very vulnerable low-income households. The argument was presented to the board to

complement the federal stimulus to raise the PCE of these families because consumer spending is

critical for the survival of the local economy.

R5: Yes, the rationale is not unfounded. Going back to ten economic principles, the circular income

flow model is explicit about PCE. A slump in PCE would exacerbate the declining economic

activities, but high PCE would boost the economy or prevent a potential recession.

The participants were sure that increased personal consumption helped boost economic

activities during the COVID-19 pandemic. However, one participant doubted that increasing PCE

could facilitate economic activities enough to restore macroeconomic stability without

complementing it with other market-driven economic approaches. This view questions Keynes's

(1936) assumption that consumption spending translates into increased economic activities.

However, most of the opinions aligned with the theoretical foundation of the Keynesian theory.

The idea in the literature, including Cheng (2015), Barker et al. (2020), Chetty et al. (2020), and

Baqaee and Farhi (2021), is that multiplier of consumption spending (M) will induce a higher

velocity (V) of money, which pushes the economy on a faster recovery path in the short run.

Overall, the empirical evidence shows a positive correlation between changes in PCE and GDP;

however, the correlation was weak.

Question 3: Do you think the changes in the unemployment rate due to COVID-19

correlate with economic activities?

The participants' responses were as follows:

R1: Certainly, I agree with that notion. The essence of expansionary economic policy is managing

business cycle changes. When the economy is hit with a severe shock, such as the current

pandemic, the automatic stabilizers become ineffective; policy intervention becomes inevitable to

close the output gap and restore the economy to equilibrium. Such policy intervention aims to

achieve full employment to keep the economy at its potential.

R2: Yes, the stimulus business is about job creation and protection. Unemployment declines when

jobs are protected or created, and more labor hours are devoted to economic productivity.

R3: I agree with this notion altogether. Yes, the unemployment rate is strongly related to changes

in economic activities.

R4: As a city economic development director, my principal duty is to promote job creation and

attract businesses. Employment is the key to economic growth. So, yes, the unemployment rate

correlates with economic activities.

R5: This question touches on one of the essential objectives of rural agricultural and urban

economic development policy. Income guarantees an average worker a wage to meet the cost of

living. Employment is the means to provide income for nearly 90% of the labor force. Therefore,

any policy that reduces unemployment contributes to productivity.

The participants' opinions reinforced the Keynesian goal of boosting economic activities

with stimulus to reduce the unemployment gap and stabilize the economy. They believe that the

interventions reduced unemployment and preserved many jobs; hence, the unemployment rate

correlates strongly with economic activities. These views validate Keynes's (1936) idea that

stimulus is necessary to reinvigorate economic activities, restore consumers' confidence, and close

the output gap to bring employment to its full potential. Eggerston (2005) evaluated FDR's fiscal

policy approach to the Great Depression and found that the key to the recovery was the successful

management of expectations, in which employment played a pivotal role. The qualitative evidence

was consistent with both theoretical and empirical evidence. In the null hypothesis for RQ3 under

the Keynesian model, unemployment robustly correlated with economic activities. However, the

statistical predictive relationship was significant in the hypotheses test; thus, the theory, empirical

results, and qualitative evidence reinforce each other.

Question 4: Can government intervention's absence increase economic activity?

The participants' responses were as follows:

R1: Although market solutions often generate efficient outcomes, in severe shocks such as this

pandemic, contemplating not doing anything will be disastrous. One option not to consider is the

absence of government intervention. We are in a different era from the classical economists.

R2: It is not an option to consider. You know what John Maynard Keynes said: we are all dead in

the long run.

R3: In this situation, the government's intervention with monetary and fiscal policy is inevitable.

If a government fails to correct market failure, we have government failure on our hands. No

government or citizens would want that. Indeed, doing nothing will not lead to an increase in

economic activities.

R4: No responsible parent can sit unperturbed to watch their child cry for help without doing

anything. Responsible governments are like that. Indeed, this is not an option; a lack of policy

intervention will get nothing done.

R5: No, the government's silence will cause the economy to degenerate into a depression. It could

reverse centuries of economic progress and retards productivity.

These responses qualitatively evaluated the overall Keynesian model. No view was

deferred from the theoretical conceptualization of stimulus and the empirical evidence. Keynes

(1937), Stilwell and Primrose (2010), and Baker et al. (2020) demonstrated that recession creates

recessionary gaps in the short run; hence, the primary reaction to shocks is to use policy

interventions to stabilize the economy. Krugman and Wells (2017) explained that expansionary

fiscal policy shifts the AD curve rightward to cover the recessionary gap faster than the monetary

policy to restore the economy to its full potential. Their views validated that Keynes's intervention

policy provides a quick growth stimulant for a depressed economy. The participants' views

validated the data analysis and theoretical results. The MR results of the Keynesian model found

statistically significant evidence. They predicted the percentage change in real GDP significantly

(F (3,27) = 1.14, p < .05). The qualitative and quantitative evidence suggests that fiscal intervention

produced a significant change in economic activities, validating the usefulness of policy

interventions. For example, the SBA-backed PPP loans helped small businesses manage their

payrolls, preserve jobs, and contribute to GDP growth.

Question 5: What is your overall assessment of the government's use of fiscal intervention

to address the COVID-19 shock?

The participants' responses were as follows:

R1: Fiscal interventions were necessary, but the government hesitated slightly, causing significant

policy lag.

R2: Fiscal intervention was remarkably effective in supporting small businesses. The problem is

the politicization, particularly the divides in Congress on political and ideological lines. However,

apart from the legislative appropriation process, handling the intervention, including economic and

public policy measures, was decently effective.

R3: I support policy interventions, but how and when the policies were implemented was

worrisome. I am not critical of government intervention; such policies' effectiveness lies in

boosting consumer spending among the most vulnerable and promoting private sector investment.

However, the interventions should target the most vulnerable. The stimulus should be directed to

households and small businesses who desperately need the money and are willing to spend it, not

families and big companies that do not need it. Recall that each round of stimulus has a price tag,

which the taxpayers will pay later.

R4: The government took appropriate policy action, but the spending came late, and the rounds

were unsustainable. The first round came in March 2020, and it took about nine months for the

second one. So, the delay created a lag in policy outcome.

R5: Economists have a disdain for government intervention. You should agree that those guys in

the Congressional Budget Office (CBO) and Council of Economic Advisers (CEA) certainly know

their jobs, but the only impediment was political.

The overall assessment of the participants is that government intervention was necessary,

but some participants deferred on the timing and methods of the policy implementation. All the

participants' views were consistent with Keynes's view that it is difficult for the market to correct

itself in the long run without intervention. During persistent aggregate demand contraction, the

economy would reach a new equilibrium with slow growth, a high UR, and a recessionary gap

(Keynes, 1937). However, all the participants admitted a need for fiscal intervention but expressed

concern that misdirected fiscal intervention could produce counterproductive outcomes. The

unsettled argument remains on what constitutes optimal stimulus and how it can generate optimal

results if the impact of the pandemic differs across households and small businesses. Susskind and

Vines (2020), Elgin, Basbug and Yalaman (2020), and Savalanli (2021) explained that the impact

of COVID-19 differs across households due to some factors such as household income,

dependency, and chronic illnesses that require a substantial part of the families' resources to

manage. It is imperative to consider these factors before choosing a policy approach. Therefore, a

targeted intervention is likely to generate a better policy outcome.

The Monetarist Model

In this study, the participants were asked five questions in the monetarist model to obtain

their expert opinions about using monetary expansion to manage the COVID-19 shock. As Wilson

(2014) noted, the essence of the interview is to use the participants' responses to help gauge the

consistency of the theory and data results. The interview responses from the participants will be

used to determine whether the monetary policy approach to managing the shock aligns with the

empirical results. Accordingly, this study asked the participants five questions to obtain an

objective policy assessment of the monetary policy measures used to respond to the economic

impact of the COVID-19 pandemic. The questions were as follows:

Question 1: To what extent do you think the interest rate (INT) reduction raises the GDP

growth rate?

The participants' responses were as follows:

R1: I think the Fed's lowering of the federal funds rate between 0% to 0.25% helped sustain credit

flow to curb the pandemic's impact.

R2: It helped small businesses to access credit and helped the labor market rebound.

R3: Monetary policy intervention was critical in the recovery effort to a great extent, using

quantitative easing (QE) to support the functioning of the financial market.

R4: It helped the labor market tremendously and helped to reduce job loss.

R5: It helped small businesses, particularly rural farmers with limited access to credit, secure loans

at lower rates. The reduction of interest rates helped most farmers to keep their farms.

All the participants expressed similar views that reducing the interest rates helped limit job

losses and enabled small businesses to access credit to sustain or expand their operations. These

views are consistent with the monetary expansion theory and align with the results of the data

analyses. For instance, Bordo and Rockoff (2013) found that reducing interest rates is more

effective in stimulating private-sector spending. Furthermore, the independent-sample test in the

monetarist model in Table 4 on page 65 indicates that INT has a standardized coefficient of -.533

that correlated with Per△GPD moderately.

Question 2: Do you think the falling unemployment rate (UR) leads to GDP growth?

The participants' responses were as follows:

R1: Yes, the falling unemployment rate contributes significantly to GDP growth. You can see

from the Small Business Administration (SBA) PPP loan that many small businesses were able to

keep their workers.

R2: During shocks, any economic policy aims to create jobs and prevent income loss from

unemployment.

R3: The essence of the Fed's relaxing regulatory requirements, promoting direct lending, and

supporting loans to small and medium-sized businesses is to reduce the unemployment rate

because it is negatively and directly related to the GDP growth rate.

R4: The mantra during the recession is jobs and more jobs. Creating new jobs and protecting the

existing ones reduces the unemployment rate and increases productivity.

R5: Yes, reducing unemployment increases consumers' liquidity and causes a significant shift in

aggregate demand. You know, aggregate demand can be used to estimate national output.

All the participants agreed that the falling UR resulting from monetary expansion

contributes to GDP growth. Guerrieri et al. (2020) found that the COVID-19 shock represents the

labor market conditions. It implies that falling unemployment restores national output to its full

potential. This view is consistent with empirical results. The MR results show that the UR strongly

correlates negatively with the Per△GPD. The participants' responses reinforce the theoretical

framework and empirical findings. Policymakers expect intervention programs to reduce the UR,

implying that a reduction in unemployment must increase the GDP.

Question 3: Do you think changes in the inflation rate (IFL) could be associated with the

change in GDP growth?

The participants' responses were as follows:

R1: One of the goals of monetary expansion during shocks is to raise the inflation rate in the short

term. Inflation can rise till the labor market attains full or maximum employment.

R2: Changes in the inflation rates are consistent with the monetary policy's desire to keep liquidity

flowing during this pandemic. That is not a problem during shocks because the Fed targets it.

R3: Of course, inflation and the unemployment rate have trade-offs. If you want to achieve growth

in GDP and a substantial decrease in the unemployment rate, you must stop worrying about the

inflation rate.

R4: No, in a situation like the COVID-19 pandemic, the focus should be on economic growth, not

inflation.

R5: Yes, a temporary rise in inflation is critical in sustaining aggregate demand and consumers'

confidence.

Four participants agreed that the IFL is associated with changes in GDP growth, whereas

one participant differed. However, Alpanda (2019) and McLeay and Tenreyo (2020) found that

the Phillips curve describes the trade-off between monetary policy utilization and inflation. The

qualitative evidence was consistent with the empirical findings. The regression results demonstrate

that inflation has a negative and weak correlation with Per△GPD, implying that variation in the

INT and money supply has an insignificant impact on GDP during economic shock.

Question 4: To what extent do you think the absence of monetary policy intervention could

lead to increased economic activities?

The participants' responses were as follows:

R1: As I said earlier, it will have no positive effect except if an alternative or fiscal policy is used.

R2: It will worsen the economic conditions and slow down growth.

R3: As much as the government has its preferred policy choice, a no policy cannot proffer any

solution. It is challenging for the market to adjust itself, as Keynes said during the Great

Depression.

R4: A sick person needs medicine to survive during illness. A sick person that fails to seek

treatment may die. The shock could be protracted if monetary policy is not applied.

R5: It is not a choice for the government in the U.S.

All participants disagreed that the absence of monetary policy intervention could lead to

increased economic activities, implying that reliance on the free money market and automatic

stabilizers will not address the shock. If doing nothing is an option, the debate of what constitutes

an optimal policy would not arise among policymakers. Fornaro and Wolf (2020) raised the policy

question of what constitutes the optimal economic policy. However, the evidence obtained from

the interview supports the theoretical framework. It is a standard monetary practice to close the

recessionary gap by increasing the money supply.

Question 5: What is your overall assessment of the government's use of monetary policy

measures to address the COVID-19 shock?

R1: Keeping money cheap would be the post-pandemic inflation effect. In the short run, the

optimal inflation rate is suitable for economic growth but is a hydra-headed monster to tame in the

long run. I think the Fed has done well, but it must continue to target the optimal inflation rate.

R2: Reopening the service-based small businesses was critical to the national economy. The Fed

helped stabilize the short-term funding markets, keeping the economy running again. The only

downside of monetary expansion is that many small businesses could still not access the funds,

particularly in rural areas.

R3: The monetary policy measure did not cost the taxpayers anything; it did not result in a fiscal

deficit, unlike the fiscal policy. The Fed was excellent in safeguarding the market functioning with

the open market operation (OMO). Additionally, the Fed took supervisory and regulatory actions

to encourage banks to dip into their capital and liquidity buffers to extend credit to borrowers

affected by COVID-19. It provided relief to many low-income households.

R4: Monetary policy was necessary for revitalizing the private sector. The only concern I have

about monetary policy is that it does not directly impact poor households.

R5: I am satisfied with the robustness of the monetary policy approach, particularly the agricultural

credit extended to the farmers through the USDA Assistant Program.

All participants expressed substantial satisfaction with the Fed's handling of the monetary

policy measures. They admitted that the Fed could use monetary expansion to keep a steady flow

of liquidity. A particular participant expressed concern that monetary policy does not directly

impact low-income families. All the participants admitted there was a trade-off between monetary

policy and inflation. They implied that inflation during economic shocks does not harm economic

growth in the short run. However, Alpanda (2019) and McLeay and Tenreyo (2020) admitted this

view in their description of the Phillips curve as the trade-off between monetary policy utilization

and inflation. For instance, Watson (2007) and Coibion and Gorodnickenko (2015) found that the

Phillips curve had maintained a flatter trend, implying that inflation has become increasingly

insensitive to standard measures of monetary policy utilization, such as unemployment. The

participants' views were consistent with the monetarist's theory; however, the reality is that since

the last quarter of 2021, the IFL has consistently maintained an upward trajectory.

CHAPTER FIVE: CONCLUSIONS

Overview

The Keynesian and monetarist theories aim to stimulate economic activities during severe

shocks such as COVID-19 despite their different methods. The Keynesian model depends on the

multipliers from fiscal spending to boost consumers' demand and cause a rise in aggregate demand.

In contrast, the monetarist model relies on the changes in monetary variables, including INT,

reserve requirements, and money supply, to provide liquidity to induce investment spending and

employment. Thus, both models support the interventionist policy to manage the COVID-19

shock. The analysis of the research questions and the hypotheses tests provided empirical evidence

to evaluate the efficacy of each model.

Furthermore, the expert views obtained through the interviews with the participants served

as the qualitative measure to validate the theoretical and empirical evidence. In the Keynesian

model, empirical evidence suggests that DPI, PCE, and UR jointly and significantly predicted the

percentage change in GDP. Unfortunately, INT, IFL, and UR did not jointly and significantly

predict the percentage change in GDP. However, a strong correlation was found between the UR

and the percentage change in GDP and between inflation and the percentage change in GDP. The

details of the empirical findings are analyzed extensively in the discussion.

Discussion

This research aims to examine the direct stimulus payments under the Keynesian model

and the variations in the INT and money supply in the monetarist models to determine their

efficacy in stimulating economic activities during the COVID-19 pandemic. The study evaluated

the outcome of the policy interventions to manage the COVID-19 shock under the two models.

The study modified the basic empirical equations and augmented them to assess the empirical

statements under the two models to identify causal relationships between the dependent and

explanatory variables. The study modified the basic regression model in Eq. 11 into the empirical

models in Eq. 12 and Eq. 13 to predict the percentage change in GDP following the method in

Creswell (2013) and Baker et al. (2020). The study uses the empirical evidence obtained from the

linear multiple regression results to test the hypotheses as an imperative to evaluate the research

questions in each model.

The Keynesian Model

RQ1: How much, if any, does the change in disposable personal income (DPI) from direct

government transfer payments raise the GDP?

The null hypotheses provide a practical solution to the research questions in the Keynesian

model. The null hypothesis for RQ1 suggests no statistically significant relationship between DPI

and the percentage change in GDP (Per△GPD), implying that a change in disposable income from

direct stimulus does not significantly raise the GDP. This finding was consistent with results from

Chetty et al. (2020) and O'Reilly and Perret (2020). Baker et al. (2020) admitted that an increase

in DPI from transfers diverts income to forced savings. O'Reilly and Perret (2020) found that fiscal

measures could stimulate demand; however, their implementation incentivizes behavior

inconsistent with the desired public health goals. The expert opinion in the qualitative assessment

validates this finding. However, the efficacy of transfer payments relies on the households' MPCs

that influence consumer behavior (Baker et al., 2020; Baqaee & Farhi, 2021).

RQ2: To what extent does the increase in personal consumption expenditure (PCE)

correlate with the changes in economic activities?

The null hypotheses test for RQ2 was insignificant. The study did not find any statistically

significant predictive relationship between PCE and the percentage change in GDP. This finding

contradicts the evidence reported by Barker et al. (2020) and Chetty et al. (2020); however, the

linear MR result indicates a weak positive correlation between changes in PCE and GDP. This

result contradicts Keynes's (1936) assumption that consumption spending translates into increased

economic activities. Cheng (2015), Barker et al. (2020), Chetty et al. (2020), and Baqaee and Farhi

(2021) ascertained that multiplier of consumption spending (M) would induce a higher velocity

(V) of money, which pushes the economy on a faster recovery path in the short run. However, if

the rounds of transfers are not enough, achieving the desired multiplier from consumption

spending would not be easy. The finding in this study reveals new evidence that an inadequate

consumption multiplier cannot induce a higher velocity of money.

RQ3: To what extent do the change in the UR due to COVID-19 correlate with economic

activities?

The null hypothesis test for RQ3 under the Keynesian model failed and was rejected; hence

the alternative hypothesis was accepted. The null hypothesis evaluated the correlation between the

UR and the change in GDP. The empirical evidence demonstrates a statistically significant

correlation between the UR and Per△GPD, indicating that a 0.56% decrease in unemployment

leads to a 1% increase in the GDP holding other variables constant. This result is consistent with

those found in Eggerston (2005), Barker et al. (2020), Chetty et al. (2020), and Baqaee and Farhi

(2021). Furthermore, the finding concerning the Keynesian model is consistent with Keynes's

(1936) idea that stimulus is necessary to induce economic activities, restore consumers'

confidence, and close the output gap to bring employment to its full potential. The qualitative

evidence overwhelmingly validates this evidence.

RQ4: To what extent does the absence of government intervention lead to increased

economic activity?

The null hypotheses test the overall significance of the Keynesian model. The empirical

evidence indicates a causal relationship that jointly and significantly predicts the percentage

change in real GDP. The null hypothesis was rejected because DPI, PCE, and UR jointly and

significantly predicted the percentage change in GDP. The empirical evidence suggests that an

11.30% variation in real GPD is attributed to the changes in DPI, PCE, and UR. The result is

consistent with findings from Stilwell and Primrose (2010) and Baker et al. (2020), which showed

that recession creates recessionary gaps in the short run, suggesting pragmatic reactions to shocks.

Keynes (1937) recommended using policy interventions to stabilize the economy. The qualitative

evidence validates both the theoretical and empirical findings. A more structured equation could

help to understand how factors relate to each other.

The Monetarist Model

RQ1: To what extent does the interest rate (INT) reduction induce growth in the GDP?

Some of the evidence in the monetarist model was consistent with the traditional monetarist

theory. Unfortunately, the findings for the hypothesis for RQ1 were not significant, implying that

INT is not a good predictor of the percentage change in GDP. This evidence contradicts the

conclusion of Bordo and Rockoff (2013) and the central theme of Freidman's (1982) monetary

expansion. The monetarists expect that INT will display a weak response to inflation without

imposing restrictions on the impact of monetary expansion on the output gap. Castelnuovo and

Surico (2010) observed that the New-Keynesian models impose restrictions within the monetary

policy that have a non-negative effect on INT. This study did not restrict economic expansion as

Castelnuovo and Surico (2010) did because of the severe nature of the investigated policy issue.

Imposing a contemporaneous zero restriction amplifies the inflation impact and makes it optimistic

(Castelnuovo & Surico, 2010). Gwartney et al. (2018) explained that the central tenet of monetary

expansion is that an increase in money supply will cause a proportionate increase in the price level

(p. 285), implying that varying INT and money supply does not significantly raise the GDP during

the pandemic. This finding is inconsistent with the monetary expansion theory and the qualitative

evidence from the participants' interviews because the current inflation trend defies the traditional

monetary theory.

RQ2: To what extent does the UR correlate with the change in GDP?

There is a strong negative correlation between the UR and percentage change in GDP, but

the monetarists model has a p-value > 0.05; thus, the hypotheses test for RQ2 fails. The result of

the monetarists model indicates that a 1.22% decrease in unemployment leads to a 1% increase in

the GDP holding other variables constant. This implies that a significant decline in unemployment

under the monetarists model is needed to realize the equivalent change in GDP in the Keynesian

model. The beta coefficient of 0.71 suggests a strong correlation between the UR and the

percentage change in GDP. The result of the data analysis was consistent with the original fiscal

theory of Keynes (1937) and recent studies, including Seidman and Lewis (2015), Guerrieri et al.

(2020), and Fornaro and Wolf (2020). The qualitative evidence validates the theoretical and

empirical evidence that the decreasing UR resulting from monetary expansion contributes to GDP

growth. However, a significant decrease in unemployment in the monetarist model is required

compared with the Keynesian model. For instance, Guerrieri et al. (2020) reported evidence that

the COVID-19 shock represents the labor market conditions, implying that decreasing

unemployment helps stabilize the national output to its full potential. Additionally, the results of

the linear MR indicate that the UR has a strong negative correlation with the Per△GPD. A general

expectation among monetarists and Keynesians alike is that low unemployment leads to a greater

GDP. The qualitative evidence validates both the theoretical framework and empirical findings.

RQ3: To what extent does the IFL correlate with the change in GDP?

The results indicate no statistically significant relationship between the IFL and the

percentage change in GDP, meaning that variation in the IFL and money supply has an

insignificant impact on GDP during the pandemic. The qualitative evidence differed from

empirical findings and aligned with the theory that the IFL is associated with changes in GDP

growth. The empirical results contradict the conclusions of Alpanda (2019) and McLeay and

Tenreyo (2020), which analyzed the behavior of the Phillips curve and described it as a trade-off

between monetary policy utilization and inflation. Guerrieri et al. (2020) modified the Phillips

curve to demonstrate that if the increase in money supply causes the IFL to rise, then monetary

policy is optimal in the short term. Hence, the optimal monetary policy optimizes the Fed's IFL,

output gap, and federal lending rate at various stages of the shock. Thus, the shock moves the IFL

and output gap in the opposite directions, reinforcing the trade-off that faces the Fed, indicating

that the optimal IFL, output gap, and federal funds rate jointly are the function of the shock (mt).

It modifies the Taylor rule in Eq. 9 and shows that the optimal IFL will rise. This finding addresses

the argument of Baker et al. (2020) and Baqaee and Farhi (2021) that the Phillips curve would

generate a smaller rise in inflation and more loss in output. However, the empirical and qualitative

evidence in this study is consistent with the results of Guerrieri et al. (2020). They found that

inflation was less responsive to changes in INT when the slope of the Phillips curve was low,

making the monetary policy less effective (Guerrieri et al., 2020).

RQ4: To what extent does the absence of government intervention lead to increased

economic activities?

This research question tested the strength of the monetarist model against the free market

approach. The null hypothesis tested the causal effect of the explanatory variables on the real GDP

to ascertain its efficacy in managing the impact of the pandemic. The overall MR model was not

statistically significant and could not significantly predict the percentage change in real GDP,

implying that all the variables, including INT, UR, and IFL, jointly, could not predict the

percentage change in GDP. The empirical evidence suggests that the monetary policy alone is

ineffective in managing severe shocks such as COVID-19. The hypothesis test addresses the

fundamental research question about monetary expansion and confirms that varying interest rates

and money supply during severe shocks do not significantly impact economic activities or alleviate

hardship. This empirical finding tends to uncover fresh evidence that suggests that leakages and

policy lag could render monetary policy ineffective as intended. For example, suppose mainstream

monetarists such as Milton Freidman (1968) and Fiebiger and Lavoie (2018) were confident that

reducing interest rates to increase the money supply makes borrowing attractive and stimulates

consumption and investment spending. In that case, contrary evidence opens a new debate in the

policy arena.

However, a do-nothing approach will not protect the economy from such severe shocks. In

this regard, all participants agreed that the absence of monetary policy intervention could not lead

to any significant increase in economic activities. The qualitative evidence reinforces the

theoretical and empirical conception of economic policy. White (2012) argued that the idea of

using monetary policy is to "restate theoretically, apply empirically and enunciate the policy

implications of the quantity theory of money" (p. 320). As detailed in Casado et al. (2020) and

Fornaro and Wolf (2020), the COVID-19 stimulus payment displayed a critical and significant

effect on spending and economic activity. The findings in this study have policy implications and

address the research questions, suggesting that the Keynesian stimulus theory is still practical if

applied appropriately.

Implications

The empirical test reinforced the theoretical foundation of the Keynesian stimulus theory

that direct cash payment and support for small businesses directly impacted by the COVID-19

shock can stimulate economic activities. The empirical evidence provides insight into the rationale

of the CARES Act, highlighting the politics of economic stimulus and the cost benefits of its

implementation. The pandemic impacts AD, and the expected future growth may not be affected

and may not preclude the shock's adverse outcomes. Fornaro and Wolf argued that drastic policy

intervention, including monetary and fiscal intervention, might be needed to forestall the negative

supply shock from severely affecting employment and productivity (Fornaro & Wolf, 2020). Baker

et al. (2020) noted that "the more cash arrives with agents that have high MPCs, the higher the

fiscal multipliers" (p. 23). The empirical results and evidence reported by Fornaro and Wolf (2020)

and Baker et al. (2020) suggest that direct government transfer payments significantly raise DPI

and increase PCE, which correlates with increased economic activities, including a decrease in the

UR. The findings in this study challenge the orthodoxy of DPI's positive correlation with the

change in GDP.

The efficacy of the Keynesian stimulus depends on several factors that have to do with the

size, scope, and frequency of the payment in the pandemic period. It raises concern and questions

the rationale for the resistance of some policymakers who think that policy comes at an

exceedingly high cost. Casado et al. (2020), Baker et al. (2020), and Weible et al. (2020) raised

the question of leadership in policy decisions because choices impose different social and

economic costs and benefits, which generates heightened public attention and policy impacts.

Considering the relevant economic indices, whether the CARES Act was used for politicking

rather than an economic panacea to avoid a recession, the policy concern should be whether the

stimulus payment produced the desired outcome and long-term implications. The strong negative

relationship between the coefficients of change in real GDP and the DPI demonstrates that DPI

from direct cash transfer diverts to forced savings. This evidence suggests that policymakers

should reevaluate the application of fiscal measures and target them appropriately. The evidence

in Casado et al. (2020), Baker et al. (2020), and Chetty et al. (2020) suggest that transfers are more

effective if they are used to replace the lost income of disengaged workers and help small

businesses better manage their payroll cost to stop further job losses. This implies that

policymakers should target stimulus transfers at households that need it to produce the desired

multiplier effect. Wrongly targeting transfers would turn Keynes's idea of economic stimulus

during recessions on its head and becomes counterproductive to the predictable Keynesian result.

Poor fiscal and monetary policy timing often produces outcomes more diminutive than the

Ricardian equivalence. The interest and rent-seeking propensities of the policymakers should not

superimpose the stimulus policy's goals.

Ultimately, the findings imply that the Keynesian theory is practical as part of the

intervention approach, and the economic consequences of the COVID-19 pandemic are

unavoidable without an objective interventionist policy. Policymakers can use this research's

findings to evaluate the outcome of earlier stimulus policies to improve the policy effectiveness if

the shock lingers longer. Another critical implication is that policymakers can use this study's

findings to target unemployment benefits and reconsider the BLS definition of unemployment,

emphasizing an individual's willingness to actively seek employment for the stimulus policy's

purpose. The correlation coefficients in both Keynesian and monetarist models indicate that

unemployment strongly predicts increased economic activity. The general notion of public policy

hinges on Fischer's reconceptualization of Dye's (1984) idea that public policy is "whatever

governments choose to do or not to do" (p. 2). The dire situation of COVID-19 requires

interventionist actions involving making the adjustment process reliant on the timing and inherent

choices associated with reactionary policy decisions that focus on reducing the UR.

In severe shocks such as the COVID-19 pandemic, using each policy approach in isolation

from the other tends to undermine the goal of stabilizing the economy faster. Keynes's (1936)

conceptualization is that stimulus relies on fiscal authorities, whereas Friedman (1968, 1982)

posited that monetary expansion depends on variations in interest rates, money supply, and debt

management. The Keynesians have continued to follow Keynes's (1936, 1937) tradition that

liquidity unleashes the animal spirit, which has remained the Keynesian theory's conceptual

foundation. Conversely, Freidman reinforced Hayek's free-market principle that lowering the

interest rates to increase money supply will increase liquidity (Freidman, 1982). These claims and

counterclaims run deep into the policy choice to manage shocks. For instance, Bordo and Rockoff

(2013) found that lowering interest rates boosts private-sector demand. Thus, the monetarists

argued that fiscal measures could not provide the desired panacea for the COVID-19 shock. In

another study, Fornaro and Wolf (2020) found a counter result: the monetarist model alone could

not sustain demand and generate multipliers that reverse the supply-demand loop. Many studies

have produced different results.

However, earlier results in Bernanke et al. (2005) provided a middle ground to combine

the Keynesian and monetarist models to achieve the goal of economic policy. Bernanke et al.

(2005) found that changing nominal interest variables combined with fiscal measures increases

higher demand for liquidity. This stimulates investment spending, sustains consumers'

expectations for future income, and boosts consumer confidence. The significant implication of

the results in this study is that the Keynesian and monetarist models reinforce each other. Keynes

and Friedman may have said the same thing but in different economic languages. When

investigated in isolation, the monetarist model was marginally insignificant at the p-value of 0.06.

If the effects of the variables in both models are measured jointly, the variables may become better

predictors of change in real GDP.

Limitations

The major limitation encountered in this research is the high level of data disaggregation

on the impact of COVID-19 on different jurisdictions, including states, counties, metropolitan

statistical areas (MSA), and regions. Each of these jurisdictions' economies differs, and the

pandemic affected each differently. However, BEA estimated their data for COVID-19 and the

impact from estimates of payment card transactions of daily spending by industry. The study used

aggregated national data of the relevant variables to predict GDP in each model. Due to the

pandemic's ongoing nature, new data emerge that most researchers calibrate data, construct the

indexes, or use proxies to impose constraints that fit their models. This study addressed critical

policy issues and relied on actual data of the variables at the aggregated form at the national level,

such as DPI, PCE, UR, IFL, and INT. However, measuring a phenomenon requires collecting the

correct data from the appropriate source (Creswell, 2013; Meier, Brudney, & Bohte, 2015).

The delays in releasing the official data remain a substantial limitation on any empirical

research. For instance, BEA released the official GDP data for the first quarter of 2022 in May

2022. There is a possibility that a new set of data can change the outcome of an empirical test or

result. If the study had added more data, the sample size (N) would have been five times greater

than N (31) used in both models. As noted in the literature, including Creswell (2014) and Meier

et al. (2015), the central limit theory stipulates that a normal or approximately normal distribution

has a sample more significant than 30.

100

Another major constraint is the difficulty of tracking the participants for interviews to

obtain their opinions on the investigated issues. The constraint on traveling to get their responses

were worth the time and resources committed to it because face-to-face interviews allowed for

visually observing the facial expressions and mannerisms of the participants during the interviews.

The problem of self-bias is common in qualitative research due to personal and ideological factors;

hence, the questions were crafted objectively to minimize the influences of personal biases. Still,

some respondents did not respond directly to the questions because they wanted to express their

opinions freely. Creswell (2014) and Nakamura and Steinsson (2018) advised that researchers

should use a straightforward design of questionnaires to align the interview questions to the main

research questions. Despite these constraints, the research followed a standard empirical process

in collecting the data, encoding them, entering them into the computer system, and following the

appropriate practical steps in obtaining and analyzing the outputs.

Recommendations for Future Research

Further work must illuminate gray areas and provide insight into how best to appropriately

design stimulus to target consumption and DPI with minimal behavioral effects. We are not yet

out of the pandemic's shock; the economic impact is ongoing, and data constraints are still

challenging to policy research because of the time lag associated with policy outcomes. Further

research could uncover fresh evidence on whether unemployment benefits during the shock

prolonged unemployment durations. As more data become available, research into a recovery plan

that utilizes fiscal recovery rebates for about six months can generate and sustain the multipliers

that drive DPI and PCE to restore the economy to its full potential. Future works that look at the

indirect impact and the influence of uncertainties in individual and household decisions can help

understudy the shock's microeconomic effects on individual and family behavior. These will

101

provide a broad perspective to mirror the efficacy of the Keynesian stimulus from a

multidimensional spectrum.

Answers to the following research questions could contribute to furthering this study:

i. Does poor stimulus payment targeting undermine the outcome of the Keynesian stimulus policy?

ii. What is the appropriate size and scope of direct stimulus payment?

iii. Does the stimulus program prolong unemployment duration and discourage household savings?

iv. How can the Keynesian stimulus program be redesigned not to undermine intergenerational

equity while dealing with the ramifications of a short-term shock?

The researcher welcomes opportunities to further this study to provide post-pandemic

answers to these policy questions. Additionally, post-pandemic research will provide broader

data to measure the full extent of the impacts of the COVID-19 pandemic under the Keynesian

and monetarist models.

102

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THE KEYNESIAN STIMULUS MODEL STIMULATING ECONOMIC ACTIVITIES WITH STIMULUS PAYMENTS

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