Australian Finance & Banking Review Vol. 5, No. 1; 2021 ISSN 2576-1196 E-ISSN 2576-120X Published by CRIBFB, USA 1 AN EXPLORATORY STUDY ON EFFICACY OF DEMONETIZATION IN INDIA: POLICY ROLLOUT ON DEMONETIZING OLD CURRENCY Abdul Masood Panah PhD Scholar Department of Commerce Mangalore University, Mangalagangothri Mangalore, Karnataka, India, 574199 E-mail: mazzpanah@gmail.com Dr. Y. Muniraju Professor & Dean Department of Commerce Mangalore University, Mangalagangothri Mangalore, Karnataka, India, 574199 E-mail: drymuniraju97@gmail.com ABSTRACT Demonetization is the process of declining the use of currency from circulation by the government or monetary authorities in a country. This research paper analyses the efficacy of Indian demonetization from common public perspectives, the policy that the government of India has implemented to fight against black money, drying the financial roots of terrorism, and direct the civilization towards digital transactions and a cashless economy. A field survey was conducted in Karnataka and Kerala’s coastal region by distributing a structured questionnaire among the common public to generate the data. The authors run descriptive statistics and ordinal regression analysis to obtain the result for the study’s objectives. The descriptive statistics result found that demonetization increased the number of bank account holders in India. There is not much impact of demonetization on controlling evasion of tax and illegal investments of black money, and the policy adversely affects regular business in the country. The findings from ordinal regression reveal that the time frame was given to the public to demonetize their old notes were sufficient; money circulation was well planned at the time of demonetization. The policy implemented at the right time and the common public, despite facing enormous challenges while purchasing goods and services at the time of demonetization, considers that demonetization implementation was effective. Keywords: Demonetization, Common Public, Efficacy, Policy Implementation, Currency. JEL Classification Codes: E5, E6, E7, E58, E60. mailto:mazzpanah@gmail.com mailto:drymuniraju97@gmail.com https://www.cribfb.com/journal/index.php/afbr Australian Finance & Banking Review Vol. 5, No. 1; 2021 2 INTRODUCTION Money is an essential instrument in the economy. Money is influencing the output and employment by affecting the cost of goods and services. It is also controlling the aggregate flow of saving and investment (M C Vaish, 2005).The majority of the people worldwide use money daily to buy or sell goods and services, pay or get paid, or write or settle contracts. Notes are central to the workings of the modern economy (McLeay & Radia, 2014). The central bank issue maintains the currency and is authorized to decline or ban the money from circulation in a country’s economy (Khiaonarong & Humphrey, 2019). Cash is the most extensively used payment instrument worldwide(Paul van der Knaap et al., 2018), but cash will no longer be king (Massi et al., 2019) as a large number of countries in the globeare trying to reduce the use of cash in their economies. Economies that are much cash-intensive tend to grow slowly and miss out on significant financial benefits. Conversely, economies that switch to digital are much successful; the switch can boost economic growth by as much as three percentage points (Massi et al., 2019). Numerous countries attempted to digitalize their economies and people by demonetizing their currencies and overcoming hyper-inflation and also, getting rid of the defects of black money and counterfeit currency (Mahajan & Singla, 2017; Chowdhury & Hosain, 2018). Demonetization is the process of declining the use of money from circulation by the government or monetary authorities in a country (Ghosh et al., 2017). It is the process where governments are stripping a currency unit or the realmoney as a legal tender, usually by replacing it with a new currency (Panah & Muniraju, 2020).Therefore, the common public cannot use the old money in their daily dealings for purchasing goods and services. India to fight black money, fake currency, which was a good source for terror groups, and also, to reduce the number of cash in circulation, which was directly related to corruption in the country, and to digitalize the economy demonetized 86% of the total currency in circulation on 8 November 2016 (Ghandy, 2016; Sivathanu, 2019). India’s government has given the common public a limited period to bring their old notes and exchange them with the new currency (Beg & Joshi, 2017). Therefore, this paper aims to study the common public’s opinions on policy reactions, understand their hardships due to the notes’ demonetization, and study whether the policy implementation was effective. The study results depict that demonetization increased the number of bank account holders in India. There is not much impact of demonetization on controlling evasion of tax and illegal investments of black money, and the policy adversely affects regular business in the country. Despite facing enormous challenges while purchasing goods and services at the time of demonetization, the common public considers that demonetization implementation was effective in India. LITERATURE REVIEW Demonetization is one of India’s most memorable economic events that affect all citizens and the economy through the liquidity side. The objectives of the demonetization were annihilating black money, counterfeit currency, drying up the financial roots of terrorism, and direct the economy of the country and civilization toward cashless transactions and cashless economy (Briceno & de Hurtado, 2019; Chodorow-Reich et al., 2020; Ghosh et al., 2017; Kumar, 2017; Mohan & Ray, 2019; Sharma, 2019; Vij, 2018). Studied the impact of demonetization on the Indian economy (Briceno & de Hurtado, 2019; Chodorow-Reich et al., 2020; Kumar, 2017), the results obtained from demonetization in India are a shortage of liquidity, changes in consumer preferences, increased inflation, decreased productive activities, the new distribution of monetary cone, and more significant electronic usage transfer and increased deposits, and decreased the banks’ credit https://www.cribfb.com/journal/index.php/afbr Australian Finance & Banking Review Vol. 5, No. 1; 2021 3 growth in India.(Mohan & Ray, 2019; Sharma, 2019; Vij, 2018), the decision of demonetization shocked the economy for a while. It impacted the economy in various sectors. The size of black money reduced to 15% of official GDP, the GDP has been hampered. There was a positive impact of the demonetization on the CPI inflation rate as the rate dropped post demonetization. The exchange rate of foreign currencies has not been significantly impacted.(Ghosh et al., 2017), studied the impacts ofdemonetization on the economy, they mentioned that demonetization doesn’t lead to a reduction in inflation as it can only be achieved through the recession; their book (2017, p15, 58, 64) discussed that demonetization resulted in the most significant adverse effect on the informal economy due to loss of liquidity, which drastically affected trading and supply chain across the country.(Dash, 2017; Lal, 2018; Samuel & Saxena, 2017; Singh, 2018), Studied positive and negative impacts of demonetization. The demonetization shows a mixed effect on various issues; the shortage of cash creates hardships and challenges in the everyday living of the common public in short-rub, but its impact will defiantly prove positive in the long- term.(Beg & Joshi, 2017; Dash, 2017; Koshy, 2017; Mahajan & Singla, 2017; Mishra, 2017; Mohindra & Mukherjee, 2018), studied the impact of demonetization on the common public. Their studies show thatdemonetization mainly impacted ordinary people rather than the people who are the main contributors to black money. Its impacts had a negative consequence on the poor. Indeed, the demonetization left many people with low access to cash in their daily lives. Different dimensions and effects of demonetization have been studied in the existing literature; this paper will address the efficacy of demonetization from common public perspectives on policy rollout demonetizing old currencies in India. Statement of Research Question Demonetization is not a recent phenomenon in India, and India has been implemented demonetization twice earlier. Suddenly, at midnight, November 2016, the government of India announced to the public that due to the existing large number of black money, destroying the financial resource of terrorist groups, and directing the country toward a digitalizedeconomy and a cashless society, the high-value currency which creates 86% of the total money in circulation will be no longer a legal tender and cannot be exchanged for purchasing goods and services until they replace it with the new currency in a short period. The next day, many of the common public rushed to the banks to exchange their old notes. ATMs had stopped working; the bank service rendered to people was not sufficient enough to reach all. The ordinary people were at the forefront of the government’s decision to suffer from the harms of demonetization and shortage of liquidity in the country. Therefore, the scope of the efficacy of demonetization from the general public perspectives makes it more attractive to the researcher to study in-depth their opinions and reactions. On the other hand, it will add to the existing body of knowledge and act as a potential reference for policymakers for better future national policy implementation. 1) Did the policy rollout on demonetization effective? 2) Does demonetization help to achieve tax-compliance among citizens? 3) Does demonetization help in curbing the misuse of black money (Illegal activities)? 4) Does demonetization affect the SME’s (petty business) Research Objectives  To study if the policy implementation was effective.  To Study the opinions of the public on policy reaction  To understand their hardships due to the implementation of the policy. https://www.cribfb.com/journal/index.php/afbr Australian Finance & Banking Review Vol. 5, No. 1; 2021 4 Research Limitation This study is limited to the common publicfrom the Coastal Region of Karnataka and Kerala.The researchers had faced a lack of understanding of local languages for interacting with the commonmasses and convincing them to respond to the questionnaire,lack of common public awareness about scientific research, and the researcher’s vast challenges for conducting this research. Many times, the respondents were not ready to spare their time responding to the questionnaire. Research Delimitation Efficacy of demonetization in India; Policy rollout on demonetizing old currency is a fascinating topic and studied by conducting a survey between the general public inthe coastal region of Karnataka and Kerala states of India. The datasets were generated from the general public through sharing the questionnaires from August 2019 to November 2019.Section three of this study discusses the research methodology and section four represents the discussion of the results, and chapter five concludes the paper. RESEARCH METHOD Population & Sample The authors obtained the variables measured for this research model from a review of relevant literature.For this study, the respondents are the general public from the Coastal Region of Karnataka and Kerala.This study’s source is from different groups; mostly, the researchers surveyed the people who do not have stable income such as, daily wage workers, agriculturalists, small business holders, retailers, and people who are part of the unorganized labor class. Non- probabilistic convenient sampling technique was conducted. The nonprobability sampling technique is mainly used in surveys where the total population is unknown or cannot be individually identified(Chawla & Sondhi, 2015; Kumar, 2011). Data & Questionnaire The authors collected the respondent’s responses to the pre-tested designed questionnaire. The questionnaire has been distributed between the common public in the coastal areas of Karnataka and Kerala. A total of 450 questionnaireswere printed and distributed among various groups of the respondents, and 274 respondents answered the survey questionnaire, of which 250 respondents considered appropriate responses for the study. The responses’ internal consistency has been checked using Cronbach’s alpha test and the scale items as 0.825, indicating that the investigation is 82.5 percent reliable. Data Analysis The authorsanalyzed the common public reactionsto demonetization policy implementation by India’s government using descriptive statistics. The researchers used ordinal regression analysis (ORA) to measure the common public’s opinions on policy rollout on demonetizing old currency to understand its efficacy from the general public perspectives. The data on the government’s appropriateness to demonetize the old money is considered as the response variable. And the money circulation planning,the time framegiven to the public to demonetize their old notes, public awareness on objectives of the policy rolled, the right time for demonetization implementation, facing problems while purchasing goods and services at the time of demonetization, lack of prior preparation, waiting in queues for exchanging old notes, and the https://www.cribfb.com/journal/index.php/afbr Australian Finance & Banking Review Vol. 5, No. 1; 2021 5 quality service rendered by the banks are considered as covariates or explanatory variables. This is done to verify the common public reactions relating to the policy rollout’s efficacy on demonetizing old currency matches and predicting the general public concerning the implemented policy’s appropriateness. Furthermore, the researchers consider the categorical measure of numerous age and professional groups as explanatory variables in the analysis to measure the policy appropriateness from different age and occupation groups’ perspectives. The case processing summary of the categorical elements and the response variable is represented in table 01. The authors classify the respondent’s age into six alphabetic groups, group A. [25-30], and group B. [31-35], are the youngsters.Group C. [36-40], and group D. [41-45], represent middle-aged people. Group E. [46-50] and group F. [50 &above] depict the veterans. Similarly, researchers categorize the occupational groups as daily wage workers, agriculturalists, small business holders, shopkeepers, and group ‘others’ representing the people from the unorganized labor class (housewives) and students. The following equation signifies the general expression of our ordinal regression model. log𝑖𝑡𝑌𝑖,𝑗 = ∝𝑗− ⌊∑ 𝛽𝑖𝑋1𝑖 + ∑ 𝑌𝑖𝑋2𝑖 + 𝛿𝐹 + 𝜍𝐵 + 𝜂𝐼 + 𝜆𝑀 + б𝑆 + 𝜃𝑇 + ԳL + ЧY + 휀𝑗 𝑝 𝑡=1 𝑛 𝑖=1 ⌋ Where, 𝑌𝑖,𝑗is thecumulative probability of the𝑗thcategory for the 𝑖thcase; log𝑖𝑡𝑌𝑖,𝑗is the log of an odds ratio⌊log 𝜓 1−𝜓 ⌋Where 𝜓probability of the respondents in favor of demonetization.∝_j is the threshold/intercept value; X1 and X2 are factors in the ordinal regression model representing the categorical variables such as age and profession group, with β and γ coefficients, respectively; n and p indicate one less than the total number of categories in the age group and professional group, respectively. The variables F, B, I, M, S, T, L, and Y represent the model’s covariates.Money circulation was well planned;the time framegiven to the public to demonetize their old notes, public awareness on objectives of the policy rolled, the right time for demonetization implementation facing problems while purchasing goods and services at the time of demonetization, lack of prior preparation, waiting in queues for exchanging old notes, and the quality service rendered by the banks.The coefficient of these covariates is δ, ζ, η, λ, б, θ,Գ, and Ч respectively. Table 1. Case processing summary statistics (Demographic Information) Panel A: Appropriateness of the Decision Demonetization: SD D N A SA Total Frequency (%): 39 (15.6) 31 (12.4) 51 (20.4) 73 (29.2) 56 (22.4) 250 (100.0) Panel B: Demographic Information Demographic Information Population of the respondents Profile Categories Frequency Percentage https://www.cribfb.com/journal/index.php/afbr Australian Finance & Banking Review Vol. 5, No. 1; 2021 6 Age 25-30 31-35 36-40 41-45 46-50 51 and Above Total 103 58 20 36 13 20 250 41.2 23.2 8.0 14.4 5.2 8.0 100.0 Gender Male Female Total 175 75 250 70.0 30.0 100.0 Education Illiterate Up to primary school Up to 10th standard school Up to 12th standard school Undergraduate Post-graduate Total 17 23 29 64 86 31 250 6.8 9.2 11.6 25.6 34.4 12.4 100.0 Family size Join family Nuclear family Total 115 135 250 46.0 54.0 100.0 Occupation Daily wage earner Agriculturalist Shopkeeper Small business Others Total 40 18 46 37 109 250 16.0 7.2 18.4 14.8 43.6 100.0 Source: Authors’ Computation based on the questionnaire The respondents’ demographic profile is reported in table 01 based on Age, Gender, Education, Family size, and occupation. 103 respondents, representing 41.2 percent of the total respondents, are 25-30, followed by age group 31-35, representing 58 respondents and 23.2 percent of the total respondents. Similarly, the age group of 46-50 and above 51 represents 5.2 and 8.0 percent of the respondents. Likewise, the gender of the respondents is dominated by male and female, 175 respondents, and 70.0 percent are male, and 75 respondents and 30.0 percent are female. We have also asked about the respondents’ education, family size, and occupation, represented in the table. Daily wage workers dominate the respondents’ occupation, agriculturalist, shopkeeper, small business andothers’ represent those who do not come under working class such as housewives (homemakers) jobless people who are a part of the unorganized labor class. The necessary model fit condition has been checked using baseline comparison and tests the ordinal regression assumptions using a multi-collinearity test and parallel line test to assess the empirical approach’s suitability. Multi-collinearity exists when the regression equation’s independent variables are positively correlated with each other(Zikmund, 2010). Table 04 shows the tolerance level and the Variance Inflation Factors (VIF) for all the model variables. The VIF values <1 or > 10 indicate that the variables are multi-collinear. The VIF between 1 and 10 in each casesuggests the absence of multi-collinearity(Gujarati & Porter, 2009). The multi- https://www.cribfb.com/journal/index.php/afbr Australian Finance & Banking Review Vol. 5, No. 1; 2021 7 collinearity in regression analysis refers to how strongly interrelated the model’s independent variables are(Zikmund, 2010). The other assumption relating to the ordinal regression is proportional odds, which specify the correlation among the response variable. The explanatory variable does not change for the response variable’s categories. The parameter estimations do not change for cut-off points as well. In ordinal regression, the assumption of the proportional odds test observes the various categories’ quality and resolves whether the assumption holds or not. Suppose the assumption does not fit, the interpretations about the results will be inaccurate(Erkan & Yildiz, 2014). Researchers examine the test assumption with support of the full likelihood ratio test that compares the fitted location model to a model with varying location parameters(Laerd Statistics, 2013). The results of these tests are discussed in the below sections. RESULT & DISCUSSION In the survey pertaining to the efficacy of the demonetization and the policy rollout on demonetizing old currencies from common public perspectives, most of the respondents favorthe policy rollout on demonetizing old currencies by the government. The summary of the survey findings shows that respondents considered the decision asappropriate. Concerning changes in the number of bank account holders’ pre and post-demonetization, the result is depicted in the following table. Table 2. Bank Account holders Bank account holder before demonetization No Yes Total Frequency 65 185 250 Percentage (26%) (74%) (100.0) Bank account holder after demonetization No Yes Total Frequency 15 235 250 Percentage (6%) (94%) (100.0) Source: SPSS output Table 2 reveals that the numbers of bank account holders among the respondents’ pre and post-demonetization.Where 185 respondents are 74% of the study respondents, have a bank account before the demonetization. Likewise, the table shows that the number of bank account holders increased from 185 to 235 respondents, which is 94% of the study respondents. Therefore, the results of table 02 indicate that demonetization increased the number of bank account holders in India. Authors measure the common public’s opinions on the efficacy of the demonetization in controlling tax evasion, controlling black money, illegal investments, and regular business transactions. The following table 03 shows the views of the common public on the efficacy of demonetization. Table 3. Opinions of Common Public on Demonetization Demonetization helps controlling tax evasion SD D N A SA https://www.cribfb.com/journal/index.php/afbr Australian Finance & Banking Review Vol. 5, No. 1; 2021 8 Frequency 22 38 75 84 31 Percentage (8.8%) (15.2%) (30%) (33.6%) (12.4%) Demonetisation helped control the investment of Black money SD D N A SA Frequency 16 28 66 80 60 Percentage (6.4%) (11.2%) (26.4%) (32%) (24%) Demonetizationadversely affected regular business transactions SD D N A SA Frequency 21 23 47 125 34 Percentage (8.4%) (9.2%) (18.8%) (50%) (13.6%) Note: Likert scale data tenets choice from 1-5, where 1 stands strongly disagree (SD), 2 stands for disagree (D), 3 stands for neutral (N), 4 stands for agree (A), and 5 stands for strongly agree (SA). The results depict that the ordinary public hasperceived that India’s demonetization policy was not effective in controlling tax evasion, illegal investments of black money. At the same time, 63.6% of the respondents agree and strongly agree that demonetization adversely affected India’s regular business transactions. In order to get a clear sign about the independent variable i.e. appropriateness of the demonetization decision by the government from the common man (general public) perspectives, researchers run ordinal regression analysis, the appropriateness of the government decision to demonetize the old currencies and introduce new currency, reflecting the insights and perceptions of common public as a response variable and the policy rollout on demonetizing old currencies measured via planning of money circulation at the time of demonetization, difficulties while purchasing goods and services, the timing of the demonetization, the time frame given to common public to demonetize their old currencies, lack of prior preparation, public awareness on objectives of the policy, waiting in lines to change the old coins, and the quality service rendered by the banks at the time of demonetization, represented in five-point Likert scale as a covariates laterally with age and occupation-based categorical variable. Table 4. Test of Ordinal Regression Assumptions Panel A: Test of Multi-collinearity Variables Collinearity Statistics Money Circula tion The time frame has given Public awarene ss on Objectiv es The right time to impleme nt Demon Lack of prior preparati on problems while purchasi ng goods Waiti ng in lines quality services rendere d by banks Tolerance 0.561 0.671 0.699 0.471 0.591 0.486 0.630 0.778 VIF 1.781 1.622 1.431 2.122 1.693 2.062 1.587 1.285 Panel B: Test of Parallel Lines Model -2 Log Likelihood Chi-Square df Sig. https://www.cribfb.com/journal/index.php/afbr Australian Finance & Banking Review Vol. 5, No. 1; 2021 9 Null Hypothesis 501.057 General 476.682b 24.375c 51 0.999 Source: SPSS output The model diagnostic statistics results have been reported in table 04 to understand whether the ordinal regression model used in the study satisfies all pre-conditions/assumptions. A Multi-collinearity test that checks forthe high degree of correlation between the explanatory variables specifies that the study’s model is free from multi-collinearity issues as the VIF values areless than 10. Tolerance values are greater than 0.10 for all the explanatory variables. The ordinal regression models used in the study are based on the fundamental assumption of proportional odds that highlights an identical effect is observed from each explanatory variable at each cumulative split of ordinal response variables(Laerd Statistics, 2013). Theparallel line test is used to prove whether the model used in this study satisfies the proportional odds assumption (see table 04, panel B). The parallel lines test result shows an insignificant probability value at a 5 % level of significance. We failed to reject the test state’s null hypothesis that the location parameters (i.e., slop coefficients) are the same across response categories. This indicates that the model used here also satisfies the assumption of proportional odds. Before discussing the ordinal regression estimates, the researchers verified the goodness of fit by comparing the baseline model with the model used. The significance of the (McCullagh & Nelder, 1989) chi-square value depicts that the model used here indicates better prediction than the simple intercept-only (baseline) model, which is similar to making a guess based on the marginal probabilities for the outcome categories{Citation}(Elamir & Sadeq, 2010). The following table, 04, shows a summary of model fitting. Table 4. Overall Model Fitting Information Model Fitting Information Model -2 Log Likelihood Chi- Square df Sig. Intercept Only 774.581 Final 501.057 273.525 17 0.000 Source: SPSS output After conducting the diagnostic check and model fit verification, the researchers further discuss the ordinal regression results. The following table, 05, reveals the estimates of ordinal regression parameters. Table 5. Estimates of Ordinal Regression Parameters Variables Coefficient Notation Estimate Std. Error Wald Sig. Odds Ratio Constant [SD] α1 3.296 0.909 13.142 0.000*** 27.005102 Constant [D] α2 4.839 0.938 26.629 0.000*** 126.31409 https://www.cribfb.com/journal/index.php/afbr Australian Finance & Banking Review Vol. 5, No. 1; 2021 10 Constant [N] α3 6.978 1.011 47.638 0.000*** 1073.2446 Constant [A] α4 9.751 1.101 78.387 0.000*** 17163.848 Money Circulation δ 0.581 0.150 15.082 0.000*** 1.7875468 Lack of Prior preparation ζ -0.063 0.138 0.205 0.651 0.9392421 Problem while Purchasing goods η -0.380 0.145 6.826 0.009** 0.6840535 Public awareness on objectives λ 0.080 0.139 0.329 0.566 1.0830595 Time frame given б 0.330 0.128 6.680 0.010** 1.3907601 Waiting in lines θ 0.089 0.146 0.372 0.542 1.0932785 Quality services rendered Գ -0.060 0.136 0.196 0.658 0.9417167 The right time to implement demonetization Ч 1.512 0.181 70.007 0.000*** 4.5372563 Age group A. [25-30] ß1 0.733 0.518 1.998 0.058* 2.0802892 B. [31-35] ß2 0.797 0.544 2.143 0.043* 2.2184108 C. [36-40] ß3 0.495 0.667 0.551 0.458 1.6404722 D. [41-45] ß4 -0.075 0.589 0.016 0.898 0.9273929 E. [46-50] ß5 0.391 0.722 0.294 0.588 1.4787684 F. [50 & Above] ß6 0a Occupation group [Daily wage workers] ϒ1 0.251 0.396 0.403 0.526 1.2855481 [Agriculturalists] ϒ2 0.043 0.554 0.006 0.938 1.043906 [Shopkeepers] ϒ3 -0.576 0.383 2.262 0.133 0.5620558 [Small Business holders] ϒ4 -0.023 0.404 0.003 0.955 0.9773644 [Others] ϒ5 0a Source: SPSS output. Note: *indicate values significant at 5% level ** indicate values significant at 1% level Table 5 shows the results of the estimate of ordinal regression parameters, among the covariates used to capture the influence on the response variable, the variables relating to the planning of money circulation, difficulties while purchasing goods and services, the time frame of the demonetization by the government, and the time frame given to the common public to demonetize their old currencies are turned to be statistically significant. And the variables such as lack of prior preparation, public awareness on objectives of the policy, waiting in lines to change the old currencies, and the quality service rendered by the banks at the time of demonetization are not statistically significant. The variable that shows the planning of money circulation at the time of demonetization indicates that a unit increase in the value of the variable will increase odds in favor of the response on the appropriateness of the decision to demonetize the old currency and introduce the new money over the answer against the demonetization decision are more significant than 1.7875468, times. Likewise, the odds of getting a response in favor of the decision to demonetize old currency are greater if respondents are in prefer, positive response relating to the reactions on difficulties while purchasing goods and services, the time frame of the demonetization by the government, and the time frame given to the common public to demonetize their old currencies as indicated by higher odds ratio relating to these variables. https://www.cribfb.com/journal/index.php/afbr Australian Finance & Banking Review Vol. 5, No. 1; 2021 11 In the categorical variables age- groups, the results show that there is more possibility that respondents in the age group (25-30), (31-40) the youngster and middle-aged individuals are more likely in favor of positive response toward the decision to demonetize old currency and introduction new currency to the economy, compared to the veterans, the statistically significant values for the age group (25-30) and (31-40) reveals that the odds are firmly in favor of the decision is 2.0802892, and 2.2184108, times greater than the veterans as shows by cumulative odds ratio values. Likewise, in the case of the occupational group, the result indicates that there is a possibility that the respondents in daily wage workers and agriculturalists are more likely in favor of the demonetization decision compare to the group’s shopkeepers, small business holders, and group others that represent the people from unorganized sectors. The daily wage workers’ statistical values indicate that the odds are firmly in favor of the demonetization decision is 1.2855481, and 1.043906 times greater than the other class of people, due to the cumulative odds ratio. FINDINGS & CONCLUSION The study explores the efficacy of demonetization from India’s common public perspectives, the policy implemented by India’s government to fight against black money, drying the financial roots of terrorism, and direct the civilization towards digital transactions anda cashless economy. The study’s main purpose is to study if the policy implementation was effective, study the public’s opinions on policy reactions, and understand their hardships due to policy implementation. The descriptive statistics results suggest that the demonetization increased the numbers of bank account holders in India. There is not much impact of demonetization on controlling evasion of tax and illegal investments of black money, and the policy adversely affects regular business in the country. The ordinal regression findings reveal that the ordinary public, despite facing enormous challenges while purchasing goods and services at the time of demonetization, consider that demonetization implementation was a practical step in directing the civilization towards the digital transaction and cashless economy, the results obtained from the ordinal regression analysis also depicts that the government implemented the policy at the right time, the government well planned the money circulation at the time of demonetization, the time frame given to the public wassufficient enough to change their old notes. Before demonetization, there was a proper system in place, and the general public has not faced any problems. The otherwise innocent common public was harassed and disturbed to their day-to-day transactions and withdrawn their complex and earned money in the name of controlling black money, terrorism, and fake currency circulation should not be punished to the common public. 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