Effectiveness of public policy in reviving the COVID-19 hit economy: Evidences from Kerala, India.

The economic crisis triggered by the COVID-19 urgently required active policy interventions to enhance the revival strategies of the world economy. This paper examines the effectiveness of policy intervention of the State Government of Kerala in India in mitigating the risks caused by the pandemic. The policy effectiveness is evaluated by analyzing the data collected from a sample of 300 beneficiaries with the help of descriptive statistics, ordered probit (OP) model, and semi nonparametric extended OP (SNEOP) model. Our results are assertive with the fact that state policies are effective in reviving the crisis-hit economy as they have primarily helped low-income groups and other marginalized communities. The majority of BPL families, self-help group members, and social security beneficiaries rated government policies as highly or fairly effective. Though the policies are found to be highly effective among those who have suffered income loss, the study does not find sufficient evidence to believe that the government interventions are effective in helping those who have lost their jobs. The level of effectiveness is inversely related to age, education, and family size. Our results suggest that an extensive fiscal package is required to help people recover from the crisis.

INTRODUCTION

John Maynard Keynes's advocacy for government intervention through public policies was a revolutionary chapter in economic thinking as it overturned the then‐prevailing idea that free markets would automatically bring about economic stability. Historically speaking, when the great depression wrecked the world economy in an unparalleled way, fiscal policies, as proposed by Keynes, were effectively put in place to revive the economies. This was followed by a surge in discussions by Keynesian economists who argue that government intervention is necessary to stabilize the booms and busts in the economic activities (Aspromourgos, 2012; Jahan and Mahmud, 2014). Thus, government intervention started to be seen as an integral element of revival mechanisms for the economies plunged into crisis.

The world today is on yet another battlefield fighting COVID‐19 and resultant economic crises of various sorts. Beginning from 24 March, 2020 India witnessed a nationwide lockdown for 68 days till the end of May, which ultimately caused an unprecedented economic crisis. The pandemic has already affected the lives and livelihoods of millions of people by causing them to lose their jobs, income, and welfare. Considering the magnitude of the shocks triggered by this pandemic and consequent closure measures, especially in the labor market, some studies prove that job losses ensuing COVID‐19 are larger than the entire effects of the Great Recession (Montenovo, 2020; Coibion et al., 2020). In such a precarious state, effective execution of policies and interventions from the part of governments is inevitable. Realizing this, governments around the globe are bent on formulating various initiatives to get the economies out of chaos.

A close investigation of various kinds of fiscal policies adopted by countries and summarized in the policy tracker of the international monitory fund (IMF) suggests that although immediate priority was given to investments in public health infrastructure and vaccination programme, later onwards several economic revival programmes were implemented. The programmes such as unemployment benefit schemes, tax rebates and tax holidays, loan repayment reliefs, food safety nets, social security provisions, and easy access to business loans have been common in revival packages announced by most of the countries (IMF, 2021). For Instance, United States announced unemployment benefits, funds to prevent corporate bankruptcy, and small business administration loans for retaining workers. Similarly, United Kingdom also extended tax holiday grants for small firms, social safety to vulnerable people, self‐employment income support scheme, and job retention scheme for employees.

Fiscal measures introduced by India's central government can be divided into two broad categories, direct expenditure measures and indirect business support and credit enhancement measures. During the early days of the pandemic, spending by the government focused on social protection and health care measures, some of them are in‐kind and cash transfers to lower income families, wage support and employment provision to low wage workers, and insurance coverage for health workers. Measures announced in the second stage include additional capital expenditure, interest free loans to states, production linked incentiveschemes for 13 priority sectors, tax concessions, credit supports, and so forth.

Among the states in India, Kerala was one of the first state to announce an economic revival package of Rs 20,000 crore on 19 March, 2020. The package includes various welfare measures such as financial aids, free food grains, health schemes, subsidized loans and so forth (Government of Kerala, 2020). In May 2020, the State Government of Kerala had disbursed 7 months' welfare pension to 44 lakh households in one go. Further, another group of 14.7 lakh BPL families who are not eligible for welfare pensions were provided a relief of Rs. 1000. A sum of Rs. 1000 per person was distributed to the informal workers who have registered with the various welfare fund boards. An amount of Rs. 2000 crore has been allocated by the government in the economic package to provide loans to self‐help group (SHG) members. Further, the state government distributed 15 kg of free food grains to all families irrespective of the type of ration card through the public distribution system during the lockdown. Free food grains were also distributed to the families not possessing ration cards. Further, the state government distributed free food kits containing 17 essential items through the public distribution system every month since the lockdown to all ration cardholders. In this paper, we analyze the effectiveness of these policy initiatives adopted by the Government of Kerala to help the people mitigate the risks associated with the pandemic.

The study uses Kerala as the reference state as Kerala is considered to be the “ground zero” of COVID‐19 in India (Sarkar, 2021). Being one of the very first states in India to report COVID‐19 cases, Kerala has grabbed the attention of international organizations and media commenting on the laudable efforts of the state to arrest COVID‐19 induced setbacks (Andrews et al., 2020; Vaman et al., 2020). Though there were criticisms from various corners regarding the state's failure to prevent the spread of the virus at some point in time, various initiatives of the state were generally taken as the ideal practices (Sadanandan, 2020). Apart from this, Kerala has a reputed health system (Travelli, 2020; Isaac & Sadanandan, 2020) nurtured by successive governments, high levels of educational attainment, and the functioning of an effectively integrated local governments. All these have made the people politically vigilant which in turn forces the governments to implement pro‐public programmes and initiatives. Thus, this study analyses the responses of randomly selected 300 sample beneficiaries about the effectiveness of state intervention in reviving from the COVID‐19 crisis.

REVIEW OF LITERATURE

Keynes's general theory helped the world move out of the great depression and unsurprisingly it still gains adherents during economic crises. Similar to Keynes, Kalecki (1943) also emphasized the importance of government investment, subsidies for mass consumption, and various forms of family allowances in generating employment and consumption. Kalecki was of the view that that involuntary unemployment is the result of the absence of government intervention (Ghosh, 2005). This asserts that the government has a huge role to play in stabilizing the economy. The discussions of government policies and interventions become contemporarily relevant as the world today is plunged into a crisis brought about by the COVID‐19 pandemic. Various studies carried out to analyze the impacts of COVID‐19 on the economy show that the pandemic has gruesomely resulted in job loss (Crayne, 2020; Coibion et al., 2020; Borjas et al., 2020; Lemieux, 2020; Costa Dias et al., 2020; Mayhew & Anand, 2020; Bell & Blanchflower, 2020), income loss (Brewer & Gardiner, 2020; Dahab et al., 2020; Qian & Fan, 2020) and happiness loss (Greyling et al., 2020; VanderWeele et al., 2021; Yang & Ma, 2020). As a result, countries all over the world have been trying all the way possible to save their economies from the shocks triggered by the pandemic.

Right from the beginning, the Kerala government has been vigilant and highly responsive to the outbreak of Covid‐19 (Sadanandan, 2020). The government has resorted to various initiatives such as tracking and tracing, providing medical and financial supports, ensuring food security, and supplying necessary food items (Sarkar, 2021). The government initiatives were broadly aimed at curbing the spread of the virus and supporting the people whose lives were affected by the pandemic through multiple channels. There are few studies already conducted discussing Kerala's response to Covid‐19, and the majority of such studies are assertive of the fact that Kerala stands out in the world (Travelli, 2020; Falerio, 2020; Isaac & Sadanandan, 2020; Menon et al., 2020) in terms of dealing with the pandemic. However, this paper adds to the existing literature as it comprehensively analyses the effectiveness of Kerala's policy responses to support the people whose lives are badly affected by the pandemic.

DATA AND METHODOLOGY

Data collection

India, similar to many other countries, has so far witnessed an alarming rate of COVID‐19 cases and deaths. Among the states in India, Kerala is one of the highly affected states. In order to support the people to come out of the crisis, Kerala has implemented various programs and schemes. Thus, the present study aims at analyzing the effectiveness of policy responses of the Kerala government adopted to mitigate the risks triggered by the pandemic. The effectiveness is analyzed based on the responses of a sample of 300 respondents selected from five districts of northern Kerala. Northern Kerala consists of Kasaragod, Kannur, Wayanad, Kozhikode, and Malappuram districts. Applying the nonlist‐based random sampling method, we have selected a sample of 60 respondents from each district. We could not collect data directly from sample respondents due to the constraints imposed by the COVID‐19 protocol. However, a well‐structured digital questionnaire is prepared in google form and shared with the sample respondents through various digital social networks. The questionnaire was prepared in regional language and clear and elaborate instructions were given about the purpose of the study, target respondents, and each question to avoid mistakes from the part of respondents. The target respondents were household heads who are economically active. Thus, after scrutiny of the received responses, we have excluded responses from students and the aged. The required data about socio‐economic features and employment and income details during the pandemic period were collected in August 2020 using a three‐month reference period. The reference period extends from the last week of March to the last week of June 2020, including 68 days nationwide lockdown implemented in India.

Methodology

The Dependent variable used in the study is the rating recorded by the individuals regarding the effectiveness of various economic policies implemented by the government to help the people recover from the financial shocks produced by the pandemic. Respondents were asked to mark their responses on a scale of 1–3 for the question: Considering various initiatives such as distribution of basic food items, streamlining the distribution of social security measures, opening community kitchens, and the like adopted by the Government of Kerala during the period of COVID‐19, how effective you feel those initiatives are? where 1 = not effective, 2 = fairly effective, and 3 = Very effective.

As the dependent variable of the study is ordinal, the present study used ordered probit (OP) model and its extended version semi nonparametric extended OP (SNEOP) model proposed by Stewart (2004).

Indexing individuals by the subscript i, the model can be written as: where y * is individual i's response and the βs are the associated linear regression coefficients; represents k independent variables and U s are mutually independent standard normal variables. The OP model yields maximum likelihood estimates of the parameters of the response function given in Equation (1).

is related to the observable ordinal variable of as follows:

where stands for the threshold value of the dependent variable at which the outcome changes.

The variable y thus indicates in what interval y falls into. Thus, the probability of outcome j{1,2,3} is. The consistency of the estimates of β, however, depends crucially on the assumed distribution of Uis. To avoid this distributional assumption, Stewart's (2004) SNEOP model is used. This model has several advantages over OP. Firstly, it relaxes the distributional assumption required by OP; instead, F(.) is estimated by maximum likelihood from a semi‐nonparametric cumulative distributions whose density function is the product of the square of an unknown polynomial multiplied by the normal density, so that:

In Equation (2), K is the order of the unknown polynomial and ɸ(z) is the standard normal density at z. After choosing K based on simple likelihood‐ratio tests and setting θ to its OP value for identification, the coefficients γ are estimated jointly with θ and β by maximum likelihood. This flexible approach can take any distribution provided it is smooth enough and its tails are not too flat. Second, SNEOP nests the OP model, which corresponds to both K = 1 and K = 2. Prior to estimating the models, the study diagnosed the multicollinearity problem and confirmed that corresponding collinearity is not too high. After estimating a standard OP model, the study tests the underlying distributional assumption by computing Stewart's (2004) likelihood ratio tests on the value of K in SNEOP. The study has also derived marginal effects of being not effective, fairly effective, and very effective with respect to all explanatory variables.

RESULTS AND DISCUSSION

Descriptive analysis

This section empirically analyses the effectiveness of government policies during COVID‐19 based on the responses of individuals. First, a descriptive analysis of the variables is made, followed by a detailed analysis of the effectiveness of public policies based on the results obtained from SNEOP model. Table 1 presents the summary statistics for the sample respondents with respect to demographic and socio‐economic features. The average age of the sample respondent is 40.5. Among the respondents, about 69% are male and 31% are female. The lower proportion of females among respondents is mainly due to the lower labor force participation rate of women in Kerala. About 74% of the respondents are married, which indicates a higher dependency on the respondents. Almost 39% of them have educational qualifications above matriculation. With respect to the social category, 76% belong to other backward classes.

TABLE 1

Summary statistics

	Mean	SD	Min	Max
Age	40.547	12.020	18	65
Gender (Female = 1)	0.313	0.464	0	1
Marital status (married = 1)	0.74	0.439	0	1
Social group (other backward class = 1)	0.76	0.4277	0	1
Education (above matriculation = 1)	0.39	0.488	0	1
Family size	4.714	1.355	2	8
Monthly income before the pandemic	11285.17	7027.408	800	40,000
Economic status (below poverty line = 1)	0.507	0.500	0	1
SHG member (Yes = 1)	0.6266	0.484	0	1
Pensioner in the family (Yes = 1)	0.32	0.467	0	1
Job loss (Yes = 1)	0.397	0.489	0	1
Income loss (Yes = 1)	0.423	0.494	0	1

An analysis of the economic background suggests that the average monthly income before the pandemic was Rs. 11,285. This is relatively higher when compared to other states mainly because of higher wage rates paid to workers in Kerala. With respect to the economic status, it was noted that nearly half of the households are below poverty line. It was found that 62% of the households have at least one SHG member in their family. Membership in SHG is helpful for poor households since women members can take loans at lower interest rates during livelihood crisis. It was further observed that 39% of the households have taken loans through the bank linkage programme of SHGs to overcome the livelihood crisis created by the COVID‐19 crisis. Similarly, almost 32% of the households have pensioners in their family, thus they are beneficiaries of pension payments distributed by the state government. An enquiry about the impact of the pandemic on the employment and income status of the respondents revealed that nearly 40% remained completely unemployed and 42% suffered complete income loss during the pandemic period.

An analysis of rating of government policies by the sample respondents revealed that the majority of the people (50.7%) are finding the government policies ‘fairly effective.’ While 22.7% of the respondents feel the policies as ‘very effective,’ 26.7% of the people feel as ‘not effective,’ (see Figure 1). Table 2 presents a bivariate analysis of the responses across scale variables. Results show that the average age of those who find the policies ‘effective’ is lower, which implies that young people are relatively more satisfied with public policies. In the case of income, the mean income of the respondents is higher among those who find the policies ‘very effective.’ However, family size is higher among those who rate the government initiatives as ‘not effective,’ which suggests that large families are less satisfied.

FIGURE 1

Effectiveness of government intervention as perceived by the sample respondents

TABLE 2

Rating of effectiveness of government intervention across scale variables

Variables	Not effective	Fairly effective	Very effective	Total
Age	42.85 (9.46)	40.41 (12.86)	38.15 (12.42)	40.55 (12.02)
Monthly income before the pandemic	10872.50 (8519.28)	10866.62 (5873.56)	12706.62 (7376.16)	11285.18 (7027.49)
Family size	5.08 (1.53)	4.65 (1.36)	4.44 (1.04)	4.71 (1.36)

Note: Figures in the parentheses are SD.

Table 3 analyses how responses vary across different categorical explanatory variables such as gender, marital status, level of education, economic status and others. The percentage of those who find the policies ‘fairly effective’ is generally higher among all the categories. Among the weaker sections, not less than 70% of the respondents find the policies as either ‘fairly effective’ or ‘very effective’. The percentage of people who have rated policies ‘very effective’ is more among weaker sections. It is interesting to note that a good percentage of females (23.4%), BPL families (28.7%), and those who are members of Self‐Help Groups (26.6%) find the government interventions as ‘very effective’. On contrary, 39.3% of nonmembers in SHGs and 38% of APL families rate the government initiatives as ‘ineffective’. The same kind of responses are noted when the analysis is made with respect to pension receiving households. About 42% of pension beneficiaries find the government policies as ‘very effective’, whereas the percentage of those who believe the policies to be ‘very effective’ is only 14% among nonbeneficiaries. These findings suggest that public policies have benefited BPL families, SHG‐ members and pension beneficiaries more.

TABLE 3

Rating of effectiveness of government intervention across categorical variables

Variables	Categories	Not effective	Fairly effective	Very effective	Total
Gender	Male	58 (28.2)	102 (49.5)	46 (22.3)	206 (100.0)
	Female	22 (23.4)	50 (53.2)	22 (23.4)	94 (100.0)
Marital status	Married	66 (29.7)	108 (48.6)	48 (21.6)	222 (100.0)
	Others	14 (17.9)	44 (56.4)	20 (25.6)	78 (100.0)
Social group	Other backward classes	68 (29.8)	112 (49.1)	48 (21.1)	228 (100.0)
	Others	12 (16.7)	40 (55.6)	20 (27.8)	72 (100.0)
Education	Above matriculation	34 (29.1)	60 (51.3)	23 (19.7)	117 (100.0)
	Below matriculation	46 (25.1)	92 (50.3)	45 (24.6)	183 (100.0)
Economic status	Below poverty line	23 (15.3)	84 (56.0)	43 (28.7)	150 (100.0)
	Above poverty line	57 (38.0)	68 (45.3)	25 (16.7)	150 (100.0)
SHG member	Yes	36 (19.1)	102 (54.3)	50 (26.6)	188 (100.0)
	No	44 (39.3)	50 (44.6)	18 (16.1)	112 (100.0)
Pension beneficiaries	Yes	24 (25.0)	32 (33.3)	40 (41.7)	96 (100.0)
	No	56 (27.5)	120 (58.8)	28 (13.7)	204 (100.0)
Job loss due to COVID‐19	Yes	44 (37.3)	48 (40.7)	26 (22.0)	118 (100.0)
	No	36 (19.8)	104 (57.1)	42 (23.1)	182 (100.0)
Income loss due COVID‐19	Yes	18 (14.2)	51 (40.2)	58 (45.7)	127 (100.0)
	No	62 (35.8)	101 (58.4)	10 (5.8)	173 (100.0)

Note: Figures in the parentheses are percentages to raw total.

Among those who have lost income due to COVID‐19, 45.7% of people consider government policies as ‘very effective’ and 40.2% as ‘fairly effective’. However, among those who have not lost income, only 5.8% of the people find the policies ‘fully effective.’ In fact, 35.8% of this category of people rate the government policies ‘not effective.’ Considering the job loss, 37.3% of workers who have lost jobs believe that government policies are ‘not effective.’ Further analysis shows that the percentage of those who find the policies to be ‘very effective,’ is more or less the same both among those who have faced and have not faced job loss. For those who have lost jobs, public policies have not created a significant impact since policies creating new jobs have been less.

Effectiveness of governmental interventions during Covid 19: Results of OP and SNEOP models

The results of OP model and SNEOP are reported in Table 4. The validity of the estimated results is contingent on the assumption of normality of the error term in Equation (1), which is rejected by Likelihood Ratio test of the OP model against the SNEOP model, whose p value <0.01 for all estimated models. In the second stage, SNEOP was assessed, and the model with k = 4 is found to be best OP model. The likelihood ratio test of SNEOP model with K = 4 compared to OP confirms the rejection of the OP model (p value <0.0001). The estimated parameters of SNEOP models and their corresponding levels of significance are displayed in Table 4. As we have noted previously in descriptive analysis, the results from econometric analysis show that the people who lost their jobs during the crisis are unhappy with the government interventions, whereas the people who faced income loss express comparatively high levels of satisfaction. The subsequent section provides a detailed discussion based on the marginal effects derived from SNEOP model which are provided in Table 5.

TABLE 4

Factors influencing the rating of effectiveness of government intervention: Estimates of OP and SNEOP models

	OP model	SNEOP models
Explanatory variables	OP estimates	SNEOP model (K = 3)	SNEOP model (K = 4)
Age	−0.0189 (0.064)	−0.029 (0.000)	−0.0244 (0.000)
Gender (male = 1)	0.1493 (0.430)	0.1678 (0.362)	0.2113 (0.189)
Marital status (married = 1)	−0.057 (0.813)	−0.1731 (0.422)	−0.1888 (0.328)
Social group (other backward class = 1)	−0.434 (0.016)	−0.374 (0.018)	−0.302 (0.050)
Education (above matriculation = 1)	−0.7240 (0.001)	−1.123 (0.000)	−1.034 (0.000)
Family size	−0.133 (0.023)	−0.1256 (0.006)	−0.0884 (0.038)
Monthly income before the pandemic	0.00025 (0.038)	0.000024 (0.081)	0.000269 (0.053)
Economic Status (below poverty line = 1)	0.645 (0.000)	0.4719 (0.008)	0.464 (0.003)
SHG member (yes = 1)	0.343 (0.038)	0.4325 (0.020)	0.483 (0.037)
Pensioner in the family (yes = 1)	0.407 (0.008)	0.653 (0.000)	0.569 (0.000)
Job loss (yes = 1)	−2.511 (0.000)	−4.197 (0.000)	−4.223 (0.000)
Income loss (yes = 1)	2.877 (0.000)	4.593 (0.000)	4.608 (0.000)
Threshold 1	−1.758	−1.757	−1.757
Threshold 2	0.3567	0.307 (0.272)	0.1461 (0.678)
Polynomial 1	——	1.176 (0.131)	0.320 (0.393)
Polynomial 2	——	0.037 (0.763)	−0.611 (0.000)
Polynomial 3	———	−0.283 (0.053)	−0.076 (0.078)
Polynomial 4	——	———	0.0843 (0.000)

Note: Number of observations = 300; OP results: Log‐likelihood: −197.46; LR Chi‐Square (with 12 degrees of freedom) 225.08 (0.000). SNEOP results: K = 3; log‐likelihood: −188.24. Wald Chi‐Square (with 12 degrees of freedom): 208.88. Likelihood ratio test of the OP model against SNEOP model: Chi‐square (with 1 degrees of freedom):18.40 (0.000). SNEOP results: K = 4; log‐likelihood: −188.15. Wald Chi‐Square (with 12 degrees of freedom): 111.82. Likelihood ratio test of the OP model against SNEOP model: Chi‐square (with 1 degrees of freedom):18.65 (0.000).; Values in the parentheses indicate level of significance.

TABLE 5

Derived marginal effects of explanatory variables

Explanatory variables	Not effective	Fairly effective	Very effective
Demographic factors
Age	0.0038 (0.002)	0.00058 (0.561)	−0.0044 (0.001)
Gender (Female = 1)	−0.033 (0.190)	−0.0050 (0.575)	0.0382 (0.172)
Marital status (married = 1)	0.0296 (0.344)	0.0045 (0.589)	0.0341 (0.324)
Social group (other backward class = 1)	0.0475 (0.126)	0.0072 (0.513)	−0.054 (0.063)
Education (above matriculation = 1)	0.1626 (0.000)	0.0247 (0.554)	−0.187 (0.000)
Family size	0.0139 (0.049)	0.0021 (0.570)	−0.0160 (0.043)
Monthly income before the pandemic	−4.232 (0.015)	−6.433 (0.595)	4.879 (0.034)
Economic Status (Below poverty line = 1)	−0.0730 (0.015)	−0.0111 (0.537)	0.084 (0.002)
SHG member (Yes = 1)	−0.0759 (0.004)	−0.0115 (0.592)	0.0875 (0.016)
Pensioner in the family (Yes = 1)	−0.0894 (0.011)	−0.0136 (0.528)	0.1030 (0.000)
Job loss (Yes = 1)	0.6637 (0.000)	0.1008 (0.545)	0.764 (0.000)
Income loss (Yes = 1)	−0.7241 (0.000)	−0.1100 (0.547)	0.834 (0.000)

Note: The marginal effects are derived from SNEOP K = 4 model. Values in the parentheses indicate level of significance.

An analysis of the responses of the people based on age, education and family size indicates that these factors have a negative impact on the level of satisfaction with government schemes. With the increase in age, level of education, and family size, the level of satisfaction with government interventions during the crisis period appears to be lower. This indicates that the expectations of the educated individuals and of old people exceed the government initiatives. The negative association between the satisfaction and the government responses among the large families must be due to the inability to meet the requirements of entire family members during the crisis.

The policies of the governments are well taken by the low‐income families as the people below poverty line rate the government interventions as effective. This shows that the economic policies rolled out by the government have helped low‐income families. Thus, welfare programmes included in the COVID‐19 economic package are found to have achieved the stated objectives of helping the poor families to mitigate the risks brought about by the pandemic. A separate analysis of the responses among the people who are entitled to pension benefits and those who are engaged in the SHG asserts that these two categories of people are highly satisfied with the government responses to the crisis. The decision of the government to streamline the distribution of various kinds of social security benefits has helped in improving the economic conditions of disadvantaged groups. As we have noted earlier in the descriptive analysis, considerable proportion of households have taken loans through the bank linkage programme of Self‐Help Groups and some also received pension payments of 7 months. Thus, it can be inferred that the allocation of special funds by the state for the provision of SHG loans and pensions payments has benefited these poor households. Similarly, opening up of community kitchens in various villages under the direct supervision of SHGs has provided income generating activities for the members of SHGs.

The satisfaction levels with government policies are different among those who faced job loss and income loss. Although the people who experienced an income fall due to the pandemic are highly satisfied with government interventions, the individuals who lost their jobs are found to be unsatisfied. This might be due to the fact that the economic policies of the government schemes like distribution of food kits and timely distribution of social security benefits have helped the people to mitigate the immediate risks of income loss. However, the interventions of the government have not helped the people in regaining the jobs lost due to the pandemic. This implies that to help the people who lost their jobs, effective policy interventions that focus more on the employment generations and bringing the people back to the job market, are essential for a complete economic revival.

CONCLUSION

This study has evaluated the effectiveness of the governmental interventions during COVID‐19 in the state of Kerala in India by analyzing the responses of the beneficiaries to public policies. The study attempted to figure out how the policy responses of the government have affected the people of different categories. The findings are assertive with the fact that public policies are effective in reviving the crisis‐hit economy. Fiscal policies of the state government have immediately helped low‐income groups and other marginalized communities. The public policies have compensated for the income losses suffered by poor sections through both cash and in‐kind transfers. BPL families and those who are entitled to social security benefits have found the policies to be highly effective. The workers who suffered income loss also found public policies very effective. This, as previously argued by Kalecki (1943), highlights the importance of government spending and initiatives in boosting the economy. However, it is worth mentioning that the government interventions are not found to be that effective in helping those who have lost their jobs due to the pandemic. Therefore, in order to support all those who are affected by job loss and income loss, an extensive fiscal package that focuses more on employment generation is needed.

y_i = 1	(not effective)	if	θ_0=−∞<y_i>θ_1
y_i = 2	(fairly effective)	if	θ_1<y_i>θ_2
y_i = 3	(very effective)	if	θ_2<y_i≤θ_3=+∞