Multidimensional effect of covid-19 on the economy: evidence from survey data

Figures & data

Figure 1. Authors own calculations based on primary data.

Source: Survey data.

Figure 2. Authors own calculations based on primary data.

Source: Survey data.

Figure 3. Authors own calculations based on primary data. Source: Survey data.

Figure 4. Authors own calculations based on primary data. Source: Survey data.

Figure 5. Authors own calculations based on primary data. Source: Survey data.

Figure 6. Authors own calculations based on primary data. Source: Survey data.

Table 1. Cronbach Alpha (α).

Name	Value
Cronbach’s Alpha	0.896

Source: Survey data.

Table 2. COVID-19 and “lifestyle, education and work management.”

Questions	Available options	Do not know		Maybe		No		Yes		Total
Did COVID-19 affect your lifestyle?		7 (0.69%)		28 (2.76%)		49 (4.83%)		931 91.72%)		1015 (100%)
If you are a student, will online classes help continue your studies during the COVID-19 pandemic?		182 (17.93%)		245 (24.14%)		371 (36.55%)		217 21.38%)		1015 (100%)
Question	Available options	No		No, I am a student		Prefer not to say		Yes		Total
Were you working before the COVID-19 pandemic?		63 (6.21%)		399 (39.31%)		35 (3.45%)		518 (51.03%)		1015 (100%)
Question	Available options	Do not work	Still going to work		Officially off		On-leave		Working from home	Total
If "Yes," how you manage to work (after the COVID-19 pandemic)?		266 (34.23%)	168 (21.62%)		168 (21.62%)		42 (5.41%)		133 (17.12%)	777 (100%)

Source: Authors own calculations based on primary data.

Table 3. Essential Questions about different variables.

Question	Available options	Not at all	Rarely	Neutral	Frequently	Most frequently	Total
How is your education affected by COVID-19?		147 (14.48%)	77 (7.59%)	133 (13.10%)	245 (24.14%)	413 (40.69%)	1015 (100%)
How is your firm affected by COVID-19?		98 (9.66%)	105 (10.34%)	301 (29.66%)	266 (26.21%)	245 (24.14%)	1015 (100%)
Do you find economic policies effective in response to COVID-19?		245 (24.14%)	189 (18.62%)	287 (28.28%)	182 (17.93%)	112 (11.03%)	1015 (100%)
Do you think that educated people responded better to COVID-19		98 (9.66%)	182 (17.93%)	175 (17.24%)	350 (34.48%)	210 (20.69%)	1015 (100%)
Do you think trade regulations should be revised to avoid any such calamity in the future?		98 (9.66%)	105 (10.34%)	217 (21.38%)	301 (29.66%)	294 (28.97%)	1015 (100%)
How do the developing countries respond to it?		133 (13.10%)	168 (16.55%)	336 (33.10%)	224 (22.07%)	154 (15.17%)	1015 (100%)
Community response to COIVD-19 in your country		175 (17.24%)	210 (20.69%)	252 (24.83%)	287 (28.28%)	91 (8.97%)	1015 (100%)
Do panic and misinformation cause more than pandemic?		28 (2.76%)	105 (10.34%)	231 (22.76%)	273 (26.90%)	378 (37.24%)	1015 (100%)
Do you think that your country has any Pandemic Response Policy?		189 (18.62%)	238 (23.45%)	301 (29.66%)	175 (17.24%)	112 (11.03%)	1015 (100%)
The government response was right or not?		63 (6.21%)	168 (16.55%)	350 (34.48%)	252 (24.83%)	182 (17.93%)	1015 (100%)
Does government response in the shape of interest rate reduction and tax relief is helpful?		119 (11.72%)	168 (16.55%)	287 (28.28%)	245 (24.14%)	196 (19.31%)	1015 (100%)
Does the government bailout package is enough?		287 (28.28%)	238 (23.45%)	315 (31.03%)	126 (12.41%)	49 (4.83%)	1015 (100%)
Should firms compensate employees in such difficult times where firms are struggling?		70 (6.90%)	210 (20.69%)	203 (20.00%)	266 (26.21%)	266 (26.21%)	1015 (100%)
Do you think globalization is the primary source of spreading the virus?		91 (8.97%)	175 (17.24%)	231 (22.76%)	189 (18.62%)	329 (32.41%)	1015 (100%)
COVID-19 helped the environment		175 (17.24%)	84 (8.28%)	175 (17.24%)	273 (26.90%)	308 (30.34%)	1015 (100%)
Health Expenditures post COVID-19 should be increased or not?		119 (11.72%)	126 (12.41%)	168 (16.55%)	259 (25.52%)	343 (33.79%)	1015 (100%)

Source: Authors own calculations based on primary data.

Figure 7. Authors own calculations based on primary data.

Source: Survey data.

Figure A1. (A) Gender Distribution. Source: Authors own calculations based on primary data. (B) Respondents' by Profession. Source: Authors own calculations based on primary data.

Table A1. Summary of the literature review.

Author(s)	Countries	Data type	Methodology	Findings
Almond, Du, & Zhang	China	China's official pollution Monitor data	Difference-in Differences	Positive effect of COVID-19 Lockdown on Air Pollution
Alon, Doepke, Olmstead- Rumsey, & Tertilt	USA	American Time Use Survey 2017–2018; American Community Survey, 2017–2018.	Survey	Disproportionate Effect of COVID-19 on Gender Equality (in terms of labour force participation, childcare needs, workplace flexibility)
Andree	Netherlands	355 municipalities	OLS	COVID19 Lockdown and positive Connections with Air Pollution Exposure:
Beland, Brodeur, Mikola, & Wright	Canada	Labour Force Survey and Canadian Perspective Survey Series	OLS	Positive effect of COVID-19 and subsequent confinement on family stress and domestic violence
Beland et al.	USA	Current Population Survey Occupational Information Network Survey	Difference-in Differences	Negative Impact of Safer-at Home Policies on Labor Market Outcomes
Brodeur, Cook, & Wright	USA	Cellular tracking and high-frequency air pollution data	Difference-in Differences/ Synthetic Control Method	Causal Effect of Safer-At-Home Orders on Pollution and Collision Externalities
Cicala, Holland, Mansur, Muller, & Yates	USA	daily cell phone mobility data	OLS	Positive Impact of Social Distancing on Emissions and Expected Health Effects Through Reduced Personal Vehicle Travel and Electricity Consumption.
Fetzer et al.	Global	A survey covering 58 countries and over 100,000 respondents	OLS	Negative Effect of Lockdown on Mental Health
Gupta et al.	USA	Cell phone data measuring work-related mobility, State-level data on initial unemployment insurance claims, Unemployment-related internet searches, and Person-level data from the monthly Current Population Surveys from January 2015 to April 2020.	Difference-in Differences	Negative Impact of Social Distancing Policies on Labor Market Outcomes
Hamermesh	USA	2012–2013 American Time Use Survey	OLS	Impacts of Spending Time “With Whom” and “How” during Lockdowns on Happiness
Harris et al. (2020)	India	Telephone survey with 448 farmers in 4 states	Ordered logit regressions	Negative livelihood and dietary effects of COVID-19 on vegetable producers
He et al. (2020)	China	1,600 monitoring stations	Difference-in Differences	Good Effect of COVID-19 Lockdown on Air Quality
Kansiime et al. (2021)	Kenya and Uganda	Online survey data from 442 respondents	Probit model	Two-thirds of the respondents experienced income shocks due to the COVID-19that increase crisis food insecure respondents increased by 38% and 44% in Kenya and Uganda
Lu et al. (2020)	China	Cross-sectional online survey	Quantile Regression	Bad Impact of Quarantine and Attitudes Towards COVID-19 on Depressive Symptoms and Happiness
Rojas et al. (2020)	USA	District and state level school closures data NCES data on school district enrollment	OLS	Negative Impact on Job Market Conditions in Response to COVID19 infections and Mitigation Policies
Tubadji et al. (2020)	UK, Italy and Sweden	Waves of a customized survey with more than 10,000 respondents	Difference-in Differences	Causal Effect of Lockdown on Mental Health
Yasenov (2020)	USA	Occupational Information Network  American Community Survey	Data Analysis	Diverse Impact of Safer-at Home Policies on Different Occupations

Table A2. Use of the following before the COVID-19 pandemic.

Categories	Available options					Total
	Not at all	Rarely	Neutral	Frequently	Most frequently
Transport	168 (16.55%)	210 (20.69%)	133 (13.10%)	266 (26.21%)	238 (23.45%)	1015 (100%)
Bank	259 (25.52%)	364 (35.86%)	238 (23.45%)	105 (10.34%)	49 (4.83%)	1015 (100%)
ATM	217 (21.38%)	280 (27.59%)	217 (21.38%)	189 (18.62%)	112 (11.03%)	1015 (100%)
Hotel services	476 (46.90%)	154 (15.17%)	196 (19.31%)	140 (13.79%)	49 (4.83%)	1015 (100%)
Hospital	315 (31.03%)	357 (35.17%)	182 (17.93%)	119 (11.72%)	42 (4.14%)	1015 (100%)
Market visits	105 (10.34%)	210 (20.69%)	175 (17.24%)	294 (28.97%)	231 (22.76%)	1015 (100%)
Buying goods	84 (8.28%)	161 (15.86%)	217 (21.38%)	357 (35.17%)	196 (19.31%)	1015 (100%)
Selling goods	714 (70.34%)	84 (8.28%)	112 (11.03%)	77 (7.59%)	28 (2.76%)	1015 (100%)
Online shopping	476 (46.90%)	238 (23.45%)	182 (17.93%)	77 (7.59%)	42 (4.14%)	1015 (100%)
Digital payment	441 (43.45%)	224 (22.07%)	140 (13.79%)	84 (8.28%)	126 (12.41%)	1015 (100%)
Tourism activities	378 (37.24%)	175 (17.24%)	203 (20.00%)	168 (16.55%)	91 (8.97%)	1015 (100%)
Hiking/Outings	308 (30.34%)	189 (18.62%)	189 (18.62%)	224 (22.07%)	105 (10.34%)	1015 (100%)
Working from home	504 (49.66%)	231 (22.76%)	168 (16.55%)	63 (6.21%)	49 (4.83%)	1015 (100%)

Source: Authors own calculations based on primary data.

Table A3. Use of the following during the COVID-19 pandemic.

Categories	Available options					Total
	Not at all	Rarely	Neutral	Frequently	Most frequently
Transport	462 (44.14%)	259 (11.03%)	133 (15.86%)	42 (11.72%)	119 (17.24%)	1015 (100%)
Bank	490 (48.28%)	343 (33.79%)	105 (10.34%)	35 (3.45%)	42 (4.14%)	1015 (100%)
ATM	336 (33.10%)	392 (38.62%)	98 (9.66%)	112 (11.03%)	77 (7.59%)	1015 (100%)
Hotel services	770 (75.86%)	91 (8.97%)	84 (8.28%)	21 (2.07%)	49 (4.83%)	1015 (100%)
Hospital	553 (54.48%)	238 (23.45%)	140 (13.79%)	28 (2.76%)	56 (5.52%)	1015 (100%)
Market visits	287 (28.28%)	336 (33.10%)	182 (17.93%)	119 (11.72%)	91 (8.97%)	1015 (100%)
Buying goods	175 (17.24%)	378 (37.24%)	161 (15.86%)	203 (20.00%)	98 (9.66%)	1015 (100%)
Selling goods	763 (75.17%)	77 (7.59%)	133 (13.10%)	14 (1.38%)	28 (2.76%)	1015 (100%)
Online shopping	567 (55.86%)	231 (22.76%)	84 (8.28%)	84 (8.28%)	49 (4.83%)	1015 (100%)
Digital payment	462 (45.52%)	210 (20.69%)	91 (8.97%)	133 (13.10%)	119 (11.72%)	1015 (100%)
Tourism activities	658 (64.83%)	147 (14.48%)	112 (11.03%)	42 (4.14%)	56 (5.52%)	1015 (100%)
Hiking/Outings	567 (55.86%)	189 (18.62%)	98 (9.66%)	98 (9.66%)	63 (6.21%)	1015 (100%)
Working from home	448 (44.14%)	112 (11.03%)	161 (15.86%)	119 (11.72%)	175 (17.24%)	1015 (100%)

Source: Authors own calculations based on primary data.

Table

Name	Whereas, A (Not at all), B (Rarely), C (Neutral), D (Some-times), and E (Often).
How is your education affected by COVID-19?	□ A   □ B   □ C   □ D   □ E
Do you think online education is beneficial?	□ A   □ B   □ C   □ D   □ E
How is your firm affected by COVID-19?	□ A   □ B   □ C   □ D   □ E
Do you find economic policies effective in response to COVID-19?	□ A   □ B   □ C   □ D   □ E
Do you think that educated people responded better to COVID-19	□ A   □ B   □ C   □ D   □ E
Do you think trade regulations should be revised to avoid any such calamity in the future?	□ A   □ B   □ C   □ D   □ E
How do the developing countries respond to it?	□ A   □ B   □ C   □ D   □ E
Community response to COIVD-19 in your country	□ A   □ B   □ C   □ D   □ E
Do panic and misinformation cause more than pandemic?	□ A   □ B   □ C   □ D   □ E
Do you think that your country has any Pandemic Response Policy?	□ A   □ B   □ C   □ D   □ E
The government response was right or not?	□ A   □ B   □ C   □ D   □ E
Does government response in the shape of interest rate reduction and tax relief is helpful?	□ A   □ B   □ C   □ D   □ E
Does the government bailout package is enough?	□ A   □ B   □ C   □ D   □ E
Should firms compensate employees in such difficult times where firms are struggling?	□ A   □ B   □ C   □ D   □ E
Do you think globalization is the main source of spreading the virus?	□ A   □ B   □ C   □ D   □ E
COVID-19 helped the environment	□ A   □ B   □ C   □ D   □ E
Health Expenditures post COVID-19 should be increased or not?	□ A   □ B   □ C   □ D   □ E

Table

□ Fear to lose the job

□ Loss job

□ Financial Uncertainty

□ Food Shortage

□ Others _____________________

Table

Namely	Whereas A (Strong Negative), B (Negative), C (Neutral), D (Positive) and, E (Strong Positive).
Business	□ A   □ B   □ C   □ D   □ E
Stock Market	□ A   □ B   □ C   □ D   □ E
Financial Market	□ A   □ B   □ C   □ D   □ E
Production (industry)	□ A   □ B   □ C   □ D   □ E
Textile	□ A   □ B   □ C   □ D   □ E
Agriculture Sector	□ A   □ B   □ C   □ D   □ E
Services Sector	□ A   □ B   □ C   □ D   □ E
Consumption	□ A   □ B   □ C   □ D   □ E
Saving	□ A   □ B   □ C   □ D   □ E
Employment	□ A   □ B   □ C   □ D   □ E
International Trade	□ A   □ B   □ C   □ D   □ E
Global value chain	□ A   □ B   □ C   □ D   □ E
Travel Industry	□ A   □ B   □ C   □ D   □ E
Tourism	□ A   □ B   □ C   □ D   □ E
Environment	□ A   □ B   □ C   □ D   □ E
Poor Population	□ A   □ B   □ C   □ D   □ E
Daily Wage earners	□ A   □ B   □ C   □ D   □ E
Workforce	□ A   □ B   □ C   □ D   □ E
The role of media in controlling COVID-19	□ A   □ B   □ C   □ D   □ E

Table

Name	Use or involvement in the following, before and after COVID-19 pandemic (check all that applies)
Transport	□ Before COVID-19 pandemic	□ During COVID-19 pandemic
Bank	□ Before COVID-19 pandemic	□ During COVID-19 pandemic
ATM	□ Before COVID-19 pandemic	□ During COVID-19 pandemic
Hotel services	□ Before COVID-19 pandemic	□ During COVID-19 pandemic
Hospital	□ Before COVID-19 pandemic	□ During COVID-19 pandemic
Market visits	□ Before COVID-19 pandemic	□ During COVID-19 pandemic
Buying goods	□ Before COVID-19 pandemic	□ During COVID-19 pandemic
Selling goods	□ Before COVID-19 pandemic	□ During COVID-19 pandemic
Online shopping	□ Before COVID-19 pandemic	□ During COVID-19 pandemic
Digital payment	□ Before COVID-19 pandemic	□ During COVID-19 pandemic
Tourism activities	□ Before COVID-19 pandemic	□ During COVID-19 pandemic
Working from home	□ Before COVID-19 pandemic	□ During COVID-19 pandemic

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