Does communicable diseases (including COVID-19) may increase global poverty risk? A cloud on the horizon.

Coronavirus epidemic can push millions of people in poverty. The shortage of healthcare resources, lack of sanitation, and population compactness leads to an increase in communicable diseases, which may increase millions of people add in a vicious cycle of poverty. The study used the number of factors that affect poverty incidence in a panel of 76 countries for a period of 2010-2019. The dynamic panel GMM estimates show that the causes of death by communicable diseases, chemical-induced carbon and fossil fuel combustion, and lack of access to basic hand washing facilities menace to increase poverty headcounts, whereas, an increase in healthcare expenditures substantially decreases poverty headcounts across countries. Further, the results show the U-shaped relationship between economic growth and poverty headcounts, as economic growth first decreases and later increase poverty headcount due to rising healthcare disparities among nations. The causality estimates show that lack of access to basic amenities lead to increase of communicable diseases including COVID-19 whereas chemical-induced carbon and fossil fuel emissions continue to increase healthcare expenditures and economic growth in a panel of selected countries. The rising healthcare disparities, regional conflicts, and public debt burden further 'hold in the hand' of communicable diseases that push millions of people in the poverty trap.

Coronavirus disease 2019

Severe Acute Respiratory Syndrome

Middle East Respiratory Syndrome

Severe acute respiratory syndrome coronavirus 2

Generalized Method of Moments

Non-communicable diseases

Tuberculosis

Particulate matter 2.5

Growth-inequality-poverty

Poverty headcount ratio

Communicable diseases

Chemicals

Carbon dioxide emissions

Fossil fuel combustion

Healthcare expenditures

GDP per capita

Population density

Lack of basic hand washing facility

Vector autoregressive

Impulse response function

Variance decomposition analysis

Sustainable development indicators

Introduction

Coronavirus (COVID-19) pandemic

Coronavirus is the global epidemic that spread it from Chinese city Wuhan to the whole World. Its abbreviation is commonly used at every forum, i.e., COVID-19, which denotes ‘CO’ for corona, ‘VI’ for the virus, ‘D’ is a disease, and ‘19′ shows 2019, as its first time visible at the end of December 2019. The COVID belongs to the viruses’ family that comes across visible by common cold and respiratory diseases, which caused severe illness. The severity of this pandemic could get analyzed by the two common respiratory syndromes that seemed like COVID-19, i.e., “Severe Acute Respiratory Syndrome (SARS)” and the “Middle East Respiratory Syndrome (MERS)”. The mutant form of this corona is also called “novel coronavirus” that officially declared to The Chinese government on January 7, 2020 and since named SARS-CoV-2 (Aljazeera, 2020). This virus spread through human-to-human transmission, coughing, sneezing, touching, etc., which shows the following symptoms, i.e., fever, shortage of breathing, coughing, etc. The more severity of illness leads to pneumonia, multiple organ failure and even death. The symptoms of this virus could be asymptomatic in infected peoples; however, usually, its symptoms could visible within 5–6 days that prolong up to 14–20 days. The COVID-19 is highly deadly virus especially for the older age population, the median the range above 60, and poor immune persons that already suffered from some underlying medical problems, including cancer, heart patients, diabetic, and severe respiratory problems (WHO, 2020). The prevention measures of the novel coronavirus are at two different levels, first an individual level, the person should have to adopt some precautionary levels, like social distancing - avoid direct contact with the others through a handshake or avoid his/her self to attend the mass gathering, avoid to contact a person that has flu, coughing, etc., as saliva droplets or discharge droplets from the nose easily transmit this disease to others through human-to-human interaction. The frequent hand washing with soap and water at least 20 s, using hand sanitizers, surgical masks, gloves, etc., to prevent it from this epidemic disease (Parker-Pop, 2020). The second level prevention measures adopted from the state to prevent their peoples from the novel coronavirus are not limited to the following, i.e., the government initially spread general awareness to the common people about COVID-19 and advise them to keep safe and stay in homes; further using print media, social media and other modes of communication to get updated information about infected cases, deaths, and newly registered cases; closed all national and international flights closed the borders, provide freely accessible soap, hand sanitizers, surgical gloves, swabs test, etc; complete banned on mass gathering; closed educational institutes and transportation; enforce partial lockdown and somewhere full lockdown the city; etc. (Miller, 2020, Business Insider 2020, etc) These measures are vital to get a fight from coronavirus and it's our prime duty to obey all the state orders to get out from this pandemic.

Coronavirus updates

The data of COVID-19 pandemic is reported by using Worldometer by April 5, 2020. The total registered case of COVID-19 pandemic is reached up to 1,201,964 (02:49 GMT), among which 64,727 affected peoples were not survived while recovered patients are 246,638. To date, USA economy largely suffered from the COVID-19 pandemic as total registered case reached up to 311,357 with a death toll of 8,452; followed by Spain and Italy that have a total active case reached to 126,168 and 124,632 with a death toll of 11.947 and 15,362 respectively. In a comparison of the US, Italy and Spain has a large death toll that needs sustained efforts and collaborative strategic actions to reduce this pandemic (Worldometer, 2020). Table 1 shows 10 highly affected countries from COVID-19 (by using a data report of April 5, 2020, 02:49GMT) for ready reference.

Table 1

Reported Coronavirus Cases (10 extremely affected countries of the World).

Countries by Rank	Total registered cases	Deaths Toll	Total recovered	Critical patients	Share of deaths in total registered casesa	Share of recovered patients in total registered casesa	Difference between total recovered patients and total death casesa
USA	311,357	8,452	14,825	8,206	2.714569	4.761415	2.046847
Spain	126,168	11,947	34,219	6,532	9.469121	27.12177	17.65265
Italy	126,632	15,362	20,996	3,994	12.13121	16.58033	4.449112
Germany	96,092	1,444	26,400	3,936	1.502727	27.47367	25.97094
France	89,953	7,560	15,438	6,838	8.404389	17.1623	8.757907
China	81,669	3,329	76,964	295	4.07621	94.23894	90.16273
Iran	55,743	3,452	19,736	4,103	6.192706	35.40534	29.21264
UK	41,903	4,313	135	4,103	10.29282	0.322173	−9.97065
Turkey	23,934	501	786	1,311	2.093256	3.284031	1.190775
Switzerland	20,505	666	6,415	391	3.247988	31.28505	28.03706
World	1,201,964	64,727	246,638	42,290	5.385103	20.51958	15.13448

Source: Worldometer (2020) and.

author’s estimation.

The data of Table 1 shows that deaths share in total registered cases are far higher than in Italy, followed by UK, Spain, France and Iran (subsequently one can easily access the rest of the orders in the given table), whereas the least share of deaths in total registered cases reported in Germany, Turkey and USA. The higher share of recovered patients out of total registered cases was in China, followed by Iran, Switzerland, Germany, and Spain. It further shows the difference between total recovered and total death cases and found that China, Iran, and Switzerland exhibit a high difference share between them. The world statistics show that total death to registered cases ratio is 5.385 that is far below than Italy, UK, Spain, France, and Iran, whereas the ratio of recovered patients to total registered cases are 20.519, which is less than the share of China, Iran, Switzerland, Germany, and Spain. The global difference in recovered patients’ data versus death ratio is 15.134 that is greater than the USA, Italy, France, UK, and Turkey. These statistics could be made for a more clear understanding of the updates of coronavirus across the world.

Review of literature

Since the early January 2020, when the epidemic of coronavirus dispersed across the globe, the greater number of research scholars writing on the given issue and suggested a number of possible policy implications to the states to get prevents it from this pandemic virus. Few of the scholarly writing presented here that related with the study theme for ready reference, i.e., Wernery and Woo (2019) discussed the vulnerability of MERS that is zoonotic viral disease affected both animals and human beings. This disease largely hit the country’s economic development projects and increase food security challenges due to the shortage of healthcare resources that lead to an increase in poverty headcounts. The close surveillance of disease is highly recommended to prevent this disease from the massive population. Adhikari et al. (2020) reviewed the scholarly literature on novel coronavirus and come to the conclusion that healthcare expenditures enormously increases along with an increase in the intensity of spreading novel coronavirus. The only given therapy is the adoption of modes of prevention from this virus, else no specific antiviral treatment yet launched, thus symptomatic treatment is given the affected people to enhance the immunity against the virus. However, it should be remembered that the novel coronavirus is highly deadly and its effect evenly to the rich and poor nations. Thus, prevention is the only remedy for this pandemic. Thienemann et al. (2020) discussed the causes and prevention of coronavirus in low-income countries. The study argued that high population density areas, increase poverty incidence, and already ill patients of HIV, TB, and other parasitic diseases largely affect the COVID-19. The need for effective healthcare policies and prevention strategies would be helpful to minimize the incidence of coronavirus in the slums area with designated social distancing policies and sanitation policies across countries. Khan et al. (2020) concluded that COVID-19 is extremely deadly and it affects both the developed and developing countries. The developed countries could have a better healthcare strategy to cope the zoonotic viral diseases including COVID-19, while developing countries, like Asian countries, could hard to find the way to escape it, as due to lack of access to basic amenities and the countries that already have an incidence of common pneumonia, which cause high fatality rate in spite of the availability of treatment and vaccination make it more vulnerable situation in low –income countries. The main factors include high poverty rate, political conflicts and instability, violence, illiteracy, poor diagnostic laboratories, and other infectious diseases competing for a shortage of healthcare resources. The issues are very severe and its impact could be put more pressure on the country’s economic and healthcare resources that need to be addressed and understand in a short while. Rollins (2020) conferred the effect of 2019-nCOVon children, their parents, and poor community, as low-income earners and the people remain in poverty face difficult time to survive with the disease, while children are less likely to affect, and their parents need to take care with their family through adopting preventive measures.

The study presented some general literature related to poverty and its main determinants in order to assess the government reforms towards pro-poor growth (see, Table 2 ).

Table 2

Current literature on diseases-poverty nexus across countries.

Authors	Country	Time Period	Determinants of Poverty	Results
Sangar et al. (2019)	India	Survey period 2004	Out-of-pocket healthcare expenditures and non-communicable diseases (NCD)	NCD put huge pressure on economic and healthcare resources that negatively impact on increase poverty headcounts in a country.
Dong et al. (2019)	China	2012–2014	TB, Hospital admission rate, number of outpatient visits, and healthcare services	Poor patients have a high admission rate in TB hospitals, while more than 15% of poor patients get good medication adherence. Thus, the poor get more benefits in terms of utilization of healthcare services in a country.
Xu et al. (2020)	China	467 respondents data	Diseases and social capital	Diseases cause poverty incidence that mediates with social capital.
Khan et al. (2019a)	7 Asian countries	2005–2017	Natural disasters, healthcare expenditures and economic growth	Natural disasters increase poverty headcounts while healthcare expenditures alleviate poverty across countries.
Tran et al. (2019)	Vietnam	2016–2017	gallstone diseases, healthcare expenditures, and health insurance	The gallstone disease put a high burden on healthcare expenditures, which largely increase healthcare insurance premium in a country.
Farooq et al. (2019)	47 countries	1991–2017	Trade, social expenditures, and healthcare expenditures	Life expectancy increases by social and healthcare expenditures while trade –induced healthcare inputs further support country’s healthcare infrastructure.
Cinaroglu and Baser (2019)	Turkey	2003–2015	Out-of-pocket healthcare expenditures, and income inequality	Healthcare disparities largely affected poor community more than non-poor.
Carrasco-Escobar et al. (2020)	Peru	2015–2017	TB and PM2.5 emissions concentration	Air pollutant and incidence of TB put a burden on poor people that cause high poverty headcounts.
Liu et al. (2020)	China	2016–2017	TB, out-of-patient healthcare expenditures, and income inequality	The larger medical expenditures associated with TB diagnosis and its prevention leads to increase the overall cost of TB care that largely affected the poorest households.
Were et al. (2019)	Kenya	2006–2013	Malaria prevalence, household income, and healthcare inequalities	The prevalence of malaria infection largely affected the poorest households as compared to the less poor.
Saleem et al. (2019)	21 countries	1990–2016	TB, healthcare expenditures, mortality rate, and environmental pollutants	The higher risk of maternal death and under −5 mortality rate is associated with high healthcare expenditures, while the depth of food deficit causes a greater chance of increase TB incidence across countries.
Batool et al. (2019)	Meta analysis	Review of 42 scholarly papers	Environmental factors and healthcare spending	Environmental pollutants directly affected the community health that cause healthcare costs across countries.
Zhou et al. (2020)	China	2017	Non-communicable diseases, farmers income, household size, and health status	Poverty increases in rural China is mainly attributed to increase non-communicable diseases that affect household head/family members. The shortage of healthcare resources cause more poverty in the region.
Sumner et al. (2020)	138 countries	2019–2030	COVID-19, economic growth, and income inequality.	COVID-19 posed serious challenges to the globalized world in the form of increasing global poverty incidence that projected to increase poor headcount by 420–580 million relative to 2018 estimates.
Van Lancker and Parolin (2020)	Europe and the USA.	2019–2020	COVID-19 and School closure	Child poverty is exacerbated due to school closures, although it was necessary to contained coronavirus through social distancing among the students.
Ahmed et al. (2020)	USA	2019–2020	COVID-19 and inequality	Healthcare inequality is the leading factor that unable to controlled COVID-19 pandemic that exacerbate global poverty.
Tosam et al. (2019)	Low and middle income countries	1940–2000	Ebola virus, healthcare disparity, and inequality	Effect of Ebola virus largely retained on the poor nations because of high inequality and healthcare disparities.
Dhama et al. (2020)	Global data	2019–2020	SARS, MERS, COVID-19	Infectious diseases should be retained by adopting preventive measures, which would be helpful to reduce social crisis.

Contribution of the study

The contribution of the study is to get awareness to the common people about the outbreak of coronavirus in different domains, first, the study assesses the impact of communicable diseases including COVID-19 on poverty incidence, as it is widely discussed that communicable diseases can push millions of people in the poverty trap, thus the given importance put forward to include epidemic diseases in poverty modeling. Secondly, climate change is more likely to affect the poorer community, as due to lack of healthcare resources and insufficient household income, they are unable to cope with the environmental pollutants that are emerged with greater use of chemical use in the manufacturing process. Sooner and later, the government reforms for the conservation of the natural environment by using renewable fuels would minimize the risk of climate change, followed with pro-poor growth policies may escape poorer from climatic vulnerabilities. Thus, the study includes chemicals –induced carbon and fossil combustion in poverty modeling to get assess poverty incidence across countries. Third, without including economic growth and income inequality, the growth-inequality-poverty (GIP) triangle could not be completed, as both the factors are important in poverty modeling to assess pro-poor growth policies. For this purpose, the study included GDP per capita and lack of access to basic amenities (a proxy for inequality) in poverty modeling to get some insights about pro-poor growth process across countries. Healthcare expenditures have a direct relationship with poverty reduction, as more of the healthcare resources devoted by the government, its trickle down to lower-income strata group, which poor can get a benefit as compared to non-poor to sustain their livelihood. Thus, social expenditures also have included in poverty modeling. Finally, the government enforced the need for ‘social distancing’ to minimize the risk of coronavirus outbreak and it could possibly be reduced by proper city planning, as the world is largely compact and becomes a global village and the high chunk of poor nations reside in slum areas that are highly dense, and the chances of spread of any infectious diseases are more likely to exposed to the poor community. On the basis of this importance, the study included population density in poverty modeling and discussed its implication in the light of COVID-19.

Objectives of the study

The study has a following research objectives, i.e.,

To examine the impact of communicable diseases, chemicals-induced carbon and fossil combustion, healthcare expenditures, and population density on poverty headcounts across countries.

To substantiate the hump-shaped relationship between income and poverty reduction.

To investigate the possible consequences of lack of access to basic handwashing facility to poverty incidence in the light of COVID-19, and

To determine the causal inferences and inter-temporal relationship among the stated variables.

The stated objectives need a sound statistical technique in order to get robust inferences. The study gets benefited from dynamic panel GMM estimator to utilize in a panel of 76 countries for a period of 2010–2019. The dynamic panel estimator fulfills all the prerequisite of noise disturbance term and corrected it through by taking the first difference of the regression factors along with desired instrumental factors. Thus, the parameter gets it through this method to exert reliable estimates.

Data and methodology

The data of communicable diseases, chemicals used, carbon emissions, fossil fuel combustion, healthcare expenditures, GDP per capita, population density and lack of sanitation is taken from World Bank (2019) database, while a data of poverty headcount ratio is taken from POVCALNET at a given poverty line of US$1.90, whereas a data of poverty headcount taken from World Bank (2019) is utilizing a national level poverty estimates. The data surveyed a panel of 76 selected countries for a period of 2010–2019. Table –A in the appendix shows the list of countries for a ready reference. Table 3 shows the list of variables for ready reference.

Table 3

List of variables.

Variables	Symbol	Measurement	Expected Sign	Data Source
Poverty headcount ratio	POV_HCR	% of population	POVCAL NET & World Bank (2019)
Communicable diseases	COM_DIS	Causes of death by COM_DIS as % of total death	+	World Bank (2019)
Chemical Use	CHEM	% of manufacturing value added	+	World Bank (2019)
Interaction terms	CHEM × CO2	Metric tons per capita	+	World Bank (2019)
	CHEM × FFUEL	% of total energy demand	+	World Bank (2019)
Health expenditures	HLT_EXP	US$	–	World Bank (2019)
GDP per capita	GDPpc	Constant 2020 US$	+	World Bank (2019)
Square of GDPpc	SQGDPpc		–	World Bank (2019)
Population density	POP_DEN	People/km2 land area	+	World Bank (2019)
Lack of basic hand washing facility (proxy for inequality)	LB_HWF	% of population	+	World Bank (2019)

The study started with the traditional endogenous growth theory where human capital is considered the main vital factor that increases a country’s economic growth. Poverty trap is another factor that largely drains the human capital resource because of a vicious cycle of poverty. The earlier studies on poverty reduction and emphasis to make growth more ‘pro-poor’ based on growth-inequality-poverty (GIP) triangle, for instance, Kakwani and Pernia (2000), Bourguignon (2004), Klasen and Misselhorn (2008), Kakwani et al. (2010), Khan et al. (2014), Kakwani et al., (2018), Zaman et al. (2019), etc. These studies argued that poverty is the function of economic growth and income inequality, i.e., higher the country’s economic growth (or household income), lesser will be the poverty headcounts or vice versa. This specification does not imply that economic growth (or household income) is the only factor to design pro-poor growth policies. The economic growth itself could be the “necessary condition” of poverty reduction but may not be the “sufficient condition” of poverty reduction. Thus, the need to identify some other possible determinants of poverty is highly desirable. In search of the factors, the judicious income distribution is the other possible factor that could be used in pro-poor growth formulation, i.e., higher income inequality will decrease economic growth, which eventually increases poverty headcounts across countries. Thus, the need to contain income inequality in GIP formulation is desirable for analyzing long-term social development programmes. This specification is shown below for ready reference, i.e.,Where, POV_HCR shows poverty headcount, INEQ shows inequality, GDPpc shows GDP per capita, and ƹ shows error term.

Equation (1) hypothesized that inequality would largely increase poverty headcount while economic growth support to reduce poverty to proceed towards make a growth more ‘pro-poor’. This study gets benefited from the above-cited studies and formulate another equation (2) by adding more important determinations in the relationship of GIP triangle, i.e.,Where, COM_DIS shows communicable diseases, CHEM shows chemicals use, GDPpc shows GDP per capita, POP_DEN shows population density, LN_HWF shows lack of basic hand washing facility, and ‘i' and ‘t’ shows the number of countries at a specific time period.

The study selected the following variables on the basis of their importance in the earlier studies and utilized it in a given study for more policy insights, i.e., poverty headcount ratio is served as a ‘response variable’ of the study, while the remaining variables are the ‘stimulus’ of the study. Poverty is subject to change by economic growth and income inequality (see, Kakwani and Krongkaew, 2000, Adams (2004); Meng et al., 2005, etc), thus the given study included GDP per capita is the proxy for economic growth and healthcare-related factors, i.e., healthcare expenditures and lack of basic hand washing facility are used to show the healthcare disparities that substitute to the ‘inequality’ factors in the study. Further, the earlier literature largely argued that poor largely affected by infectious diseases and environmental changes (see, Haines et al., 2006; Weiss and McMichael, 2004; King and Bertino, 2008; Khan et al., 2019b, etc), thus the study included communicable diseases, chemicals used in manufacturing, carbon emissions, population density, and fossil fuel combustion for robust inferences. The square of economic growth and some moderation factors also used in the study in order to assess the hump-shaped relationship and synergetic impact of environmental factors on poverty headcounts across the countries.

Equation (2) shows that it is the likelihood that economic growth tends to show an inverted U-shaped relationship with poverty headcount, further there will be a non-linear relationship between chemicals used and poverty headcount with moderation of carbon emissions and fossil fuel combustion. The other variables, i.e., communicable diseases, population density, and lack of sanitation will exert a negative relationship with poverty headcount while healthcare expenditures would likely to impact positively on poverty reduction across countries. Fig. 1 shows the research framework of the study.

Fig. 1

Research framework of the study.

Fig. 1 shows that high chemicals used in the manufacturing process and population density leads to an increase communicable diseases, which could negatively affect poverty reduction outcomes. Further, economic growth would be a positive tool in order to increase healthcare expenditures and lessen income inequality that ultimately tends to decrease poverty headcounts and carbon-fossil combustion across countries. The following tentative statements need to be checked through sophisticated statistical techniques, i.e.,

There is a likelihood that communicable diseases, lack of sanitation, and population density will increase poverty headcounts.

The inverted U-shaped relationship is expected between economic growth and poverty reduction, and

Increase healthcare expenditures would decrease poverty headcounts to support trickle-down hypothesis across countries.

The study used the following sequential econometric techniques in order to get reliable parameter estimates, i.e.,

Dynamic Panel Generalized Method of Moment (GMM) Estimator

The dynamic panel GMM estimator is used in this study to get robust inferences. The differenced GMM estimator is utilized as developed by Arellano-Bond (1991) specification. The given estimator not only minimizes the possibility of endogeneity while it reduces the possibility of autocorrelation in the given data set. The important features of the estimator are as follows: i) it includes lagged dependent variable as a regressor, ii) list of vector factors are included in the instrumental list, iii) it works on a large panel of countries with a limited time period, iv) AR bond at first and second lagged allow to check any persistent issue related with autocorrelation in a given model, v) The instrumental validity has been checked by J-statistical value and instrumental rank, and vi) this estimator works under differenced operator. The given estimator is more powerful than any other available instrumental techniques, like two-stage least squares, three-stage least squares, robust least squares regression, etc. These estimators covered the known endogeneity issues and minimize the possible outliers, and work under different conditions, while the given estimator is work under unknown endogeneity issues and handle simultaneity issues accordingly. The differenced GMM estimator works where the cross-section identifiers are larger than the time period. Thus it works in a more constraint environment and provides robust inferences. The following equation is used for the specification of differenced GMM estimator, i.e.,Where Δ shows first difference of the variables and ‘Z’ shows instrumental variables list. .

Equation (3) included the first difference of the variables along with lagged dependent variable and instrumental lists, which would give a dynamic effect of the explanatory variables to the explained variable.

VAR Granger Causality by Wald Test

The causal relationship between the stated variables is analyzed by a Wald test under VAR specification. This test statistics allow finding four different plausible hypotheses, i.e.,

When two variables jointly move in the same direction and both causes each other at a given time interval. Thus we can say that both the variables have a bidirectional causal relationship with each other.

Unidirectional Causality: When one variable like ‘X’ causes to the ‘Y’ variable but ‘Y’ variable does not Granger cause ‘X’, it implies that the given causal relationship is one-way directional that moves from variable ‘X’ to ‘Y’ but not ‘Y’ to ‘X’. Thus we can say that the given variable has a unidirectional causal relationship with the other variable.

Reverse Causality – Unidirectional Relationship too: The causal relationship earned a same features than the above stated unidirectional causality, however, the distinction is that the causal inferences are revert back and the direction of causality is not running from like variable ‘X’ to ‘Y’ but now at this time its moving from variable ‘Y’ to ‘X’. Thus, it also exhibits a one-way linkage with reverse causality, and

Flat/No Causality: In many cases, the relationship between the two variables in correlation and regression apparatus is significantly positive or either negative and have exerted an important policy aspects, however, during estimating the causal relationship between the two variables, the relationship could not fall either in any above three stated causality conditions. In this scenario, both the variables, although have a strong appearance in regression apparatus, would unable to determine any causal inferences between with each other. Thus we can say that both the variables have a flat/no causal relationship during a given time interval.

The modified version of Granger causality is estimated by the following system equations, i.e.,Where, A1 is the 7 × 1 matrices of coefficients with A0 is the 3 × 1 identity matrix.

Inter-temporal Relationship under VAR Specification

The study further estimated an inter-temporal relationship between the stated variables over a time horizon by two different innovation accounting matrices. The first one is “impulse response function (IRF)” that assessed either a positive or negative relationship among the variables through different structural shocks, which could happen among the selected cross-sections in the next coming years period. Another one is “variance decomposition analysis (VDA)” that assessed the variance error shocks among the regressors due to a one standard shock pertaining to the response variable. Both the methods work under forecasting mechanism and evaluated the relationship between the variables for the next 10 years time period. The given technique breakdown the forecast error for the variables and established a VAR system, i.e.,Where ‘Pt’ is the response variable and . is the noise error term.

Results and discussion

Table 4 shows the descriptive statistics of the candidate variables. The average value of poverty headcount, communicable diseases, and chemical use is about to 15.395%, 13.078% of total death, and 9.694% of manufacturing value-added respectively. The maximum value of carbon emissions and fossil fuel combustion is 23.811 metric tons per capita and 99.977% of total energy with an average value of 5.578 metric tons per capita and 72.323% respectively. The maximum value of GDP per capita is US$110742.3 and an average value of US$17230.83 with a standard deviation of US$19392.71. The healthcare expenditures, lack of sanitation, and population density on average shows a value of US$1474.137, 27.559% of the population, and 145.428 people/km2 of land area respectively.

Table 4

Descriptive statistics.

Statistics	POV_HCR	COM_DIS	CHEM	CO2	FFUEL	GDPpc	HLT_EXP	LB_HWF	POP_DEN
Mean	15.395	13.078	9.694	5.578	72.323	17230.83	1474.137	27.559	145.428
Maximum	72.300	71.500	47.931	23.811	99.977	110742.3	9871.742	99.083	2017.274
Std. Dev.	14.956	15.423	6.625	4.644	22.492	19392.71	1936.618	22.322	266.143
Skewness	1.192	2.181	2.350	1.521	−1.252	1.929	1.790	1.157	4.499
Kurtosis	4.636	7.058	11.965	5.418	4.147	7.594	5.755	3.647	25.494

Table 5 shows the correlation matrix and found that communicable diseases and lack of sanitation both increases poverty headcounts with a correlation coefficient value of 0.587 p < 0.000 and 0.487 p < 0.000 respectively, whereas healthcare expenditures and economic growth decreases poverty headcounts with a correlation values of −0.551 p < 0.000 and −0.567 p < 0.000 respectively. There is a negative correlation found between healthcare expenditures and communicable diseases while there is a positive relationship exhibit between lack of sanitation and poverty headcount across countries. Population density increases carbon and fossil fuel combustion during a study time period.

Table 5

Correlation matrix.

Variables	POV_HCR	COM_DIS	CHEM	CO2	FFUEL	GDPpc	HLT_EXP	LB_HWF	POP_DEN
POV_HCR	1
	–
COM_DIS	0.587	1
	(0.000)	–
CHEM	−0.158	0.003	1
	(0.000)	(0.933)	–
CO2	−0.435	−0.396	0.087	1
	(0.000)	(0.000)	(0.022)	–
FFUEL	−0.167	−0.388	0.148	0.297	1
	(0.000)	(0.000)	(0.000)	(0.000)	–
GDPpc	−0.567	−0.360	0.147	0.611	−0.024	1
	(0.000)	(0.000)	(0.000)	(0.000)	(0.520)	–
HLT_EXP	−0.551	−0.327	0.185	0.556	−0.063	0.929	1
	(0.000)	(0.000)	(0.000)	(0.000)	(0.097)	(0.000)	–
LB_HWF	0.487	0.607	−0.071	−0.414	−0.361	−0.405	−0.385	1
	(0.000)	(0.000)	(0.061)	(0.000)	(0.000)	(0.000)	(0.000)	–
POP_DEN	−0.175	−0.078	0.048	0.302	0.210	0.077	0.015	−0.103	1
	(0.000)	(0.039)	(0.201)	(0.000)	(0.000)	(0.041)	(0.686)	(0.007)	–

Note: small bracket show probability values.

Table 6 shows the dynamic panel GMM and VAR Granger causality estimates and found that there is a positive relationship between communicable diseases and poverty headcount, which implies that an increase in communicable diseases lead to increases in poverty headcount in a panel of selected countries. The results are discussed in the light of novel coronavirus (COVID-19) as the global economy is largely affected with an unwanted epidemic plaque in recent dates. The latest report of World Economic Forum (2020) discussed the vulnerability of coronavirus disease for poorer countries that are already drowning in debt and unable to spend sufficient amount on healthcare expenditures, while on the another side, rich nations spending billions of dollars to build new hospitals for the prevention of this epidemic outbreak. This inequality leads to many negative outcomes in the prevention of communicable diseases that could be sustained by transferring healthcare equipment, knowledge transfer and lessening the debt burden to the poorer nations. Aljazeera (2020b) report shows that millions of poor are directly hit by the outbreak of the novel coronavirus during the lockdown in India, thus the high need for making adequate planning to tackle this plaque is required to save economic and human toll. Rasmi (2020) argued that United Nation doing hard struggle to generate around US$2 billion for the poorer countries and war-torn countries to control the coronavirus pandemic. The limited COVID-19 testing swaps, inadequate medical care facilities, and limited funds to fight against the spread of epidemic raised many unsolved questions that need to be answered by joint economic collaboration.

Table 6

Dynamic panel GMM and VAR granger causality estimates.

Variables	Coefficient	Standard Error	t-statistics	Probability value
POV_HCR(-1)	0.778	0.048	15.940	0.000
COM_DIS	0.006a	0.003	1.940	0.052
CHEM	0.073	0.163	0.452	0.651
CHEM × CO2	0.002938a	0.001673	1.755	0.079
CHEM × FFUEL	0.000370a	0.000202	1.829	0.067
GDPpc	−3.08E-05	1.66E-05	−1.856	0.064
SQGDPpc	3.21E-10	1.74E-10	1.843	0.065
HLT_EXP	0.000166a	9.41E-05	1.763	0.078
LB_HWF	0.010a	0.003	3.334	0.000
POP_DEN	−0.000141	0.001488	−0.094437	0.924
Statistical Tests
J-statistic	24.715 (0.590)	Prob. J-statistic	0.590
Instrumental rank	37	AR(1)	−2.906 (0.003)
		AR(2)	1.360 (0.173)
VAR Granger Causality by Wald Test
LB_HWF→COM_DIS	POP_DEN→CHEM × CO2	CHEM × CO2→GDPpc	HLTEXP→GDPpc	POP_DEN→GDPpc
CHEM→HLT_EXP	CHEM × FFUEL→HLT_EXP

Note:

shows value estimated on the basis of exclusion criteria.. → shows unidirectional relationship between the variables.

Further, the results show that the moderation effect of chemical use with carbon and fossil fuel combustion on poverty headcount becomes positive, which implies that greater use of the chemical in manufacturing industries leads to highly toxic carbon and fossil fuel combustion that exacerbates poverty headcount across countries. The Guardian (2020) report argued that coronavirus leaves many unanswered questions related to the global fight against climate change and poverty reduction. The epidemic plaque would be occurred due to high climatic vulnerability, which leads to an increase in poverty incidence across countries. Kolinjavjivadi (2020) blamed the global environmental policies that were unsatisfactory to tackle climate changes thus it’s exacerbated an outbreak of many communicable diseases like coronavirus.

The U-shaped relationship found between economic growth and poverty reduction gives food-for-thoughts to the policymakers to reduce growing inequalities that arise through high income that leads to an increase in poverty headcounts across countries. The New York Times (2020) showed that poorer nations that hold a higher income inequality and poverty headcounts are more likely to catch with the coronavirus epidemic due to healthcare disparities. Although outrage of coronavirus does not discriminate poorer and/or richer, however, a few incidences further reported that slums area caused citywide COVID-19 accelerant. Finally, inadequate healthcare expenditures and low basic hand washing facility lead to an increase in poverty headcount by adopting a transmission channel of exacerbated infectious diseases including COVID-19. The Conversation (2020) report shows that fight with coronavirus could become more difficult where the assessment of basic hand washing facilities are less available, as a case study of India, the report argued that Indian economy could face the main challenge of lack of basic sanitation facility, which can extremely affect millions of poorer in a country. UNICEF (2020) report highlighted the urgent need to use basic healthcare sanitation facilities including hand washing with soap and water to fight against coronavirus. The report shows the fact sheet where numerous countries unable to provide basic sanitation facilities and billions of people are ‘out of reach’ from access to basic hand washing facility. Thus it could affect largely to the poorer nations that have low healthcare spending and less access to basic healthcare sanitation facilities. The similar insights get from the latest report issued by Business Standard (2020) for Indian economy where a lack of basic amenities among poorer community get harder to fight against this novel coronavirus. The diagnostic tests confirmed that the given results are reliable as it falls under the basic statistical assumptions. The J-statistic value, instrumental rank and AR-values confirmed that the instruments used in the given model are accurate and reliable, and there is no existence of possible autocorrelation in a given model. Table 7 shows the sensitivity analysis performed by panel quantile regression for ready reference.

Table 7

Sensitive analysis test by panel quantile regression.

Variables	Coefficient	Standard Error	t-statistics	Probability value
Constant	23.419	1.533	15.078	0.000
COM_DIS	0.115	0.045	2.517	0.012
CHEM	0.281	0.176	1.591	0.111
CHEM × CO2	0.046	0.007	6.449	0.000
CHEM × FFUEL	−0.008	0.002	−3.768	0.000
GDPpc	−0.0007	6.87E-05	−10.387	0.000
SQGDPpc	5.00E-09	4.54E-10	11.016	0.000
HLT_EXP	0.00074a	0.0003	2.165	0.030
LB_HWF	0.054	0.033	0.814	0.100
POP_DEN	−0.002b	0.0017	−0.1.260	0.207

Note:

and.

shows the quantile regression estimates at 10th quantiles and 30th quantiles respectively.

The sensitivity analysis results endorse the findings of earlier parameter estimates obtained by panel differenced GMM estimates. Fig. 2 shows the U-shaped relationship between per capita income and poverty headcounts for ready reference.

Fig. 2

U-shaped Relationship between Per capita Income and Poverty Headcounts.

The VAR Granger causality analysis shows that lack of basic hand washing facility Granger cause communicable diseases that confirmed the unidirectional casualty relationship between them. The other results are as follows, i.e., population density Granger cause chemical-induced carbon emissions and economic growth; chemical-induced carbon and fossil fuel combustion Granger cause economic growth and healthcare expenditures respectively; chemical use Granger cause healthcare expenditures, and healthcare expenditures turned to cause economic growth across countries. The causality results imply that lack of basic amenities menace to increase communicable diseases, which is the forefront issue in fighting against the coronavirus (Thomas-Rüddel et al., 2020; Wax et al., 2020, etc). The other result implies that high population density increases chemical-induced carbon emissions that lead to increase country’s economic growth on the cost of an emerging pandemic like COVID-19 (Qiu et al., 2020; Ficetola and Rubolini, 2020, etc). The chemical-induced carbon and fossil fuel combustion leads to an increase in healthcare expenditures, which remains to create a hurdle against the fight of coronavirus. Thus climate changes exacerbate the incidence of a communicable disease including COVID-19 across countries. Table 8 shows the IRF and VDA estimates for ready reference.

Table 8

IRF and VDA estimates.

Response of POV_HCR
Period	POV_HCR	COM_DIS	CHEM	GDPpc	HLT_EXP	LB_HWF	POP_DEN
1	1.177120	0	0	0	0	0	0
2	1.278452	−0.042108	−0.011849	−0.018587	−0.010475	0.030398	−0.010222
3	1.273717	−0.039477	−0.004983	−0.021486	−0.015936	0.022882	−0.021908
4	1.260108	−0.030463	−0.000103	−0.016656	−0.019218	0.023733	−0.034008
5	1.245165	−0.021712	0.004164	−0.008747	−0.022539	0.026216	−0.046817
6	1.230025	−0.013216	0.008414	0.000369	−0.025871	0.028656	−0.060553
7	1.214901	−0.004957	0.012912	0.010011	−0.029247	0.031053	−0.075381
8	1.199829	0.003053	0.017755	0.019938	−0.032679	0.033417	−0.091462
9	1.184814	0.010819	0.022984	0.030072	−0.036179	0.035747	−0.108968
10	1.169851	0.018352	0.028618	0.040392	−0.039756	0.038046	−0.128085
Variance Decomposition of POVHCR
Period	S.E.	POV_HCR	COM_DIS	CHEM	GDPpc	HLT_EXP	LB_HWF	POP_DEN
1	1.177120	100	0	0	0	0	0	0
2	1.738806	99.88764	0.058643	0.004644	0.011427	0.003629	0.030563	0.003456
3	2.156178	99.85589	0.071658	0.003554	0.017361	0.007823	0.031138	0.012571
4	2.498054	99.83984	0.068258	0.002648	0.017380	0.011747	0.032225	0.027899
5	2.791892	99.82107	0.060694	0.002342	0.014896	0.015922	0.034616	0.050456
6	3.051725	99.79223	0.052674	0.002721	0.012469	0.020513	0.037790	0.081600
7	3.285849	99.74860	0.045663	0.003891	0.011683	0.025616	0.041528	0.123015
8	3.499666	99.68635	0.040330	0.006004	0.013545	0.031301	0.045726	0.176744
9	3.696952	99.60179	0.036996	0.009245	0.018754	0.037627	0.050325	0.245262
10	3.880494	99.49100	0.035816	0.013830	0.027857	0.044648	0.055290	0.331559

The results show that communicable diseases, chemicals use, continued economic growth, and lack of sanitation issues will likely to increase poverty headcounts while increase healthcare expenditures will likely to decrease poverty headcount over a time horizon. The result implies that communicable disease will more likely to affect negatively to the poorer communities due to lack of access to basic amenities, low healthcare expenditures, and rising inequality. Thus, the need for rapid economic and healthcare policies is desirable to minimize the threat of epidemic plaque at a global scale. Fig. 3 shows the complete descriptions of the forecast parameters for ready reference.

Fig. 3

Irf estimates.

The VDA estimates show that population density will likely to increase poverty headcount with a variance error shock of 0.331%, followed by lack of access to basic healthcare facilities, healthcare expenditures, communicable diseases, and economic growth, while the least influenced will be of chemicals used in manufacturing sector that likely to exert negatively on poverty headcount. Fig. 4 shows the complete descriptions of the forecast parameters by VDA approach for ready reference.

Fig. 4

Vda estimates.

Conclusions

The emergence of novel coronavirus threat to human health that evenly affected developed and developing nations, however, due to rising healthcare disparities in poorer nations, the shortage of healthcare services and provision of basic healthcare services are likely exposed to the poorer nations more in front line of this epidemic plaque. Thus the need of collaborative efforts to fight against this epidemic plaque is imperative for long-term sustained growth. This study identifies the potential risk of communicable diseases including COVID-19 that largely affect the poor community in a panel of 76 countries by using a time series data of 2010–2019. The dynamic panel GMM estimates show that increase risk of spreading communicable diseases including COVID-19, chemical-induced carbon and fossil fuel combustion, and lack of sanitation largely affected poorer peoples while increased use of healthcare resources decreases poverty headcounts across countries. The non-linear relationship is found between economic growth and poverty reduction that follow a U-shaped relationship between them. The causal inferences can be deduced by the following given directional relationship, i.e., causality mainly running from lack of access to basic amenities to communicable diseases, healthcare spending to country’s economic growth, and chemicals use to healthcare spending. The forecast relationship among the stated variables corresponds that communicable diseases, chemicals –induced carbon and fossil combustion, economic growth, and lack of access to basic amenities will likely to increase poverty headcounts for the next 10 years time period.

Policy implications

The following policy implications are recommended for healthcare specialists, environmentalists, and Government officials to get benefited from it and make use for against coronavirus pandemic, i.e.,

To designed national prevention policies for epidemic plaque and assure that everyone could respond and act upon them. Further, the need of some strict action plans are desirable, firstly, everyone including children, adults and old age peoples should have to be wear preventive masks when go outside the home, else some possible penalty should be imposed; secondly, during a lockdown, it should be assured that no one could get outside except in some genuine cases, however, they should be protective enough to come out, and thirdly, special take care required for the poorer communities, they mostly worked on daily wages and they largely affected by the lockdown, so the government should have to collect the data of the needy peoples and allocate some emergency funds for their foodstuff so they can spend these days in prevention. The case study of Pakistan for taking these measures is an exemplary figure in this regard.

Communicable diseases evenly affected poor and rich nations so no one can escape out from this epidemic if not taking preventive measures, however, due to wide healthcare disparities among the globalized world, the poorer nations could get more likely to expose with this epidemic plaque as compared to the rich nations. The same situation exists for the poor and rich person, both can evenly affect with the infectious disease, however, prevention is better than cure, so rich can prevent his/her self more likely as compared to the poor person due to having sufficient knowledge about this pandemic and have an ability to fight against this disease, while on the other side, due to dearth of economic and healthcare resources with the poorer, they exposed more likely to this epidemic and get trap in a vicious cycle of poverty. Now there are two possible options with the government, first, if the country has enough resources, then the government should have to devote their all resources for the prevention of infectious diseases and side-by-side used expansionary fiscal and monetary policy instruments, thus due to less imposition of taxes and get some subsidy to the business sector, while when a business enterprise get a loan on less interest, so the businesses could gain momentum and it will ultimately lessen the economic depression phase. Further, the rich countries should allocate some funds to the poorer countries in order to defeat this epidemic and shared knowledge, technical, and healthcare expertise as well, thus poorer nations could be able to defeat the epidemic like COVID-19. Secondly, if the nation has fewer resources and have engaged with some political and regional turmoil, further engaged with high debt and bear some economic sanctions from the world community, thus it should have to re-correct their policies and resolve all political and regional disputes in these unprecedented times and get back the expertise from the developed world and to get swap their debt with healthcare resources that could get able to prevent it from COVID-19 pandemic.

The greater need of basic access to hand washing facilities is a precondition to escape with the novel coronavirus. The countries that have a lack of access to basic amenities are largely affected by this epidemic outbreak, thus every government should have to provide frequent soap and water to their peoples to prevent their selves with this pandemic. Few solutions are suggested, i.e., soap and hand sanitizers should be freely available at less price and the government should have to monitor their prices, secondly, drinking water should be boiled first and use it semi hot, it will also help to get a person more healthy, and thirdly, keep avoid contact to others. The good thing is most of the precautionary measures are already used by the global world and spread it by the media and word-of-mouth.

The climate change always remains a hot debatable topic in the environmental literature, as industrialization process keep producing new toxic air pollutants, most of them likely to the mutant and converted into novel coronavirus, although there is not get any substantial evidence in this regard, however, we cannot ignore the hidden reality that most of the infectious diseases caused by air pollutants that need environment sustainable policies like advancement in the cleaner production technologies, and adopting strict environmental regulations, so it would be helpful to minimize the risk of infectious disease across countries, and

Poverty reduction remains a key agenda of the United Nation and in 2015, the United Nations proposed 17 sustainable development indicators (SDIs) among which the first and second foremost goal of SDIs are related with poverty and hunger-reduction. The infectious disease can be more likely to spread through lack of knowledge, lack of income, and lack of healthcare resources, which unintentionally multiply by many folds as in a case of coronavirus. Although there is no such evidence found with the given data set that poverty cause communicable diseases, however, the result of the study found that lack of basic amenities increases communicable diseases that affect poor citizen more than non-poor. Thus we may not ignore the possibility of existence of more causal channels between the stated factors in future. The government should give indemnity to the poor community to resolve their hunger issues at priority, secondly, the government need to establish a social safety net and include in a priority agenda to reduce half of the poverty and hunger as per the agenda of the United Nation till 2030 would be reduced, thirdly, government should give provide jobs to the poorer in a way to register them free of cost in any hands-on-training centre and give some little honorarium so the poor can be able to get benefited from it, forth, government officials should have to fortnightly visit the slums area of nearby their residencies and enlist all their problems and related issues, and get resolve it in a stipulated time period, so if God forbid, any disaster happens, then the people could be able to sustained and cope with the problem, and finally, monitoring and command system should be vigilant, the governments should devise a policy for the betterment of the countries and took strict actions against those that get find some undue favour by using their authority.

These suggested policies are framing under the need to escape from novel coronavirus, which related to the study topic. The future challenges can be reduced by taking some hardcore decisions at present, the government should have to design a layout about poverty reduction and get a plan to fight against infectious disease, as both are interlinked issues at the end. The pro-poor growth and healthcare policies should be designed in a way to reduce poverty through increase healthcare spending, thus the provision of technical knowledge should be given to the poorer, which enables the poor to get escape out of from infectious diseases and poverty trap.

Table A

List of Countries

“Albania, Algeria, Armenia, Australia, Austria, Bahrain, Belarus, Belgium, Bolivia, Bosnia and Herzegovina, Brazil, Bulgaria, Canada, Chile, China, Colombia, Costa Rica, Croatia, Ecuador, Egypt, Estonia, Ethiopia, Finland. France, Georgia, Greece, Hungary, Iceland, India, Indonesia, Iran, Ireland, Italy, Japan, Jordan, Kazakhstan, Kenya, Korea, Kyrgyz Republic, Latvia, Lithuania, Luxembourg, Malaysia, Malta, Mauritius, Mexico, Mongolia, Morocco, Namibia, New Zealand, Panama, Peru, Philippine, Poland, Portugal, Romania, Russia, Senegal, Serbia, Slovak Republic, Slovenia, South Africa, Spain, Sri Lanka, Sweden, Tanzania, Thailand, Tunisia, Turkey, Ukraine, UK, USA, Uruguay, Vietnam, Yemen, and Zimbabwe”.