Determinants of Household Healthcare Expenditure: A Study in Purulia District, West Bengal

 

Santanu Bhattacharya¹, Sebak Kumar Jana²

¹M. Phil Research Scholar, Department of Economics, Vidyasagar University,

Midnapore 721102, West Bengal, India.

²Professor, Department of Economics, Vidyasagar University, Midnapore, 721102, West Bengal, India.

 

ABSTRACT:

According to World Health Organization health is considered as "state of complete physical, psychological, and social well-being, and not merely the absence of disease or infirmity”. Healthy people are considered to be the heart of the nation’s growth. Household healthcare expenditure has been a subject of discourse in the field of Health Economics for quite some time now. This is due to the fact that healthcare expenditure has been on the increase globally and there is the need to determine the factors that influencing household healthcare expenditure. The specific objectives of the present study are to analyze the socio-economic and health expenditure characteristics of households in Kashipur block, to determine the factors that influencing healthcare expenditure of the households in Kashipur block. We have visited randomly selected three villages of Kashipur block in Purulia district, West Bengal and surveyed two hundred households.  We have used some simple statistical tools and bar diagram for the pictorial exposition of the data. For statistical analysis of data we have used One-Way ANOVA and Tukey’s method as our post hoc test and multiple regression model. The findings of the study reveal that monthly average health expenditure differs across social category and major source of income of the households. The explanatory variables like monthly average family income, social category, household head’s education, number of members in a household above 60years, having at least one member who consume medicines during the whole year and mode of treatment are found to have significant impact on the household average monthly health expenditure. Some explanatory variables like at least one member in a households having Swasthya Sathi card or not, having livestock or not and number of meals a household consume per day becomes insignificant bur their coefficient has expected sign.

 

 

 

1. INTRODUCTION:

In the framework of human development, health is considered as an individual right, which describes not only the absence of illness but also the entire state of physical, psychological and social well-being. Healthcare is essential not only for achieving demographic benefits through a strong and creative workforce and achieving universal happiness, but also for achieving the goal of population stability.

 

Against this background, access to adequate and significant health care is seen to be an important factor in improving the quality of human life, especially in developing and least developed countries. According to World Health Organization health is consider as "state of complete physical, psychological, and social well-being, and not merely the absence of disease or infirmity”. Healthy people are considered to be the heart of the nation’s growth. Healthy people do their work effectively and as a result create wealth. The mutual prosperity of these healthy people increases the wealth of the nation and makes the economy stronger. Healthcare and it’s attain is a constant concern of social thinkers. The nations of the world have decided that enjoying the uppermost attainable standard of health without any distinction of race, religion, political affiliation and economic or social status is a fundamental human right. In addition to its inherent value to the individual, improving health and protection is also central to overall human development and poverty reduction. The Millennium Development Goals, imitative from the UN Millennium Declaration, promise countries to halve extreme income poverty and improve health by 2015.Three of the eight MDG goals are based on health issues and target for a two-thirds reduction in child mortality, a three-quarters reduction in maternal mortality, and a halt to the spread of HIV/AIDS, malaria and tuberculosis. India is far from achieving the target and the circumstances in West Bengal generally healthier than the Indian average, also needs considerable improvement. In addition to achieving those goals, significant improvements are needed in all aspects of public health, especially to prevent illness and death, as well as to make accessible and affordable healthcare services accessible to all. There is a close link between poverty and poor health, with the poorest people in each society generally experiencing much higher levels of infant and maternal mortality. Poor people have low capacity (low nutritional status, lack of awareness, marginalized lifestyle), poor access to health services and lack of funds forcing them to face more health-related shocks. Poor people are vulnerable to these shocks and tend to experience lower well-being because inadequate social security provisions. The poor people go through worse health and die younger. Poor people have higher average child and maternal mortality, higher levels of disease burden and restricted access to health care. Gender inequality further adversely affects the health of poor women and girls. Health is also a very important economic resource, especially for the poor. Their livelihood depends on it. When a poor or socially vulnerable person becomes ill or injured, the whole family may be stuck in a downward spiral of lost income and higher health care spending. The cascading effect may include changing the time of taking care of the sick from income or going to school; they may be forced to sell the assets need to make a living. Poor people are more at risk from this downward spiral because they are more prone to disease and have limited access to healthcare and social insurance. The relationship between health and poverty works on both sides. Poor health can be a catalyst for increasing poverty and as a result, poverty can create and perpetuate poor health conditions. Low productivity and low-level income is the cause of low levels of nutrition. Poor nutrition makes the poor extremely susceptible to disease. In addition, living conditions and sanitation facilities are poor due to low income. These in turn increase the burden of the family. Wage earners in the family remain unemployed for a long time due to frequent and chronic illness. Increased vulnerability to the disease can lead to miserable sales of assets, leading to indebtedness. Some of the factors associated with income poverty are also determinants of illness, malnutrition and high fertility. These consist of high level of female illiteracy, lack of access to clean water, food insecurity, poor family care practice, heavy work demands and lack of fertility control, as well as low access to preventive and primary healing care. The proportion of household income spent on health care is generally higher in the low-income groups than in the high-income groups in Asia. Catastrophic illnesses often put close-knit families in great financial trouble. We have already mentioned our motivation of the present study. Now we will highlight specifics objectives of the study The Specific objectives of the present study are as follows: i) To analyze socio-economic and health expenditure characteristics of sample households. ii) To determine the factors that influencing healthcare expenditure of sample households.

 

2. REVIEW OF RELATED STUDIES:

In this section we will briefly review the underlying theory explaining determinants of household health expenditure. The selected review of the literature on intra-household resource allocation is dictated by the scope of this study. The literature survey is concerned with both inside and outside the India. Newhouse (1992) analysed the relative importance of ageing in respect to increase in per capita health expenditures over the period from 1940 to 1990 in the United States. He used Paasche’s index number in his analysis and concluded that there existed constant age-specific health expenditures in respect to ageing of an increased proportion of population in the age group more than 65 years. In the United States there exists differential rate of growth for healthcare spending per person among the old-old and the young elderly. Annual growth of per-person health expenditure spending on the elder person was about 8 percent which significantly higher than the annual rate of growth 4.7 percent for those aged 1-64 years over the period 1953-87 (Cutler and Meara, 1997). Pannarunothai and Mills (1997) did a comparative study in Thailand to analyze households out of pocket health expenditure through household health interview surveyed for a large urban area. A discriminatory pattern of out-of-pocket health expenditure had found in perspective of income quintile and per capita. The deprived had initiate to be least possible to be enclosed by the government health care schemes while the civil servants made least out-of-pocket payments and did not even contribute to the government medical benefit fund. That showed the discrimination in access to not just good services but also to government assistance schemes which were mainly focussed towards poor people but do not benefit them much. In 1999, Zweifel, Felderand and Meiers indicates that in Switzerland rising payouts in a sample of elderly people of a sick fund, with a 90 years old member spending twice as much to the sick fund as a 65-years-old. Kochar (1999) using data from Pakistan, and highlighted the points acute care hospital-based treatment is obviously more expensive than ambulatory care. The informal home-based care is the least costly in compared to institution-based care. Informal home-based care become to be less expensive because in some traditional societies the degree of care, whether acute or long-term, received depends directly on the economic contribution of the elderly, and that may be small. Following Anderson and Hussey (2000), in the 1990s the ratio of per-capita spending of the age group 65 aged years and above to per capita spending in the age group less than 65 years, ranged from 2.7 to 4.8 in seven OECD countries. Study of Peters et al. (2002) highlighted that health policies is concerned not only with improving health status of population but also with protecting households from financial catastrophe of illness. Several studies (Narayanan et al., 2000; Peters et al., 2002; Pradhan, 2002) had indicated that the poor in India become poorer when they seek medical intervention for major ailments. Results from those studies also revealed that, every year, about one quarter of the hospitalized people slip into poverty due catastrophic payment for availing such care. Mugisha et al. (2002) in their worked calculated of out-of-pocket health care expenditure on 800 urban and rural households at Nouna health district in Burkina Faso. They used descriptive analysis and a multivariate analysis using the Tobit model as their methodology. They concluded from their result that near about 80 percent of the households coincided with high out of pocket health expenditure. Xu et al. (2003) argued that share of out-of-pocket expenditure should be taken in terms of household’s capacity to pay. They defined household’s capacity to pay as income after accounted for median level of food consumption in society. Regression analysis had used for a cross-country household survey of 59 countries used variables associated with out-of-pocket health care expenditure. They defined catastrophic health expenditure as more than 40% of household income left over after meeting the necessary needs. Damme et al. (2004) described the consequence of out-of-pocket healthcare expenditure on income of households and how in the rural areas it forced the household to debt. In Cambodia they surveyed 26 households over two years and founded that out-of-pocket healthcare expenditure could caused to indebtedness and subsequently poverty. They also suggested that it could correct through accessible public health care system with some provision to poor. Sen and Rout (2007) concluded that in urban Orissa the household’s income has significant and education has insignificant influence on health expenditure. The requirement of Out of pocket payments is particularly hard on the poor. Private health expenditures includes 98percent share of out of pocket health expenditure in Pakistan. In compared to other country the private expenditures on health as a percentage of the GDP are small in Pakistan. In a global context private expenditure share on health as a percentage of total health expenditure is relatively high (Christian Lorenz, 2009). Raban et al. (2013) examined out-of-pocket spending and catastrophic health expenditures through household surveys in India. They analysed that the catastrophic health expenditures and other inpatient and outpatient costs were raised from 2004-05 to 2009-10. Woo Choi et al. (2016) used the Korean welfare panel study during 2009-2012. Catastrophic health expenditure was 40percent greater than the of the household’s ability to pay. Household with people who experienced changes in job status from employed to unemployed or were unemployed with no status change were more likely to incur CHE than those containing people who were consistently employed. In a Low-income families with member who had either lost a job or where already unemployed were more likely to incur CHE than those with family members with a consistent job. Sheikh and Singh (2017) analyzed the healthcare expenditures in seven South Asian countries (India, Pakistan, Sri Lanka, Maldives, Nepal, Bangladesh and Bhutan). They took panel data for 19 years from 1995 to 2013 and examined the out-of-pocket healthcare expenditure in those countries. They compared the per-capita health expenditure differences and also develop a panel data pooled OLS model for out-of-pocket expenditure. The factors which affected the out-of-pocket expenditures were per capita health expenditure, household final consumption expenditure and public health expenditure. They suggest that the per capita health expenditure highest in Maldives and India had the highest out-of-pocket health expenditure as a percentage of total expenditure on health. One important aspect of their worked is that to explained healthcare financing in respect to the developing economics. According to Tur-Sinai, Magnegi and Grinvald–Forgel (2018), Single person household health expenditure had a positive relation with age and the differences in most expenditure category are significant the current study looks into the Direct and indirect affects income, gender, socio economic status on health insurance and other out of pocket health expenses were significantly affect the health expenditure. Among single person households there exist is a direct link between income, gender, and socio economic status. Vasudevan, Akkilagunta, and Kar (2019) wanted to estimate the proportion of households incurring out of pocket expenditure and the average amount spent by the household for healthcare. They conducted a cross-sectional study in Puducherry. 67 percent of the households reported out of pocket expenditure. The resources in Puducherry are much more available and accessible compared to other part the country. A comparative study on health expenditure in Purbo Barddhaman, West Bengal includes that of Dalui, Banerjee and Roy (2020), they did cross-section study of 235 households in Bhatar block and showed that around 64percent of the household under the health insurance coverage and catastrophically spend on health around one sixed of the household. The distance from public health care facility and gender of head of the household head were significantly influence the catastrophic health expenditure, on the other hand the education of the head of the family, caste category and the distance from public health care facility were significantly influence the out-of-pocket health expenditure.

 

3. PROFILE OF THE STUDY AREA:

At present there are total 20 blocks in Purulia district, out of which we have collected our primary data from Kashipur block. We consider Kashipur block as our primary survey area because most of the area in kashipur block belongs to rural area and different castes can be seen living here. From this Kashipur block we randomly selected three villages as our primary survey areas namely i) Bar Daikiari ii) Talajuri iii) Majuramura. Now we have highlighted some important facts about Kashipur block and the above mentioned three villages. Kashipur block is a community development block in Raghunathpur subdivision of West Bengal state in India. According the 2011 census Kashipur block covered an area of 451.31 Sq.kms and total the population of this block was 200083. As per the 2011 census the total number of literate persons in Kashipur block was 125307, which was the 71.06 per cent of the total population above six years. Out of them 82.33 per cent were males and 58.9 per cent were females. The literacy gap between male and female was 23.92 per cent. According to the 2011 census the livelihood of people in Kashipur block were i) cultivation (18.32%) ii) agriculture labour (43.68%) iii) household industry (2.97%) iv) other workers(35.03%). As per census 2011 data total working population of this block was 41.48 per cent of the total population. According to the 2011 census Kashipur block has 25 Primary health sub centres. As per the information from 2011 census there were 209 villages under Kashipur block. We have randomly selected the above-mentioned villages as our survey area. In the following we have discussed the demographical and occupational features of those three villages namely i) Bar Daikiari ii) Talajuri iii) Majuramura.

 

Table 1: Demographic and Occupational distribution of sample villages

(Source: Census 2011, Govt of India)

 

According to the 2011Census Talajuri village has the highest number of households (542) with 2305 total population among three sample villages. Bar Daikiari village don’t have any schedule tribe population. Majuramura village has the lowest average literacy rate in compare to other sample villages. In Majuramura village ST community has the maximum share compare to other categories. Female average literacy rate is highest in Talajuri village.

 

4. METHODOLOGY:

To prepare the report we have used some simple statistical tools like percentage, mean, median, mode and coefficient of variation. We have also used bar diagram for the pictorial exposition of the data. For statistical analysis of data we used One-Way ANOVA. To determine paired wise comparison, select Tukey’s method as our post hoc test which is the popular method for comparing all possible groups. For econometric analysis we fitted multiple regression model.

 

Table 2: Variable Description

 

Table 3: Descriptive statistics of the Sample Households

(Source: Calculated based on field survey)

 

5. DATA AND VARIABLE DESCRIPTION:

We have visited randomly selected three villages of Kashipur block in Purulia district, West Bengal and surveyed two hundred households. The primary was conducted during Jan 2021- March 2021. We have surveyed the households using a constructed questionnaire. The questions are primarily on four aspects - (i) Basic details of the households ii) food and education expenditure of the households iii) health expenditure and health seeking behaviour of the households iv) family profile details of the households.

 

6. SOCIO-ECONOMIC CHARACTERISTICS OF SAMPLE HOUSEHOLDS:

In this section we will analyse socio economic characteristics of sample households with the help of descriptive statistics and some statistical tools. We found all types of social categories in our sample General, SC, ST, and OBC. About 29 per cent and 35.5 per cent of the sample households are associated to General and OBC category while the SC and ST households cover 26 per cent and 9.5 per cent respectively. In the sample OBC category has the maximum share and ST category has the lowest share. 29.50 per cent of our sample households are above the poverty line, 37.50 per cent households below the poverty line and the reaming 33 per cent are under the Antyodaya Anna Yojana scheme. During survey we got various types of occupations of the household members. In case we get more than one earning members in any family, we have considered the highest earners occupation as major source of income. We divide the major source of income of the households into three broad categories namely i) Agriculture ii) govt service or pension iii) non-firm labour. Major source of income earner of the 71 per cent of the households are engaged in nonfarm labour.15.5 per cent and 13.5 per cent households major income are comes from agriculture and govt service or pension respectively.

 

Now we represent the descriptive statistics of the sample in the following table and try to analyze the basic characteristics of the relevant variables.

 

Average age of the sample household’s head is near about 48 years and mode is 40 that indicate the age 40 years occurs more frequently than any other ages. Coefficient of variation is little high that is 25.76 per cent. In the sample maximum age of household’s head is 80 and minimum age is 21. The average year of schooling of the household’s head is near about 7 years which shows that the household’s heads are not well educated. Mode of household’s head education is zero which indicates that most of household’s head are illiterate. In the sample maximum education of the household’s head is 20 years on the other hand, the minimum is zero. Average monthly family income of the sample household is ₹10876 and mode is 6000. Coefficient of variation in terms of monthly family income is very high (103.26) which indicate high inequality of family income. In the sample, the highest income of the family is ₹74000 and the lowest is ₹1000. Mean of the monthly food expenditure is ₹3117. Highest and lowest food expenditure of the sample households is ₹10000 and ₹1000 respectively. Coefficient of variation is near about 51 which indicate the inconsistency of the households in terms of monthly food expenditure. Average monthly education expenditure of our sample households is near about ₹776 with coefficient of variation 148.67. Mode of Average monthly education expenditure occurs 0 because the cost of education can be seen in those families where any member of the family is still studying. Here also the coefficient of variation is high. Mean of the monthly average health expenditure is ₹594 with coefficient of variation 197.67. Highest monthly health expenditure of our sample households is ₹10000 and minimum is ₹50. Average monthly total expenditure of our sample households is ₹4546 and coefficient of variation is 65. Our sample households reported that they consume on an average 3 times meals per day. Coefficient of variation is not too high in terms of consumption of food per day. Average household size of our sample households is 4.1. Mode of household size is 4 which indicate most of our sample households have 4 members. The maximum number of members in our sample households is 10 and the minimum number of members is one. The average number of children in a sample household under six years is 0.37 and the coefficient of variation is 176.41. Mode of number of children in a family is 0 because most of the households do not have any children less than 6 years of age. The highest number of children in our sample households is 3 and lowest is 1.

 

As per the survey data we divide mode of treatment into two broad categories namely i) Both government and private places and ii) only government hospital. We represent this mode of treatment by social category in the following figure.

 

Figure 1: Household generally go for treatment by Social Category

(Source: Calculated based on field survey)

 

From the above diagram we can see that 62 per cent of the general households get treatments in both government and private places, whereas 38 per cent of the households get treatment only from government hospitals. 51 per cent of OBC households seek treatment in both public and private places, while 49 per cent of the households seek treatment only from government hospitals. 46 per cent of the SC households usually go to both public and private places for treatment but 54 per cent of the households seek treatment only in government hospitals. 37 per cent of the ST households seek medical help for general treatment in both government and private places whereas only 20 per cent of the households seek treatment in government places only.

 

7. RESULT AND ANALYSIS – I:

Now we want to examine that is there exists any statistical difference between mean of monthly average health expenditure of the sample household respect to their social category, village residence and major source of income. To do so we used one way analysis of ANOVA. However the Analysis of Variance results do not make out which particular differences between pairs of means are significant, so we select Tukey’s method as our post hoc test for comparing all possible groups.

 

Hypothesises are given below,

i) H₀: µ₁ (gen) = µ₂ (obc) = µ₃ (sc) = µ₄ (st)

                                                       H_A: all µ are not equal

Our null hypothesis is that mean of average monthly health expenditure of sample households not statistically differs respect to their social category. On the other hand other alternative hypothesis is that at least one of them is unequal.

 

Table 4: Analysis of health expenditure characteristics of the sample households

(Source: Calculated based on field survey)

 

ii) H₀ : µ₁ (Bar Daikiari) = µ₂ (Talajuri) = µ₃ (Majuramura)

                                            H_A : all µ are not equal

 

Our null hypothesis is that mean of average monthly health expenditure of sample households not statistically differs respect to their village residence. On the other hand other alternative hypothesis is that at least one of them is unequal.

 

iii) H₀: µ₁ (agriculture) = µ₂ (govtservice/pension) = µ₃ (nonfarmlabour)

                               H_A: all µ are not equal

Our null hypothesis is that mean of average monthly health expenditure of sample households not statistically differs respect to their major source of income. On the other hand other alternative hypothesis is that at least one of them is unequal.

 

We may present our results in a summary in the following table.

 

From the Analysis of Variance we find that p-value is statistically significant respect to their social category and major source of income which indicates that our null hypothesis (i) and (iii) are rejected. So we can interpret the result as mean of monthly average health expenditure of the sample households differ respect to their social category and major source of income. P-value is statistically insignificant corresponding to their village residence which indicates that mean of monthly average health expenditure of the sample households not varies respect to village residence. We have four social categories General, OBC, SC, and ST so we need six comparisons to cover all combinations of groups. Major source of income into three broad categories namely agriculture, Government service/pension holder and non-farm labour, so we need three comparisons to covers all combinations of groups. In the post hoc test p-values point out the group comparisons that are significantly different. From the result we found that only ST-GEN comparison has statistically significant that shows the mean of monthly average household health expenditure differs between these two categories. The mean difference of ST and GEN is -0.67 point. Rest all other comparisons have statistically insignificant that indicates the mean of monthly average household health expenditure not significantly differs from other pairs. In respect to major source of income the above output shows only Government service/pension holder and Agriculture comparison has statistically significant that means the mean of monthly average household health expenditure differs between these two income sources. The mean difference of Government service/pension holder and Agriculture is .7043181 points. Rest all other comparisons have statistically insignificant that indicates the mean of monthly average household health expenditure not significantly differs from other pairs.

 

8. Result and Analysis – II:

Now we have intended to do some deeper analysis on our target area. In specific we are interested to determine the factors that affecting household health expenditure. The present chapter is based on field survey (primary data). To determine the factors influencing household health care expenditure we have consider multiple regression model. The estimation strategy relies on OLS method.

 

LOGMHEXP_i = α +β₁ LOGMHEXP_i + β₂ CATEGORY_i + β₃ HGEND_i + β₄ HEDU_i + β₅ HH6_i + β₆ HH60_i + β₇ CHRONIC_i + β₈ SHI_i+ β₉ PROVIDER_i + β₁₀ LIVESTOCK_i + β₁₁ NOOFMEALSPERDAY_i + u_i

We may present our multiple regression results in a summary in the following table.

 

Table 5: Factors Influencing Healthcare Expenditure of Sample Households

(Source: Calculated based on field survey)

 

The F value 61.13 with a p-value 0.000 implies that our model as a whole fit significantly. The regressors monthly average family income, social category, household head’s education, number of members in a household above 60years, having at least one member who consume medicines during the whole year and mode of treatment have significant impact on the household average monthly health expenditure. Moreover their co efficient has expected sign. It is clear from the above result that healthcare expenditure of the sample household is inelastic in income. As household’s income increases, its health expenditure increases less than proportionately. Our explanatory variable CATEGORY is basically a dummy variable which represents social category of our sample households if unreserved then 1, otherwise 0. The coefficient of CATEGORY is interesting because it measures the average difference in household health expenditure between unreserved and reserved households who have the same level of other explanatory variable. The unreserved households spent on 22.2per cent more in monthly average health expenditure than reserved households. The more accurate estimate implies that the unreserved households spend on average [{exp (0.222) -1}100] = 24.85per cent more compare to reserved household. If we keep the effect of all other explanatory variables constant then monthly average household health expenditure increases by 1.7per cent for every additional year of schooling. Positive coefficient of the HH60 variable indicates that one more member above 60 years in a household increases the monthly average household health expenditure by 16.1per cent. The coefficient of CHORNIC is positive which measures the average difference in household health expenditure between presence and absence of at least one member who consume medicines during the whole year. When the effect of all other explanatory variables constant then the households having at least one member who consume medicines during the whole year spent on an average 23.7per cent more than others households. The household who are seeking medical help from both government and private health care facilities are treated as 1 and if household generally go only government hospitals for their medical help treated as 0. The household who are taking medical help from both type of government and private facilities are spending 78.7per cent more compare to those households who seeking medical from only government. From the above result it is clear that among all explanatory variables considered, the marginal effect of mode of treatment on average monthly health expenditure has been the highest, followed by monthly average family income. The explanatory variables, at least one member in a households having Swasthya Sathi card or not, having livestock or not and number of meals of a household consume per day becomes insignificant bur their coefficient has expected sign. Co-efficient of the explanatory variable having Swasthya Sathi card or not is negative which implies that the monthly average health expenditure of the households who having Swasthya Swati card is less on an average compare to others. The coefficient of the having livestock or not and the number of meals that a household consume per day are negative which shows these two explanatory variables are negatively related with monthly average household health expenditure. The value of R² for the estimated model is 0.7815 which implies that that our all-explanatory variables together explained nearly 78per cent of the average monthly health expenditure. In multiple regression model the value of adjustment R² is near about 0.77 which is much more important.

 

9. CONCLUSION:

The present study is an attempt to investigate the several aspects of household healthcare expenditure in the context of Kashipur block in Purulia district, West Bengal. The Specific objectives of our study are to analyze socio-economic and health expenditure characteristics of sample households and to determine the factors that influencing healthcare expenditure of sample households. We have visited randomly selected three villages of Kashipur block in Purulia district, West Bengal and surveyed two hundred households. To check that is there any difference between mean of monthly average health expenditure of the sample households corresponding to their social category, we fitted one way analysis of ANOVA. From result we find that mean of monthly average health expenditure differs across social category. To find pair wise comparisons of means we used Tukey’s method as our post hoc test. Only ST-GEN comparison has statistically significant that shows the mean of monthly average household health expenditure differs between these two categories. Rest all other comparisons have statistically insignificant that indicates the mean of monthly average household health expenditure not significantly differs from other pairs. To examine that is there any difference between mean of monthly average health expenditure of our sample households corresponding to their village residence, we used one way analysis of ANOVA. From the result we find that mean of monthly average household health expenditure are not differs corresponding their village residence. From one way analysis of ANOVA we find that mean of monthly average health expenditure differs according their major source of income. To find pair wise comparisons of means we used Tukey’s method as our post hoc test. From the result we conclude that only Government service/pension holder and Agriculture comparison has statistically significant that shows the mean of monthly average household health expenditure differs between these two income sources. Rest all other comparisons have statistically insignificant that indicates the mean of monthly average household health expenditure not significantly differs from other pairs.

 

To determine the factors that influencing household health expenditure we have used multiple regression model. The explanatory variables monthly average family income, social category, household head’s education, number of members in a household above 60years, having at least one member who consume medicines during the whole year and mode of treatment have significant impact on the household average monthly health expenditure. Moreover their co efficient has expected sign. From result and analysis portion we found that healthcare expenditure of the sample household is inelastic in income as household’s income increases, its health expenditure increases less than proportionately. The unreserved households spent more in terms of monthly average health expenditure compare to reserved households when all other variables constant. We find that if we keeping the effect of all other explanatory variables constant then household head’s education is positively related with monthly average household health expenditure. The number of members in a household above 60 years has positive coefficient that means as number of members in a household above 60 years increases household average monthly health expenditure also increases. When the effect of all other explanatory variables constant, then the regressor households having at least one member who consume medicines during the whole year positively influencing the household average monthly health expenditure. The household who are seeking medical help from both type of government and private facilities are spending more on an average compare to those households who seeking medical from only government. From the result it is clear that among all explanatory variables considered, the marginal effect of mode of treatment on average monthly health expenditure has been the highest, followed by monthly average family income. The explanatory variables, at least one member in a households having Swasthya Sathi card or not, having livestock or not and number of meals of a household consume per day becomes insignificant bur their coefficient has expected sign. Co-efficient of the explanatory variable having Swasthya Sathi card or not is negative which implies that the monthly average health expenditure of the households who having Swasthya Swati card is less on an average compare to others. The coefficient of the having livestock or not and the number of meals that a household consume per day are negative which shows these two explanatory variables are negatively related with monthly average household health expenditure.

 

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