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Health Policy 78 (2006) 167–177

Ill health and its potential influence on householdconsumptions in rural China

Hong Wang a,∗, Licheng Zhang b, William Hsiao c

a Yale University School of Public Health, Yale School of Medicine, LEPH, 60 College Street, Suite 315,New Haven, CT 06520, USA

b Beijing University School of Public Health, Beijing 100083, Chinac Harvard University School of Public Health, Cambridge, MA 02138, USA

bstract

Ill health is very expensive and could have significant impact on household consumptions. The purpose of this study is toxamine the differences in household consumption patterns among households with or without ill health family member(s)n rural China. We also examine the opportunity cost of ill health by estimating the marginal effects of medical spending ononsumption patterns.

The data used in this study are from the baseline survey of a community-based rural health insurance study in a poor ruralrea of China conducted in 2002. The unit of analysis in this study is the household; 4553 households are included in this survey.ractional Logit model is used as our prediction model. Ill health is measured by the presence of hospitalization and presencef diagnosed chronic disease(s) in a household.

Findings from this study reveal that ill health and medical expenditure reduces household investment in human capital,hysical capital for farm production, and other consumptions that are critical to human well-being. Subgroup analysis displayed

hat the impacts of medical expenditure on household consumption patterns described above are more significant in low-incomeouseholds than in high-income households. In addition, the decline of the percentages of other consumptions is much largeror households with hospitalization than for households with chronic diseases.

2005 Elsevier Ireland Ltd. All rights reserved.

; Rural

l

eywords: Ill health; Medical expenditure; Household consumption

. Introduction

Since the economic reform in 1978, continuedncome growth has enabled farmers to improve their

∗ Corresponding author. Tel.: +1 203 785 6230;ax: +1 203 785 6193.

E-mail address: hong.wang@yale.edu (H. Wang).

a(2Ft[l

168-8510/$ – see front matter © 2005 Elsevier Ireland Ltd. All rights resedoi:10.1016/j.healthpol.2005.09.008

China

evel of consumption significantly in rural China. Thennul living expenditure increased from 116 YuanUS$ 14) per capita in 1978 to 1834 Yuan (US$22) in 2002, a 16-fold increase in the last 25 years.

ood expenditure as a percentage of total consump-

ion declined from 78.6% in 1978 to 46.3% in 20021,2]. However, due to escalating medical costs andack of insurance coverage, medical spending often

rved.

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auses financial hardship for many rural families [3].everal international studies showed that once a house-old member experiences ill health, under the stressnd anxiety of disease, people have no choice but toay the fees requested by health providers even if theoney is more than what they can afford [4]. Ill health

ot only affects an individual’s quality of life or lifexpectancy, but also have an important impact on theesource allocation in the household, which could jeop-rdizes both short- and long-term economic welfare,uch as incurring debts, selling off productive assets,r sacrificing investment in future productivity by cur-ailing children’s education [5–8]. Even small costs forommon illnesses can be financially disastrous for poorouseholds with no insurance coverage [9]. Althoughany studies have addressed the issues of poverty due

o ill health and high medical expenditure, very fewtudies, have examined differences in household con-umptions between households with and without illousehold member(s) and the trade-off between healthxpenditure and other household consumptions.

Household is an important unit of analysis in ruralreas of developing countries. From an economic per-pective, the household is generally assumed to be therincipal locus of decision-making about consumptionnd production [10]. Using a unique dataset, we exam-ne the differences in household consumption patternsmong households with or without ill family mem-er(s) in rural China. Ill health is measured by theresence of hospitalization and presence of diagnosedhronic disease(s) in a household. We also examine thepportunity cost of ill health by estimating the impactf medical spending on expenditure patterns while con-rolling for demographic characteristics. The analysesre stratified by income levels to examine the interac-ion effects between ill health and income on householdonsumption patterns.

. Methods

.1. Data and variables

.1.1. Dataset

The data used in this analysis are from the baseline

urvey of a community-based rural health insurancetudy in the poor rural areas of China conducted in 200211]. The original purpose of this study is to examine if

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y 78 (2006) 167–177

subsidized community-based rural health insurance isviable health care financing strategy under the newlyeveloped market-oriented economic environment inural China.

Six townships in Guizhou and Shanxi Provinces par-icipated in this study. These two provinces are bothelatively poor provinces in China. The selection ofownships is based on their representativeness to theocal economic development status and their willing-ess to participate in this study. The baseline surveyopulation was chosen through multistage samplingethod. In the first stage, random sampling was used to

hoose 22 villages from selected townships. In the sec-nd stage, all households with family members in thehigh-risk” population, as well as about one out of threether households chosen at random, were selected.High-risk” was defined as single elderly and disabled;aving dementia; women who were pregnant in the pre-ious year or at the time of interview; those admitted tohe hospital in the previous year; or those with a severeisease condition. “High-risk” households were iden-ified by village doctors working in the villages. Theample includes 4553 households, which is the unitf the analysis for this study. Sample weight variableas created based on different sampling probabilities

or the households with or without family member(s)n the “high-risk” population and other households foreighted analysis. In order to reduce the recall bias,

xpenditures were separated into monthly expendi-ures’ and annual expenditures’ categories. Monthlyxpenditures include expenses that occur daily andrequently such as food, social activities, and utili-ies. Annual expenditures include expenses that occurot very frequently such as education, durable goods,nd farming expenses. In general, expenditure infor-ation is provided by the household heads or their

pouses.

.1.2. Dependent variablesSince we are interested in the impact of ill health

nd health expenditure on other goods’ consumptions,e developed a set of dependent variables that mea-

ures the percentage of all post-tax expenditure spentn 13 expenditure categories. The definitions of these

3 expenditure categories are listed in Table 1, alongith their means, by ill health status as measured by

he presence of hospitalization and presence of chronicisease(s) in a household.

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Table 1Descriptive statistics of household characteristics (percentage (%)/mean)

Variables Descriptions Totalhouseholds

Households withhospitalizaiton

Households withchronic disease

No Yes No Yes

Ill health statusHosp 1 if household has any hospitalization in the previous year of the interview, 0 if

otherwise5.8 0.0 100.0 3.1 10.7

Chronic 1 if household has any presence of diagnosed chronic disease in the previous 6months, 0 if otherwise

35.1 33.3 65.5 0.0 100.0

Age Mean age of household head 48.4 48.4 49.6 48.3 48.7Male 1 if household head is a male, 0 if otherwise 94.3 94.4 94.0 94.4 94.3

Marital statusUnmarried Household head is unmarried. Reference group in regression analysis 4.2 4.3 2.3 4.2 4.3Married 1 if household head is married, 0 if otherwise 89.2 89.0 93.9 89.5 88.9Divorced 1 if household head is divorced or separated, 0 if otherwise 6.5 6.7 3.8 6.4 6.8

Education statusIlliterate Household head is illiterate. Reference group in regression analysis 18.0 18.2 14.4 15.9 21.7Elementary 1 if household head finished elementary school, 1–6 years, 0 if otherwise 44.1 44.1 44.0 42.4 47.2Junior high 1 if household head finished primary school, 7–9 years, and above, 0 if otherwise 38.0 37.8 41.6 41.7 31.2

Income Mean total post tax consumption per capita (100 Yuan) 20.4 19.9 28.5 20.6 20.1Num-household Mean number of household members 3.6 3.6 3.9 3.6 3.6Num18 1 if there is at least one person under 18 years old, 0 if otherwise 51.7 51.3 57.7 53.5 48.3Num65 1 if there is at least one person over 65 years old, 0 if otherwise 38.6 38.3 44.9 38.7 38.5D-village Mean distance (km) from home to village health post 2.1 2.1 1.8 2.0 2.2D-township Mean distance (km) from home to township center 10.7 10.7 10.5 9.9 12.4D-county Mean distance (km) from home to county center 60.7 61.1 54.3 63.4 55.6

Regional variablesKaiyang Household is from Kaiyang county. Reference group in regression analysis 25.8 25.7 26.1 19.6 34.5Tiechang 1 if household is from Tiechang, Zhenan county, 0 if otherwise 17.5 18.4 10.1 25.2 6.8Zhangjia 1 if household is from Zhangjia, Zhenan county, 0 if otherwise 14.5 14.0 18.8 15.4 13.3Gaofeng 1 if household is from Gaofeng county, 0 if otherwise 16.5 16.4 17.8 17.0 15.9Baiyun 1 if household is from Baiyun district, 0 if otherwise 15.3 14.8 19.3 9.0 24.2Machang 1 if household is from Machang county, 0 if otherwise 10.4 10.7 7.9 13.9 5.4

Numbers of household 4553 4070 483 2662 1891

Note: The percentages and means here are all weighted results, excepts the number of households.

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.1.3. Independent variablesFor the analyses of examining the differences in

ousehold consumption patterns among householdsith or without ill health family members, the indepen-ent variable of interest in this study is the householdember’s ill health status, which is defined by two

ummy variables: with or without any hospitalizationn the previous year of interview, and with or withouthronic disease(s) in the previous 6 months of inter-iew in the household. For the analyses of examininghe opportunity cost of medical expenditure, we usedousehold per capita medical expenditure in the previ-us year of the interview as the independent variable tostimate the impact of health spending on expenditureatterns (Table 2).

.1.4. Control variablesWe recognize that households with or without ill

ealth member(s) differ in ways other than health statusnd that household consumption patterns can also bexplained by other factors [12,13]. We controlled foremographic variables, including household income,ousehold size, age composition of the household, edu-ation of the head of household and household location.able 2 describes these variables and provides sum-ary statistics for each. It is worth mentioning here

hat the income used in this study is measured byelf-reported expenditure rather than by self-reportedncome because income is more likely to be misre-orted and actual expenditures are better proxies foresources available for spending [14].

.2. Method of estimation

We considered the household as the relevantecision-making unit. We used this unit for several rea-ons. A well-respected theory of household suggestshat household with two or more individuals is thenit of decision-making for household consumptionatterns [15]. Households allocate limited resourceso maximize utility. Although there is a debate onhether this theory is representative of householdehaviors since individual household members mayave different preferences, the farm-household is usu-

lly viewed as highly intertwined with production andonsumption occurring at a central level, thus is stillonsidered as “a complete economic unit by itself”16].

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y 78 (2006) 167–177

Empirically, we used the Fractional Logit (Flogit) asur prediction model [17,18]. Since our dependent vari-bles are the percentage of expenditures allocated toach expenditure category, the predicted values of theseercentage of expenditures derived from this model isounded within the unit interval [0, 1].

Multiple regression analysis was used to controlor variations of expenditures due to other householdariables. There are two sets of models used to pre-ict the relationship between ill health (hospitalizationr chronic disease) and consumption patterns. Eachet has 13 regressions for the 13 expenditure cat-gories. After constructing prediction models of illealth on expenditure patterns, the “recycling” pre-iction method is used to predict the percentage ofach expenditure categories based on ill health statushospitalization or chronic disease), assuming all otherariables being equal [19,20]. Weights were used toccount for the sampling scheme. Six township indi-ator variables are included in the model to control foregional differences.

In order to examine the marginal effects of medi-al expenditure on consumption patterns, we also usededical expenditure as independent variable to esti-ate its effects on other 12 consumption categories.

. Empirical results

.1. Descriptive analyses of expenditure patternsnd household characteristics

The descriptive analyses indicate that 5.8% ofouseholds have family member(s) utilized hospitalervices in the previous year of the interview; 35.1%f households have the family member(s) with diag-osed chronic diseases in the previous 6 months of thenterview (see Table 1). Comparing households withnd without hospitalization, household heads are lessikely to be unmarried or divorced and are more likelyo have higher education in households with hospital-zation than in households without hospitalization. Theousehold income per capita (represented by house-old post-tax expenditure) is higher in households with

ospitalization than in households without hospital-zation. Even after we exclude medical expenditure,ousehold income per capita is still higher in house-olds with hospitalization than in households without

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Table 2Average household consumption (post-tax) by ill health status

Categories of consumptions Descriptions Total Households with hospitalization Households with chronic disease

Mean Percent (%) No Yes No Yes

Mean Percent (%) Mean Percent (%) Mean Percent (%) Mean Percent (%)

1. Foods Rice, flour, meets, vegetables,fruits, spices, etc.

446.6 21.9 445.5 22.3 465.6 16.3 462.4 22.5 417.6 20.7

2. Education Tuitions, books, educationrelated expenses

245.3 12.0 243.4 12.2 276.7 9.7 245.8 11.9 244.4 12.1

3. Social activities Gifts, wedding, funeral,entertainments, etc.

240.3 11.8 236.6 11.9 300.5 10.5 255.0 12.4 213.2 10.6

4. Farming expenses Equipments, fertilizers, seeds,etc.

218.1 10.7 218.8 11.0 206.0 7.2 211.7 10.3 229.8 11.4

5. Smoking and drinking Cigarette, tobacco, wine,liqueur

164.4 8.0 162.4 8.1 197.2 6.9 170.0 8.3 154.2 7.6

6. Medical expenses Outpatient and inpatientservices, drugs

161.6 7.9 123.4 6.2 786.0 27.5 113.4 5.5 250.4 12.4

7. Utilities Electricity, water, heat, cooking,renting, etc.

150.4 7.4 149.3 7.5 167.6 5.9 150.8 7.3 149.7 7.4

8. Daily goods Soap, paper, pen/pencil, newspaper, transportation,communication, etc.

137.9 6.7 135.9 6.8 169.8 5.9 149.4 7.3 116.7 5.8

9. Clothes Clothes 87.4 4.3 87.4 4.4 86.6 3.0 98.5 4.8 66.9 3.310. Saving Savings 76.9 3.8 78.5 3.9 51.9 1.8 88.0 4.3 56.5 2.811. Durable goods TV, furnitures, radio, etc. 37.1 1.8 38.0 1.9 22.8 0.8 41.5 2.0 29.2 1.412. Insurance Insurances for property, farm

product, health, etc.12.2 0.6 12.1 0.6 14.2 0.5 15.6 0.8 6.1 0.3

13. Other expenses The expenses not included inabove categories

65.1 3.2 62.4 3.1 109.3 3.8 55.3 2.7 83.3 4.1

Total expenses 2043.4 100.0 1993.8 100.0 2854.2 100.0 2057.2 100.0 2018.0 100.0

Note: The percentages and means here are weighted results.

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ospitalization (see Table 2). Households with hospi-alization are also more likely to have household mem-er(s) whose age is(are) below 18 or above 65 yearsld and their homes are closer to the county centerhan households without hospitalization. Contrary toesults from comparison of households with and with-ut hospitalization, household heads are less likely toave higher education and less likely to have house-old member(s) whose age is(are) below 18 years oldn households with the chronic diseases than in house-olds without chronic diseases. Similar to results fromomparison of households with and without hospital-zation, households with chronic diseases are closer tohe county center, but farther from the township center,han households without chronic diseases (Table 1).

The descriptive statistics in Table 2 indicate that, onverage, households in this sample spend about 7.9%f their total expenditure on medical services, mak-ng medical expenditure the sixth ranking consumptionategory. For households with hospitalization, medicalxpenditure comprises 27.5% of total cash expendi-ure and is the largest category of consumption. Forouseholds with chronic diseases, medical expenditureomprises nearly 12.4% of total cash expenditure and

s the second largest category of consumption.

Comparing households with and without hospital-zation, medical expenditure and percentage of medicalxpenditure are 6 and 4.5 times higher in households

cTds

able 3esults of regression analyses of ill health and percentage of household con

ategories of consumptions Households with hospit

oods −0.19 (0.0394)***

ducation −0.3059 (0.0846)***

ocial activities −0.069 (0.0450)arming expenses −0.1461 (0.0466)***

moking and drinking −0.1143 (0.0571)**

edical expenses 1.3182 (0.0617)***

tilities −0.0916 (0.0469)*

aily goods −0.1079 (0.0443)**

lothes −0.1352 (0.0475)***

aving −0.6263 (0.2376)***

urable goods −0.159 (0.2088)nsurance −0.0604 (0.0983)ther expenses −0.3843 (0.1782)**

ote: Robust standard errors in parentheses. The results here are after contrf household head, household income, presence of person under 18 years oounty centers, and sample regions.* Significant at p < 0.10.

** Significant at p < 0.05.** Significant at p < 0.01.

y 78 (2006) 167–177

ith hospitalization than in households without hos-italization, respectively. Although the major expendi-ure categories, such as food, tuition, social activity,nd daily goods, are higher in household with hos-italization than in household without hospitalization,ertain categories, such as farming expenses, savings,nd durable goods are lower in households with hos-italization than in households without hospitalizationTable 2).

Comparing households with and without chroniciseases, medical expenditure and percentage of med-cal expenditure are 2 and 2.5 times higher in house-olds with chronic diseases than in households withouthronic diseases, respectively. All expenditure cate-ories are lower in households with chronic diseaseshan in households without chronic diseases (Table 2).

.2. Predictive analyses of expenditure patterns

We used multiple Flogit regression models to ana-yze the relationship between ill health and expenditureatterns and predict expenditure patterns (representedy the percentage share for each category of consump-ion) of households with or without ill health status after

ontrolling for all available household characteristics.ables 3–5 list the regression analysis results and pre-ictive consumption patterns by household ill healthtatus.

sumption categories

alization Households with chronic diseases

−0.0682 (0.0266)**

−0.0443 (0.0508)−0.1507 (0.0291)***

−0.0635 (0.0266)**

−0.1291 (0.0365)***

0.7807 (0.0436)***

−0.0436 (0.0268)−0.1017 (0.0291)***

−0.1728 (0.0299)***

−0.2684 (0.1757)0.0666 (0.1592)0.0441 (0.0640)0.0797 (0.1151)

olling for household size, age, gender, marital status, and educationr over 65 years old, the mean distance from home to township and

H. Wang et al. / Health Polic

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y 78 (2006) 167–177 173

Results in Table 3 indicate that after controlling forifferences in households characteristics, the percent-ges of expenditure in all major categories, such asood, tuition, social activity, smoking/drinking, dailyoods, clothes, and savings, are significantly lowern households with hospitalization or chronic diseaseshan in households without hospitalization or chroniciseases. The relationship between ill health status andercent expenditure of social activity is only significantn the chronic diseases model, while the relationshipetween ill health status and percent expenditure ofavings is only significant in the hospitalization model.

Based on regression results in Table 3, we estimatedhe predicted values of each expenditure categories,isted in Tables 4 and 5. These predicted results indicatehat households we interviewed spend 8.7% of theirotal cash expenditure per capita per year on medicalervices. After controlling for household characteris-ics, households with hospitalization spend nearly 450uan per capita per year on medical services, which isearly 300 Yuan more than the spending per capita perear for households without hospitalization. The per-entage of medical spending per capita in householdsith hospitalization is 14.6 percentage points higher

han the percentage of medical spending per capita inouseholds without hospitalization.

Due to high spending on medical services, manyxpenditure categories were crowded out in householdsith hospitalization. Comparing to households with-ut hospitalization, the top five expenditure categoriesith the greatest decline in absolute value are food

84.3 Yuan), tuition (53.8 Yuan), farming expense (37.5uan), smoking and drinking (23 Yuan), and socialctivities (22.2 Yuan). The top five expenditure cate-ories with the greatest percentage decline are savings47.4%), education (26.2%), durable goods (17.3%),oods (15.9%), and clothes (15%) (Table 4).

After controlling for household characteristics,ouseholds with chronic diseases spend nearly 250uan per capita per year on medical services, which

s 127 Yuan more than the spending per capita perear for households without chronic diseases. The per-entage of medical spending per capita in the house-olds with chronic diseases is 6.2 percentage points

igher than the percentage of medical spending perapita in households without chronic diseases. Com-aring to households without chronic diseases, the topve expenditure categories with the greatest decline in

1 h Policy 78 (2006) 167–177

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74 H. Wang et al. / Healt

bsolute value are social activity, foods, smoking andrinking, clothes, and farming. The top five expendi-ure categories with the greatest percentage decline areavings, clothes, social activity, smoking and drinking,nd farming (Table 5).

.3. Marginal effects of medical expenditures onther expenditure categories

Results in Table 6 displayed the marginal effectsf medical expenditures on the percentages of otherxpenditure categories. Assuming that medical expen-iture has causal relationship with other expenditures,s medical expenditure increases, the percentages ofther expenditure categories will decline due to theonstraint of the total household budget. The results ofhis marginal effect analysis show that with each 100uan (about 4.9% total expenditure with the averageer capita expenditure is 2034.4) medical expendi-ure increase, the percentage of food consumption willecline by 0.67 percentage point (14 Yuan decline)fter controlling for other household characteristics.he percentage of consumption on social activity, farm-

ng, smoking and drinking, and tuition will also declinerom 0.22 to 0.34 percentage points (4.5–6.9 Yuanecline).

If we break down the total sample based theirer capita income into L-income, M-income, and H-ncome households (L-income households are thosehose annual per capita income is in the lowest5 percentile; M-income households are those whosennual per capita income is in the 25–75 percentile;-income households are those whose annual per

apita income is in the top 25 percentile), the marginalffects of medical expenditure on the percentages ofther expenditure categories are much larger in L-ncome than in H-income groups. The percentage ofonsumptions on food, farming, smoking and drink-ng, tuition, and social activity will decline 4.1, 2.4,.2, 2.0, and 1.2 percentage points, respectively, in-income households (29.6, 17.3, 15.9, 14.4, and.7 Yuan decline, respectively, with the average perapital expenditure is 721.8 Yuan in L-income group),hile in H-income households, the decline of percent-

ge of consumptions on other categories are very smallith each 100 Yuan (about 13.9% total per capita

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H. Wang et al. / Health Policy 78 (2006) 167–177 175

Table 6Marginal effects of per 100 Yuan medical expenditure on household consumption patterns

Expenditure categories Total L-income M-income H-income

Foods −0.6677 (0.0690)*** −4.1362 (0.7440)*** −1.5678 (0.1660)*** −0.2771 (0.0480)*

Education −0.3372 (0.0740)** −1.9667 (0.4570)*** −0.9931 (0.1430)*** −0.2665 (0.0620)*

Social activities −0.2217 (0.0370)*** −1.2497 (0.3520)*** −0.5398 (0.1040)*** −0.1464(0.0330)Farming expenses −0.2227 (0.0410)*** −2.3746 (0.4410)*** −0.3563 (0.0870)*** −0.1019 (0.0270)***

Smoking and drinking −0.2891 (0.0450)*** −2.1743 (0.4820)*** −0.6448 (0.1110)*** −0.1561 (0.0330)**

Utilities −0.1145 (0.0240)*** −0.5327 (0.3320) −0.1700 (0.0550)** −0.0857 (0.0240)**

Daily goods −0.1182 (0.0270) −0.3695 (0.1950) −0.3110 (0.0620)*** −0.0812 (0.0240)Clothes −0.1189 (0.0190) −0.3667 (0.1770) −0.2706 (0.0440)*** −0.0675 (0.0140)Saving −0.0582 (0.0320)** −0.0002 (0.0000)* −0.3293 (0.1020)*** −0.1033 (0.0600)Durable goods −0.0277 (0.0140)*** −0.0041 (0.0020)* −0.0456 (0.0320)** −0.0475 (0.0280)***

Insurance −0.0034 (0.0020) 0.0213 (0.0180) 0.0014 (0.0080) −0.0026 (0.0010)Other expenses −0.0904 (0.0260)*** −0.0457 (0.0540) −0.1556 (0.0490)*** −0.1048 (0.0260)***

Note: Robust standard errors in parentheses. The results here are after controlling for household size, age, gender, marital status, and educationof household head, household income, presence of person under 18 years or over 65 years old, the mean distance from home to township andcounty centers, and sample regions.

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. Discussion

Although it has been realized that ill health increasesedical care utilization, and therefore increases med-

cal expenditure, few studies have closely examinedhe impacts of ill health and medical expenditure onousehold consumption patterns, which could havemmediate harm on household family members, par-icularly in poor families where basic needs often gonmet. Although medical services can help patientsecover from ill health, the excessive burden of medicalxpenditure crowd-out other goods and services, andherefore can have several potential negative impactsn productivity and human well-being, and thus wouldmpact the overall economy and social development of

society. The findings from this study reveal that illealth and medical expenditure reduces the householdnvestment in human capital, physical capital for farmroduction, and other consumptions critical to humanell-being:

1) Human capital investment. Expenditures on educa-tion for children or adults are human capital invest-ments that tend to increase productivity in the long

run. The results from this study indicate that medi-cal expenditures, especially due to hospitalization,significantly influence households’ investment oneducation expenditure. Households with ill health

members have less investment on education thanhouseholds without ill health members. For exam-ple the households with hospitalization spend 54fewer Yuan per capita on education than the house-hold without hospitalization, a 26% difference ininvestment on education. The increase of each 100Yuan in medical expenditure leads to 0.34 per-centage point decline (6.9 Yuan decline) in thepercentage of education expenditure. Thus, house-holds forgo long-term benefits to meet immediatehealth needs, especially among poor families.

2) Physical capital investment in farm production.The results of this study also suggest that house-holds with ill health members spend less on physi-cal capital investment for farm production, such asfarming tools and seeds, than households withoutill health members. The increase of each 100 Yuanin medical expenditure leads to 0.22 percentagepoint decline (4.5 Yuan decline) in farming invest-ment. Thus, excessive medical expenditure reducesphysical capital investment in farm production, andtherefore reducing farming productivity.

3) Investment in development of human well-being. Inaddition to economic development, many house-

holds’ consumptions are critical for the humansurvival and well-being, such as food, clothes, andsocial activity. Households with ill health membersand high medical expenditures must sacrifice their

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76 H. Wang et al. / Healt

consumptions on those categories, which couldhave both short- and long-term negative impactson human development.

Based on subgroup analysis, this study also demon-trated that the impacts of medical expenditure onousehold consumption patterns described above areore significant in low-income household than in high-

ncome households. The percentage of consumptionsn food, farming, smoking and drinking, tuition, andocial activity will decline 4.1, 2.4, 2.2, 2.0, and 1.2ercentage points, respectively, in L-income with each00 Yuan (about 13.9% total per capita expenditure in-income group) medical expenditure increase.

This study also revealed that hospitalization is morexpensive than chronic diseases in the fixed termin 1-year period), and therefore have more signifi-ant impact on household consumption patterns thanhronic diseases. The declines of the percentages ofonsumptions are much larger in households with hos-italization than in households with chronic diseases.owever, the decline of the percentage of social activ-

ty consumption is larger in households with chronicisease than in households with hospitalization. Thisesult indicated that the chronic disease is an impor-ant barrier for patients and their family to enjoy socialctivities.

This study used unique detailed data on consump-ion in rural China to better understand what familiesre foregoing in order to utilize medical services in ruralhina. However, there are several limitations. First, theata refer to cash consumption and do not include in-ind consumption. Thus, foods produced by farmershemselves are not captured in the data. Further, allonsumption data are based on self-reported informa-ion. In addition, since the data are cross-sectional, were not able to draw causal relationships between illealth/medical expenditure and other consumptions.

. Conclusion and policy implication

We found that ill health, especially hospitalization,ncreases medical expenditure significantly. Expendi-

ure on medical services clearly takes away from otherreas. In Chinese rural areas, expenditure on medi-al services occurs at the expense of food, education,nvestment in farming, and participation in social activ-

[

y 78 (2006) 167–177

ties. The reduction of these expenditures by medicalxpenditures could have negative impacts on the well-eing of individuals as well as household economicpportunities. These effects provide further evidenceo promote the health policy related to the preventionf catastrophic illness (i.e. hospitalization) and chroniciseases, cost control for medical services, and healthnsurance development to reduce financial uncertaintynd out-of-pocket payments for medical expenses.

cknowledgements

The data used in this study is from a Rural Mutualealth Care (RMHC) social experimental study carriedut by a Harvard research team. This study is fundedy the Guanghua foundation.

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