Socioeconomic status and children's health: Evidence from a low-income country

Social Science & Medicine 130 (2015) 23e31

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Socioeconomic status and children's health: Evidence from a lowincome country Ardeshir Sepehri a, Harminder Guliani b, * a b

Department of Economics, University of Manitoba, Winnipeg, MB R3T 5V5, Canada Department of Economics, University of Regina, Regina, SK S4S 0A2, Canada

a r t i c l e i n f o

a b s t r a c t

Article history: Available online 27 January 2015

There has been a growing empirical literature on the relationship between household socioeconomic status (SES) and children's health, and in particular, whether this SES gradient is constant or varies in strength across different life stages. Much of this literature focuses on the developed countries and less evidence has been presented for developing countries. Using Vietnam's rich National Health Survey (2001e02) and appropriate multilevel modeling this study empirically assesses the SES gradient in health and whether it varies in strength across different life stages of children aged 15 and younger (N ¼ 45,448). The results for the interaction terms between the natural logarithm of household consumption and age groups indicate no evidence of a steeper health gradient for older children. However, health-consumption gradients are found to be sensitive to the functional form of the regression model as well as the model specification. The results for the interaction terms between consumption expenditure quintiles and age groups indicate that gradients vary in strength across ages. Not only are children from the poorest households worse off, compared to those from the richest households, but this relative disadvantage is greater among the 0e3 year olds. The inclusion of parental health status in the regression model weakens the gradients for all age groups as does the inclusion of household sources of drinking water. However, poorer children are still relatively worse off, specially the 0e3 year olds. This suggests that absolute deprivation may help explain the relative health disadvantage of younger children. Better measures of poverty alleviation are hence needed to improve children's health in a low-income country such as Vietnam. © 2015 Elsevier Ltd. All rights reserved.

Keywords: Vietnam Child health gradient Low-income country Multi-level analysis

1. Introduction There has been a growing empirical literature on the relationship between household socioeconomic status (SES) and children's health, and in particular, whether this SES gradient is constant or varies in strength across different life stages. The evidence on how the child health gradient evolves is mixed. The family income gradient in parent-reported child health is found to strengthen as children grow older in a number of studies focusing on the US (Case et al., 2002; Condliffe and Link, 2008; Fletcher and Wolfe, 2014), Canada (Allin and Stabile, 2012; Currie and Stabile, 2003). Consistent and robust evidence of a significant family income gradient in child health is also reported for England, though the slope of the gradient is found to be very small and increases little as children

* Corresponding author. E-mail address: [email protected] (H. Guliani). http://dx.doi.org/10.1016/j.socscimed.2015.01.045 0277-9536/© 2015 Elsevier Ltd. All rights reserved.

grow older (Currie et al., 2007). Other research from the UK indicate that SES gradients in health that are present in earlier childhood flatten or disappear in adolescence, as the effects of the secondary school, the peer group, and youth culture overshadow those of the family background (West, 1997; West and Sweeting, 2004). By contrast, the income gradient in child health is found to remain fairly constant throughout childhood and adolescence in the US (Chen et al., 2006), UK (Apouey and Geoffard, 2013; Currie et al., 2007; Propper et al., 2007) and Germany (Reinhold and Jürges, 2012). The existing literature on the nature of the relationship between SES and children's health focuses on developed countries and less evidence has been presented for developing countries. Using data from waves 2 and 3 of the Indonesia Family Life Survey (IFLS) Park (2010) finds that general health status is strongly correlated with household income among children younger than seven, but not so among the older school-aged children, as schooling is found to have a positive impact on the health status of children of low-income

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families (Park, 2010). By contrast, using wave 3 of IFLS and both subjective and objective measures of child health Cameron and Williams (2009) find that although the health status of children from poor families is compromised by their families' resources, there is no evidence that this disadvantage is greater among older children as evidence for developed countries suggest. One potential source of differences in the nature of gradients between developed and developing countries may lie in the nature of illnesses that threaten child health, with acute conditions such as diarrhea and acute respiratory infections playing a greater importance than chronic conditions in parents-assessed child health in developing countries (Cameron and Williams, 2009). Moreover, in contrast to chronic conditions which tend to be revealed as children age, acute illnesses are generally less common and less severe among older children. Even though acute illnesses are more common among children from low-SES households, they are less likely than chronic conditions to have a cumulative negative health effect due to their short-term nature. Another potential source of differences in the nature of gradients between developed and developing countries is selective mortality resulting from the SES-gradient in infant and child mortality rates. This selectivity bias raises the average health of surveying children from the low-SES households and hence artificially flattens the SES-gradient in health. Implicit in Cameron and Williams' argument regarding the differences in the nature of illnesses between the developed and developing countries are the assumptions that acute conditions play a greater importance than chronic conditions in parent-assessed child health status and that the burden of the chronic conditions among low- and high-SES children in Indonesia is similar to the one in developed countries, with children from low-SES households experiencing more severe and/or more new chronic conditions as they age. It remains an empirical question whether these assumptions are actually met in the context of developing countries in general and low-income developing countries in particular. Data on chronic conditions from a low-income country such as Vietnam suggests little variation in the prevalence of chronic conditions among low- and high-SES children younger than 15 (MoH and GSO, 2002). If there is indeed no clear income gradient in the burden of chronic conditions and their severity, then one would expect younger children from poor households to be relatively more disadvantaged, at least to the extent to which acute illnesses are more common and more severe among younger children as compared to older children, and more common and severe among low-SES children compared to high-SES children. Moreover, younger children from the poor households are likely to be at a greater risk in many low-income countries where communicable diseases remain the leading cause of child mortality and morbidity and where over 45% of under-five deaths are attributable to under nutrition (UNICEF et al., 2013; WHO, 2013). The risks posed by communicable diseases and household food security are further compounded by limited access to safe drinking water, sanitation and hygiene, inadequate access to health care services, and insufficient maternal, infant and young child feeding and caring practices. In the context of many low-income countries household resources may then play a greater role in protecting children's health than in developed countries, as household resources allow parents to purchase better nutrition, better quality medical care and to provide a safer environment for the their children. The differential prevalence of chronic and acute illnesses between the developed and the low-income developing countries raises the question of whether the effect of poverty on children's health is constant or varies in strength across different life stages. With much of the literature being focused on developed countries, this study is the first to investigate the SES gradient in children's

health for a low-income developing country. Although Vietnam's record on poverty reduction over the past two decades has been remarkable, 20.7% of Vietnam's population is still poor, including 27% in rural areas, and 8% of the population remains extremely poor (World Bank, 2012). Moreover, 22% of rural children under five and 30% of ethnic minority children are still identified to be underweight and micronutrient deficiencies remain a significant problem (GSO and UNICEF, 2007; UNICEF, 2010). Using Vietnam's rich National Health Survey (VNHS) and appropriate multilevel modeling this study empirically assesses the influence of household resources on child health while controlling for the confounding factors previously found to be important determinants of child health. More specifically, it is hypothesized that children from the low-SES households are more likely than children from the high-SES households to have poorer health, and that the younger children from the low-SES households are relatively more disadvantaged. Since poor health in childhood is likely to be associated with lower educational attainment and poor lifetime health outcomes and consequently poor labor market outcomes as adults, understanding SES gradients in health is critical both for maximizing children's health and for early prevention efforts to improve health across lifespan. 2. Data and methods 2.1. Data The data in this study are from the VNHS (MoH and GSO, 2002). The VNHS covers about 158,000 individuals from 36,000 households collected as a three-stage random stratified cluster sample with a non-response rate of less than 2% for the survey at the national level. The VNHS's health check questionnaire collected, among other things, parents' (subjective) evaluations of children's general health status, anthropometric measures, blood pressure and smoking/drinking habits of the household members aged 16 and over. General information on various socio-economic variables, such as age, gender, education and ethnicity as well as on the presence of chronic conditions, symptoms of various acute illness, injuries and accidents and health seeking behavior were also collected through the household questionnaire. A commune leader questionnaire was also separately administrated to collect detailed background information on the sampled communes/wards, including the number of poor households, the main grassroots public health care facilities, and the socio-economic situation of the commune/ward. Once we merge individual and household records of the sample population, our sample consists of 45,448 children and 22,303 households. 2.2. Methods To the extent that children's general health is influenced by household unobserved characteristics, the reported health measures are likely to be correlated among the children from the same household. In this case, the application of standard ordinal regression models such as an ordered probit model leads to bias (Rabe-Hesketh and Skrondal, 2005). The dependence among the household members' health status can instead be explicitly modeled using a random-intercept ordered probit model. To assess the overall degree of homogeneity in general health status among children within a household we estimate a two-level (individual and household) random intercept ordered probit model without including the observed covariates. The estimated random-intercept b is 2.08, implying an estimated residual intravariance ( j) household correlation of 0.39. A two-level random-intercept ordered probit model is then used to empirically assess the

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relationship between child health and household resources while controlling for observed individual-, household- and commune/ ward-level confounding factors. In light of the estimation technique used in this study, rather than splitting the sample into the relevant age groups as done in the child health literature, we use the entire sample and interact SES markers with age-group dummies to test the hypothesis that the SES-health gradient varies with the child's age. Since the VNHS data uses a three-stage stratified cluster sampling methodology, the clustering of responses by the primary sampling unit (commune/ward) raises the possibility of intra-commune correlation. Standard errors of the estimated coefficients are thus corrected for intra-commune correlation (heteroscedasticity). We also apply appropriate sampling weights to produce unbiased population estimates. STATA version

25

12.1 was used for all data analysis. As the analysis rely exclusively on secondary data, ethics approval was not required for this study. 3. Study variables In line with the existing literature, child health (the dependent variable) is parent-reported child health status, and it is represented by a three-point ordinal indicator: (1) very good or good; (2) fair; and (3) poor or very poor. Because of the small number of “very good” and “very poor” responses, “very good” and “good” as well as “very poor” and “poor” are collapsed into one category. The VNHS provides three measures of household living standards. One measure is constructed from household consumption of thirteen key food items that account for a large share of food consumption

Table 1 Definitions and summary statistics. Variable name

Description

Parents assessed child general health Health status very good or good Health status fair Health status poor Weight-for-height Z score Height-for-age Z score Weight-for-age Z score Gender Age 0e3 years 4e7 years 8e11 years 12e15 years Parent age Mother age Father age Mother's education Parent's general health status Mother: Very good/good health Mother: Fair health (ref.) Mother: Poor health Mother: Missing Father: Very good/good health Father: Fair health (ref.) Father: Poor health Father: Missing Log of household size Ethnicity Source of drinking water Piped city water Drilled well water Dug well water (ref.) Rain River/lake/spring Piped mount.spring Bought Others Log of household consumption Log of household food consumption Household consumption quintiles Quintile 1 (poorest) Quintile 2 Quintile 3 Quintile 4 Quintile 5 (ref.) Urban Regions Red River Delta Northeast (ref.) Northwest North Central Coast South Central Coast Central Highlands Southeast Mekong Delta Poverty rate in commune/ward

1 1 1 1

Mean

Std dev.

1.733 0.321 0.624 0.054 0.846 1.459 1.514 0.514

0.552 0.467 0.484 0.227 0.983 1.190 0.937 0.500

0.172 0.237 0.303 0.288

0.377 0.425 0.460 0.453

35.598 38.243 0.872

7.366 7.842 0.334

1 ¼ if very good/good health, 0 otherwise 1 ¼ if fair health, 0 otherwise 1 ¼ if poor health, 0 otherwise 1 ¼ if information is missing, 0 otherwise 1 ¼ if very good/good health, 0 otherwise 1 ¼ if fair health, 0 otherwise 1 ¼ if poor health, 0 otherwise 1 ¼ if information is missing, 0 otherwise ln (No. of individuals residing in household) 1 ¼ if kinh or Chinese, 0 otherwise

0.163 0.686 0.142 0.009 0.232 0.611 0.105 0.052 1.658 0.791

0.369 0.464 0.349 0.095 0.422 0.488 0.307 0.222 0.293 0.406

1 ¼ if source is city piped, 0 otherwise 1 ¼ if source is drilled well, 0 otherwise 1 ¼ if source is dug well, 0 otherwise 1 ¼ if source is rain, 0 otherwise 1 ¼ if source is river/lake/spring, 0 otherwise 1 ¼ if source is piped mount.spring, 0 otherwise 1 ¼ if bought water, 0 otherwise 1 ¼ if source is others, 0 otherwise ln (real annual household consumption in VND (000)) ln (household consumption of 13 key food items in the past 4 weeks in VND (000))

0.138 0.203 0.380 0.107 0.096 0.055 0.010 0.011 5.889 9.613

0.345 0.402 0.485 0.310 0.295 0.227 0.098 0.105 0.883 0.528

0.279 0.207 0.187 0.172 0.156 0.271

0.448 0.405 0.390 0.377 0.363 0.444

0.165 0.152 0.049 0.118 0.095 0.087 0.138 0.197 0.152

0.371 0.359 0.215 0.322 0.293 0.282 0.345 0.397 0.131

¼ ¼ ¼ ¼

if if if if

very good/good health; 2 ¼ if fair health; 3 ¼ if poor/very poor health very good/good health, 0 otherwise fair health, 0 otherwise poor/very poor health, 0 otherwise

1 ¼ if male, 0 ¼ female 1 1 1 1

¼ ¼ ¼ ¼

if if if if

child child child child

is is is is

in in in in

this this this this

age age age age

group, group, group, group,

0 0 0 0

otherwise otherwise otherwise otherwise

Age in years Age in years 1 ¼ if completed primary education, 0 otherwise

1 1 1 1 1 1

¼ ¼ ¼ ¼ ¼ ¼

if if if if if if

consumption quintile is 1, 0 otherwise consumption quintile is 2, 0 otherwise consumption quintile is 3, 0 otherwise consumption quintile is 4, 0 otherwise consumption quintile is 5, 0 otherwise resides in urban area, 0 otherwise

1 ¼ if household resides in this region, 0 otherwise 1 ¼ if household resides in this region, 0 otherwise 1 ¼ if household resides in this region, 0 otherwise 1 ¼ if household resides in this region, 0 otherwise 1 ¼ if household resides in this region, 0 otherwise 1 ¼ if household resides in this region, 0 otherwise 1 ¼ if household resides in this region, 0 otherwise 1 ¼ if household resides in this region, 0 otherwise % of households in commune/ward classified as poor

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among households and yet being different in value and price across poor and non-poor households. A second measure is derived from household total expenditures using the estimated expenditure parameters obtained from the 1998 Vietnam Living Standard and the common variables in both surveys. The third measure is based on the commune/ward leaders' ranking of living standards for the 30 selected households in each commune/ward. In this study we use the household food consumption and total expenditures rather than the community ranking to assess the SES gradients in health. The community assessments reflect the community leaders' subjective ranking of households within their own localities and they may lack comparability across communities. Moreover, the community assessments can be subject to misuse as the commune/ ward leaders may overestimate the poverty in their locality in order to receive more assistance from the central/provincial governments or alternatively underestimate poverty as a way of demonstrating their achievements in poverty reduction (Bales, 2003). Although the focus of our analysis is on the relationship between child health, household resources, and age, we control for the confounding factors previously found to be important determinants of child health. In our baseline specification, controls include gender of child, logarithm of household size, age of parents, household's ethnicity, geographical location (region and urban/rural) and the poverty rate in the community as a marker of area deprivation. Controls were initially included indicators of the presence of parents in the household. Only 3 and 9% of households in the sample, respectively, reported no biological mother and father present in the household. Since none of the estimated coefficients on the indicators of the presence of parents in the base regression were statistically significant we chose to make our analysis conditional on the presence of parents in the household. In a series of sensitivity analyses, we test whether the inclusion of additional variables representing parental health status and education, and household sources of drinking water may mediate the relationship between SES and child health. Children's health may be correlated with their parent's health through several channels. Children may inherent their parents' genetic predisposition for illness, lower quality of care by sick parents, and exposure to common, yet unobserved, environmental risk factors. In addition, to the extent to which parents' poor health reduces the income-producing capacity of the household, poor health is transmitted from parents to children and consequently the failure to control for parents' health could results in attributing the effect of these omitted variables to household resources (Case et al., 2002). Though it has its own potential pitfalls, we follow Case et al. (2002) and Cameron and Williams (2009) and address this issue by including parents' general status in the regression model.

Fig. 1. Child health status and household consumption quintiles.

Similarly, in the presence of high correlation between parents' education and household resources, the exclusion of parents' educational attainment may load onto the coefficients of the interaction terms involving household consumption quintiles. Indeed, parental education, particularly the mother's education, has been found to be an important determinant of child health (Cameron and Williams, 2009; Stauss and Thomas, 1995). The third control variable used to assess the robustness of our results is access to improved drinking water. Improved access to water and sanitation infrastructure are found to substantially alter childhood mortality and morbidity (Fewtrell et al., 2005; Jalan and Ravallion, 2003; Lavy et al., 1996). The risk factor “water, sanitation, and hygiene” comprises a number of interrelated transmission pathways for causing a large number of diseases, with fecaleoral diseases accounting for an important part of this disease burden (Prüss et al., 2002). To the extent to which access to improved water and sanitation is correlated with household resources, the observed SES gradient in child health may include the indirect effect of improved access to water and sanitation operating through household SES. The VNHS provides information on household sources of drinking water and types of toilet facilities. Since data on types of toilet facilities were available for about 80% of the total sample households and since none of these indicators were statistically significant, they were excluded from the controls. Definition of these variables and their summary statistics are provided in Table 1.

Table 2 Health status and household resources: the results for the interaction terms between household resources and age groups. Log of household consumption

Ages Ages Ages Ages

0e3 4e7 8e11 12e15

ra Log likelihood N (level 1) N (level 2)

Log of household food consumption

Coef.

(95% CI)

0.464*** 0.416*** 0.444*** 0.405*** 0.347 31030.469 45,430 22,258

(0.545, (0.494, (0.519, (0.481,

0.382) 0.339) 0.368) 0.329)

Coef.

(95% CI)

0.198*** 0.157*** 0.166*** 0.153*** 0.349 31061.767 45,378 22,243

(0.245, (0.201, (0.209, (0.197,

0.150) 0.112) 0.123) 0.108)

Note: The dependent variable is child health status (1 ¼ very good or good, 2 ¼ fair, 3 ¼ poor or very poor). *** significant at 1%. CI ¼ Confidence Intervals. Controls include a set of dummies for each age group, gender, mother's age, father's age, natural log of household size, ethnicity, urban/rural residence, region of residence, the poverty in the commune/ward of residence. a Intra-household correlation.

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4. Results 4.1. Descriptive analysis Fig. 1 presents the average level of general health by consumption quintile and age. Two features of Fig. 1 are worth noting. First, the gradients for all four age groups slope downward, indicating that the protective effect of health increases as households become richer. Note that better health is here indicated by a smaller number. For instance, parent-reported health status among the youngest age group (0e3) varies from 1.9 for children from the poorest households to 1.81 for those from the second poorest households and these differences are statistically significant at the 1% level. Second, among the children from the poorest households, the youngest age group is relatively more disadvantaged. For example, among the children from the poorest households, the older ones (12e15) are on average healthier than the younger ones (0e3) by 0.17 (¼1.9e1.73) and this difference is statistically significant at the 1% level. By contrast, the corresponding difference in health among children from the second lowest quintile is 0.1 (¼ 1.81e1.71) and this difference is not statistically significant. These results suggest that the standard log-linear functional form which is often used to assess the relationship between household resources and children's health in the literature may mask changes in health gradients in a low income country such as Vietnam. 4.2. Econometric results To investigate how health-SES gradients are influenced by many other parental, household, and child-specific factors, we run a two-

27

level random-intercept ordered probit model while controlling for factors previously found to be important determinants of child health. Table 2 reports the interaction terms between household resources, as measured by household total consumption and consumption of thirteen key food items, and age groups. All the interaction term coefficients are negative and statistically significant in both models, indicating that children's general health improves with family resources. Moreover, the estimates show little variations across the four age groups in the consumption based model. Similar results are obtained when SES is proxied by household consumption of thirteen key food items. Although the coefficients on the interaction term involving age group 0e3 (0.198) is slightly larger in absolute value than the estimates for older age groups, these differences are not however statistically significant. These results suggest a common gradient across all four age groups. To assess the robustness of our results to the functional form of the regression model we re-estimate the regression model using household total consumption expenditure quintiles rather than the natural logarithm of consumption. The results for the interaction terms between consumption quintiles and age groups are presented in the upper panel of Table 3. The coefficients are all positive and generally statistically significant except for the interaction term between age group 4e7 and quintile 4. The point estimate on the interaction term between quintile 1 and age group 0e3 is 0.781, indicating that the youngest children in the bottom quintile are more likely to be in a poorer health than those in the top quintile (the reference category). The coefficients on the interaction terms between age group 0e3 and quintiles, as shown in the first row of Table 3, get smaller as households become richer. The results for

Table 3 Health status and household consumption expenditure quintiles: the results for the interaction terms between quintile and age group.

Controls 1a Ages 0e3 Ages 4e7 Ages 8e11 Ages 12e15

Quintile 1 (the poorest)

Quintile 2

Coef.

(95% CI)

Coef.

(95% CI)

Quintile 3

0.781** 0.612*** 0.657*** 0.617***

(0.640, (0.483, (0.536, (0.494,

0.921) 0.741) 0.778) 0.738)

0.419*** 0.455*** 0.452*** 0.479***

(0.276, (0.327, (0.326, (0.359,

0.561) 0.583) 0.571) 0.598)

0.540*** 0.303*** 0.329*** 0.296***

(0.404, (0.178, (0.211, (0.177,

0.677) 0.427) 0.447) 0.416)

0.250*** 0.224*** 0.208*** 0.217***

(0.111, (0.101, (0.092, (0.100,

0.388) 0.348) 0.324) 0.333)

0.368*** 0.172*** 0.200*** 0.162***

(0.222, (0.044, (0.079, (0.039,

0.515) 0.301) 0.322) 0.284)

0.150** 0.136** 0.122** 0.120**

(0.010, (0.011, (0.005, (0.002,

0.291) 0.262) 0.240) 0.238)

Coef.

Quintile 4 (95% CI)

Coef.

(95% CI)

0.288*** 0.240*** 0.292*** 0.340***

(0.141, (0.109, (0.175, (0.224,

0.216*** 0.093 0.130** 0.200*** 0.347 31014.433 45,440 22,258

(0.077, 0.362) (0.037, 0.223) (0.013, 0.246) (0.083, 0.318)

0.171** 0.053 0.115** 0.117**

(0.030, 0.312) (0.073, 0.178) (0.000, 0.230) (0.004, 0.231)

0.127* 0.026 0.011 0.073 0.292 28769.4583 44,679 22,243

(0.014, (0.151, (0.103, (0.042,

0.268) 0.100) 0.125) 0.188)

(0.045, (0.146, (0.070, (0.070,

0.091 0.060 0.021 0.033 0.292 28626.428 44,649 22,231

(0.050, (0.185, (0.135, (0.082,

0.232) 0.066) 0.093) 0.148)

0.434) 0.370) 0.409) 0.456)

rb Log likelihood N (level 1) N (level 2) Controls 2c Ages 0e3 Ages 4e7 Ages 8e11 Ages 12e15

r Log likelihood N (level 1) N (level 2) Controls 3d Ages 0e3 Ages 4e7 Ages 8e11 Ages 12e15

r Log likelihood N (level 1) N (level 2)

0.097 0.020 0.046 0.044

0.239) 0.107) 0.162) 0.159)

Note: The dependent variable is child health status (1 ¼ very good or good, 2 ¼ fair, 3 ¼ poor or very poor). Quintile 5 ¼ The reference category. *** significant at 1%; ** significant at 5%; * significant at 10%; CI ¼ Confidence Intervals. a Controls 1 includes a set of dummies for each age group, gender, mother's age, father's age, natural log of household size, ethnicity, urban/rural residence, region of residence, the poverty rate in the commune/ward of residence. b Intra-household correlation. c Controls 2 includes all variables in controls 1 plus the parents' self-reported health status dummies. d Controls 3 includes all variables in controls 2 plus the parents' educational attainment dummies and the sources of household drinking water dummies.

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other age groups also show a similar pattern, indicating that the protective effect of household resources on children's health decreases as households get richer. Testing the hypothesis of equality of the coefficients of interaction terms across each consumption quintile indicates no significant differences in the estimates except for the youngest children from the poorest households. The point estimates on the interaction term between age groups and quintile 1, as shown in the first column of Table 3, vary from 0.781 for the youngest age group to 0.612e0.657 for older age groups, indicating that among children from the poorest households the youngest ones (0e3) are relatively more disadvantaged. The existence of this break suggests that the use of natural logarithm of household resources may not be appropriate when estimating children's health gradients. The top panel of Table 3 does not control for parental health status. To examine whether parents' general health status is a channel through which household resources translates into the child's general health status, we included parents' general health status in the control variables. The inclusion of parental general health status improves considerably the model fit and the coefficient estimates of parental general health dummies are all large and statistically significant, particularly for the mother's health. The middle panel of Table 3 reports the results for the interaction terms. Two main observations can be drawn from these results. First, the results show that the inclusion of parental health status reduces the protective effect of household resources on child health, and the reduction is more pronounced for the children from the poorest households than for the children from the non-poor households, and more pronounced for older children (aged 4þ) than younger children. When we test for the equality of coefficients of the interaction terms between each age group and adjacent quintiles we find no significant differences in the point estimates, indicating a flat health gradient for the children from the better-off households. Second, among children in the bottom quintile the 0e3 year olds are still relatively more disadvantaged than older children (4þ). The point estimate of the interaction term between age group 0e3 and quintile 1 is 0.540 versus 0.296e0.329 for other age groups, and these differences are all significant at the 1% level. These differences are also larger by about 64e80% than the corresponding differences in the point estimates when parental health status is not controlled for. To investigate the influence of parental education on gradients in child health, parental education was interacted with the four child age groups. The indicator of education (completed at least primary education) was found be negative and statistically significant only for the interaction term between the mother's education and the youngest age group (0e3). The inclusion of the mother's education reduces the impact of household resources on the health of the youngest children from the poorest households by about 8%, but it does not alter the overall pattern of the gradients e the results are available from the authors upon request.

Finally, the extended model was re-estimated using an additional control variable, household source of drinking water. Household sources of drinking water is measured by eight dummies, “piped city water”, “drilled well water”, “dug well water”, “rain”, river/lake/springs, “piped mount. springs”, “bought” and “other”. Most of the indicators of drinking water sources are found to be statistically significant, with piped city and drilled well water being associated with better health while river/lake/springs and piped mount. springs with poorer health. The results for the interaction terms between age groups and consumption quintiles are reported in the bottom panel of Table 3. The inclusion of drinking water sources reduces the estimated coefficients on all interaction terms and alters the pattern of gradients. None of the point estimates of the interaction terms between age groups and the third and fourth top quintiles are statistically significant, indicating a flat health gradient for children from the better-off households. By contrast, the point estimates of the interaction terms between age groups and the lowest two consumption quintiles are all statistically significant and they are still large, especially for those in the poorest consumption quintile. Among the children from the poorest households the youngest age group is still relatively more disadvantaged than older children (4þ), with the point estimate of the interaction term between age group 0e3 and quintile 1 being between 1.8 and 2.3 times the estimates for older age groups (0.368 versus 0.16e0.20). These differences are all significant at 5% level. Our extended model was also subjected to additional robustness checks. More specifically, following Case et al. (2002) regression models were re-estimated using a linear probability model in which the dependent variable is whether the child is in poor health. The overall pattern of health gradients were found to be robust to dichotomizing the dependent variable. Our results were also found to be robust to alternative age ranges for younger preschool children. To test the robustness of our results to alternative age ranges, the age group 4e7 was split into two separate age groups, 4e5 and 6e7. The overall pattern of health gradients were found to be robust, with the youngest age groups, 0e3 year olds from the poorest households being relatively more disadvantaged than the older children. Moreover, with self- and parents-reported measures of general health status being likely to suffer from some nonrandom measurement errors (Strauss and Thomas, 1998), we assessed the robustness of our results to alternative measures of child health by using three anthropometric measures of child health, standardized weight-for-height, height-for-age and weightfor age. These anthropometric measures reflect the short and long term nutritional status of a child. Since the overall degree of homogeneity among children within a household was low e intrahousehold correlation ranged from 0.08 to 0.14 without including our standard control variables e the relationship between the objective measures and household resources was estimated using OLS regression models. Table 4 provides the OLS results for the

Table 4 Objective health measures: health status and log of household consumption. Standardized weight-for-heighta

Ages Ages Ages Ages N

0e3 4e7 8e11 12e15

Standardized height-for-age

Standardized weight-for-age

Coef.

(95% CI)

Coef.

(95% CI)

Coef.

(95% CI)

0.205*** 0.239*** 0.184*** NA 2530

(0.155, 0.254) (0.188, 0.291) (0.123, 0.245)

0.551*** 0.538*** 0.479*** 0.468*** 46,070

(0.494, (0.490, (0.437, (0.428,

0.495*** 0.492*** 0.408*** 0.365*** 46,070

(0.440, (0.443, (0.370, (0.325,

Note: CI ¼ Confidence Intervals. *** Significant at 1%. Quintile 5 ¼ Reference category. Controls same as in Table 2. a Data for standardized weight-for-height are available only for children aged nine and younger.

0.609) 0.587) 0.520) 0.508)

0.550) 0.541) 0.445) 0.405)

A. Sepehri, H. Guliani / Social Science & Medicine 130 (2015) 23e31

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Table 5 Standardized height-for-age: the results for the interaction terms between consumption expenditure quintiles and age groups.

Ages Ages Ages Ages N

0e3 4e7 8e12 12e15

Quintile 1 (the poorest)

Quintile 2

Coef.

(95% CI)

Coef.

(95% CI)

0.700*** 0.769*** 0.728*** 0.706***

(0.804, (0.848, (0.640, (0.770,

0.621*** 0.629*** 0.573*** 0.448***

(0.731, (0.708, (0.521, (0.510,

0.594) 0.689) 0.409) 0.641)

Quintile 3

0.511) 0.551) 0.395) 0.385)

Quintile 4

Coef.

(95% CI)

0.525*** 0.546*** 0.458*** 0.351***

(0.640, (0.626, (0.521, (0.412,

0.409) 0.466) 0.395) 0.290)

Coef.

(95% CI)

0.383*** 0.398*** 0.374*** 0.237*** 46,070

(0.497, (0.478, (0.440, (0.300,

0.270) 0.317) 0.308) 0.175)

Note: CI ¼ Confidence Intervals. *** significant at 1%. Quintile 5 ¼ Reference category. Controls same as in Table 2.

interaction terms between the log of household consumption and age groups. The results indicate a positive and significant association between all three objective measures of child health and household resources, with the protective effect of household resources being far greater on height/weight by age than on weight by height measure of child health. Moreover, when we test for the equality of coefficients of the interaction terms across the age groups we find no significant differences in the point estimates in the weight by height regression model, indicating a common gradient across all three age groups. By contrast, the point estimates on the older age groups (8e11 and 12e15) are smaller than those for the younger age groups (0e3 and 4e7) in both the height by age and weight by age regression models, and these differences are statistically significant. In the height by age regression model, the point estimates drop from 0.54-0.55 for the youngest two age groups to 0.47e0.48 for the oldest two age groups, and these differences are all significant. These results suggest that children from the wealthier households are, on average, taller than children from the poorer households and differences in height weaken as the child ages. The objective health measures regression models were also estimated using consumption quintiles. The results for both the height by age and weight by age measures of child health indicate that the log consumption model is likely to mask variations in the protective effect of household resources on the child's health among the poor and non-poor households. Table 5 presents the results for the interaction terms between consumption quintiles and age groups for the height by age regression model - the weight by age regression model yielded similar health gradients. The coefficients on the interaction terms between age group 0e3 and quintiles, as shown in the first row of Table 5, get smaller in absolute value as households become richer, indicating that the youngest children from the less wealthy households are more likely to be shorter than those from the wealthiest households (the reference category). The results for other age groups also show a similar pattern. However, a comparison of the point estimates across age groups shows greater variations in the protective effect of household resources on child's height among children from the non-poor households than from the poorest households. When we test for the equality of the coefficients of interaction terms involving quintile 1 and age groups, as presented in column 1 of Table 5, we find no significant differences in the protective effect of household resources on child's height across ages. By contrast, beyond the poorest quintile, the point estimates on the older age groups (8þ) becomes smaller in absolute value, indicating that the health gradient weakens as child ages. In the case of children in the second lowest quintile, the point estimate drops in absolute value from 0.62 for the younger children to 0.44 for the oldest age group. The results suggest that children from the poorest households are relatively more disadvantaged and unlike our results from the subjective health measure regression model this disadvantage is greater among the older children. Controlling for parental education and health as well as for sources of drinking water has little

effect on the pattern of gradients. 5. Discussion and conclusions Using the VNHS data and a multilevel ordinal probit modeling framework this paper empirically assesses the relationship between household resources and child health, or health gradient, while controlling for a wide range of individual- and householdlevel factors. The results support the main finding from developed and developing countries, which show that household resources are a powerful determinant of children's health status. Moreover, the point estimates of the interaction terms between household resources (as measured by the natural logarithm of household consumption) and age groups are found to be not significantly different from each other, indicating a common gradient across all four age groups. However, the healthconsumption gradients are found to be sensitive to the functional form of the regression model as well as the model specification. Rather than using the natural logarithm of household consumption as a functional form we instead estimate the regression model using household consumption quintiles. Consistent with descriptive findings, the results indicate a steeper gradient for younger children (0e3) than older children (4þ) from the poorest households. Not only are children from the poorest households worse off, compared to those from the richest households, but this relative disadvantage is greater among the younger children. However, these results may be subject to selection bias if higher infant and child mortality rate among the low-SES children raises the average health of surviving children from low-SES households and artificially flattens the SES-gradient in health. Selection bias may also arise if the mortality rates have declined over time. In the latter case, the health-SES gradient flattens more for older children as the decline in mortality rates increases the average health of surviving children. The reported results may thus underestimate the “true” protective effect of household resources on child health. However, the direction of bias is less clear when assessing differences in the health-SES profiles across age groups. These potential biases may be small given Vietnam's overall relatively low infant and under-five child mortality rates as compared to those for other low-income countries (30 and 39 versus 80 and 120 per 1000 live births) (UNDP, 2004). Moreover, the reported results are based on cross sectional data and may not adequately capture many changes taking place in a rapidly changing transitional economy. Clearly, additional research remains to be done to understand the unseen household level determinants of parent-reported health. Indeed, the results from the extended regression model suggest that the strength of health-SES gradients across age groups is affected by parental health and household sources of drinking water. Controlling for parental health status reduces the coefficients of the interaction terms involving household consumption quintiles and age groups, and the reduction in the protective effect of household resources is found to be greater for the children from the poorest households than for the children from the non-poor households,

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and greater for older children (aged 4þ) than younger children. Parental health has also been found to play a significant role in mediating the relationship between household income and child health in two studies focusing on the UK and Australia (Khanam et al., 2009; Propper et al., 2007), with the income gradient in health being eliminated once maternal health and behaviors are included in the analysis. The finding that the reduction in the protective effect of household resources is greater for the poor households than non-poor once we control for parental health may suggest the exposure of children from the poor households to common, yet unobserved, environmental risk factors. Although controlling for households sources of drinking water affects the pattern of consumption-health gradient by eliminating the health gradients for children from the better-off households, younger children from the poorest households remain relatively disadvantaged. Access to improved drinking water has the potential of altering the burden of illnesses common among younger children, but this potential may not be realized in practice if other important and often complementary health inputs such as hygienic water storage, boiling water and oral re-hydration therapy are absent (Jalan and Ravallion, 2003). For example, in a study on the impact of piped water on diarrhoea prevalence and duration among children under five in rural India, Jalan and Ravallion (2003) found striking differences in the child-health gains from piped water according to household income. While there were significant health gains overall from access to piped water, they found no evidence of significant gains for the lowest two income quintiles. The results from the height-for-age regression model suggest that children from the poorest households are, on average, likely to be shorter/thinner than children from the non-poor households and that unlike our results from the parental-assessed general health status regression model this disadvantage is greater among the older children (8þ). These results provide further evidence in support of the exposure of children from the poor households to common, yet unobserved, environmental risk factors. As an overall measure of a child's physical development and adequate growth, height-for-age reflects the child's medical history and long-term nutritional status. Both child's growth and health have a common precedent in deprivation, with adverse economic and environmental factors leading - largely through dietary inadequacy and infection in poor communities - to growth failure and ill health (United Nations, 1990). While inadequate nutrition and infection in a poor community may cause both growth failure and sickness, growth failure itself does not lead to increased sickness. Moreover, as noted earlier, acute illnesses such as diarrhea and acute respiratory infections are likely to play a greater importance than chronic conditions in parental-assessed child health status in developing countries and due to their short-term nature these acute illnesses are less likely than chronic conditions to have a cumulative negative health effect. According to the VNHS data, the overall prevalence rate of symptoms of acute illnesses varies among children from as high as 54% for the youngest age group (0e3) to as low as 26% for oldest age group (12e15) versus 0.7e1.9% for chronic conditions. Among the reported acute illnesses, the consumption gradient was more pronounced for the case of diarrhoea, with the prevalence rate for younger children from the poorest rural households being twice the rate for their counterparts from the richest rural households. By contrast, there were little variations in the presence of reported chronic conditions across consumption quintiles. Although household access to clean water in Vietnam' s rural areas has improved considerably over the last two decades from 17% of the households in 1993 to 29% in 2002 and 57% in 2010, unsafe water and sanitation remains a major challenge in Vietnam, causing about half of the communicable diseases in the country

(UNICEF, 2010; World Bank, 2012). In our sample population, two thirds of the poorest rural households reported to use water from dug wells and lake/river/springs as their main source of drinking water as compared to 28% for the richest rural households. Apart from inadequate access to improved drinking water and sanitation, inadequate knowledge and poor hygiene habits such as use of dirty cooking utensils, lack of hygienic toilets, defecation into fields and rivers and use of untreated human excreta mean increased risk of diarrhoea and parasitic and bacterial infections (UNICEF, 2010; World Bank, 2012). Likewise, although Vietnam's record on poverty reduction over the past two decades has been remarkable, the poverty rate remains high, especially among ethnic minorities. In our sample population, among those classified as “hungry” by the community assessment, 78.5% were in the lowest quintile. Better measures are needed to protect the poor and vulnerable households from food poverty, and inadequate access to improved drinking water and sanitation (World Bank, 2012). Clearly, additional research remains to be done to understand the unseen household level determinants of the observed worsening of SESrelated health status gradients among the younger children from the poorest households. Acknowledgments The authors would like to thank the two anonymous reviewers and the editor for their invaluable comments and suggestions on an earlier version of this manuscript. References Allin, S., Stabile, M., 2012. Socioeconomic status and child health: what is the role of health care, health conditions, injuries and maternal health? Health Econ. Policy Law 7 (2), 227e242. Apouey, B., Geoffard, P., 2013. Family income and child health in the UK. J. Health Econ. 32, 715e727. Bales, S., 2003. Technical Documentation for the Vietnam National Health Survey 2001e2. Vietnam Ministry of Health and Statistics Sweden International, Hanoi, Vietnam. Cameron, L., Williams, J., 2009. Is the relationship between socioeconomic status and health stronger for older children in developing countries? Demography 46 (2), 303e324. Case, A., Lubotsky, D., Paxson, C., 2002. Economic status and health in childhood: the origins of the gradient. Am. Econ. Rev. 92 (5), 1308e1334. Chen, E., Martin, A.D., Matthews, K.A., 2006. Socioeconomic status and health: do gradients differ within childhood and adolescence? Soc. Sci. Med. 62 (9), 2161e2170. Condliffe, S., Link, C.R., 2008. The relationship between economic status and child health: evidence from the United States. Am. Econ. Rev. 98 (4), 1605e1618. Currie, A., Shields, M.A., Price, S.W., 2007. The child health/family income gradient: evidence from England. J. Health Econ. 26 (2), 213e232. Currie, J., Stabile, M.A., 2003. Socioeconomic status and child health: why is the relationship stronger for older children? Am. Econ. Rev. 93 (5), 1813e1823. Fewtrell, L., Kaufmann, R.B., Kay, D., Enanoria, W., Haller, L., Colford, J.M., 2005. Water, sanitation, and hygiene interventions to reduce diarrhoea in less developed countries: a systematic review and meta-analysis. Lancet Infect. Dis. 5 (1), 42e52. Fletcher, J., Wolfe, B., 2014. Increasing our understanding of the health-income gradient in children. Health Econ. 23, 473e486. General Statistical Office (GSO) & UNICEF, 2007. Multiple Indicator Cluster Survey 2006 (MICS): Monitoring the Situation of Children and Women. Statistical Publishing House, Hanoi. Jalan, J., Ravallion, M., 2003. Does piped water reduce diarrhea for children in rural India? J. Econ. 112, 153e173. Khanam, R., Nghiem, H., Connelly, L., 2009. Child health and the income gradient: evidence from Australia. J. Health Econ. 28, 805e817. Lavy, V., Strauss, J., Thomas, D., de Vreyer, P., 1996. Quality of health care, survival and health outcomes in Ghana. J. Health Econ. 15, 333e357. Ministry of Health (MoH) & General Statistical Office (GSO), 2002. Vietnam National Health Survey 2001e2002. Ministry of Health, Hanoi. Park, C., 2010. Children's health gradient in developing countries: evidence from Indonesia. J. Econ. Dev. 35 (4), 25e44. Propper, C., Rigg, J., Burgess, S., 2007. Income and maternal mental health from a UK birth cohort. Health Econ. 16 (11), 1245e1269. Prüss, A., Kay, D., Fewtrell, L., Bartram, J., 2002. Estimating the burden of disease from water, sanitation, and hygiene at a global level. Environ. Health Perspect.

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