Impact of education and health on poverty reduction: Monetary and non-monetary evidence from Fiji

Economic Modelling 29 (2012) 787–794

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Impact of education and health on poverty reduction: Monetary and non-monetary evidence from Fiji☆ Rukmani Gounder ⁎, Zhongwei Xing School of Economics & Finance, Massey University, Palmerston North, New Zealand

a r t i c l e

i n f o

Article history: Accepted 22 January 2012 JEL Classification: C25 I32 R29 Keywords: Poverty Econometrics Monetary models Non-monetary models Fiji

a b s t r a c t Fiji signed the United Nations 2015 target of halving extreme poverty from its 1990 level, but like many developing countries it is facing challenges in meeting this goal. This paper presents the economic modelling using Fiji's Household Income and Expenditure Survey 2002/03 dataset to examine the economic and social factors crucial for poverty reduction. Two hypotheses are tested: first, we estimate the monetary effects of education at the aggregate and disaggregated returns to education (primary, secondary, tertiary levels) and by income quartiles, and second, test the non-monetary education and health factors as channels of impact promulgated as effects against poverty prevalence. The monetary results indicate that all income quartile households (i.e. lowest to highest) benefit from additional skills obtained through formal education. While those at the lowest income quartile in particular benefit the most from formal education, however it cannot sustainably prevent people with only primary education from falling into poverty. The results for non-monetary models show that education has a positive and significant influence on the tendency of the people to engage in health prevention activities and in acquiring good housing facilities. © 2012 Elsevier B.V. All rights reserved.

1. Introduction The theoretical and empirical literature on the impacts of education and health on poverty reduction note that education benefits both the individual and society and that health is central to wellbeing for reducing poverty. At the household level, an increase in an individual's productivity and the level of income are two possible outcomes that is realised with higher education levels. Similarly, at the country level, an educated workforce is considered to be the building block for a knowledge-based economy and thus a significant contributor to economic growth. Studies suggest that investment in human capital is the precondition for developing countries to absorb modern technology and improve productivity, which in turn leads to higher income and improved economic performance (Barro, 1991; Mankiw et al., 1992; Romer, 1990). Also, an improvement in a country's educational level not only increases its citizens’ understanding of their rights and opportunities, but also has an empowering effect on women which can lower the fertility rate and child mortality (World Bank, 2005). ☆ The authors thank the Fiji Islands Bureau of Statistics for providing the 2002/03 HIES dataset. We appreciate the valuable comments from participants of the XXth Annual Conference, Contemporary Issues in Development Economics, Jadavpur University (Kolkata, India, 20–21 December, 2010), Sunlou Liuvaie, Massey University and two anonymous referees. The usual caveat applies. ⁎ Corresponding author. Tel.: + 64 6 3505969; fax: + 64 6 3505660. E-mail addresses: [email protected] (R. Gounder), [email protected] (Z. Xing). 0264-9993/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.econmod.2012.01.018

Poverty is much more complex than simply income deprivation as it entails the lack of empowerment, knowledge, opportunity as well as access to income and capital. The benefits of schooling increase welfare via increased ability to acquire higher income and positively influence socio-economic outcomes. The motivation of this study derives from the growing concern about high incidence of poverty and the different channels of impact factors crucial for poverty reduction. To our knowledge no study has analysed the monetary and nonmonetary effects of poverty reduction in the case of Fiji. We examine the hypotheses that the influence of education on reducing poverty goes beyond its impact on income and wages by analysing the monetary and non-monetary effects which relate to the impacts on income poverty and other social dimensions. The monetary effect explains the returns to education where the higher level of education of the population the lower is the number of people in poverty as skills/knowledge lead to higher wages/income and fulfils the standard of living. The non-monetary effect explains the channels of impact through social indicators such as health, education and housing factors promulgated as effects against poverty prevalence. While education is both an outcome and a means to poverty alleviation, the returns to education can also be a useful tool for policymakers in a number of ways. In particular, it can determine which sector of the education system the government should invest in. Notably, the efficient and effective allocations of public resources require that these should be directed to the levels of education that yield the highest returns. This is critical in optimising the use of scarce public resources, and is crucial for poverty reduction. A number of studies

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provide evidence of positive significant association between education and health outcomes, also, that substantial social and economic gains have been achieved from women's education (Auster et al., 1969; Friedman, 2002; Fuchs, 1980; Lee, 1982; Leigh, 1981). Education is essential for a satisfying and rewarding life, and good health is a prerequisite for increasing productivity. Education empowers people to engage in health practices, decision making and adequate health relies on successful education. Both these factors are necessary components for economic growth and social development. Such dual roles as both inputs and outputs give health and education central importance in economic development. While Fiji saw to most of its development challenges since its independence in 1970, the political instabilities, economic and environmental vulnerability, and limited access to the global markets have led to major obstacles in achieving improvements not only in economic growth, but also social development. The effects of global economic crisis and a decline in the agriculture sector due to floods in 2009 and 2010 have significantly reduced the income earning capacity of the people, and many of whom are on the very edge of poverty. Fiji's 46 th Human Development Index (HDI) ranking (out of 175 countries) in 1994, has been high compared to larger developing countries as Brazil (68 th), the Philippines (98 th), China (108 th) and India (138 th) (United Nations Development Programme (UNDP), 1997). However, Fiji's 1994 HDI ranking dropped from 46 th to 108 th in 2007 (out of 177 countries) (UNDP, 2009). The incidence of poverty in Fiji increased between 1977 and 2002/ 03. While the most recent poverty estimates show a decline in poverty levels from almost 35% in 2002/03 to 31% in 2008/09, rural poverty has consistently increased since 1977 from 21.4% to 43% in 2008/09. The human poverty index (HPI) rank has slipped from 6th (out of 85 developing counties) in 1998 to 45 th (out of 95 developing countries) in 2007.1 Some improvements have been noted in the education and health sectors but challenges are high in terms of improving housing infrastructure, especially those who live in squatter settlements (Naidu et al., 2007). The Fiji government since 1971 has set its efforts to increase access to education via primary education at zero-fee tuition, however poor households are still not able to have access to it due to high direct and opportunity costs. 2 As a result, there has been a high rate of school dropout rates, and to some extent, the denying of schooling for girls. These effects contribute to the perpetuation of poverty cycle since it reduces the income-earning potential of a child and further hinder productivity, receptivity to change and capacity to improve quality of life. The interim government in 2007 has taken some actions to assist those experiencing hardships through education and welfare benefits, but major improvements in housing, health care facilities and service provision are required. As the impact of global economic crisis continues to be imminent, it is anticipated that this may lead to an increase in poverty levels therefore posing difficulties for Fiji in meeting its poverty reduction target of the United Nations Millennium Development Goal (MDG). Using Fiji's Household Income and Expenditure Survey (HIES) 2002/ 03 dataset, comprising of 4977 households, the socio-economic characteristics (income, schooling, age, gender, ethnicity, children, rural and urban areas, young parents and disability) are utilised to estimate the monetary and non-monetary models. In analysing the returns to education, we first test the impact of monetary factors by income quartiles and different levels of education followed by non-monetary hypothesis of education and health measures of engaging in health prevention 1 Fiji's gender development index ranked at 69th in 2009 improved from 2005 to 07 rankings but its gender empowerment measure slipped to 71st ranking in 2009 from 47th in 1996 and 68th 1997 (UNDP, 2009). 2 Direct cost of uniforms, learning materials and transportation may well be beyond the means of a poor family, also when the family has several children of school age (Gounder, 2005). Opportunity costs for poor households are high, i.e. loss of labour input and the forgone income which increases the likelihood of school dropout rates.

activities and sanitation. The model specifications and methodologies employed to analyse the monetary effects are presented in Section 2 followed by non-monetary returns to education and health in Section 3. The quantile and logistic regression techniques are applied using STATA to estimate various models by total households, levels of household income distribution function, aggregate and disaggregated education levels, and health prevention activities. The results reported in the penultimate section are followed by the implications of findings and conclusion in the final section. 2. Models for poverty reduction The emphasis on multi-dimensionality of poverty includes economic and noneconomic dimensions of deprivation (Alkire, 2002; Sen, 1985, 1999; Stewart et al., 2007). Poverty is defined as “unacceptable human deprivation in terms of economic opportunity, education, health, and nutrition, as well as lack of empowerment and security” (World Bank, 2001, p. 15). Notions of deprivation and well-being are noted where structural barriers prevent the poor from both accessing external assets (e.g. credit, land, infrastructure, common property) and internal assets (e.g. health, nutrition, education). This multi-dimensionality raises the standards for more complicated and complex strategies for poverty alleviation (Stewart et al., 2007; World Bank, 2005). As such, poverty requires a measurement as comprehensive as its definition in order to achieve adequate results. Major approaches to poverty measurement are the monetary approach, capability approach, basic needs approach, social exclusion approach and participatory approach. Based on these approaches the variables included in the monetary and non-monetary models identify the household characteristics and how the chances of a household being non-poor increases. The approaches are examined below. 2.1. Econometric framework: monetary models of poverty reduction The causal relationship between education and income earnings has been one of the most highly and carefully explored subjects in the empirical literature of labour economics.3 The analysis of such a relationship has used a variety of econometric tools on diverse data sets. A difficulty in isolating the causal impact of additional education on earnings has been noted by Gujarati (1995, 1999), Stanovnik (1997), and Wooldridge (2003). Hence, it is crucial to obtain accurate measures for earning premiums associated with adopting education and poverty reduction policies. From the private returns viewpoint, under certain conditions, it provides a measure of the return to investment in addition to schooling. The social point of view notes that returns to education could give an indication of the relative scarcities experienced by people with different levels of education and therefore may provide a guide for educational policies (Psacharopoulos and Ng, 1994). The underlying monetary model of education used in this study is associated with Mincer's (1974) wage function. The model is extended to control for a number of other factors related to personal household characteristics rather than just schooling factors. Fiji's information on the income for each household is available and thus we consider the purpose of addressing poverty conditions that are not determined exclusively by labour income of the individuals, but also by any available income to the household as a whole.4 The semi-logarithmic monetary framework applied in this study takes the following form: lnY i ¼ f ðSi ; X i ; Z i Þ þ ui 3

ð1Þ

See Card (1999) and Son (2010) for a comprehensive review. Total household income can reflect the household's standard of living characteristics, for e.g. the ability to purchase expensive durable household goods. But it may suffer from the weakness that there may be larger (or smaller) numbers of income earners in each household, and the same income may need to be spread out over a larger (or smaller) number of occupants. It is therefore important to adjust for household size (Narsey, 2006). 4

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where lnY is logarithm of the total income of the household. S is completed years of schooling of the household head i; X is a vector of characteristics of the head of household i; Zi is a vector of characteristics of the household i; u is a random error term that captures unobserved characteristics; and i is 1,…N, households. Eq. (1) is further classified into two functional forms that estimate the monetary effects of education for income poverty reduction. The first functional specification (Eq. (2)) measures the aggregate level of household head's completed years of schooling, and Eq. (3) computes the disaggregated returns to schooling (i.e. primary, secondary, tertiary level of education). These specifications, respectively, take the following forms: 2

lnTHAIi ¼ β0 þ β1 Schooli þ β2 Agei þ β3 Agei þ β4 Femalei

ð2Þ

þβ5 Ethnicityi þ β6 Childreni þ β7 Rurali þ u1i 2

lnTHAIi ¼ α 0 þ α 1 PRIM þ α 2 SEC þ α 3 TER þ α 4 Agei þ α 5 Agei

þa6 Femalei þ α 7 Ethnicityi þ α 8 Childreni þ α 9 Rurali þ u2i

ð3Þ where lnTHAI is logarithm of total household annual income; School is the household head's completed years of schooling; Age is the age of household head; Age 2 is squared of the household head's age; Female is dummy variable that represents the female household head; Ethnicity takes value of 1 if the household head is Indo-Fijian and 0 if Fijian; Children is number of children in the household 5; Rural is the households in rural areas; PRIM is the household head with primary education (1–8 years); SEC is the household head with secondary education (9–13 years); TER is the household head with tertiary education (>13 years); u1i, u2i are random error terms; and i is 1,…N, households. The dependent variable in Eqs. (2) and (3) is total household income rather than individual's wages seen in Mincer's wage equation as it avoids the potential biases and omitted variables. 6 Also, in the cross-sectional studies, the years of schooling and wages have not been able to identify the impact of income from the effects of unobserved characteristics (such as family background, school quality, individual ability and other factors) that may be correlated with income. Therefore, incorporating appropriate variables to identify the effects from unobserved factors (i.e. ability, family background or exogenous influences on schooling decisions) are taken besides the household-community characteristics to avoid measurement errors. 7 Aside from the question of ability the choice of years of schooling for an individual cannot necessarily be regarded as independent of expected earnings of that person. Also, if earnings are an extreme variable, then the current earnings may dominate future earnings and the household may choose to reduce schooling when labour market prospects are buoyant. In this case, the Ordinary Least Square (OLS) schooling coefficient would be a downward biased estimator of the true return (Card, 1999). The problem of endogeneity issue is overcome by using instrumental variables for schooling in this study. 8 5

Children are defined in the present study as those who are 14 years of age or under. Stanovnik (1997) and Wooldridge (2003) argue that the standard log wage equation does not include any measure of ability. Since the ability and schooling variables are positively correlated omitting measures of ability would result in biased upward schooling coefficients. 7 Blackburn and Neumark (1992) use IQ scores as a proxy variable for ability in the standard log wage equation to measure the rates of return to education in the United States. They find the estimated return to education without the proxy IQ variable to be 6.5%, and the return to education falls to 5.4% when IQ is added, i.e. omitted bias amounts to 1.1% decline in the return to education. Similar evidence is found by Hernstein and Murray (1994, cited in Wooldridge, 2003). 8 See Angrist and Krueger, 1991; Card, 1993; Harmon and Walker, 1995; Wooldridge, 2003; Zuluaga, 2007 for a discussion on endogeneity issues. See also Coudouel et al. (2001). 6

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Following Zuluaga (2007) we utilise exogenous variations of schooling attendance of the individuals. The first instrumental variable reflects an expansion in Fiji's educational sector in the 1970s. The Government introduced the tuition-free education since 1973 for primary school intake (Ministry of Education, 2000), thus in this regard, PRIMFree1973 variable is included to identify exogenous influences on schooling decisions resulting from free primary education scheme. The second instrumental variable captures the impact of schooling of young parenthood (YPi) to identify the individuals that have become head of households before reaching the age at which secondary school is normally culminated. The third instrumental variable reflects the impact of school attendance of the head of household due to his/her physical/mental underprivilege, as people with disabilities are more likely to remain in poverty and also lack access to employment and education.9 The disability variable (Disable) is used to identify whether the household head withdrew from school or has less schooling years which reflects their likelihood of being poor given the low education levels. A dummy variable is used to measure this impact. The above noted issues are incorporated in Eq. (4), which takes the following specification: Schooli ¼ δ0 þ δ1 PRIMFree1973i þ δ2 YP i þ δ3 Disablei þ δ4 Agei þδ5 Age2i þ δ6 Femalei þ δ7 Ethnicityi þ δ8 Childreni

ð4Þ

þδ9 Rural þ ei where School is the household head's completed years of schooling; PRIMFree1973 is 1973 tuition-free scheme; YP is young parenthood; Disable is disable people with school attendance; e is random error term. Other variable descriptions are similar to those in Eq. (3). The effects of education are measured by household income quartiles to distinguish the impact for poverty conditions. 10 It draws attention to the lowest and highest household income groups and estimates the returns to schooling at different quartiles of the conditional distribution of earnings, and reflects the distribution of unobserved ability. Using quantile econometric technique captures the relationship between the covariates and any conditional response variable. We next present the non-monetary models. 2.2. Econometric framework: non-monetary models of poverty reduction The influence of education in reducing poverty goes beyond its impact on income and wages. As the level of education increases, certain decisions and behaviours of people also change and this in turn reduces the likelihood of people falling into poverty. It increases the probability of success needed to reach their basic needs, such as health, housing, water and sanitation, and other services (Sen, 1985, 1999). However, the vulnerable households fall deeper into poverty due to lack of health services, illness, high fertility and malnutrition.11 Two non-monetary models are used to analyse the impact of different channels such as education and health effects to reduce poverty. The hypotheses are: first, whether education has a positive influence on the tendency of people to engage in health prevention decisions such as acquiring life-accident insurance policy or medical and therapeutic appliances. The second hypothesis tests whether higher education level of the household head provides for his/her family's basic needs for improving housing conditions (sanitation — safer water 9 It has been estimated that nearly 90% of children with disabilities in developing countries do not attend school, and the global literacy rate for adults with disabilities is as low as 3 per cent and 1 per cent for women with disabilities in 1998 (United Nations, 2009). An estimated 386 million of the world's working-age are disabled in 2008 (International Labour Organization, 2009). In the case of Fiji 3 per cent of the sample household heads are with disabilities. 10 See studies by Koenker and Bassett (1978), Portnoy (1991), He and Shao (1996), Portnoy and Koenker (1997), Koenker and Hallock (2001), Koenker (2005). 11 According to the World Bank (2002) poorer regions tend to have health facilities that are of low quality and lack many basic medicines.

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supply and flush toilet). The specification in Eq. (5) captures the nonmonetary effects of household head's educational attainment and other characteristics for satisfying the basic needs, it takes the form as follows: P ij ¼ f ðEi ; yi ; X i Þi ¼ 1…N

ð5Þ

where P is the probability of the household i to reach the basic needs j; E is a vector of educational variables for the household i; y is the per capita income of the household i; X is a vector of characteristics of the head of the household i; and i is 1,…N, households. Based on which levels of education contribute to the likelihood of improving the basic needs, Eqs. (6), (7) and (8) include dependent variables for health prevention (HlthPVT) and Sanitation measures. The logistic regression techniques are applied to estimate these equations which take the following specific forms: HlthPVT i ¼ b0 þ b1 Schooli þ b2 lnTHAIi þ b3 Agei þ b4 Femalei

ð6Þ

þb5 Ethnicityi þ b6 Rurali þ v1i

ð7Þ

þc5 Ethnicityi þ c6 Rurali þ v2i Sanitationi ¼ d0 þ d1 PRIM i þ d2 SEC i þ d3 TERi þ d4 lnTHAIi þ d5 Agei þd6 Femalei þ d7 Ethnicityi þ d8 Rurali þ v3i

ð8Þ

where HlthPVT represents whether or not the household engages in health prevention activities such as acquiring life-accident insurance, medical and therapeutic appliances; Sanitation captures whether or not the household has access to metered water supply and flush toilet. School is the household head's completed years of schooling, lnTHAI is total annual income of household; Age is age of the household head; Female identifies the female household head; Ethnicity is dummy variable that takes the value of 1 if it is Indo-Fijian and 0 if it is Fijian; Rural is the household living in rural areas; PRIM is the household head with primary education; SEC is the household head with secondary education; TER is the household head with tertiary education; v1i, v2i, v3i are random error terms; and i is 1,…N, households. The health prevention and sanitation qualitative dependent variables are computed by: "

# P HlthPVT HlthPVT i ¼ Zi ¼ ln 1−P HlthPVY i ¼Z

"

Sanitation Li

Dependent variable: total household annual income (lnTHAI) (Eq. (2)) and schooling (Eq. (4)) IV estimates (Eq. (2))

Reduced form OLS (Eq. (4))

Variable

2SLS

Standard error

OLS

Standard error

School Age Age2 Female Ethnicity Children Rural PRIMFree1973 YP Disable Constant R-Square F-Statistic Observation

0.0349** 0.0332*** − 0.0003*** − 0.2250*** − 0.0994* 0.0043 − 0.3928***

0.03 0.004 0.001 0.04 0.06 0.006 0.03

8.15*** 0.1941 132.96*** 4977

0.44

0.0318* − 0.0019*** − 1.3091*** − 2.1570*** − 0.1122*** − 1.0433*** 0.2971* − 6.0476*** − 1.7802*** 16.55*** 0.1727 98.13*** 4977

0.03 0.0003 0.22 0.16 0.04 0.15 0.27 2.34 0.45 0.83

Notes: ***, **, * significance at the one, five and ten per cent, respectively.

Sanitationi ¼ c0 þ c1 Schooli þ c2 lnTHAIi þ c3 Agei þ c4 Femalei

HlthPVT Li

Table 1 Monetary impact of education on income.

P Sanitation i ¼ ln 1−P Sanitation i

#

Sanitation

where LHlthPVT is odds in favour of engaging in health prevention; LSanitation is the likelihood of a household with access to metered water supply and flush toilet; P/(1-P) is the odds ratio; ln[P/(1-P)] is natural log of P/(1-P) regarded as logit coefficient. The value of P is between from 0 to 1 (i.e. as Z varies from −∞ to +∞), logit L value is between −∞ and +∞. 3. Empirical Results In this section, we report the estimates for different models described in Section 2 based on two sets of estimated results, these include the monetary and non-monetary factors that influence income poverty reduction and social dimensions of poverty reduction. Monetary models have been extended to reflect the disaggregated effects of education and also by income distribution of the five income quartile groups. The second set of result presents the non-monetary effects based on the relationship between the covariates of education and health prevention activities for poverty reduction. Appropriate techniques of two-stage least squares (2SLS), quantile regression and logistic regression methodologies are applied to the data from the Fiji Islands' Bureau of Statistics HIES 2002/03. The

variable description and definitions are presented in the Appendix Table A1. 3.1. Results for monetary models The estimated 2SLS monetary model results (Table 1) show the relationship between income and education. The School coefficient for the returns to education of the household head increases total income of the household by around 3.5%. 12 Other covariates such as Age of the household head increases income while the older household head's (Age 2) income declines. Also, being a Female household head leads to a lower income level as that of Ethnicity (i.e. Indo-Fijian household head) and for those in the Rural areas. The key finding for reduced form schooling Eq. (4) indicates that the three instrumental variables are statistically significant at the conventional levels. The free primary education estimate (PRIMFree1973) is positive and significant providing support to the view that free education schemes tend to encourage more children to attain primary schooling leading to higher income. However, the coefficients for young parenthood (YP) and being disabled (Disable) show the impact on these two groups' income earnings are negative and they also have higher deleterious effects than other factors. These circumstances adversely affect their income levels and cause them to remain in poverty. In Tables 2 and 3, we present the results using quantile regression estimations for the aggregate education level (based on Eq. (2)) and disaggregated education levels (based on Eq. (3)), respectively. The computed coefficients for the years of schooling (School) are statistically significant and positive for all income quartiles, which indicate that an extra year in schooling increases the household's income by 3.35% at the 10th quartile, 3.52% at 25th, 3.57% at the 50th quartile and 3.4% at the 90th income-level group. This reflects that all income quartiles benefit from additional skills obtained through formal education. Moreover, it is substantially higher for the 25th to 75th income quartiles. The empirical results support the hypothesis that returns to education increases income levels and thus plays an important role in poverty reduction. The estimates by income quartile households for the older household head (Age 2), gender (Female), Indo-Fijian household heads (Ethnicity) and region (Rural) indicate declines in income which also lead to these households falling into poverty. The negative Rural 12 This estimated rate of returns to education impact is comparable to the findings of Trostel et al. (2002) study for Czechoslovakia (4.5%) and East Germany (4.4%) though the instrumental variables used in their study are father's, mother's and spouse's education level for educational impact.

R. Gounder, Z. Xing / Economic Modelling 29 (2012) 787–794 Table 2 Quantile regression results by income groups and aggregate education level. Variable

10th quartile

25th quartile

50th quartile

75th quartile

90th quartile

School

0.0335*** (0.0034) 0.0482*** (0.0087) − 0.0004*** (0.0001) − 0.3282*** (0.0620) − 0.1174*** (0.0344) 0.0099 (0.0111) − 0.3977*** (0.0357) 7.0843*** (0.2068)

0.0352*** (0.0023) 0.0358*** (0.0064) − 0.0003*** (0.0001) − 0.2714*** (0.0443) − 0.0982*** (0.0247) 0.0130* (0.0066) − 0.3809*** (0.0236) 7.6520*** (0.1469)

0.0357*** (0.0027) 0.0342*** (0.0051) − 0.0003*** (0.0001) − 0.2059*** (0.0408) − 0.1349*** (0.0306) − 0.0044 (0.0073) − 0.3610*** (0.0298) 8.1185*** (0.1320)

0.0350*** (0.002) 0.0331*** (0.0064) − 0.0002*** (0.0001) − 0.1122*** (0.0358) − 0.1140*** (0.0301) − 0.0007 (0.0075) − 0.3933*** (0.027) 8.5635*** (0.1578)

0.0340*** (0.0029) 0.0237*** (0.0082) − 0.0001* (0.0001) − 0.1809*** (0.0459) − 0.0994*** (0.0373) − 0.0081 (0.0097) − 0.3796*** (0.0362) 9.1505*** (0.1814)

Age Age2 Female Ethnicity Children Rural Constant

Notes: ***, **, * significance at the one, five and ten per cent, respectively. Standard errors are in parentheses.

coefficient suggests that rural households are more susceptible and likely to be associated with the incidence of poverty than urban households. This result supports that rural poverty levels are greater than urban poverty levels (Narsey, 2006). The households in the lowest income quartile also have a greater level of disadvantages of low income earnings and living in rural areas than those in the upper tail income distribution. The estimated Ethnicity coefficient suggests that Indo-Fijian household heads have lower income levels compared to Fijian household heads, a finding that is also consistent with the view reported by Narsey (2008) that the poorest group in Fiji is the rural Indo-Fijian households and the incidence of poverty is higher for this group. An important and decisive result for the disaggregated schooling groups (PRIM, SEC, TER — Table 3) is that the lowest income group (10th quartile) benefits substantially from obtaining formal education. The primary education estimate for the 10th quartile shows an increase in income by 15.97%, 24.97% for secondary education, and 48.79% for tertiary education. Thus primary education is vital as it increases income for the lowest 10th quartile. The gender (Female) coefficients for all quartile groups are negative and significant, i.e. the Table 3 Results for disaggregated education level. 10th quartile

25th quartile

50th quartile

75th quartile

90th quartile

PRIM

0.1597* (0.0866) 0.2497*** (0.0861) 0.4879*** (0.08) 0.0504*** (0.0091) − 0.0005*** (0.001) − 0.3044*** (0.0727) − 0.1262*** (0.0375) 0.0101 (0.0102) − 0.3841*** (0.0334) 7.1178*** (0.2215)

0.0349 (0.0584) 0.1359*** (0.0566) 0.4161*** (0.0549) 0.0424*** (0.0071) − 0.0004*** (0.0001) − 0.2674*** (0.0433) − 0.1080*** (0.0258) 0.0092 (0.0064) − 0.3375*** (0.0250) 7.6620*** (0.1819)

− 0.0484 (0.0575) 0.0481 (0.0524) 0.3983*** (0.0549) 0.0417*** (0.0048) − 0.0003*** (0.0001) − 0.2209*** (0.0361) − 0.1572*** (0.0279) − 0.0027 (0.0068) − 0.3247*** (0.0262) 8.1634*** (0.1299)

− 0.0296 (0.0558) 0.0577 (0.0511) 0.4704*** (0.0494) 0.0455*** (0.0058) − 0.0004*** (0.0001) − 0.1469*** (0.0405) − 0.1008*** (0.0254) 0.0026 (0.0054) − 0.3259*** (0.0287) 8.3907*** (0.1495)

− 0.0438 (0.0784) 0.0501 (0.071) 0.4454*** (0.0731) 0.0372*** (0.0091) − 0.0002** (0.0001) − 0.2198*** (0.0501) − 0.0742** (0.0352) 0.0065 (0.0088) − 0.3575*** (0.0352) 8.9405*** (0.2158)

TER Age Age2 Female Ethnicity Children Rural Constant

female household heads have less income than the male household heads. While such a disadvantage slightly decreases at the higher income distribution, their income however is still lower with respect to male household heads in the same income quartile. The household head's age (Age) is significant and positive for poverty reduction for all quartiles but both Female and Age 2 results are consistent with the hypothesis that gender and older household heads explain their lower income levels and their likelihood to face the problems of poverty. The estimated Ethnicity coefficient is negative and significant for all quartiles, i.e. Indo-Fijian household heads’ income is lower than Fijian household heads. The magnitude of this coefficient decreases at the higher income quartiles, i.e. at the 75th and 90th quartile. The Children estimates (number of children b 15 years) though positive are insignificant for all income groups (Table 3), this is due to the fact that children do not earn high income to sufficiently contribute to total household income. However, at the aggregate school level result (Table 2) the computed Children estimate is positive and significant at the 25th quartile level. It suggests that families at lowincome levels with more children need a higher disposable income and their labour supply complements total income. On the other hand, children can be potential helpers in the family business and domestic duties, which may reduce the level of poverty through theirs' and parents' income. Also, the results show that households located in the rural areas tend to have lower income than those in urban areas. The rate of returns to education by income quartiles (Fig. 1) illustrates the comparison of earnings by education levels for PRIM, SEC and TEC levels. The impact of TER education on earnings is significantly higher than the returns to SEC and PRIM levels for all income quartiles. The returns from PRIM and SEC educational levels have substantially lower impacts on earnings at higher income quartiles. In particular, the returns to PRIM education became statistically insignificant and negative from the 25th quartile onward. This suggests that primary education is the initial stage for lifting the poorest people out of poverty but it cannot sustainably prevent people with primary education falling into poverty. This is particularly more in the circumstances of unforeseen events such as natural disasters, effects of political instability, and global economic crisis that Fiji has experienced over time as it reduces the capacity for income earning opportunities of those with lower education levels. Higher years of schooling and higher education levels are crucial to lift people out of poverty and to prevent them falling into the poverty trap. 3.2. Education, health and housing: results for the non-monetary model

Variable

SEC

791

Notes: ***, **, * significance at the one, five and ten per cent, respectively. Standard errors are in parentheses.

We now turn to the estimated logit results for non-monetary models (Eqs. (6), (7), and (8)), with health and aggregate and disaggregated education nexus. The health factors for reducing poverty are measured in terms of health prevention activities such as buying a life and accident insurance policy, or medical and therapeutic appliances, and decision-making over households’ living arrangement of having a living tenure with better sanitation facilities (i.e. safe water supply and a flush toilet). Results in Table 4 show that the School coefficient

Fig. 1. Returns to education by income quartiles.

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Table 4 Logit model of education–health nexus: health prevention. Dependent Variable: Health Prevention (HlthPVT) — Eq. (6)

School lnTHAI Age Female Ethnicity Rural Constant No. of observation 2 LR x(6) Log likelihood McFadden R2 Correctly predicted (%) 2 H-L x(8)

Coefficient

Standard error

Marginal effect

0.0303*** 1.0138*** 0.0083*** 0.1023* 0.8752*** − 0.7711*** − 8.5168*** 4977 688.38 − 2120.5911 0.1396 80.89 24.68

0.008 0.06 0.003 0.12 0.08 0.08 0.60

0.0390 0.1314 0.0011 0.0129 0.1145 − 0.1038

Notes: ***, **, * significance at the one, five, and five percent, respectively.

has a positive significant influence on the tendency of the people to engage in health prevention activities. Thus, education of the household head is vital in health decisions of buying a life and accident insurance policy, and medical and therapeutic appliances. The marginal effect of School provides a contribution of 3.9% in increasing the likelihood of households' health prevention activities (Table 4, Eq. (6)), and 6.5% in increasing the likelihood of better housing conditions for decisions over households living arrangements of living tenure with safe water supply and flush toilet (Table 5, Eq. (7)). The impact of income (lnTHAI) on the probability of buying lifeaccident insurance policy, medical and therapeutic appliances or better living environment is statistically related to household income. The marginal effect shows that income contributes to a very high increase (i.e. 13.14%) in health prevention activities. What is important is to examine the separate impacts at various educational levels (Table 5, Eq. (8)). The disaggregated education levels (PRIM, SEC, TER) estimates are statistically significant providing support to the view that all educational levels are central in modifying the behaviour and decisions of people with respect to their health. The marginal effect for household head with tertiary education has the highest impact at 12.07% for tenured safe water supply and flush toilet compared to those with secondary education at 5.08%, and primary education at 3.69%. The other estimated coefficients such as Age, Female and Ethnicity are positive and statistically significant showing the likelihood of

engaging in health prevention activities and better sanitation, Tables 4 and 5). The Female household heads are more likely to acquire life-accident insurance or medical and therapeutic appliances compared to their male counterparts. The coefficient of Female shows a contribution of 1.3% (Table 4) and around 2% (Table 5) in increasing the likelihood in engaging health prevention activities and sanitation, respectively. Although the estimated marginal effect of Age for the household head is less than 0.11% for all coefficients, it is an important determinant of health prevention activities. Thus, the age of the household heads (either for responsibility or obligation) is a predictor of health decisions or outcomes — older household heads tend to develop higher prevention habits than young household heads (who face less risk of acquiring illnesses). The Ethnicity coefficients are positive and significant (Tables 4 and 5) indicating that Indo-Fijian households tend to a have higher possibility of engaging in health prevention activities and better sanitation. The marginal effects of 11% (Tables 4 and 5) suggest a high level of preference for engaging in health activities and sanitation. The results also show that there is a disadvantage for Rural households in engaging with health prevention activities and sanitation as major healthcare and better housing facilities are located and seen largely in urban areas (cities/towns). The negative marginal effects in health activities and access to sanitation facilities (33%) suggest that rural households have low participation in attaining proper levels of health activities which negatively affects family health status.

4. Implications of the findings The econometric analysis of monetary and non-monetary models for Fiji's income and education-health nexus supports the view that returns to education provide a vital platform for acquiring higher income and regular prevention habits, risks and external shocks that raise awareness of health prevention activities. These factors are crucial in sustainably preventing the people falling into poverty. As labour is the single main asset of the low-income poor households, any factor that favourably affects the quality of such an assets (i.e. high income earning through schooling, attaining higher levels of education, direct investment in health, indirect investment in health through educational investment) is considered relevant in reducing poverty and therefore should be enhanced. From these findings, it can be said that health is not only an end in itself but also a means to reach other goals. Also, the findings provide strong evidence for Fiji government and the households to invest their resources in

Table 5 Logit model of disaggregated education–health nexus: sanitation. Dependent variable: access to sanitation Eq. (7) Coefficient PRIM SEC TER School lnTHAI Age Female Ethnicity Rural Constant No. of observation 2 LR x(6) Log likelihood McFadden R2 Correctly predicted (%) 2 H−L x(8)

0.0760*** 0.7597*** 0.0103*** 0.2660** 1.2645*** − 3.0337*** − 5.4733*** 4977 1818.4*** − 1692.8993 0.3494 83.58% 13.08

Eq. (8) Standard error

0.009 0.069 0.003 0.13 0.09 0.12 0.65

Notes: ***, **, * significance at the one, five, and five percent, respectively.

Marginal effect

0.0065 0.0652 0.0009 0.0211 0.1123 − 0.3387

Coefficient

Standard error

Marginal effect

0.4903*** 0.6581*** 1.4017***

0.19 0.18 0.19

0.0369 0.0508 0.1207

0.7571*** 0.0123*** 0.2513* 1.2476*** − 2.9828*** − 5.5896*** 4977 1831.73*** − 1686.2328 0.352 83.54% 6.13

0.06 0.003 0.13 0.09 0.12 0.67

0.0649 0.0011 0.0200 0.1106 − 0.3310

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education which would accrue to higher benefits and lower costs over time. As higher educational levels enhance the possibilities of individuals in attaining formal employment, it also facilitates health prevention activities. Education also makes an individual more aware of the essence of staying healthy, access to better sanitation, and how to cope with health risks. Given that people in rural areas do not actively engage in such health prevention activities and do not have easy access to infrastructure and social services like higher education, wellequipped housing facilities and health prevention programmes, the provision of these facilities are necessary to be considered in any poverty reduction programmes. Addressing wage inequality for female household heads' income compared to their male counterparts with similar educational levels would also reduce poverty through the provision of those activities for good health and obtaining higher educational levels. Children obtaining higher levels of schooling reduce the chances of falling in the poverty cycle of low income and poor health outcomes. Appropriate education and training for people with disability would increase their participation in the workforce and income earning opportunities to avoid poverty situations. A number of health economics studies have established that schooling is associated with better health outcomes, even when the factors such as income are controlled for. A well-educated person is more likely to select the right food needed to attain proper levels of nutrition even with little money, which positively affects family health status and decreases mortality (Friedman, 2002). Education not only enlarges people's possibilities to engage in health promotion behaviours but also raises awareness on the importance of developing health risk coping strategies. As better health has positive externalities, such as reducing contagious diseases or influencing the utility of others, many of the benefits of improved health will have a spill over effect into the community. 13 Effective poverty reduction policies are therefore crucial in addressing long-term poverty consequences and enabling Fiji to meet the poverty related targets of MDGs. Strategic investments in all levels of education therefore will contribute not only to an increase in economic opportunities for households but also enable them to adopt healthy decisions which are critical to reduce the incidence of poverty.

793

The quantitle models for the monetary effects for the distributional function of household income show that the impacts of education on income vary between the lowest and upper tails of income groups. In general, people benefit from additional skills obtained through formal education for all income quartiles. In particular, the lowest income quartile group benefits more from obtaining formal education compared to others in the higher income quartile group. The findings suggest that primary education is the initial stage for lifting the poorest people out of poverty, but it cannot sustainably prevent people with primary education falling into poverty when unforeseen events occur, notably as the returns of primary education after certain income quartile will not able to meet the demands of providing the basic needs and health prevention activities. The logit non-monetary results for education and health nexus show that education has a positive and significant influence on the tendency of the head of household to participate in health prevention activities and acquire good housing conditions. Overall, an educated household head has several direct benefits of engaging in obtaining health prevention activities like life-accident insurance, medical and therapeutic products and also have tenured facilities like metered water supply and flush toilet for the benefit of the family's health. Education provides that awareness for good standard of living conditions. For the Government to address poverty reduction and the poor to break from the cycle of poverty, policy makers must identify and remove barriers to attaining higher levels of education, school retention, the low-income families pursuing higher learning, employment-related training and professional education. Hence, secondary and tertiary education and/or professional levels of training provided to those in the urban and rural poor households may sustainably prevent people falling into poverty and reduce poverty levels. It is also central to address social exclusion and ensure access to health facilities and services including the participation of low-income households with disability that require additional support. Assistance in both education and health services to the poor in rural households will be crucial for the sustainable reduction of poverty in the long term.

Appendix Table A1. Variable Description and Definitions 5. Conclusion This analysis of monetary and non-monetary effects of poverty reduction has devoted much attention to the identification of its impact on income, education and health in the case of Fiji. The approaches to poverty measurement seen in the monetary, capability, basic needs, social exclusion and participatory approaches are analysed using relevant variables here. In examining the hypothesis that returns to education are not limited to monetary impact on wages and income, the effects show that there are relevant non-monetary returns resulting from the influence of education on the health behaviours and subsequently decision making of the individuals. The empirical findings suggest that the resources invested in education can bring future returns to individuals, not only reflected in monetary earnings, but also in higher levels of satisfied basic needs. This implies that certain crucial decisions related to poverty conditions are positively influenced by education, particularly as education affects health, mortality, fertility, housing conditions and health activities. 13 Lee (1982) notes that in South Asia women with education are more likely to be involved in the immediate care of children and in critical decisions about food, sanitation and general nurturing, all of which influence the children's health and development. Grossman (2005) finds that an increase in extra year of schooling will increase the quantity of health demand by 0.21 per cent. Sen and Sekhar (2007) show a similar correlation between schooling levels and health in Urban Orissa state in India. Education level is important to modify the behaviour and decisions of individuals in regard to health conditions in Colombia (Zuluaga, 2007).

Variables

Definition

Dependent lnTHAI School HlthPVT Sanitation

variables Natural logarithm of total household income Household head's completed years of schooling Household engages in health prevention (Yes = 1, No = 0) Household has access to metered water supply and flush toilet (Yes = 1, No = 0)

Explanatory variables Age Age of the household head (completed years) Age2 Age of the household head squared Female Dummy variable (Female household head = 1, Male household head = 0) Ethnicity Dummy variable (Indo-Fijian household = 1, Fijian household head= 0) Children Number of children ages of 14 or under Rural Dummy variable (Rural areas = 1, Urban areas = 0) PRIM Dummy variable (Household head with primary education = 1, otherwise = 0) SEC Dummy variable (Household head with secondary education = 1, otherwise = 0) TER Dummy variable (Household head with tertiary education = 1, otherwise = 0) PRIMFree Dummy variable (Household head in the 1973 tuition-free scheme = 1, 1973 Otherwise = 0) YP Dummy variable (Young parenthood = 1, otherwise = 0) Disable Dummy variable (Household head is disabled = 1, otherwise = 0)

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