The impacts of environmental income on household livelihoods are analysed in two cases: (1) comparison of indices with and without environmental income; and (2) comparison of indices across different household livelihood strategies.
Environmental income and poverty
This study uses headcount, poverty gap and poverty severity indices to measure poverty. Cavendish (1999) and Thondhlana and Muchapondwa (2014) used these indices to investigate the poverty incidence and depth of poverty. For example, Cavendish (1999) analysed the impact of
environmental income on rural poverty and inequality using a data set of 213 households from rural Zimbabwe. The results showed that environmental income contributed to a significant 50% decrease in measured poverty and a 30% decrease in measured inequality. Similarly, Thondhlana and
Muchapondwa (2014) studied the dependence on environmental resources and their influence on households’ welfare in South Africa. The findings showed that the poverty incidence and poverty gap decreased by 13% and 7%, respectively, with the inclusion of environmental income.
Vietnam National Poverty line
Vietnam currently has two poverty measurement approaches developed by the Ministry of Labour, Invalids, and Social Affairs (MOLISA), the General Statistical Office of Vietnam (GSO) and the World Bank. The first method identifies poverty lines based on income, which is suitable for guiding poverty reduction targets; whereas, the second method is based on consumption and is more useful for monitoring poverty over time (Demombynes & Hoang Vu, 2015).
In this study, the poverty line proposed by MOLISA is used for two reasons: (1) the study uses income as the welfare indicator; and (2) MOLISA identifies two separate poverty lines for urban and rural areas. In particular, the official MOLISA poverty lines for the period 2011-2015 are VND 400,000 per person per month ($1.33 per day) and VND 500,000 per person per month ($1.66 per day)1 for rural areas and urban areas, respectively. With the focus on rural areas, this current study only uses the rural poverty line, which is VND 400,000 per person per month ($1.33 per day).
Compare indices with and without including environmental income
First, this current study subtracts environmental income from total household income. Secondly, indices for total income with and without environmental income are computed. Thirdly, when
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comparing the indices with and without environmental income, the estimation of the impact of the environment on poverty is achieved.
Comparison of indices across household livelihood strategies
This study will apply the decomposition of poverty indices by population sub-group as proposed by Foster et al. (1984). Babulo et al. (2009) has adapted the equation from Foster et al. (1984), as in equation (6). The authors assumed that the population is divided into k distinct groups of households (i = 1, 2,…, k). 𝑃𝛼= ∑𝑞 1𝑛 (1) 𝑗=1 [ 𝑍− 𝑌𝑗(1) 𝑍 ] 𝛼 + ∑𝑞𝑗=1(2)1𝑛 [𝑍− 𝑌𝑗(2) 𝑍 ] 𝛼 + ⋯ + ∑𝑞𝑗=1(𝑘)1𝑛 [𝑍− 𝑌𝑗(𝑘) 𝑍 ] 𝛼 (6)
where 𝑞(𝑘) is the number of people below the poverty line in a sub-group k, 𝑛
𝑘 is the population size of sub-group k, 𝑌𝑗(𝑘) is the income of the jth household in the sub-group k with the income below the poverty line. This method provides the effect of changes in sub-group poverty on total poverty.
The quantity ∑ 1 𝑛 𝑞(𝑘) 𝑗=1 [ 𝑍− 𝑌𝑗(𝑘) 𝑍 ] 𝛼
indicates the total contribution of a sub-group k to the overall poverty
index. Meanwhile, 100 ∑𝑞𝑗=1(𝑘)1𝑛 [𝑍− 𝑌𝑗 (𝑘)
𝑍 ] 𝛼
/ 𝑃𝛼 is the percentage contribution of sub-group j.
Environmental income and inequality
This study uses the two most common measures of inequality, the Lorenz Curve and Gini coefficient, to analyse the impact of environmental income in terms of the inequality effect. Similar to poverty analysis, the inequality status is analysed in two cases: (1) with and without environmental income; and (2) across different household livelihood strategy groups.
Lorenz curve
A step-by-step procedure to build the Lorenz curve is adapted from Bellù and Liberati (2005): Step 1: Sort the income distribution.
Step 2: Identify the percentage of income owned by each household and the percentage of the population corresponding to each household.
Step 3: Identify the accumulative percentage of income and population.
Step 4: Identify the equidistribution line. The equidistribution line is constructed by following Steps 1 to 3 with the assumption that everyone has the same level of income.
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Step 5: Plot the accumulative percentage of income against the accumulative percentage of the population.
Gini coefficient
Similar to poverty analysis, this current study estimates the Gini coefficient in two cases: (1) Gini coefficients with and without environmental income; and (2) Gini coefficients across household livelihood strategies by the decomposition of Gini index by population sub-group, as proposed by Yao (1999). However, the analysis of inequality is slightly extended by using the decomposition method of Lerman and Yitzhaki (1985) to estimate the marginal effect of environmental income on total household inequality.
Compare Gini coefficients with and without environmental income
First, environmental income is deducted from total household income. Secondly, Gini coefficients for total income with and without environmental income are computed. Thirdly, a comparison of the Gini coefficients with and without environmental income provides an estimation of the impact of environmental on inequality.
Compare Gini coefficients by household livelihood strategies
Yao (1999) divided the total population into a finite numbers of sub-groups in his study. The decomposition of Gini coefficient by household livelihood strategies is, as follows:
𝐺 = 𝐺𝐴+ 𝐺𝐵+ 𝐺𝑂 (7)
where G is the Gini coefficient for the whole population and GA is the intra-class element of G. If no income inequality exists within each of the classes, then GA = 0. GB is the inter-class element of G. If the mean incomes of every class are the same, then GB = 0. GO is the overlapped element of G. If the richest person in any low income class I is not better off than the poorest person in any high income class J, then GO = 0. 𝐺 = 1 − ∑ 𝑝𝑖(2 ∑𝑖 𝑄𝑖− 𝑤𝑖 𝑘=1 ) 𝑛 𝑖=1 (8) 𝑄𝑖 = ∑𝑖 𝑤𝑖 𝑘=1 (9)
where n is the number of income group (a group can contain just one household), Qi, wi, mi, pi denotes, respectively, the cumulative income share, the income share, per capita mean income, and relative population frequency of the ith group ( i= 1, 2, 3…., n).
31 𝑄𝐼 = ∑𝐼 𝑤𝐼
𝐾=1 (11)
where QI denotes for the cumulative income share up to I, S is the number of population classes, pI and wI are population and income shares of the Ith class ( I = 1, 2, …., S) in the population.
𝐺𝐴= ∑𝑆 𝑤𝐼
𝐼=1 𝑝𝐼𝐺𝐼 (12)
where GI is the Gini coefficient for the Ith sub-population. There are S Gini coefficients for S classes.
GO = G – GA - GB (13)
where GA, GB, GO > 0
Marginal effect of environmental income on inequality
Lerman and Yitzhaki (1984) assumed that the population (n) consists of N households (n=1, 2, …., N) and that the total income of each household is the sum of incomes from k different sources. The Gini coefficient for total income inequality (G) with k exclusive income components can be decomposed as follows:
𝐺 = ∑𝑘 𝑅𝑘𝐺𝑘
𝑘=1 𝑆𝑘 (14)
Where Rk is the Gini correlation between income component k and total income, Gk is Gini of income component k, and Sk is income component k’s share of total income
Decomposition of the Gini coefficient by income source presents the effect of changes in environmental income on overall income inequality in comparison with other income sources. This study assumes that a change in each household’s income from source k equal eYk, where e is close to 1. The marginal effect of a particular income source k, as follows:
𝜕𝐺
𝜕𝑒𝑘= 𝑆𝑘(𝑅𝑘𝐺𝑘− 𝐺) (15)
The relative marginal effect of a particular income source is shown in equation (16) 𝜕𝐺/𝜕𝑒𝑘
𝐺 = 𝑆𝑘𝐺𝑘𝑅𝑘
𝐺 − 𝑆𝑘 (16)
Equation (16) shows the change in overall Gini coefficient when income from source k increases by 1%.
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