Comparison of Income and Subjective Well-Being in Vietnam

The comparison income and subjective well-being in the socialist transitional developing country of Vietnam – Panel data

Abstract

This study examines the relationship between income and individual happiness by using unique data from Viet Nam Access to Resources Household Survey (VARHS). The VARHS (waves 3, 2012–2016) consists of a representative sample of 6,575 respondents from 12 provinces of rural areas in Vietnam. This study presents an empirical analysis of the importance of absolute, comparative income for individual wellbeing or happiness in Vietnam.  We use a fixed-effects regression method to get rid of individual heterogeneity and find that people’ subjective wellbeing not only be influenced by absolute but also closely be impacted by the comparision income. This study is important as it provides a new explanation about the asymmetric versus symmetric of the income comparison effects.

1 Introduction

The age-old question of whether earning more money makes people happier seems especially needs for policy-makers as well as almost people at any time in the progress of using effectively the scarce resources. Nearly a half of century of research by economists shows that this topic is complex and has been one of the most discussed and debated in the literature on subjective well being (SWB) since the early 1970s (Ferrer-i-Carbonell, 2005; Sengupta et al., 2012). Despite the vast international literature, data on the effect of income on happiness in Vietnam remains scarce, especially panel data. This research aims to fill this gap. There are two main contributions of this research. First, at a specific level, this study is the first empirical research that uses panel data to examine the relationship between income and happiness in Vietnam. Second, the study sheds new light on the research about income and happiness by using a new approach – relative income for the sub-sample of comparison instead of absolute income ranking. This study is important as it provides a new explanation about the asymmetric versus symmetric of the income comparison effects.

Using the unique data from Vietnam, we focus on the impact of income per capita on the happiness of rural people during the period of economic recovery, after the global crisis, from 2012 to 2016. By using panel data, we not only explore the effect of income in different people but also in the same individuals over time. Moreover, panel data help us to reduce the bias of estimating by using fixed effect models. In other words, the use of panel data has more advantages to the use of cross-section micro-data (Ferrer-i-Carbonell, 2005). We also investigate other potential determinants of happiness, including age, gender, marital status, education, and shocks.

The paper is structured as follows: Section 2 discusses related literature about the association between income and happiness; Section 3 describes the data and estimation procedures; Sect 4 presents the analytical results and discussion, and Sect.5 concludes.

2. Income and happiness

Happiness, life satisfaction, and subjective well-being

Economists have often viewed happiness as subjective well-being (Bruno S Frey & Stutzer, 2000). In other words, subjective well-being is a scientific term for happiness. The construct of subjective well-being is often expressed as happiness or life satisfaction in the literature (Diener & Ryan, 2009). Subjective well-being represents people’s evaluation of their lives and includes happiness, pleasant emotion, life satisfaction, and a relative absence of unpleasant moods and emotions (Parnami, Mittal, & Hingar, 2013). Veenhoven (1991) one of the most influential researchers of happiness, considers the term happiness as a synonym of life satisfaction. Though the interchangeable use of  “happiness,” “subjective well-being” and “life satisfaction” is still debatable, a wide range of different research has been carried out in this context (Binstock, George, Cutler, Hendricks, & Schulz, 2011; Diener & Seligman, 2004; George, Ellison, & Larson, 2002; Helliwell, 2007; Tran, Nguyen, Van Vu, & Doan) and it is suitable for empirical study (Soukiazis & Ramos, 2016) and is more relevant to policy (Gilbert, Colley, & Roberts, 2016). Thus, happiness, subjective well-being, and life satisfaction are often used interchangeably in this research.

Absolute income and well-being

One of the two main mechanisms to explain the association between income and happiness is absolute income. Absolute income refers to the idea that money buys things that lead to happiness (including both material goods and services). The research on income and well-being consistently shows that absolute income has from small to significant positive effectiveness on happiness (Blanchflower & Oswald, 2004; Boes & Winkelmann, 2010; Daniel & Angus, 2010; Diener, Ng, Harter, & Arora, 2010; Bruno S Frey & Stutzer, 2002; Layard, 2005; Lelkes, 2006; Luhmann, Schimmack, & Eid, 2011; Tao & Chiu, 2009; Tsui, 2014). Absolute income has a significant positive effect on those people who have a relatively low income. The relative low threshold estimated to be around 10,000 USD per year according to (Bruno S. Frey, 2002) or 15,000 USD according to (Layard, 2005). In other words, absolute income has a strong positive impact on well being of those people whose income is insufficient or just only be enough for their basic needs. Also, empirical evidences show that the poor, who do not meet several material standards that allow them to satisfy the basic needs, are far less likely to be satisfied with their lives than people who can achieve those standards (Borghesi & Vercelli, 2012; Diener, 2000). 

Among relatively rich people, the impact that wealth has on well-being, however, is less certain. Various researchers claim that income correlates only weakly and diminishingly with the well-being of people who are already fairly well-off so that continuous income growth does not lead to ever-happier individuals (Easterlin, 1974, 1995; Mentzakis & Moro, 2009). This economics issue is the so-called Easterlin paradox.

Generally, absolute income buys-off unhappiness, but it does not seem to buy all levels of happiness (Mentzakis & Moro, 2009).

Cheung and Lucas (2016) is one of the good recent reviews about the relation between absolute income and happiness. In their research, they recommend that multiple reviews shown the robust and replicable of the positive association between income and happiness.

Comparision income and happiness

The other main explanation mechanism for the relationship between income and happiness is “comparison income” or “relative utility effect”. This theory shows that individual income perception is subjected to the individual’s income compared with the income of other people. This also the key to the happiness puzzle called the Easterlin paradox. It reflects the importance of the relative position of individuals in society for their satisfaction with life. Studies that have included relative income (defined in a range of different ways with a range of different reference groups) suggest well-being is strongly affected by relativities incomes (Dorn, Fischer, Kirchgässner, & Sousa-Poza, 2007; Ferrer-i-Carbonell, 2005; Ferrer‐I‐Carbonell & Ramos, 2014; Huang, Wu, & Deng, 2016; Yamamura, Tsutsui, & Ohtake, 2015).

The relative income is anticipated to be negatively correlated with individual SWB. This suggests that additional income may not increase well-being if those in the relevant comparison group also gain a similar increase in income. In other words, the higher the income of the reference group, the less satisfied individuals are with their income. However, existing research on this relationship has resulted in inconsistent findings.  Low relative levels of income and consumption do not seem to affect subjective wellbeing among low-income sample in rural Ethiopia (Akay & Martinsson, 2011) and Venezuela (Kuegler, 2009). Good review about this relation can be found in the research of Brown, Gray, and Roberts (2015) and Cheung and Lucas (2016); (Dumludag, 2014)Brown et al. (2015)

About to what extent relative income impact on SWB varies at different levels of absolute income. Some researchers show that relative income is stronger at higher levels of absolute income, both within and across countries (Akay & Martinsson, 2011; Becchetti, Corrado, & Rossetti, 2011; Clark, Frijters, & Shields, 2008; Easterlin, 1974, 1995; Ravallion & Lokshin, 2010). The exception is found in some studies (Clark & Senik, 2010; Peng, 2017; Stutzer, 2004). Individuals in lower-income households are found to attach higher importance to relative income.

Regarding to the “symmetry” versus “asymmetry” of interpersonal comparison, base on the idea that the typical individual looks “downwards”, the models of status are investigated. The researchers hypothesize that the existence of a feeling of “relative elation” which can be conceptualized as the dual of relative deprivation and arises under virtue of being richer than others (Chakravarty, 1997; Kingdon & Knight, 2006). Dissent from that view, Stutzer (2004) argues that “people look upward when making comparisons…Wealthier people impose a negative external effect on poorer people, but not vice versa”.  Ferrer-i-Carbonell (2005) finds that Stutzer’s statement holds for Western Germans but not for Eastern Germans.

Recently, Corazzini, Esposito, and Majorano (2012) show that interpersonal comparison takes place by both looking upward and looking downward along the income scale. His experiment study’ s result reveals that not only there a negative effect arising from the presence of people with higher achievements, but also a positive effect seems to originate from looking “downward” and seeing that there are people with lower achievements.

In conclusion, adverted to the consensus in the happiness literature about the growth but diminishing return impact of absolute income on happiness, the negative impact of relative income on happiness and the asymmetric/symmetric comparison have not been confirm. The literature provides divergent results (see the review of Reyes-García et al. (2016). Moreover, the studies about the relationship between income, including both absolute and relative income and happiness in transition and developing economies is insignificant (Dumludag, 2014; Reyes-García et al., 2016).

To the best of my knowledge, there is no study about this relationship in Vietnam; and the study, which uses the panel data to examine the relationship between income and happiness in Vietnam is scant. This study aims to fill this gap.

3. Data and estimation procedure

3.1 The data

This research will make use of the Viet Nam Access to Resources Household Survey (VARHS), which was first implemented in 2002. From 2006 onwards, households from twelve provinces in the Red River Delta, Northern Mountains, the Central Coast and the Mekong River Delta in Vietnam were surveyed biennially between June and August each year.

The sample of VARHS households, to a large extent, is similar to the samples in the Vietnam Household Living Standards Survey (VHLSS) datasets and the 2009 population census (Brandt and Tarp, 2017).  Since 2012, it concludes an approximate total of 3700 households from 437 communes, 130 districts in the 12 provinces and offers a balanced panel of more than 3,500 households during the period from 2012 to 2016.

The VARHS was designed to supplement the information collected in VHLSS by the General Statistics Office (GSO). The VARHS is not a nationally representative survey. Instead, the purpose of VARHS was to develop a unique panel of households. The VARHS and VHLSS are best understood as complementary sources of information (Brandt & Tarp, 2017). In detail, the provinces were selected to facilitate the use of the survey as an evaluation tool for Danida – supported programmes in Viet Nam which were located in the North-West and Central Highlands. Thus, the sample are over-sampled the relatively poor and sparsely populated regions.

These surveys have been conducted in collaboration with two Vietnamese partners: the Central Institute for Economic Management (CIEM) of the Ministry of Planning and Investment of Vietnam (MPI), and the Institute of Labour Science and Social Affairs (ILSSA) of the Ministry of Labour, Invalids, and Social Affairs of Vietnam (MoLISA). 

The VARHS provides comprehensive information at three levels including individual (such as age, gender, marital status, health status, education, employment, occupation status social capital etc), households (housing conditions, asset, economic situation, household income  and expenditure, shock etc) , and commune (general information, average income per capita of the commune, migration and many other interesting information). Especially, the survey collected data on subjective well-being since the 2012 survey rounds. Only one person in each household answered question about subjective well-being, typically but not always the household head will answer the question “taking all thing together, would you say you are (1) very pleased/happy with your life; (2) rather pleased/happy with your life; (3) not very pleased/happy with your life; (4) Not at all pleased/happy with your life. This item is reverse coded such that higher values represented higher levels of life satisfaction. According to Cheung and Lucas (2016) review, the single-item life satisfaction measures have shown satisfactory reliability and validity.

Regarding happiness, the re-interviewed respondents from 2012 to 2016 provide 6,575 observations equivalent to sample sizes for those three years were 2,116; 2,242 and 2,217 observations respectively.

3.2. Econometrics model and analytical steps

Econometric model

The main purpose of this study is testing the importance of not only own but also other income on individual well being. The following equation was used to examine this relationship:

SWB _ihjt= +HI_hjt + X_1ihjt3 + X_2hjt4 + e_ihjt  (e.q.1)

In this function, “SWB” is an estimated cardinal/ ordinal response variable; HI is category refers to income-related factors; X₁ is the vector individual variables; X₂ is the vector of household variables; “i” is individual; “h” is household; “j” is commune; “t” is time (year) and “” is the error term.

The literature suggests that happiness is associated with a large number of different factors. Following (Bjørnskov, Dreher, & Fischer, 2008; Dolan, Peasgood, & White, 2008; Sinnewe, Kortt, & Dollery, 2015; Tran, Nguyen, Van Vu, & Doan, 2016), we specify happiness as a function of gender, age, years of schooling, ethnicity, marital status, health status (at the individual level); and dependent children, household size, household head born in the commune, shocks to the household in last 2 years and household members passed away in the last 12 month (at the level of household).

 In our estimation, SWB is constructed with a value ranging from 1 to 4 corresponding to not at all pleased/happy; not very pleased/happy; rather pleased/happy; very pleased/happy.

 In modelling the determinants of SWB, life satisfaction or happiness can be used as cardinal or ordinal, depending on researchers’ assumption (Ferrer‐I‐Carbonell & Frijters, 2004; Tran et al., 2016). Many studies have confirmed that the results remain practically unchanged whether on models happiness as either a cardinal variable (e.g., using an Ordinary Least Square (OLS) estimator) or an ordinal variable (e.g., using ordered logit/probit estimator). Howerver, OLS coefficients directly denote the marginal effects (Wooldridge, 2016) and thus are more intuitive and interpretable by a wide range of readers (Jiang, Lu, & Sato, 2012). Moreover, using OLS will help partly dealt with the endogenous problem by making use of panel data. Thus, for the ease of estimation and interpretability of the regression coefficient, we opt to treat SWB as a cardinal variable and use a conventional OLS regression model for panel data to investigate the relationship between income and happiness. However, the Ordered Probit model will be also estimated in several cases to check for the robustness to the model specifications.

Regarding potential endogeneity, in the SWB literature, some researchers such as (Frijters, Haisken-Denew, & Shields, 2004a, 2004b; Gardner & Oswald, 2007) adequately addressing the issue of endogeneity by focusing on the subsamples of the population (i.e., lottery winners). However, their results cannot be adopted and generalized when the main interest lies in the income happiness relationship in the general population. To deal with the problems of endogeneity, we make use of the panel structure of our dataset. By using the individual fixed effect model and all models include year dummy, the unobserved endowments, such as ability, preferences, and personality or family background and the difference caused by year will be controlled. This method was used in the study of (FitzRoy, Nolan, Steinhardt, & Ulph, 2014), (Fitzroy & Nolan, 2018), (Di Tella, Haisken-De New, & Macculloch, 2010). 

Analytical steps

In terms of income category, we first examine the influence of absolute income on SWB. The simplest specification is one which includes, besides X_1, X_2, only annual income per capita at household level (Absolute income) as a determinant of SWB. This will be the first specification presented in the empirical analysis. A common assumption in economics is that household income is positively related to SWB. Often, the utility or objective well-being function is believed to be concave in income and, consequently, income is introduced in logarithmic form. This approach is followed here.

Different from present studies which divided the sub-sample base on the absolute income ranking (e.i. the richer country/area and the poorer country/area), we divided the sample from 2012 to 2016 into two groups, namely “Relative-higher- income” and “Relative-lower -income”. In which, “Relative-higher income” is defined as the group of individuals who have their own income per capita larger than the average income per capita in the commune where they are living; “Relative lower income” is defined as the group of individuals who have their own income per capita smaller than the average income per capita in the commune where they are living. Then, to compare the degree of the influence of absolute income on happiness of different groups of income, we will examine the impact of absolute income on each group. Common assumption in economics is that absolute income has a larger positive relation to SWB in the lower income group in comparison with other groups. We also assume that absolute income has a smaller positive relation to SWB in relative higher income groups in comparison with other groups.

The second specification will explore the relationship between relative income and SWB. Relative income refers to the vertical comparison between individuals and income in the larger society. Easterlin (1995) implicitly assumes that individuals compare themselves with all the other citizens of the same country. Persky and Tam (1990) assumes that all individuals living in the same region are part of the same reference group. Following the geographic approach used in the previous research (Blanchflower & Oswald, 2004; Ferrer-i-Carbonell, 2005; Huang et al., 2016), the present study defines relative income as the average income per capita in the district of group people. It reflexes the comparison between individuals in a household and other people in the same district in terms of income. We chose that because people are more likely to compare themselves locally when assessing their relative standing (Ferrer-i-Carbonell, 2005; Reyes-García et al., 2016). The relative income is defined as the mean of average income per capita in the district by year of the reference group (all individuals who live in the same district). We applied the logarithmic transformation on this variable.

The third specification assumes that SWB depends on the distance between the individual’s own and the reference group income. This is done by including the difference between the logarithm of the individual’s own income and the logarithm of the average income of the reference group. It is estimated by Log (Absolute income) – Log(relative income). This variable is expected to have a positive impact on SWB, indicate the richer an individual is in comparison with others, the happier she will be (Ferrer-i-Carbonell, 2005; Oshio, Nozaki, & Kobayashi, 2011).

A fourth specification, following (Duesenberry, 1949; Dumludag, 2014; Ferrer-i-Carbonell, 2005; Holländer, 2001; Oshio et al., 2011), we find the answer for the question “Is income comparisons asymmetric or symmetric”. In this context, asymmetry means that, while the happiness of individuals is negatively affected by an income below that of their reference group, individuals with income above that of their reference group do not experience a positive impact on happiness or well being, i.e. richer individuals do not get happier from knowing their income is above that of their co-citizens. Similarly, symmetry means that, the happiness of individuals is negative affected by an income below that of their reference group, individuals with income above that of their reference group experience a positive impact on well being, i.e. richer individuals get happier from knowing their income is above that of their co-citizens and the poorer get less happy from knowing their income is below that people in the reference group. To test the asymmetry and symmetry, two groups poorer and richer are often added and examined.

Richer = log(Absolute income) – log(Relative income); if log(Absolute income) > log(Relative income)

Poorer = log(Relative income) – log(Absolute income); if log(Absolute income) < log(Relative income).

Similar to previous research works, two variables namely richer and poorer are tested in this study. However, base on the view of (Corazzini et al., 2012), our hypotheses is that interpersonal comparison takes place by both upward and downward (not only there a negative effect arising from the presence of people with higher achievements, but also a positive effect seems to originate from looking “downward” and seeing that there are people with lower achievements). Instead of using the experimental method in (Corazzini et al., 2012), our panel data allows us to estimate the degree of impact of looking upward and looking downward in the interpersonal comparison.

Thank to the new approach in the division of the whole sample into two sub-samples above, we can explain more detail about the impact of poorer and richer on SWB. Instead of regression the poorer, richer variable for absolute higher-income group (individuals who live in developed country/richer area) and absolute lower –income group (individuals who live in  developing/poorer place), the richer is estimated by the relative higher income group and the poorer is estimated by the relative lower income group. The detail explanation as follows:

Comparative Situation 1.1: Individual (A) is richer than individuals who have the same age bracket, year of schooling and live in the same district and his income is higher than the average income of individuals who live in the same commune. In this case, Individual (A) enjoys additional positive satisfaction. Thus, the coefficient of variable richer is expected to be significant and positive. This is the pure value of being richer.

For example:

 CS1.1

 Individual A with his own income equivalent to 18

Richer than reference group = (12, 14, 16, 18, 20); Relative higher income in the commune (11, 13, 15, 18, 21).

Comparative Situation 1.2: Individual (A) is richer than people who have the same age bracket, year of schooling and live in the same district but his income is lower than the average income of individuals who live in the same commune. In this case, Individual A experiences both positive and satisfaction coming from being richer. Thus, the coefficient may be not significant or at least smaller than the CS.1.1. This is the mixed value of being richer.

For example:

 CS1.2 Individual A with his own income equivalent to 18

Richer than reference group = (12, 14, 16, 18, 20); Relative higher income in the commune (15, 18, 21, 23, 25).

Comparative Situation 2.1: Individual (A) is poorer than people who have the same age bracket, year of schooling and live in the same district but his income is higher than the average income of individuals who live in the same commune. In this case, Individual (A) experiences both positive and negative satisfaction coming from being poorer. Thus, the coefficient might be insignificant. This is the mixed value of being poorer.

For example:

 CS2.1 Individual A with his own income equivalent to 8

Poorer than reference group= (6, 8, 10, 12, 14); Relative higher income in the commune (2, 4, 6, 8, 10).

Comparative Situation 2.2: Individual (A) is poorer than people who have the same age bracket, year of schooling and live in the same district and his income is lower than the average income of individuals who live in the same commune. In this case, Individual (A) suffers an additional negative satisfaction. Thus, the coefficient of variable poorer is expected to be significantly negative and larger than the CS.2.2. This is the pure value of being poorer.

For example:

 CS2.2 Individual A with his own income equivalent to 8

Poorer than reference group = (6, 8, 10, 12, 14); Relative higher income in the commune (7, 9, 11, 13, 15).

In general, if people only look upward or downward the coefficients are neither different between CS.1.1 and CS1.2 nor between CS.2.1 and CS.2.2. In other words, the coefficient of the variable richer/poorer is not different between the whole sample and the two sub-samples (relative-higher-income group and relative-lower-income group). According to our hypothesis, people not only look upward but also look downward so the coefficient of the variable richer is expected to be non-significant, or at least of a smaller value in CS.1.2 situation than in CS.1.2 situation. Similarly, the coefficient of the variable poorer is expected to be non-significant, or at least of a smaller value in CS.2.1 situation than in CS.2.2 situation. It is mean that individual who is richer than both reference groups (CS.1.1) is happier than who is richer in the whole sample (including CS.1.1 and CS.1.2). Similarly, an individual who is poorer in both reference groups (CS.2.2) suffers more negative satisfaction than those in the whole sample (including CS.2.1 and CS.2.2).

4. Analytical results

4.1. Descriptive Analysis

Before discussing regression results, we first present a rough picture of happiness in the rural area of Vietnam from 2012 to 2016

Figure.1 shows that the number of happy people and others is quite equal in 2012. There was an increase in the number of people who were not very happy in 2014 that make the number of people who are not happy more than the count part. Adversely, the numbers of people who felt happy were as twice as not very happy ones in 2016.                   

Figure.2

Figure 2 shows the relationship between real income per capita (income after deflating to the base of 2012) and happiness of rural people in Vietnam in 2012, 2014 and 2016. In general, people who estimated themselves very happy have the highest income level in comparison with other counterparts. Only these people have the mean of absolute income larger than the benchmark of 1,500 USD (the threshold of satisfy basic need income (Layard, 2005))

Table 1 Definition, measurements and summary statistics of included variables

Variable	Definition	Obs	Mean	Std. Dev.	Min	Max
Happiness	1= very happy/ please; 2= happy/ please; 3= Not very happy/ please; 4= Not at all happy/ please	6,575	2.551939	.6666935	1	4
Absolute income Log_hh_income_pc_real	The average income per capita in the household during the last 12 month, ‘000 VND (CPI- adjusted 2012)	6,575	21838.08	29085.11	271	858457
Relative income	Logarithm of the average income per capita in a year of all individuals who live in the same district.	6,547	9.526934	.6223106	7.090077	10.86504
Distance income	Log(Absolute income) – log( Relative income)	6,547	.0707971	.8670024	-3.617181	4.728578
Richer (richer_district)	log(Absolute income) – log(Relative income); if log(Absolute income)  > log(Relative income)	6,575	.5715589	.4948905	0	1
Poorer (poorer_district)	log(Relative income)- log(Absolute income) if log(Absolute income)  < log(Relative income)	6,575	.4041065	.4907556	0	1
age	Age of respondents	6,575	49.51498	13.56318	17	97
age2_1000	Age*age/1000	6,575	2.635665	1.456468	.289	9.409
ethnicity	1= Kinh (majority group), others=0	6,575	.6503422	.4768981	0	1
Household head born in the commune (hh_head_born_in_commune)	Household head born in the commune=1; others=0	6,575	.4250951	.494395	0	1
Household size (hhsize )	Number of people in the household	6,575	4.322129	1.864342	1	15
Ill day (ill_day )	Number of days cannot work due to ill	6,575	9.051559	26.67211	0	365
Married	Married=1; others=0	6,575	.815057	.3882808	0	1
sex	Male=1; Female=0	6,575	.7254753	.4463084	0	1
Dependent children (dep_children)	Number of children under 15	6,575	1.126844	1.199217	0	9
Years of schooling  (schoolyear)	Number of years schooling	6,573	6.895177	4.28615	0	18
Shocks to the household in last 2 years (shock)	Did the household suffer any type of shock during the last two years? 1= Yes; 0=No	6,575	.4234221	.4941386	0	1
Weather (good_weather)	Has the weather in general been favorable for agriculture during the last 12 month? 1=Yes;  0= No	6,575	.3955894	.4890141	0	1
HH member death (hhm_death)	Have any household members passed away in the last 12 month: 1=yes; 0=no	6,574	.0290538	.1679703	0	1

Note: (…) is name in regression result

The table 1 presents descriptive statistics on the explanatory variables discussed above on subjective well being. The first column shows the definition of each variable. The second column shows the number of observations. The third and the fourth columns present the mean and the standard deviation of each variable. The last two columns show the minimum and the maximum value of each variable. During three years, the average happiness` of rural people in Vietnam was slightly above the midpoint of our measure of subjective wellbeing (mean=2.551939, SD=0.6666935).

4.2. Empirical Results and Discussion

This section presents the results of estimating the impact of elements on SWB from Eq.1, which accommodates for the 5 different specifications presented in section 4.2. The fixed effect panel regression and robutness checks are used in every estimations. The Order Probit models with robutness checks are estimated as well for the check of the model. The discussion hereafter focuses on the income coefficients. The associations between other variables and SWB were comparable with the SWB literature (e.g., age has a u-shape with a minimum SWB at around 40 years old, married and highly educated individuals reported greater satisfaction). In fixed-effect models, some variables such as sex, ethnicity, married, dependent children are excluded since they vary very little or not at all over time. However, they are all significant in Order Probit models.  The  R² for all regressions are at about 0.11 to 0.20. This result  agrees with the general finding in the literature that only about 8% to 20% of individual SWB depends on objective variables and thus can be explained (Ferrer-i-Carbonell, 2005).

First, the results for the first specification, in which only household income and the control variables are included, are given in Table 2. It is shown that the income coefficient is significant at the 1% level and positively related to happiness for all three subsamples, i.e. the whole sample, the relative higher income group, and the relatively lower income group. This result is in accordance with the usual findings: namely, that the richer individuals are, ceteris paribus, happier than their poorer co-citizens. The absolute income is relatively more important for the relative low-income people than for the relative high-income ones. The coefficients for the total sample, higher-income group, and lower-income group are 0.07; 0.2 and 0.34 respectively. The Ordered Probit analyses for the total sample show the agreement with the results reported by the regression of fixed-effect model.

Table2. First specification results

Table 3 presents regression results for the second specification, in which, besides absolute income, the relative income is included. The inclusion of the relative income is almost unchanged the household income coefficient. As expected the relative income has a negative impact on SWB (Ferrer-i-Carbonell, 2005; Huang et al., 2016). The coefficient of absolute income is unchanged in comparison with the first specification (b=0.07, p<0.001) while the relative income negatively associated with SWB (b= – 0.04, p=0.104). For a household with a similar demographic background and relative income for social comparison, a 1 % increase in household income per capita raised the happiness score by about 0.07 points on the 4-point scale. In contrast, for a household with a similar level of absolute income, a 1% increase in relative income (The average income level of one person’s reference group) reduced the happiness score by about 0.04 point for the whole sample. The impact of relative income on SWB is heterogeneous within the population: the coefficient of people who are in relative higher-income group is 0.045 point larger than those belonging to the relative lower income group. Similar with the result from the ranking of absolute income the relative income has more impact on the richer than the poorer (Corazzini et al., 2012)  and differ with the study of (Peng, 2017; Stutzer, 2004) (the wealthy have smaller relative income effect than the poor) . The Ordered Probit estimation shows the same result reported by fixed effect model. However, the result of relative income is non-significant (The non-significant of relative income could be caused by the high correlation).

****significant at 1%; ***significant at 5%;** significant at 10%;* significant at 20%

Table 4.1 presents the results for the third specification, in which the average income of the reference group is substituted by distance income (the difference between the household income per capita and relative income). As expected, the coefficient of the difference is positive, indicating that the larger individual’s own income is in comparison to the reference group income, the happier the individual is. According to the results, only individuals in relative lower-income group feel happier when their own income lager than the average income of the reference group. The coefficient of the distance income is significant at p=0.10 for the total sample and at p=0.084 for relative higher-income group. However, the absolute income coefficient now becomes non-significant for both subsamples. The results are similar to the result of Ferrer-i-Carbonell (2005) and Oshio et al. (2011). The higher p value of distance income does not necessarily suggest that the comparison hypothesis is not relevant to household income. The non-significant of distance income could be partly caused by the high correlation between the distance income and the income of the individual, both calculated on the absolute income basis. If the absolute income variable is removed from the model then the regression coefficients of distance income are all positive and significant (see table 4.2).

Table 5.1 provides the results of the fourth specification, which includes the variables richer and poorer. Similar to the result of Ferrer-i-Carbonell (2005), the absolute income coefficient is, as for the third specification, positive but non-significant for the total sample and two other sub-sample and the comparisons is asymmetric for the whole sample. This result yields the conclusion postulated by (Duesenberry, 1949). However, our study obtains somewhat different results. While the coefficient for richer is non-significant and smaller than the coefficient poorer in Ferrer-i-Carbonell (2005), our study presents the significant and large coefficient for the richer and non-significant and smaller coefficient for the poorer. The table 5.1 shows that for the other two subsamples the comparison income effects are also asymmetric, i.e. either the coefficient for richer or poorer is non-significant or they are different while this is symmetric for West-Germany and asymmetric for the East – Germany in the study of Ferrer-i-Carbonell (2005).

****significant at 1%; ***significant at 5%;** significant at 10%;* significant at 20%

According to our justification above, the non-significant coefficient of absolute income variable might be partly due to the multicollinearity between this absolute income and the richer/poorer ones; the non-significant of poorer and richer coefficients due to people look both downward and backward in comparison. If we remove absolute income and estimate the poorer by the relative lower-income group, and the richer by the relative higher-income group, the coefficients are significant in all sub-samples (table 5.2). The results in table 5.2 show that individuals look more downwards than upwards in comparison. The coefficient of the richer is larger than that of the poorer. While, the increase of 1% in the result of  log (absolute income) minus log(relative income) with conditional of log (absolute income) larger than log(relative income) lead to the coefficients of happiness in the whole sample and relative higher–income groups increase 0.1119865 point (significant by 0.01) and  0.2578456 point (significant by 0.01), respectively, the decrease of 1% in the result of log(relative income) minus  log (absolute income) with conditional of log (absolute income) smaller than log(relative income) lead to the coefficients of happiness in the whole sample and relative lower – income groups decrease 0.043131 point (significant by 0.1) and  0.2212174 point (significant by 0.01), respectively. Similar with what was expected; the coefficients of sub-samples higher-income and lower-income are higher than the whole samples (see the CS1.1, CS1.2, CS.2.1 and CS2.2 above).

5. Conclusion

This study expands upon the work of previous papers, especially (Ferrer-i-Carbonell, 2005)  that have presented empirically the importance of income and comparison income for individual well-being or happiness of Germany people. The data used is sub-samples of Viet Nam Access to Resources Household Survey (VARHS), which provides the panel information of households from twelve provinces in rural area of Vietnam.

The relevance of this study lies in two features. First, it contributes to the scant empirical literature on the impact of income on individual well-being in a transitional-developing-socialist country of Vietnam. This is the first study which tests four hypotheses about the impact of interdependent preferences on individual. Second, it differs from other studies which test the asymmetric versus symmetric of income comparisons base on looking either “upwards” or “downwards” hypotheses, our study hypothesis that individuals both looking “upwards” and “downwards” in comparisons.

The main conclusion can be summarized as follow:

(1) Absolute income has positive impact on individuals’ happiness; the effect of income is significant when compared with other variables and has more effect in relative lower-income group than other groups;

(2) Holding the absolute income unchanged, the increase in the relative income lead to reduce the individuals’ wellbeing but the effect of relative income on happiness smaller than the effect of absolute income in all subsamples; relative income has the largest impact on relative higher-income group. Interestingly, the degree of the effect of absolute income nearly unchanged when the relative income is added in the model;

(3) The lager an individual’s own income is in comparison with income of reference group the happier the individual is; and

(4) the comparison effects are asymmetric as individuals look more downwards than upwards.

It means that the individuals have more positive effects on SWB when they are relatively richer than negative effects on SWB when they feel relatively poorer. In other words, individuals feel happier when they are richer than the reference group than feel less happy when they are poorer than other people. They are much happier if they both feel richer than the individuals in the reference group and their neighbour. They also feel unhappier if they are poorer than people who have the same age group, similar education, live in the same district and people live in the same commune.

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