The Determinants Of Happiness: Empirical Evidence Of Java Island

Artikel History: Artikel masuk Artikel revisi Artikel diterima Penelitian tentang kebahagiaan di bidang ekonomi semakin berkembang sejak kemunculan Easterlin Paradox. Studi ini bertujuan untuk menganalisis determinan kebahagiaan di Pulau Jawa, Indonesia. Data bersumber dari Survei Pengukuran Tingkat Kebahagiaan (SPTK) 2017 yang dilaksanakan oleh BPS dengan mengambil observasi sebesar 23.456 responden. Sebanyak 13 variabel bebas diuji pengaruhnya terhadap kebahagiaan dengan menggunakan analisis regresi logistik biner. Hasil penelitian menunjukkan bahwa pendapatan, pendidikan, kesehatan, hubungan sosial dengan keluarga dan masyarakat, kondisi lingkungan, serta kehidupan yang bermakna berpengaruh terhadap kebahagiaan. Secara umum temuan ini memperkuat beberapa temuan dari penelitian-penelitian sebelumnya. Kata Kunci: kebahagiaan, subjective well-being, regresi logistik biner


Figure 1. Comparison of World Suicide Rate
Source: World Bank, processed (http://data.worldbank.org) Eventually, some experts and government officials not only began to realize the importance of measuring welfare that was not only based on income but also encouraged the thoughts about measuring welfare in more representative ways (Forgeard, Jayawickreme, Kern, & Seligman, 2011). We can not only assess human welfare materially, but also have to pay attention to the quality of relationships with others, the pleasant feeling because of sharing with others, the comfortable natural environment, and good governance (Johns & Ormerod, 2007).
It has increasingly recognized that it is crucial to find welfare measures that not only based on economic measures but also led to "subjective well-being" conditions (Forgeard et al., 2011;Frey dan Stutzer, 2018;Graham, 2011).
The study of happiness has increasingly developed and carried out by various experts, including economists. Economists focused on researches and debates on how happiness could be a proxy for the utility, which was the central concept of well-being (Graham, 2011). These did not mean ignoring macro indicators that have long been used as development achievements. 14 overly broad definitions and a variety of terms such as well-being, happiness, quality of life, and life satisfaction. Diener and Seligman (2004) argued that a more systematic approach is needed to measure happiness. Some researchers sometimes disregarded this term diversity and assumed these terms could use interchangeably.
The We consider that subjective well-being in Indonesia is very interesting for further discussion. Several studies of happiness determinants in Indonesia have been carried out previously using data from the 2007 Indonesian Family Life Survey (IFLS). However, to the best of our knowledge, there has been no happiness research that uses the 2017 SPTK data from BPS, which focuses on Java. The 2017 SPTK samples are spread throughout provinces in Indonesia so that the data will be more representative in describing happiness in Indonesia. In this study, researchers will analyze the determinants of subjective well-being (henceforth, we will use the term "happiness"), which focus on provinces in Java Island.

Happiness on Economic View
In economic literature, happiness has a close relation to consumer satisfaction, which is known as a utility. The utility concept is defined as a measure (numerical score) of the relative satisfaction level obtained by consumers from the consumption of goods and services (Pyndick & Rubinfeld, 2013;Sexton, Fortura, & Kovacs, 2016). In everyday life, we uasually call a utility as a benefit or well-being (Pyndick & Rubinfeld, 2013). Nicholson and Snyder (2012) state that utility refers to overall satisfaction, which is influenced by various factors so that the measurement is always assumed to be ceteris paribus (other things being equal). Besides, the utility is also closely related to consumer preferences, so the measurement must meet the characteristics of consumer preferences, namely completeness, transitivity, and continuity (Nicholson & Snyder, 2012).
Subjectivity in utility concept allows someone to express his opinion about the happiness or satisfaction of life they experience (Frey & Stutzer, 2002). Happiness measurement can be considered into categorical data (ordinal) and analyzed with econometrics.
Higher grades are assumed to represent a higher level of happiness. The econometric function of happiness can be written as follows: In addition to these theories, Huang in Rahayu (2016)  2. Life satisfaction has a smaller scope and closer to income, but the response to this question is generally similar to happiness.
3. Subjective well-being includes all the ways a person states his welfare, which covers satisfaction to different aspects of life, such as work, health, education, and others.
According to Frey and Stutzer (2018), we could use several methods to measure happiness, namely surveys, brain activity, day reconstruction method, and U-index. Among these methods, the survey method is the most widely used. Some examples of happiness surveys are the General Social Survey, the World Value Survey, and The Eurobarometer Survey. At the same time, other methods are rarely used because it requires a longer time and higher cost.

Earlier Studies
An interest in the study of happiness in economics began with the emergence of Richard Easterlin's research in America in 1974 (Frey & Stutzer, 2002). As happiness studies develop rapidly in various science, especially in the economy, governments all over the world are increasingly aware of and begin using happiness data in public policy decisions. Increased happiness can be considered as an appropriate indicator to measure social progress and public policy goals (Helliwell, Layard, & Sachs, 2015). The happiness studies in economics mostly analyze to find determinants of happiness by using the ordered probit technique as an analysis tool.
Several researchers conducted studies on the determinants of happiness in Indonesia, Similar studies were also carried out by Knight et al. (2009) and Appleton and Song

Data Source
This research is a quantitative-based study by utilizing microdata from the 2017 In total, SPTK 2017 consists of 75,000 household samples and spread evenly on all provinces of Indonesia. Samples were randomly selected using the two-stage-one-phasesampling method. This survey successfully recorded 72,317 households. The observations in this study will focus on the provinces in Java. From the overall responses sample, there were 23,456 observations in six provinces in Java.

Variables and Research Model
The independent variables in this study are "generally how happy the sample is." In the SPTK 2017 questionnaire, this question asks respondents to rate their overall happiness in life, by giving a score of 0-10. A value of 0 indicates the worst condition, and 10 indicates the best condition. Nevertheless, to simplify the analysis, in this study, the data were reclassified into binary form (happy for a score of 6-10 and not happy for a score of 0-5). Meanwhile, we will analyze 13 independent variables for their effects on happiness. They are 1) age, 2) quadratic age, 3) marital status, 4) health status, 5) presence/absence of chronic disease, 6) education, 7) classification residence, 8) homeownership, 9) monthly household income, 10) family harmony, 11) social relationships, 12) environmental conditions, and 13) meaning of life (eudaimonia).
Age and age squares are ratio scaled. Age is the age of respondents based on their last birthday. Quadratic elements are included to see whether the age variable has an effect on Ushaped, like the majority of previous studies. Marital status is divided into two, married and single-the single consists of respondents who are single and divorced. The respondent's health condition was approached with two variables, health status and the presence of chronic disease.
Health status represents the intensity of respondents experiencing physical disorders due to symptoms of the disease, which is categorized as healthy and unhealthy. Meanwhile, chronic diseases are diseases that require a relatively long time to appear or cure. This variable is categorized as present and absent.
Education represents the highest level of education completed by respondents. This variable is categorized as less than junior high school and senior high school above.
Classification of residence is a classification of the area of residence of the respondent, in rural or urban areas. Homeownership is ownership of residential buildings occupied by respondents and their households, which are categorized as their own and not their own. Monthly household income is the average income earned by all household members, which is categorized under/equal to Rp 1,800,000 and above Rp 1,800,000. goals, and self-acceptance. Like the independent variables, on these four variables, the respondent was asked to give a score of 0-10, which illustrates his perception. However, in this research these four variables will be categorized binary (code 0 for score 0-5, and code 1 for score 6-10). Overall the variables used in this study are presented in the following Table 1. In binary logistics analysis, there are several tests to assess whether the model is meaningful or not, simultaneously and partially. The simultaneous test is used to determine whether all independent variables together affect the dependent variable, using the G 2 statistical test (likelihood ratio test) as follows: Where j = 1, 2, 3, …, k (k = number of independent variables).    percent. The partial test also shows significant results (p-value <0.01), which indicates that each independent variable included in the model affects the dependent variable.

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In Table 3, column 4 shows the odds ratio, which shows the probability of the characteristics of the independent variable for happiness. Marital status shows significant results in the model. This result is in line with several previous studies (Frijters, Haisken-DeNew, & Shields, 2004;Kalyuzhnova & Kambhampati, 2008;Knight et al., 2009;Sohn, 2010). The odds of a married respondent being happy compared to a single respondent is 1,2638, which means marrying people more likely to be happy. According to Frey and Stutzer (2018) marriage can be a counterweight and reduce stress feelings because of work and loneliness. Economically, marriage also provides financial guarantees in the adverse economic conditions, and also provide higher capital accumulation (Stutzer & Frey, 2006).  The level of education also shows significant results, supporting the research by Chyi and Mao (2012), Landiyanto, et al. (2011), also Senasu and Singhapakdi (2017). The odds of respondent graduated from high school and above being happy compared to those who graduated from junior high school or below is 1.8406, which means higher education people are more likely to be happy. By reaching higher education, people will have better opportunities and broader networks in employment (Chen, 2012;Frey & Stutzer, 2018). The odds of the urban respondent being happy compared to the rural respondent is 1.2174, which means that urban people are more likely to be happy. This result is contrary to Hudson (2006), Gerdtham (2001, and Graham and Felton (2006) who find that someone who lives in a big city reports lower happiness. However, according to Sohn (2010), Indonesian urban people are happier because they tend to be more educated than rural people.
Health has a positive effect on happiness. People with better health conditions tend to be happier than those who do not (Oswald & Powdthavee, 2008;Shields & Price, 2005). The odds of respondents who have never/rarely been sick in being happy compared to those who frequently/highly often get sick is 1.5194, which means healthy people are more likely to be happy. In addition, respondents who did not have chronic disease had a 1.3191 higher probability of being happy than respondents who had a chronic disease. The results support the findings of Fijters et.al (2004), Sohn (2010), and Landiyanto et.al (2011).
Happiness research on economic mostly includes income variables. The estimation results show that respondents with higher household income are 1,9621 times more likely to be happier than respondents with lower income. This result is in line with many studies, including Appleton and Song (2008), Chyi and Mao (2012), also Eren and Asici (2017). However, at a certain point, the increase in happiness will be smaller as income increases (Johns & Ormerod, 2007). Also, materialistic leads to unhappiness (Eren & Aşıcı, 2017;Frey & Stutzer, 2018).
Besides, respondents who own their own homes have a probability of 1.3701 times happier than respondents who do not have their own homes. This finding is in line with the findings of Chyi and Mao (2012) which show that homeownership has a positive effect on happiness.
The odds of respondents who were satisfied with the harmony of their families compared to those who were dissatisfied are 4.3799, which means people are more likely to be happy if they have good relationships among families. Likewise, respondents who are satisfied with their social relations have a probability of 2.0842 times happier than dissatisfied respondents. These show that good social relations with family and society are essential aspects that influence happiness (Frey & Stutzer, 2018;Knight et al., 2009;Sohn, 2010). An excellent social relationship is also a source of social capital formation. BPS (2010BPS ( , 2016 said that social capital is a form of horizontal human relations that can affect community productivity. In several studies, social capital has a positive impact on happiness (Bartolini & Bilancini, 2010;Sarracino, 2012;Tokuda, Fujii, & Inoguchi, 2010). Veenhoven (2000) states that environmental feasibility reflects environmental quality where people can get what they need. Not only nature, but the feasibility of the environment also includes social life in it. Respondents who are satisfied with their environment condition have a probability of 2.1559 times happier than dissatisfied respondents. Also, the odds of respondents who feel meaningful life compared to those who have a meaningless life is 7.2758, means that people are more likely to be happy if their life feels meaningful. Eren and Asici (2017) include proxy variables for psychological well-being in their research and found that hopes and expectations for a better future will make people happier.

CONCLUSION AND SUGGESTION
This study tries to examine the determinants of happiness in Indonesia, which focused on the provinces in Java Island. The results found that the factors that can increase the probability of happiness are higher education, higher income, living in urban areas, better