Indonesian Farmers’ Subjective Well-being: Determinants and Effect on Discounting Behaviour

Abstract

The challenges prevalent in agriculture substantially impact the well-being of farmers compared to that of many other occupations. The national average rating of well-being may not provide a true representation of farmers. This article empirically examines the determinants and the effect of subjective well-being (SWB) on discounting the behaviour of farmers by estimating data from the 2014/2015 Indonesian family life survey. The results show that 72% are among the poorest three on a six-step hypothetical economic ladder. Few farmers (38%) are very satisfied with life as a whole. Having a higher level of education and income increases the chance of a higher SWB. Also, SWB decreases with an increasing level of poor health, feeling unsafe, job dissatisfaction and being older. The results examining the effect of SWB on discounting behaviour suggest that unlike the affective component (positive affect) of SWB, the cognitive component (life satisfaction) does not have a statistically significant effect on discounting behaviour.

Keywords

Happiness life satisfaction discounting decision-making

Introduction

Measuring well-being is vital for measuring the quality of life, appraising policy, monitoring progress, improving future policies, and judging national performance (Deeming, 2013; Dolan et al., 2011; Kwon & Choi, 2021; Veronese et al., 2017). Thus, over the years, increasing importance has been attached to measures of national well-being. However, there is justification for measuring the well-being of a subgroup within a country as the national average rating of well-being and of life satisfaction does not provide the real picture of different subgroups (Deeming, 2013). As a unique occupational sector, agriculture presents specific challenges that potentially differentiate farmers’ subjective well-being (SWB) in low- and middle-income countries (LMICs) from those in other occupations. Farmers in LMICs often face multifaceted challenges, including unreliable access to water, land and credit, and volatile market conditions. There is evidence of the impact that the challenges and stress prevalent in agriculture have on farmers’ physical and mental health and well-being (Bhattacharyya et al., 2020; Merriott, 2016). In addition, the proportion of the population in many LMICs engaged in agriculture and residing in rural areas is high. Thus, farmers’ well-being could be an important indicator of the rural quality of life. In addition, the well-being of individuals in other occupations may arguably be driven by a different set of factors. For example, the close linkage between farmers’ well-being and external factors including environmental, economic and social factors that shape farmers’ lives in LMICs makes their situation peculiar.

Further, a farm cannot be sustainable if the well-being of farmers is ignored (FAO, 2021). This implies that the SWB of farmers holds significant implications for the advancement of sustainable agriculture. Farmers’ well-being can influence their agricultural practices and decisions, which in turn impact the environment and overall sustainability. For example, previous studies have shown that farmers’ SWB is associated with the adoption of sustainable agricultural practices (Brown et al., 2021; Lei et al., 2023). It could be argued that such findings show that a positive outlook fosters a long-term perspective, encouraging farmers to invest in technologies, more so in those technologies that may have delayed rewards. Moreover, meeting aspirations for a better quality of life can stimulate a sense of belonging and collaboration, leading to knowledge-sharing and collective efforts toward a sustainable and productive society (Haim-Litevsky et al., 2023; Lai et al., 2021).

Besides, the issue of farmers’ well-being has become more topical because farmers (have an intention to or) are exiting farming at unprecedented rates (Peel et al., 2016; Peng, 2013). For example, in Indonesia (the country whose farmers constituted the sample analysed in this article) had over 5 million fewer farmers in 2013 compared to 2003 (Iswara, 2020). Recently, the government has prioritized improving the welfare of farmers (Oxford Business Group, 2018). However, there are few empirical well-being studies on which such policy decisions could be based or informed. In addition, previous studies, for example, Brew et al. (2016), found that the well-being of farmers in rural areas is worse than their non-farming counterparts. Similarly, De Neve and Ward (2017) show that farmers in Southeast Asia, East Asia, sub-Saharan Africa, and Central and Eastern Europe evaluate the quality of their lives lowest. However, this limited number of evidences may not be sufficient to support or dispute the findings that farmers have lower SWB than the non-farming population. Improving the SWB of farmers can result in increased happiness and satisfaction leading to reduced likelihood of farmers abandoning farming (Peel et al., 2016) and increased adoption of sustainable agricultural practices (Brown et al., 2021; Lei et al., 2023). These positive outcomes benefit individual farmers and contribute to the overall growth and resilience of the agricultural sector and, more broadly, contribute to economic productivity (DiMaria et al., 2020; Tenney et al., 2016).

However, despite these findings, the literature examining the well-being of farmers in many LMICs is scant. Thus, it is not only that the empirical evidence on farmers’ well-being is limited but also that there are aspects yet to be understood about the determinant of farmers’ well-being and the extent to which farm economic decisions are shaped by well-being.

The contribution of this article is fourfold. First, this study expands the previous measures of SWB¹ of farmers. Many studies in the extant literature either focused on the hedonic aspects of SWB or relied on a single measure of SWB. Since different measures of SWB have different predictors, the estimate in this article is based on several proxies of SWB. Second, the article empirically examines the determinants of SWB. Although previous studies have investigated drivers of the differences in well-being, these studies mainly focus on one or a few of these drivers. This gap has been highlighted in previous studies (e.g., Degutis & Urbonavicius, 2013; Diego-Rosell et al., 2018). Thus, the estimates in this article are based on a combination of the factors that are prominent in the literature and relevant to LMICs. Third, this article contributes to the scarce evidence of the impact of SWB on farmers’ behaviours and economic decision-making. Finally, the focus of the SWB measure in this article is overall well-being, which extends the boundary beyond studies that focus on specific life domains.

The article thus provides answers to the following research questions. Is there a correlation between farmers’ SWB measured via the cognitive and affective components? What theory or theories best explains farmers’ perception of their well-being? What independent variables have predictive power on SWB? Does farmers’ SWB differ from that of the non-farming population? From the questions and supported by the literature, seven hypotheses for the study are formulated.

Literature Review

Background of Subjective Well-being: Concepts and Theories

SWB is an individual’s perception of life, irrespective of what others think. Stone and Mackie (2013) referred to well-being as how individuals experience and evaluate their lives or some aspect of it. According to Veenhoven (1997), it considers the extent to which an individual life feels good, the degree to which it meets expectations and how desirable the individual believes life is. The assessment of both objective and SWB has progressively become essential for shaping and assessing policies. However, the latter takes a more central role in psychology, economics and the social sciences (Pontin et al., 2013). Notably, previous studies suggest that SWB is related to objective measures of well-being (Abokyi et al., 2022; Oswald & Wu, 2010). However, other studies find that the association is weak, suggesting that they represent separate facets of well-being (Muffels & Headey, 2013). From a different perspective, Phillips (2006) argues that in a precise sense, there are no measures that can be definitively labelled as objective well-being but rather are ‘collectively subjective’ measures. Although these views and findings are worth mentioning, the debate is outside the scope of this article.

Diener (2000) proposed that SWB refers to an individual evaluation of own life based on cognitive and affective appraisals. The cognitive dimension of SWB is based on evaluations or judgments of life satisfaction. The distinctive feature of the cognitive component of SWB is its dependence on response acquired from self-reports in which individuals assess the quality of their lives generally or selected aspects of it. In most cases, the questions take a relatively uncomplicated format. For example, individuals may be asked, ‘How satisfied are you with your life as a whole?’, ‘Overall, how worthwhile are the things that you do in your life’ or ‘Considering all things, how happy would you say you are?’. Other approaches have employed multiple items to elicit responses to specific aspects of SWB, with the corresponding results being more reliable than the former.

On the other hand, the affective component refers to evaluations of individual day-to-day experiences. It measures the frequency and intensity with which a person experiences positive (e.g., joy and excitement) and negative (e.g., sadness, anger, nervousness and fear) emotions and mood. It is widely measured using two subscales (positive and negative) to rate how often individuals experience various states (for example, the Scale of Positive and Negative Experience by Diener et al. 2010). Previous studies have found differences in affective and cognitive SWB and in the factors that explain them. This aspect is elaborated on in the section on empirical findings.

There are a handful of theories of SWB, which in terms of the theoretical frameworks, are essentially different.² A succinct discussion of some of the theories set the foundation for the hypotheses developed in this article. First, according to the Telic theories of SWB, attaining an endpoint, for example, achieving a goal, leads to happiness. The subject of controversy surrounding the telic theories is what constitutes a ‘certain endpoint’. This limitation makes it difficult for the telic theories, for instance, to accommodate situations where fulfilment is attained from moving towards a desire other than achieving the goal itself. Popular among these theories is the liking theory, otherwise known as the hedonic happiness theory, based on maximizing pleasure and minimizing pain (Peterson et al., 2005). The consequence of increased pleasure and reduced pain is happiness (Brandt, 1989; Parducci, 1995). Broadly, the need theories postulate a set of elements that an individual needs that are crucial to attaining SWB, irrespective of the person’s values (Durayappah, 2011). Attaining these needs usually results in happiness.

From another perspective, the top-down theories propound that a person’s interactions with the wider world are determined by their inherent tendency to experience the world in a particular way. The implication is that an individual whose state of mind is more positive is likely to have a better experience; thus, the positive disposition as opposed to the objective events are the driver of well-being.

Second, individuals may evaluate their lives holistically in terms of overall life satisfaction. Notably, the bottom-up theories advance that a person perceives SWB as an amalgamation of the positive and negative moments experienced (Diener & Ryan, 2009; Headey, 2014), that is, holistic life satisfaction is the sum of its parts constituted of many concrete areas of life. The more an individual is satisfied with these components, the greater the satisfaction with life as a whole (Loewe et al., 2014). On that basis, the hypothesis tested is as follows

Hypothesis 1: Farmers SWB measured via the cognitive and affective components are positively correlated but not identical.

Third, the multiple discrepancy theory postulates that an individual compares experiences or emotions to some standard. According to Michalos (1985), SWB arises from the assessment of the perceived gaps between the present self with reference to several standards of comparisons, including but not limited to what the individual has and wants, what closely connected people have, and the best past conditions or what the individual thinks they deserve or need. Among these, the greatest determinant of SWB is arguably self-wants discrepancy. An individual’s satisfaction is low when the discrepancy arises from an upward comparison (e.g., when expectation exceeds reality). On the other hand, a downward comparison (e.g., when reality exceeds expectations) gives greater satisfaction. Thus the following is hypothesized:

Hypothesis 2: Farmers’ perception of their well-being aligns with the predictions of the multiple discrepancy theory

Empirical Findings on the Determinants of Subjective Well-being

The literature on the determinants of SWB is robust. There are empirical findings showing the correlation between SWB and income (both absolute and relative), job satisfaction, housing quality, health, work-life balance, education and skills, family and social networks, civic participation and governance, environmental quality, marital status, health and trust. Other characteristics such as age, gender, marital status, family size, ethnicity and religion have been found to influence SWB (Aghababaei, 2014; Diener et al., 2000; Ngamaba, 2017; Wu et al., 2019). The selected studies on these findings are summarized in Table 1 (see Bak-Klimek et al., 2015; Dolan et al., 2008 for a detailed review). Although the direction of the relationship reported in Table 1 are the findings that are widely reported, a few studies report contrary findings or observe no statistically significant relationship between these factors and SWB. Besides, the association between some of the factors in Table 1 and SWB is not always linear. For example, an inverted U-shape has been observed between household savings and SWB (Chen et al., 2021), and a weakening relationship between housing satisfaction (Wu et al., 2019) and education (Jin et al., 2020).

While most of these studies examine the relationship of SWB with one or a few of these factors, this article investigates a more comprehensive set of factors prominent in the literature (and summarized in Table 1). Further, a justification for this article is that the findings from one country or geographical region cannot be generalized to others, as several studies have shown (Fors & Kulin, 2016; Hochman & Skopek, 2013; Ngamaba, 2017). These reasons justify the need to test the hypothesized associations.

Table 1.

Findings from Selected Studies Examining Determinants of Subjective Well-being and Its Impact on Individual Behaviours or Economic Decisions.

Factors	Studies	Main Findings in the Literature and the Hypotheses Tested in this Article
Determinants of Subjective Well-being
Health (physical and mental)	Wicker and Frick (2017) Asadullah et al. (2018) Cross et al. (2018)	Better health increases SWB
Income	Cummins (2000) Mentzakis and Moro (2009) Diener et al. (2009) Reyes-García et al. (2016)	Higher income increases SWB
Education	Asadullah et al. (2018)	Education is positively correlated with SWB
Job satisfaction	Bowling et al. (2010) Calaguas (2017) Ariza-Montes et al. (2018)	Positive relationships exist between job satisfaction and SWB
Work-life balance	Gröpel and Kuhl (2009) Pop (2014) Khan et al. (2020)	Better work–life balance increases SWB (when certain factors, e.g., intrinsic motivation, need fulfilment etc. play a mediating role)
Family and social networks	Li and Cheng (2015) Wang (2016) Bian et al. (2020)	Size of social network is correlated with subjective well-being; i.e., SWB is significantly higher when among the socially engaged
Housing quality	Tsuchiya-Ito et al. (2019) Caffaro et al. (2019)	Poor housing quality has a negative association with SWB
Environment	Sulemana et al. (2016) Yuan et al. (2018)	Better environment is positively correlated with SWB
Personal security	Franc et al. (2012) Sarriera et al. (2021)	Feelings of safety is positively correlated with better SWB
Trust	Hommerich (2012) Ngamaba (2017)	Higher trust for people increases SWB
Civic engagement and governance	Choudhary and Gupta (2017) Wray-Lake et al. (2019)	Positive correlation exist between civic engagement and SWB
Impact on Individual Behaviours or Economic Decisions
Discount rates	Kennedy (2020)	Individuals who devalue future outcomes are less satisfied and less happy
Migration	Ivlevs (2014)	Higher levels of SWB positively affect decision to migrate
Pro-social decisions (e.g., volunteering, pro-social spending, charitable behaviour)	Oishi et al. (2009) Aknin et al. (2012)	Higher levels of SWB positively influence pro-social decisions
Buying preferences	Bhardwaj et al. (2011) Abbas et al. (2016) Usman et al. (2021)	SWB positively influence attitude to switching or buying intention and behaviour
Risk taking	Goudie et al. (2014) Guven and Hoxha (2015) Delis and Mylonidis (2015)	SWB is a driver of risk-avoiding behaviour
Savings	Guven (2012)	Higher levels of SWB result in increased savings
Investment	Rao et al. (2016)	SWB positively influence investment
Consumption	Guven (2012)	Higher levels of SWB have a negative association with marginal propensity to consume
Inter-temporal choices	Yang and He (2015)	Higher levels of SWB result in greater patience towards monetary rewards

Hypothesis 3: SWB is influenced by income, job satisfaction, housing quality, health, work life balance, education and skills, civic engagement and governance, environmental quality and personal security

Hypothesis 4: Farmers’ SWB measured via the cognitive and affective components are associated with different groups of independent variables

Farming and SWB

Previous empirical studies within a country (e.g., Fujiwara & Lawton, 2016; Tang, 2018) and between countries (e.g. Vartanova & Gritskov, 2021), as well as review studies (e.g., Erdogan et al., 2012), have reported differences in SWB depending on the occupation. Also, it is estimated that around 78% of the world’s poorest people live in rural areas and depend largely on agriculture (World Bank, 2014). For example, the association between income and SWB may be more prevalent among rural farmers as many rural dwellers have relatively lower incomes. However, studies (for example, Davey et al., 2009) have found that despite the low socioeconomic conditions of poorer farmers, their SWB is not much different from the general population. On these debates, the hypothesis tested is as follows:

Hypothesis 5: There is no association between occupation and SWB. That is, farmers SWB measured via the affective or cognitive component of SWB does not differ from that of the non-farming population.

Empirical Findings from Studies Examining the Objective Impact of Subjective Well-being

Unlike the robust literature examining the factors that determine SWB, the literature examining the effect of SWB on economic decisions is relatively small. The empirical evidence that supports the effect of SWB on behaviour and decision-making is highlighted using selected studies (Table 1). There is evidence that SWB explains migration, pro-social decisions, risk and time preferences, savings and investments. Similar to studies examining determinants of SWB, a few studies report contrary findings or observe no statistically significant effect between SWB on these factors presented in Table 1. On the basis of these findings, the following hypotheses were tested:

Hypothesis 6: The prevalence of positive emotions and mood is positively associated with discounting behaviour

Hypothesis 7: Life satisfaction is positively associated with discounting behaviour

Materials and Methods

Data

The results in this article are from analyses of the 2014/2015 Indonesian family life survey (IFLS). The survey consist of a nationally representative sample of about 83% of the Indonesian population that is made up of over 30,000 individuals across 13 of the 27 provinces. The suitability of the IFLS dataset in examining the relationship determinants of SWB and its impact on economic decisions is accredited to the data covering information on relevant measures of SWB, time preferences, and farm and farmer characteristics. After excluding non-farmers and subjects with missing information, the analysis was on data from 3,151 farmers.

Description of Variables

As shown in Table 2, self-reported life satisfaction was used to measure the cognitive component of SWB. The participants were asked the following question: ‘Please think about your life as a whole. How satisfied are you with it?’. The response was measured on a five-point Likert-type scale with the options ranging from ‘Completely satisfied’ to ‘Not at all satisfied’. The affective component of SWB was elicited from questions on subjects’ affect measure, for example, ‘Would you say you were very happy, happy, unhappy or very unhappy?’, as well as a ranking of the intensity of different affects (i.e., happy, enthusiastic, content, frustrated, sad, lonely, worried, bored, angry, tired, stressed and pain). Finally, the discounting behaviour was elicited through hypothetical questions that required subjects to choose between an immediate lower payment or a future larger payment.

Table 2.

Variables Used to Examine Determinants of Subjective Well-being and the Effect of Subjective Well-being on Discounting Behaviour.

Variables	Measure
Determinants of Subjective Well-being
Response
SWB (cognitive)	How satisfied are you with life as a whole?
SWB (affective)	Intensity of the experiences of positive and negative emotions
Explanatory
Income	What was your monthly income when you were working at that job?
Job satisfaction	How satisfied are you with your current job
Housing quality	What is the main material used in the outer wall of this house
Health	In general, how is your health?
Work life balance	What is the approximate number of hours you work per week?
Education and skills	What is the highest grade completed at that school?
Civic engagement and governance	During the last 12 months did you participate in program to improve the village/neighbourhood
Environmental quality	Where does this household drain its sewage?
Personal security	How safe do you consider this village?
Effect of Subjective Well-being on Inter-temporal Choice
Response
Economic decision (Discounting behaviour)	You have won the lottery. You can choose between being paid A. 1. Rp1 million today or 2. Rp500,000 in 5 years Which do you choose? B. 1. Rp1 million today or 2. Rp4 million in 5 years Which do you choose? C. 1. Rp1 million today or 2. Rp10 million in 5 years Which do you choose? D. 1. Rp1 million today or 2. Rp2 million in 5 years Which do you choose?
Explanatory
SWB	Please think about your life as a whole. How satisfied are you with it?

Note: Rp refers to Indonesian rupee.

Estimation Methods

The choice of ordered probit in this article is because the dependent variable is an ordinal response. Besides, the ordered probit estimation does not require that the distance between the responses be equal. The ordered probit procedure chooses b to maximize Σln(p_i), where the estimated probability of the observed response is represented by p_i and the Σ is over the entire observations in the data set. In this article, the responses for cognitive SWB, for example, are categorized as the following: 1. Not at all satisfied, 2. Not very satisfied, 3. Somewhat satisfied, 4. Very satisfied, 5. Completely satisfied. Given that i = 5, the probability of observing level i is as follows:

\begin{array}{l} p_{5} = F (x^{'} b) \\ p_{4} = F (a_{2} + x^{'} b) - F (x^{'} b) \\ p_{3} = F (a_{3} + x^{'} b) - F (a_{2} + x^{'} b) \\ p_{2} = F (a_{4} + x^{'} b) - F (a_{3} + x^{'} b) \\ p_{1} = 1 - F (a_{4} + x^{'} b) \end{array}

where F represents the cumulative normal distribution.

Results

Description of Respondents

The summary of respondent characteristics shows that 51% are female, and the average age is 39 years. A total of 59% of farmers had, at most, junior high as the highest level of education. The average income of farmers in the sample is Rp15,300,000. A summary of perception of relative wealth suggests that most farmers (72%) reported that they are among the poorest three on a six-step hypothetical economic ladder. Compared to the stated position five years prior, 82% of the proportion reported being on the same step. A Pearson correlation test shows that the perception of higher relative wealth is positive, albeit weakly correlated with SWB at the 1% level (r = 0.207; p < .01 and r = 0.099; p < .01 for the current and the past, respectively). This finding relates to previous studies (Boyce et al., 2010) where findings favour the rank-income hypothesis, that is, the ranked position of an individual income relative to others in a comparison group determines happiness and life satisfaction. In addition, the result of farmers’ classification as being among the poorest three on a six-step hypothetical economic ladder, yet about 38% being very satisfied with life as a whole, supports the multiple discrepancy theory H2 that farmers’ perception of their well-being aligns with the predictions of the multiple discrepancy theory.

As shown in Figure 1, 4% of farmers are completely satisfied compared to 38% that are very satisfied. A higher proportion of farmers have a lower level of SWB compared to the general population, but not by much. We compare this proportion to the rest of the non-farming population and find that there is enough evidence to suggest an association between occupation and SWB, that is, and that the level of SWB is dependent on the occupation group (χ²(5) = 21.47, p < .01). Thus, H5, which postulates that there is no association between occupation and SWB, is rejected.

Figure 1.

Distribution of Subjective Well-being of Farmers (Measured as Satisfaction with Life Overall).

Regression Results Examining the Determinants of Subjective Well-being

Recall that the dependent variable is coded as 1. Not at all satisfied, 2. Not very satisfied, 3. Somewhat satisfied, 4. Very satisfied, 5. Completely satisfied. Thus, the interpretation of positive coefficients in the ordered probit regression is that there are greater chances of observing a higher level of SWB. The results in Table 3 show that income, job satisfaction, health, personal security, age and gender have a statistically significant effect on SWB. Specifically, being female and having a higher income increases the chance of being satisfied with life in general (SWB). The results corroborate Cummins (2000), Reyes-García et al. (2016) and Asadullah et al. (2018). Further, the probability of a higher SWB decreases with an increasing level of poor health, feeling unsafe, job dissatisfaction and being older. This finding aligns with a priori expectations and previous studies (Wicker & Frick. 2017; Ariza-Montes et al., 2018; Sarriera et al., 2021), and it affirms the postulation (in H3) that at least one of the selected factors would be associated with SWB.

Table 3.

Ordered Probita Regression of the Determinants of Cognitive Subjective Well-being.

Independent Variable	(1)		(2)		(3)
Independent Variable	Coef.	Std Err.	Coef.	Std Err.	Coef.	Std Err.
Income	0.038**	0.019	0.035**	0.015	0.047**	0.020
Job satisfaction	–0.612***	0.043	–0.603***	0.035	–0.613***	0.043
Housing quality	0.096*	0.052	0.087**	0.042	0.094*	0.052
Health	–0.134***	0.036	–0.171***	0.029	–0.135***	0.036
Work life balance	0.000	0.000	0.000	0.000	0.000	0.000
Environmental quality	0.052	0.108	0.126	0.094	0.058	0.108
Personal security	–0.126***	0.046	–0.136***	0.038	–0.124***	0.046
Civic engagement	–0.004	0.026	0.044**	0.020	–0.010	0.026
Age	–0.006***	0.002			–0.033***	0.010
Age squared					0.000***	0.000
Gender (Female)	0.066**	0.028			0.074***	0.028
Education and skills	0.010	0.010			0.011	0.010
Land owned	0.010	0.012			0.010	0.012
cut1	–3.430	0.375	–3.341	0.282	–3.831	0.400
cut2	–2.327	0.369	–2.232	0.277	–2.728	0.395
cut3	–0.867	0.367	–0.817	0.276	–1.264	0.393
cut4	0.782	0.368	0.827	0.277	0.389	0.393
Observation	3151		3151		3151
Wald chi²(12), (8), (13)	286.86		421.684		295.13
Prob > chi²	<0.001		<0.001		<0.001
Log pseudolikelihood	–2470.746		–3694.48		–2466.61

Notes: ***p < .01, **p < .05, *p < .1.

A Spearman’s correlation was estimated to assess the relationship between cognitive and affective SWB. There was a strong positive correlation between cognitive and affective SWB, which was statistically significant, r = 0.309, p = < .01. This result affirms H1, that farmers SWB measured via the cognitive and affective components are positively correlated but not identical. Further, the result of the affective measure of SWB presented in Table 4 are similar to those in Table 3. However, H4, that farmers’ SWB measured via the cognitive and affective components are associated with different groups of variables, cannot be rejected. Thus, these findings corroborate Luhmann (2017), who reported that certain variables are more likely to be associated with cognitive SWB compared to affective SWB.

Table 4.

Ordered Probit Regression the Determinants of Subjective Well-being (Using an Affective Component).

Independent Variable	Coef.	Std Err.
Income	0.135***	0.020
Job satisfaction	–0.451***	0.041
Housing quality	0.028	0.052
Health	–0.185***	0.035
Work life balance	0.000	0.000
Environmental quality	0.089	0.115
Personal security	–0.176***	0.045
Civic engagement	–0.008	0.026
Age	–0.014***	0.002
Gender (female)	0.022	0.028
Education and skills	0.018*	0.010
cut1	–2.439	0.370
cut2	–1.385	0.365
cut3	1.372	0.366
Wald chi²(11)	348.730
Prob > chi²	<0.001
Log pseudolikelihood	–1984.62

Notes: ***p < .01, *p < .1.

Regression Estimates of the Effect of Subjective Well-being on Discounting Behaviour

A description of farmers’ discounting behaviour shows that most farmers chose the immediate smaller payment over the larger future payment. In eliciting the discounting behaviour, this article employs the methods used in previous studies (Sohn, 2017; Begho & Ambali, 2021), where subjects’ discounting is inferred directly from their preferences. A farmer ‘always discounts the future’ if the farmer rejects Rp4 million and Rp10 million in 5 years for Rp1 million today or ‘often discounts the future’ if they reject Rp4 million but accept Rp10 million. On the other hand, a farmer ‘sometimes discounts the future’ if the farmer accepts Rp4 million but rejects Rp2 million, and ‘never discount the future’ if the farmer accepts both Rp4 million and Rp2 million over Rp1 million today. The results show that 74% farmers consistently rejected the future payment and chose the immediate payment for all options.

The results of the regression estimation of the effect of SWB on discounting behaviour suggest that the affective component (positive affect) of SWB does not have a statistically significant effect on discounting behaviour (Table 5). Thus, H6, that the prevalence of positive emotions and mood is positively associated with discounting behaviour, is rejected. However, there is evidence that the effect of SWB on discounting behaviour is from the affective component (positive affect) of SWB. Specifically, a higher level of affective SWB increases the chances of the farmer preferring future monetary payoffs over immediate payoffs. This result indicates that H7 .that life satisfaction is positively associated with discounting behaviour. is affirmed. This finding corroborates the findings of Yang and He (2015), and it aligns with the predictions of the affect infusion model.

Table 5.

Ordered Probit Regression Estimating the Effect of Subjective Well-being on Discounting Behaviour.

Independent Variable	(1)		(2)
Independent Variable	Coef.	Std Err.	Coef.	Std Err.
Cognitive component of SWB	–0.017	0.047
Affective component of SWB			0.074**	0.035
Age	0.000	0.002	0.000	0.002
Gender (female)	–0.022	0.042	–0.027	0.042
Education	–0.016	0.017	–0.019	0.016
Culture	0.144**	0.070	0.164**	0.070
cut1	0.639	0.253	0.862	0.213
cut2	1.333	0.255	1.559	0.216
cut3	1.896	0.261	2.124	0.224
Wald chi²(5)	5.746		9.976
Prob > chi²	0.332		0.076
Log pseudolikelihood	–891.48		–889.37

Note: **p < .05.

Discussion

The article provides empirical evidence on farmers’ SWB, which improves our understanding of the determinants of the well-being of farmers’ and the extent to which SWB shapes farm economic decisions. The results are discussed in the context of LMICs. The result of farmers’ classification as being among the poorest three on a six-step hypothetical economic ladder, yet about 38% being very satisfied with life as a whole, may explain why SWB does not necessarily increase linearly with increasing wealth in society. These results also suggest that there is the possibility that social comparison within a group manifested in perceived income inequality. This perception of relative wealth supports the multiple discrepancy theory.

The benefit of higher SWB on people’s increased contribution to national wealth has been highlighted in previous studies (Lucas & Diener, 2008). Similarly, farmers with higher SWB may be more productive and may contribute better to ensuring a nation’s food security, particularly in low-income countries with persistent food insecurity issues. Although SWB is important in measuring national progress, SWB also impacts the economic outcomes of the individual farmer. This article disentangles the components of SWB into the affective and cognitive components to identify the component that influences the discounting behaviour. The insights from the results are that farmers can make better economic decisions when they experience pleasant emotions, moods and feelings.

The awareness that affective SWB impacts decisions can help farmers approach management decisions critically. Besides, positive mood induction can be tractably embedded in external actions to intervene to assist farmers in making better decisions and in improving their quality of life. The evidence that lower levels of affective SWB are likely to reduce the chances of a farmer preferring future monetary rewards has implications for agriculture. Many previous findings show a negative correlation between higher discount rates and profitable agricultural investments. Similarly, positive affective SWB is known to increase farm productivity.

Considering that the occupation-specific stressors and health risks are substantially higher in farming, the result in this article highlights the importance for farmers to focus on maintaining good health. Most importantly, the public, private and voluntary sectors need to increase support for farmer-centred health projects aimed at improving the health, safety and well-being of farmers. With respect to job satisfaction, improving the existing advisory support, providing a platform for better community involvement for farmers, and implementing participatory and action-oriented programmes to improve farmers’ working conditions will increase farming satisfaction and contribute to a higher SWB. These suggestions are in line with Herrera et al. (2021). Also, a focus on economic prosperity may improve a few drivers of well-being, for example, the income or wealth of farmers. Within the lower-income category, increased income will allow farmers to meet basic needs with effects on SWB that are relatively quick and conspicuous compared to high-income farmers (Eckersley, 2005). However, as the results in this article show that there is no sole driver of higher SWB, and other non-economic drivers need to be considered collectively to meet the goal of holistic life satisfaction.

Conclusion

The article examined the level of SWB of farmers, the determinants of SWB, and the relationship between SWB and farmers’ discounting behaviour. It builds on past studies that examine the relationship of SWB with one or a few of these drivers by accounting for a larger number of factors informed by the literature. The results showed that farmers rated themselves to be among the poorest in the population—a categorization that conforms with many empirical studies in LMICs. The results also suggested that having a higher level of education and income increases the chance of a higher SWB. In addition, the probability of a higher SWB decreases with an increasing level of poor health, feeling unsafe, job dissatisfaction and being older. Crucially, the article distinguishes the components of SWB that impact economic decisions. These results merge two aspects of the literature—life evaluation and behavioural measures. The importance of this study is also in its potential to contribute to the empirical evidence that informs policies. An amalgamation of evidence from measuring SWB with an objective measure of well-being will strengthen policies. From a development perspective, these results have important implications. Addressing the barriers to better SWB will also have a positive spillover for the rural communities where most farmers are located.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Notes

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