Maria Ngarachu 1. Abstract. March 2014

Transcription

1 Can an Intervention Aimed at Improving Economic Inclusion make Recipients Less Happy? Findings from a Randomized Control Trial of a Targeted Youth Wage Subsidy in South Africa. Maria Ngarachu 1 March 2014 Abstract Young Africans are the most marginalized group in the South African labour market experiencing unemployment rates of over 60 percent. Findings from a Randomized Control Trial of a targeted hiring voucher suggest that individuals in the treatment group were more likely to be employed one and two years later compared to those in the control group. However and contrary to the vast body of literature showing that employment is correlated with higher levels of happiness; individuals of the treated group do not report to be any happier with life in general. This result is driven primarily by the types of jobs these respondents obtained. Keywords:, Youth Wage Subsidy, Job Search, Welfare effects JEL: D63, I38, J08, J22 1 African Micro-Economic Research Unit, University of the Witwatersrand; Maria.Ngarachu@students.wits.ac.za 1

2 1 Introduction Despite being classified as an upper middle income country, South Africa is still plagued by massive unemployment, poverty and inequality. While youth unemployment is a global problem, the youth (aged 20-24) constitute the most disadvantaged group in the South African labour market. As figure 1 below illustrates, young Africans in particular experience unemployment rates in excess of 60%. 2 Thus, unemployment is a particularly acute problem among an already marginalized group of South Africans young Africans with relatively low level of skills. Figure 1: Broad Youth Unemployment Rates by Race (Age 20-24) 3 Source: Statistics South Africa s Quarterly Labor Force Survey (QLFS) The early twenties seem particularly important in shaping labour market trajectories. A large majority complete high school of which some proceed with tertiary education, search for employment opportunities and have their initial practical work experience during this period. Figure 2 below confirms that employment rates increase substantially for year olds compared to the age group. Furthermore, previous work experience is highly correlated 2 Throughout this paper and following Kingdon and Knight (2006) as well as Lloyd and Leibbrandt (2013) who document the importance of including discouraged workers in the context of mass unemployment; we use the broad definition of unemployment as opposed to the official narrow definition. 3 The final period, quarter 2 of 2012, corresponds to the final survey period in this randomized control trial. 2

3 with employment probabilities at a later age (Banerjee, Galiani, Levinsohn, McLaren, & Woolard, 2008) and thus finding a first job is crucial for the lifetime work trajectory of individuals. Nevertheless, young Africans still experience high unemployment levels into their 30s. Hence, the age group is a key focus for government policy when aiming to create an inclusive economy. Figure 2: Labor Market Status by Age (Africans) 4 Source: Statistics South Africa s Quarterly Labor Force Survey (QLFS) As a policy response, in 2009 the South African government tabled in Parliament a targeted youth wage subsidy for discussion in order to facilitate entry in the job market for young job seekers by creating an incentive for firms to hire inexperienced youth. In the same year, the African Micro-Economic Research Unit (AMERU) was commissioned by the National Treasury of South Africa to investigate the impact of a targeted youth wage subsidy through a Randomized Control Trial (RCT). One year after allocation of the voucher, individuals in the treatment group were 7.4 percentage points (approximately 25 percent) more likely to be 4 The period used, quarter 2 of 2012, corresponds to the final survey period in this trial. 3

4 employed than individuals of the control group (Levinsohn, Rankin, Roberts, & Schöer, 2014). Furthermore, voucher holders were more likely to be in employment two years after the experiment even after the voucher had expired. Thus, getting employed positively affected their labour market trajectories and therefore a policy such as this creates a more inclusive labour market (Banerjee et al., 2008). Accordingly and in line with existing literature which indicates a positive correlation between employment and life satisfaction, we would expect individuals in this group to report higher levels of happiness. However, it appears that individuals of the treated group do not report to be any happier 5 with life in general when compared to individuals in the control group. Given the random allocation to treatment, this atypical observation could point to a few possibilities worth exploring. Firstly, it is likely that individuals in the treated group who expected to get employment but did not find a job despite the voucher expressed greater dissatisfaction (possibly due to unfulfilled expectations) leading to a cancellation of the increased satisfaction that individuals in the treated group who obtained employment may have expressed. Secondly, this may be due to the duration of time individuals in the treated group have been employed as it is likely that satisfaction increases momentarily immediately after one obtains employment but then levels out to a predetermined baseline. Third, the type of jobs obtained by those in the treated group may have played an influential role in the reported levels of satisfaction. If the treated group were unhappy with the type of jobs they got, this may account for the lack of observed increase in contentment. Fourth, it may be that although there exists a strong positive correlation between employment and satisfaction; an exogeneous shock to employment may not necessarily lead to an increase in happiness. Last but not least, it could very well be that general life satisfaction is not a function of employment for the year age group. The aim of this paper is to unpack this peculiar phenomenon using survey data from the RCT study and investigate which of the aforementioned hypotheses offer a plausible explanation. 5 In this paper and in line with Arrow & Dasgupta (2009), happiness is synonymous with self-reported happiness, life satisfaction and well-being. 4

5 The paper is structured as follows: Section 2 provides a brief survey of the existing literature on employment and subjective well-being; Section 3 introduces the dataset and presents the descriptive statistics; Section 4 presents the methodology employed and discusses estimation issues; Section 5 presents and discusses the estimation results; and, Section 6 concludes. 2 Literature Review The determinants of life satisfaction can be broadly classified into four aggregate groups: political, institutional, economic and, human development and culture 6 (Bjørnskov, Dreher, & Fischer, 2008). The latter two categories are most relevant in the literature which has concentrated on exploring the individual-level as opposed to country-specific determinants of life satisfaction. In this part of the literature, there is fairly broad consensus on the main determinants of subjective well-being and these are remarkably similar across countries (Frey & Stutzer, 2002; Argyle, 2003; Diener & Seligman, 2004; Dolan, Peasgood, & White, 2008). First and foremost, higher absolute income and higher relative income increase an individual s self-evaluation of happiness (Clark & Lelkes, 2005). Secondly, life satisfaction depends non-linearly on age with satisfaction roughly decreasing until people reach their mid-40s after which satisfaction increases again (Blanchflower & Oswald, 2004a). Third, women appear to report higher happiness scores than their male counterparts (Clark & Oswald, 1994). Fourth, higher levels of education (Blanchflower & Oswald, 2004b) and health status (Shields & Wheatley Price, 2005) tend to be positively associated with life satisfaction. Fifth, regular engagement in religious activities is often found to be a significant contributor to happiness (Clark & Lelkes, 2005). Sixth, social capital in its various latitudes is conducive to life satisfaction (Bjørnskov, 2003). Seventh, social relationships are a major source of well-being: marriage has the strongest effect with the married being on average the most happy while the divorced and separated the least (Helliwell, 2003); having children may also be so (Haller & Hadler, 2006), although causality is not yet entirely clear. Last but not least, it is well documented that the unemployed report lower levels of happiness than their employed counterparts (Clark & Oswald, 1994; Gerlach & Stephan, 1996; 6 The last group can be disaggregated into socio-demographic, cultural, genetic and personality factors (Frey, 2008). 5

6 Winkelmann & Winkelmann, 1998; McKee-Ryan, Kinicki, Song, & Wanberg, 2005; Carrol, 2007; Krause, 2011; Ohtake, 2012). When life satisfaction scales are treated as continuous, studies consistently find life satisfaction scores of magnitudes 5-15% lower for the unemployed (Di Tella, MacCulloch, & Oswald, 2001; Frey & Stutzer, 2002; Helliwell, 2003). This almost unambiguous 7 finding has been confirmed using different measures of well-being 8, different data sets, different countries, different time periods and different methods of analysis (Frey & Stutzer, 2002; Dolan et al., 2008). Di Tella et al. (2001) demonstrate that this finding holds at a macro level as well since individuals responses to life satisfaction surveys moved systematically with their nation s level of joblessness. Furthermore, Winkelmann and Winkelmann (1998) show that the negative effect of unemployment on life satisfaction far exceeds the effect on life satisfaction associated with loss of income. By including both unemployment and income as explanatory variables, the authors are able to capture the direct effect of unemployment on life satisfaction ceteris paribus (that is, keeping income constant) and that these non-pecuniary costs surpass the pecuniary costs which stem from the loss of income while an individual is unemployed. Once again, Di Tella et al. (2003) show that this result holds at a macro level: recessions create large psychic losses that extend beyond the fall in gross domestic product and rise in the number of people unemployed. These non-pecuniary costs arise mainly because employment is not only a source of income but has several latent functions and is thus a provider of social relationships (outside the nuclear family), identity in society and individual self-esteem (Feather, 1990; Darita & Goldsmith, 1996; Krause, 2011). Stanca (2010) reveals that the well-being costs of unemployment are larger in countries where unemployment is high and income is high reinforcing the relevance of the psychological and social repercussions of unemployment. In addition, decreased well-being may express itself through adverse outcomes such as marital dissolution, criminality, increased mortality and suicide risk (Björklund, 1985; Jensen & Smith, 1990; Junankar, 1991; Carrol, 7 Graham and Pettinato (2001), and Smith (2003) are two of the few exceptions to the finding of a strong negative unemployment effect which could be attributed to the small numbers of unemployed in their datasets. 8 These include but are not limited to: life satisfaction/ happiness, mental distress, sleeplessness, stomach pain, depression, anxiety, self-esteem and an individual s locus of control (Darita & Goldsmith, 1996). 6

7 2007). Although some studies indicate evidence of adaptation 9 to unemployment, the general finding of a strong negative effect on satisfaction persists. The main issue of contestation in the early literature was that although a negative correlation was evident, one could not claim causation from unemployment to lower self-reported satisfaction due to the possibility that dissatisfied individuals select themselves into unemployment. Put differently, inherently dissatisfied individuals are more likely to get fired or quit their jobs. Secondly, the wide-spread use of cross-section data in the early literature failed to account for unobservable determinants of life satisfaction (such as health which is often difficult to measure accurately) and may have yielded biased estimates for the unemployment coefficient (that, is the omitted variable bias). However, using panel data and fixed effects (or random effects) model, subsequent literature corroborated previous cross-section evidence of a robust negative effect of unemployment on satisfaction (Winkelmann and Winkelmann, 1998; Winkelmann, 2005; Ferreri-Carbonell & Gowdy, 2007). Although panel data decreases biases caused by unobserved individual specific factors as well as biases caused by individuals anchoring their life satisfaction scale at different levels, it does not address the main issue of causality. To get around this, Winkelmann and Winkelmann (1998) make an assumption of unemployment being endogeneous or exogeneous based on respondents age groups 10 as well as other information (for example, bankruptcy) indicating the extent to which unemployment was involuntary. The authors found that both older individuals and the involuntarily unemployed experience substantial and significant reductions in happiness and thus their case for a causal interpretation. Taking into account the satisfaction score a year before unemployment, the period of unemployment and a year after unemployment; Lucas et al. (2004) showed that the unemployed do not start out with low satisfaction scores and that these drop by at least half a point (on a 0-10 scale) during unemployment. This result mirrored Winkelmann and Winkelmann s (1998) earlier finding of minimal selection effects. 9 Clark and Oswald (1994) find that the negative coefficient on unemployment reduces with the length of unemployment whereas Lucas, Clark, Georgellis, and Diener (2004) find a more negative reaction to unemployment of those who have been unemployed for more than a year. 10 The authors assumption is that unemployment for younger workers is more likely to be endogeneous as they do not yet have established careers. 7

8 Expanding on the seminal work of Winkelmann and Winkelmann (1998), Kassenböhmer and Haisken-DeNew (2008) explicitly identify truly exogenous unemployment entries. In addition to controlling for unobserved individual heterogeneity, they explicitly include the reason for job termination (voluntary, being fired and plant closure) and entry into unemployment in a multivariate regression analysis. While the fixed effects results indicate some underlying unobserved negative characteristics of those being fired or voluntarily unemployed, they do not explain the entire negative effect of unemployment on satisfaction. In contrast, with company closures (a truly exogenous event), there is evidence of substantial negative non-pecuniary psychological costs in the order of several log points of income. Nevertheless, in spite of this causal inference, there has has been little evidence that active labour market policies which increase employment result in an increased level of self-reported life satisfaction. To advance the literature, Crost (2012) shows using a regression discontinuity design that individuals experience a sudden and significant drop in self-reported happiness at the end of the duration of a subsidized employment project in Germany. Consequently, relative to the counterfactual of unemployment, subsidized employment can have a large positive effect on the happiness of their participants. This is noteworthy since jobs created by active labour market policies are often jobs on the lower end of the spectrum and hence from an income and social status point of view, may not have had the desirable characteristics that would cause an increase in happiness. 11 On the other hand, Dorsett and Oswald (2014) demonstrate that active labour market policies intended to create an inclusive economy can have adverse effects. In a social experiment carried out in the United Kingdom in which extensive in-work support was randomly assigned among the disadvantaged, the authors found reductions in all six measures of well-being (four of which were statistically significant) of the treatment group even though on these individuals ended up with higher earnings than the control group. Long after eligibility had ceased, the treatment group had considerably lower life satisfaction, perceived their financial situation as worse, ran out of money more often, worried about money to a greater extent, had more trouble with debts, and were less likely to have money left over at the end of the week. 11 From a policy perspective, this would suggest that subsidized employment projects may be a more effective way of increasing the well-being of the unemployed than cash transfers (Crost, 2012). 8

9 Dorsett and Oswald (2014) attribute their troubling finding to the curse of raised expectations. McBride (2010) also finds evidence to support an aspiration-based theory of happiness. He finds that expectations and social comparisons significantly affect reported satisfaction: an increase in aspirations has a negative on a subject s reported satisfaction whereas a relative improvement in the outcomes of others in an individual s comparison group also has a negative impact albeit of a smaller magnitude. Accordingly and in the context of this paper, it is possible that voucher recipients had higher expectations than non-recipients on their prospects of employment in the ensuing year and are therefore likely to report lower levels of satisfaction especially if some recipients obtain employment and they are unsuccessful in doing so. In another experimental study, Ifcher & Zarghamee (2013) show that postive affects impacts individuals preference for present over future utility and that emotions and mood play an important role in economic decision making in general. Although the authors study is of a somewhat different nature, this implies that expectations, like mood, are likely to affect the outcome of an experiment (in the context of our study and that of Dorsett and Oswald (2014), self-reported happiness). Thus, they underscore the need for policy-makers to account for mood in the decision-making environment. 12 This paper s addition to to the existing literature is three-fold. Firstly and analogous to Crost (2012), it investigates the impact of an active labour market policy on the average level of happiness particularly in the developing world context. In so doing, it could aid in informing policy-making which aims to increase economic inclusion. Secondly and akin to Dorsett and Oswald (2014), it augments the literature on randomized control trials of economic policies in which well-being variables are taken as the principal outcome criteria. Lastly and parallel to Kassenböhmer and Haisken-DeNew (2008), it enhances the causal implication of (un)employment to (dis)satisfaction. 12 The first and third hypotheses which this paper will explore hinge on the role of expectations. 9

10 3 Data and Descriptive Statistics This paper uses data from the second and third waves of the Labour Market Entry Survey (LMES) conducted by the African Micro-Economic Research Unit (AMERU) based at the University of the Witwatersrand (Wits). The survey was conducted to assess the impact of a targeted wage subsidy on the employment prospects of young people in South Africa following a policy response by the South African government in 2009 to facilitate entry in the job market for young job seekers by creating an incentive for firms to hire inexperienced youth. 13 The Randomized Control Trial was run over four years with two baseline studies in 2009 consisting of around 4000 individuals 75% of whom were re-interviewed in 2010; the allocation to the treatment after the second baseline survey in 2010; and, follow-up surveys in 2011 and The survey is restricted to Africans between the ages of years residing in the Gauteng, KwaZulu-Natal and Limpopo provinces. 14 The LMES was conducted across forty five enumeration areas (EAs) in the three provinces. The majority of these enumeration areas were chosen on the basis of the 2001 National Census. A smaller subsample was drawn from respondents who visited Department of Labor Centers (LCs) situated close to these enumeration areas. These labour centres offer a variety of services including the payment of unemployment insurance and keeping a record of registered unemployed persons. The EAs sample should be more representative of the population whereas those in the LCs group have selected themselves by registering or visiting a labour centre. As those registered in LCs may be considered more motivated or may have held a job that made Unemployment Insurance Fund (UIF) contributions, the two groups are likely to be different. However, confining our study to individuals who are not registered with labour centres results in a loss of approximately 40% of the observations. Thus, due to the small nature of our sample; we do not take this approach. Instead, we include a dummy variable to determine if there are significant differences between the two groups. 13 The total value of the subsidy was R5000. Payments were made in monthly installments to the firms employing the individuals entitled to a subsidy and covered a portion of the wage these firms paid to these individuals. 14 The three provinces give a good mix of individuals: Gauteng is relatively developed; KwaZulu-Natal is relatively metropolitan; and, Limpopo is relatively rural. 10

11 Proportion of individuals (%) General life satisfaction is measured on a 5-point likert-scale with 1 being Very Unhappy, 2 being Unhappy, 3 being OK, 4 being Happy and 5 being Very Happy. 15 However, a measure of self-reported happiness was included in the survey questionnaire only in 2011 and 2012 and thus we focus our attention on the last baseline year, 2010, and Figure 3 below helps set the scene for the relationship under investigation. It is clear that in terms of Very Unhappy and Very Happy, the control and treatment groups are relatively balanced. However, there appears to be a greater proportion of OK and Happy individuals in the control group compared to a greater proportion of Unhappy individuals in the treatment group. Figure 3: Comparison of of the Control and Treatment Groups (2011) Control Treatment General Before considering the control and treatment groups in isolation, it is worth noting from figure 4 below that within each randomization group and in line with existing literature (Winkelmann & Winkelmann, 1998); the employed are on average more satisfied in life than their unemployed counterparts. Disaggregating in terms of economic status, we see that for the employed, the 15 We assume cardinality (Ferrer-i-Carbonell and Frijters, 2004) and transform Very Unhappy = 1 and Very Happy =5 16 With subsequent waves, it would be interesting to consider the panel dimension and use either random-effects or fixed-effects regressions to control for unobserved heterogeneity as some of the international literature has done (D'Addio, Eriksson and Frijters 2007). Due to the observed high attrition (possibly non-random) in 2012, we restrict our study to the year

12 treatment group is marginally happier. In contrast, for the unemployed individuals, the treated group is unhappier than the control group and this group of individuals may be driving our results in figure 3 above. In the Regression Analysis and Discussion section, we test whether these differences are statistically significant. Before doing so, it is important to bear in mind that these differences (significant or not) may be merely because the two groups are in some way different: specifically, that in spite of the increase in employment of the treated group (and hence the marginal greater satisfaction observed in figure 4 below), the treatment group may be intrinsically less satisfied with life in general than the control group (as depicted in figure 3). Alternatively, it could very well be that the unemployed in the treated group had expectations of finding employment and failure to do so resulted in their reporting of lower levels of satisfaction (Ifcher & Zarghamee, 2013; McBride, 2010). Table 1 below gives the mean values of the variables of interest for both the control and treatment groups in 2010 and 2011 and indicates with an asterisk wherever the means are statistically different. Evidently, the two groups are remarkably balanced in both years the only differences being that there are more singles, less individuals with children and residing in Gauteng in the control group in 2010 and more employed in the treatment group in Particularly of interest is the balance of the two groups in terms of self-reported health status which the literature documents is highly correlated with life satisfaction (see Laborde & Powers, 1985 for an illustration). Thus, although we do not have baseline results for life satisfaction, one could conjecture that the two groups were relatively balanced in terms of life satisfaction in

13 Proportion of Treatment (%) Proportion of Control (%) Figure 4: Comparison of between the Two Groups and Across Economic Status (2011) Employed Unemployed General Employed Unemployed General 13

14 Table 1: Comparison of the treatment and control groups in 2010 and 2011 Variable Control Treatment Difference Control Treatment Difference Female Age Race African Coloured Indian Highest level of education Degree Diploma Certificate Matric Incomplete secondary Primary Marital status Single * Married Parenthood Proportion with children * Number of children Province Gauteng * Kwazulu-Natal Limpopo Economic status Employed * Unemployed Learnership Contract work

15 Hours worked a week Earnings Health status Excellent Good Okay Poor Very poor Methodology Given that this paper uses data from a Randomized Control Trial, a difference-in-difference approach would be expected to assess the change in life satisfaction following the assignment of the wage subsidy voucher. However and as was mentioned in the preceding section, the question on life satisfaction was only posed post-2010 and thus we cannot use this approach directly. First, we run preliminary Ordinary Least Squares (OLS) and ordered probit (oprobit) regressions to get a better idea of the relationship between treatment and general life satisfaction: Where: Y i = βx + ε (1) Y i is the level of happiness and is measured on a 5-point likert scale as elaborated in the Data and Descriptive Statistics section; X is a vector of individual-specific variables including age, gender, education, marital status, number of children, province of residence and dummy variables for the treated group and health status; ε is the disturbance term. In line with the body of literature on satisfaction, the hypotheses that the treated group is more content that the control group 17 is tested (Clark & Oswald, 1994). 17 As individuals in the treatment group are more likely to be in employment following the assignment to the voucher. 15

16 Although the nature of the RCT means the treatment and control groups were balanced in 2009; when the voucher was actually assigned in 2010, the sample was down from 4009 to 3064 individuals. Thus and as was evident in the Data and Descriptive Statistics section, we use propensity score matching (PSM) to rebalance the groups in terms of observable characteristics. Our main matching method is the nearest neighbor matching algorithm with replacement 18 where an individual from the comparison group is chosen as a matching partner for a treated individual if the former is closest to the latter in terms of the propensity score. We ascertain the results we obtain using other matching algorithms (radius matching, kernel density matching and stratification matching) although due to space constraints, we will not report these if they are consistent. We do so first for the full sample then consider the employed individuals and unemployed individuals in isolation. Should we not detect any differences in life satisfaction between the control and treatment groups post-matching, we attempt to unpack the finding to determine which of the hypotheses offer a possible explanation for this result. Limitations First and foremost and as alluded to earlier, we do not have a baseline on life satisfaction prior to the allocation of the wage subsidy. Hence, there is a possibility that the treated group was initially less happy although the likelihood is not significant as both the treatment and control groups were balanced on most observable characteristics (including self-reported health status) as detailed in table 1 in the preceding section. Secondly, our sample in 2011 is likely to have suffered from non-random attrition albeit to a lesser extent than would be the case in In particular, the treated who were unsuccessful in obtaining employment may have remained in the survey in the hope that they would be more successful the second-time round. Accordingly, as a robustness check, we attempt to use Lee bounds (Lee, 2009) to test treatment effect bounds should we find significant treatment effects. Third, following Imai, Keele and Yamamoto (2010), it is imperative to consider the causal mechanism between treatment, happiness and employment. However, which of the three is the mediator and which is the outcome is not clear to establish in the context of our study. 18 Replacement increases the quality of the match and decreases the bias as well as overcomes the problem of obtaining different results depending on the order in which observations get matched (Caliendo & Kopeinig, 2008). 16

17 Lastly, a huge limitation to the method employed in this paper is that we rebalanced the control and treatment groups in terms of observable characteristics. Consequently, should unobserved characteristics be important in determining self-reported life satisfaction, our results will be subject to a hidden bias and should therefore be interpreted with caution (Caliendo & Kopeinig, 2008). Given the panel nature of our dataset, accounting for unobserved heterogeneity using say fixed effects would be an attractive first option. However, as our independent variable of most interest (that is, assignment to the treatment group) is time-invariant; we would be unable to detect any differences in the average level of happiness between the two groups of individuals. Accordingly, we attempt to use Becker s and Caliendo s (2007) Mantel-Haenszel bounds (mhbounds) to check for the sensitivity of our results to unobserved factors. 5 Regression Analyses and Discussion Effect of Treatment We begin our analysis with the Ordinary Least Squares (OLS) regressions for the full sample (wage employed 20 and non-wage employed individuals). The empirical results can be found in the first and second columns of table A1 in Appendix A of this paper while the summary of results with the key variable is presented in table 2 below. As is evident from the first column, individuals in the treated group are on average less content than individuals in the control group which is contrary to what we would expect given that the treated group experienced an increase in employment (Levinsohn et al., 2014). The addition of covariates in column two does not change the sign of the coefficient and instead shows that their exclusion results in a slight downward 21 bias of the result. However, this result is insignificant even at the 10% level with residence in Limpopo, earnings and health status being the only significant contributors to life satisfaction. Turning to the ordered probit (oprobit) regressions in the third and fourth column of the table, it is apparent that the results are relatively unchanged in terms of the magnitude (albeit 19 For clarity, the key statistical results of the study are presented in the body of the paper with the corresponding detailed regression output relegated to appendices A, B and C for sections 5.1, 5.2 and 5.3 respectively. 20 For ease of analysis, we focus on the wage-employed and exclude the 80 self-employed individuals as it is possible that a number of them may have opted to work independently having failed to obtain wage employment. Nevertheless, as a robustness check, we include them in the analyses and find that the results (not shown) are relatively unchanged. Henceforth, we refer to the wage employed as the employed and the non-wage employed as the unemployed. 21 In the context of a negative coefficient, the direction of the bias is based on the absolute value. 17

18 slightly lower than the OLS specification), significance and most importantly, the negative sign of the coefficient. Table 2: Effect of Treatment on General for the Full Sample Variables (1) (2) (3) (4) (5) OLS OLS Oprobit Oprobit PSM Radius (0.001) Treatment (0.0410) (0.0405) (0.0435) (0.0438) (0.043) Controls No Yes No Yes Yes Observations 2,358 2,358 2,358 2,358 2,322 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 As detailed in the Data and Descriptive Statistics section of the paper, it is quite possible that despite the two groups being remarkably balanced in the year 2010; the differences (marital status, sampling province) may account for the observed results above as these may be important determinants of happiness for this group of individuals. Consequently, to ascertain the aforementioned negative impact on happiness for the treated group; we rebalance the 2011 sample based on observable characteristics in Figure A1 in Appendix A reports the histograms of the estimated propensity scores for the two groups prior to matching. Although relatively similar, it is quite clear that the histogram of the treated group is associated with slightly higher probabilities. In spite of their similarity (the average estimated propensity score for the control group is whereas that of the treated group is ), as a robustness check to the OLS and ordered probit results above; we proceed with the matching process. From column 5 in table 1 above, it is evident that even after matching, individuals in the treatment group are on average unhappier than those in the control group. Altering the radius to 0.01 ( ) and 0.05 (-0.019) leaves the result unaltered while the other matching algorithms yield similar results ( on the lower end in kernel density and on the upper end in nearest neighbour). Again, the result is not statistically significant. Could this insignificance be the result of two offsetting effects? For example, could the treated unemployed be less happy while the treated employed are more happy? To determine this, we 18

19 first consider the unemployed individuals taken in isolation in table 3 below. Albeit still insignificant, the treated unemployed are unhappier than the unemployed in the control group across all three specifications. Worth noting too is that the magnitude of the coefficient is approximately four times larger (in absolute value) than in the preceding full sample results. As with the regression output for the full sample, it is evident from the second and fourth columns of table A2 in Appendix A that sampling province and health status (but not earnings) remain the only individually significant determinants of life satisfaction. Table 3: Effect of Treatment on General for the Unemployed Variables (1) (2) (3) (4) (5) OLS OLS Oprobit Oprobit PSM 22 Radius (0.001) Treatment (0.0567) (0.0566) (0.0607) (0.0608) (0.061) Controls No Yes No Yes Yes Observations 1,229 1,229 1,229 1,229 1,195 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Turning to the employed individuals in table 4 below, we see a slightly different picture than that of the full sample and unemployed. Although insignificant even at the 10% level and in line with what we would expect; individuals in the treated group are more content with life in general than individuals in the control group. This result is invariable whether one uses the OLS or ordered probit specification or whether one includes control variables or not. Moreover, as can be seen in table A3 in Appendix A; sampling province and health status cease to be important explanatory variables on their own and instead, age and earnings plays an increasingly influential role in the determination of happiness. 22 Yet again, nearest neighbor matching yields the upper limit average treatment effect of whereas a radius of 0.01 yields the lower limit of

20 Table 4: Effect of Treatment on General for the Employed Variables (1) (2) (3) (4) (5) OLS OLS Oprobit Oprobit PSM 23 Radius (0.001) Treatment (0.0684) (0.0685) (0.0748) (0.0762) (0.074) Controls No Yes No Yes Yes Observations Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Had the results of the unemployed and employed individuals considered on their own been significant, one could have come to the conclusion that the greater dissatisfaction amongst the treated unemployed offset the greater satisfaction of employed individuals in the treated group the first hypotheses. However, the results suggest that this is not the case and hence we look to the other hypotheses for possible explanations. Before doing so, it is interesting to note that individuals in the treated group who were previously 24 employed are unhappier than those in the control group (see table A4 in Appendix A) whereas individuals in the treated group who obtained employment in 2011 are happier than those in the control group (see table A5 in Appendix A). Nonetheless, these results still remain insignificant. Could this unhappiness among the formerly employed be the result of the voucher not being particularly useful? Correspondingly, could the insignificance among the newly employed be a result of the type of jobs obtained or the time elapsed since obtaining employment? We explore this in the subsequent sections. 5.2 Duration of Employment As detailed in the literature review of this paper, Crost (2012) shows using a regression discontinuity design that individuals experience a sudden and significant drop in self-reported happiness at the end of the duration of a subsidized employment project in Germany. Equivalently and in the context of this paper, it could very well be that individuals reported 23 The lower bound average treatment effect is (0.01 radius) while the upper bound is (stratification matching). 24 That is, individuals who already had employment in 2010 and remain employed in

21 higher levels of life satisfaction in the period of time shortly after obtaining employment and that this greater level of contentment was short-lived. Put differently, an individual has an innate level of happiness whereby exogenous shocks or events result in changes or spikes to an individual s self-reported life satisfaction and that this returns to the innate level after some time. Accordingly, we consider the effect the duration of employment 25 may have on individuals selfreported life satisfaction. From table 5 below, it is evident that across both specifications and regardless of the inclusion of control variables, there is a negative relation between the duration of employment and individuals self-reported happiness. Unlike the anticipated hump-shaped relation described in the preceding paragraph, we observe a downward trend: that is, reported life satisfaction is highest immediately after beginning employment and that this decreases when one remains employed. Interestingly, although the results of columns (1) to (4) are relatively unchanged when one includes the treatment dummy (not shown); restricting the results to the treatment group as in columns (1) and (2) of table B2 in Appendix B yields significant results (and an improvement in significance where previously significant in the cases with covariates) and of magnitudes approximately double that of the two groups in aggregation. This could indicate that the significant negative relation among the treated employed is offset by the relation between the duration of employment and life satisfaction among the untreated employed. Table 5: Duration of Employment on General for the Employed Variables (1) (2) (3) (4) OLS OLS Oprobit Oprobit Duration * ** ( ) ( ) ( ) ( ) Controls No Yes No Yes Observations Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p< As individuals were interviewed at different times in the year, this was calculated as the difference between the date of the interview and the date which an individual began employment. This gives us an accurate figure for the duration of employment particularly for individuals for whom the wage subsidy voucher was instrumental in obtaining employment in

22 To unpack this, we make use of interaction effects using dummy variables as in Gujarati (2003) as follows: Y = α + β 0 Tr + β 1 Du + β 2 Tr*Du + ε (2) Where: Y is the level of happiness and is as defined in the methodology section of this paper; Tr is the treatment dummy and is also as defined in the methodology; Du is the duration of employment in months; Tr*Du is the interactive term between treatment and duration; ε is the disturbance term. α gives us the intercept of the control group whereas β 1 gives us the slope. β 0 and β 2 give us the differential intercept and slope respectively of the treatment group should they be statistically different from zero. However, from columns (5) and (6) of table B2, we see that we are unable to determine the underlying relationship as all the coefficients are insignificant: controlling for other determinants of satisfaction leaves the results unaltered. Moreover, attempting different functional forms by adding a quadratic and cubic term (not shown) to the analysis above in table 5 and table B2 does not change the conclusion as the negative sign for the duration coefficient persists while those of its quadratic and cubic terms are interchangeable and highly insignificant. Cognizant of the likelihood that we are unable to detect the significance of the quadratic and cubic terms due to the fact that the duration of the employed is strictly positive, we repeat the analysis above and include the unemployed by setting their duration of employment equal to zero. As can be seen in table 6 below, the signs of the coefficients are in line with the anticipated hump-shaped relationship but these become insignificant with the inclusion of covariates. Using job satisfaction 26 (not shown) as the dependent variable does not improve the significance. Yet 26 A simple regression of job satisfaction on general life satisfaction with and without controlling for other relevant variables yields highly statistically significant results which is consistent with the body of literature (see Rice, Near, 22

23 again, it is evident from columns (5) and (6) that the inclusion of the quadratic and cubic terms does not offer us any more insight than table B2 did as to what the structural differences between the control and treatment groups might be. Table 6: Duration of Employment on General for the Employed and Unemployed Variables (1) (2) (3) (4) (5) (6) OLS OLS Oprobit Oprobit Oprobit Oprobit Duration *** *** (0.0124) (0.0108) (0.0130) (0.0112) (0.0115) (0.0119) Duration ** ** ( ) ( ) ( ) ( ) ( ) ( ) Duration e e e e e e-06 (6.87e-06) (4.97e-06) (7.17e-06) (5.16e-06) (5.09e-06) (5.30e-06) Treatment (0.0493) (0.0535) Interaction ( ) ( ) Controls No Yes No Yes Yes Yes Observations 2,358 2,358 2,358 2,358 2,358 2,358 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 The possibility that the effect of the duration of employment on satisfaction is discontinuous as opposed to continuous as we have assumed thus far may be the reason we failed to pick up the anticipated hump-shaped relationship as described at the onset. This is plausible as an individual s self-reported status in the first few months is likely to differ from the ensuing months. Consequently, we repeat our analysis using dummy variables which enables us to detect any structural changes in the relationship over time. As the ordered probit results in the preceding discussions of this sub-section were consistent with those of the OLS specifications both in terms of significance and magnitude, we consider the regressions of the OLS specifications only in table B4 in Appendix B. The benchmark category is individuals who have been in employment for months 27 ; their average level of satisfaction is given by the & Hunt, 1980 for a review of the literature). As with general life satisfaction, job satisfaction is measured on a 5- point likert-scale with 1 being Very Unhappy through to 5 being Very Happy. 27 These constitute approximately 5% of the employed population in the year

24 intercept term. With life satisfaction as the dependent variable, we see from columns (1) and (2) that unlike the continuous case in table 4; the downward trend is insignificant regardless of the inclusion of covariates. However, with job satisfaction as the dependent variable; the previously determined negative relation (albeit not smooth) persists, is significant but levels off with time. Using the results in column (3) to illustrate, it is evident that those who have been employed for less than six months report the highest average level of job satisfaction ( =3.314), those who have been employed for between 18 to 24 months report the second lowest average level of job satisfaction ( =3.157) whereas those in employment for between 24 to 36 months have an average level of job satisfaction no different to that of the benchmark category. As a robustness check, we redefined the dummy variables, for example to every three month period as opposed to every six month period, and found the relationship to be consistent. The inclusion of interactive terms in this specification was not insightful due to the various dummies representing the duration of employment. For rigour and to rule out the possibility that the hump-shaped relationship is obscured by those who have been in employment for a significant amount of time; we repeated the analysis above (not shown) but restricted it to individuals who were unemployed in the year 2010 but obtained employment in We found this not to be the case: the negative relationship persists but unlike the results in tables 4 and B2, is insignificant even with the inclusion of covariates or the restriction to the treatment group. This may indicate that there are other more important factors than the duration of employment for the newly employed. Unlike in table B4 with all the employed; for the discontinuous case, we made use of three-month dummies 28 which confirmed that the duration of employment is not an important factor even when one uses job satisfaction as the dependent variable. Thus far, our results do not indicate any strong evidence of a hump-shaped relationship as the inclusion of covariates rendered the quadratic and cubic terms irrelevant where such a relationship appeared to exist. Hence, we continue with our investigation of the other hypotheses. 28 This time round, the benchmark category is the months in employment. 24

25 5.3 Types of Jobs The third explanation for the lack of increase in self-reported life satisfaction is that individuals were unsatisfied with the jobs they obtained. This is a plausible explanation as wage subsidies often target jobs on the lower end of the spectrum. Warr (1999) classifies the job-related determinants of job satisfaction (and consequently life satification) into ten broad categories. These include: income, opportunity to use one s skill, supportive supervision, environmental clarity (including job security), physical security, personal control, job demands, variety, interpersonal contact, and a valued social position. 29 Table C1 in Appendix C gives a detailed overview of the job characteristic variables used in our analysis as well as how they link to Warr s (1999) theoretical framework. Table C2 makes a comparison between the job characteristics of the control and treatment groups in the year 2011 and could offer an explanation for the lack of increase in happiness should these characteristics be significantly worse for the latter group. From the table, it is clear that the only statistically significant job characteristic is that the control group has a higher rand pension contribution than the treatment group. It is plausible to assume that this is not sufficient to result in an offsetting effect to any increase in reported life satisfaction owing to the increase in employment. Nevertheless, the above result holds when each job characteristic is considered on its own and thus it is important to go a step further and investigate if job characteristics explain treatment. Accordingly, when we control for the other factors, we see in the first column of table C3 that the treated group has lower income, less pension yet more medical benefits than the control group and that these are highly significant and may offer an explanation for our observed phenomenon. Once again, it might be useful to see if the job characteristics of those already employed in the year 2010 differ from those who only obtained employment in 2011 (a greater proportion of whom were recipients of the wage subsidy). From table 7 below, it is clear that although the occupations are similar across both groups of employed individuals; significant differences in income, number of hours worked a week, promotions and fringe benefits exist which may explain the lack of increase in life satisfaction observed in section 5.1. The only significant difference in favour of the newly employed is that a greater proportion of them have relatives in 29 The first, a pecuniary factor; the rest, non-pecuniary 25