72599 THE NEWLY UNEMPLOYED AND THE UIF TAKE-UP RATE: IMPLICATIONS FOR THE WAGE SUBSIDY PROPOSAL IN SOUTH AFRICA Haroon Bhorat and David Tseng The World Bank Human Development Unit Africa Region The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank, its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. THE NEWLY UNEMPLOYED AND THE UIF TAKE-UP RATE: IMPLICATIONS FOR THE WAGE SUBSIDY PROPOSAL IN SOUTH AFRICA Haroon Bhorat and David Tseng1 August 2011 Abstract This paper investigates the take-up rate or claim-waiting period rate of the unemployed under the South African Unemployment Insurance Fund (UIF) system. The goal is to identify disincentive effects that income replacement rates (IRR) and accumulated credits may have on the claimant’s behaviour in terms of their claim waiti ng period rate (or how quickly they apply for UIF benefits).2 Utilizing nonparametric and semi-parametric estimation techniques, we find that there is little evidence, if any, for job disincentives or moral hazard problems. More specifically, the majority of claimants that are quickest to claim the UIF benefits are those who have worked continuously for at least four years and accumulated the maximum allowable amount of credits. We also note that claimants’ waiting periods are indifferent with regard to levels of income replacements yet extremely sensitive to the amount of credits accumulated. Ultimately, the recipients of the UIF benefits do not rely heavily on the replacement incomes and prefer waiting longer for employment opportunities as opposed to exhausting their accumulated credits. The semi-parametric Cox’s Proportional Hazard (PH) model confirms that there is a positive relationship between the claimant’s accumulation of credits and the associated take-up rate of the UIF. We use this detailed information then to analyse the extent to which the wage subsidy proposal of government, currently stalled in negotiations, can be manipulated and managed through the UIF instead of employers, as is currently the proposal . Acknowledgment: This report was financed by the World Bank’s - Spanish Trust Fund for Impact Evaluation and Results-based Management in Human Development Sectors 1 All comments and queries to haroon.bhorat@uct.ac.za 2 The claim-waiting period is defined as the time taken by workers who are out of job to apply for unemployment insurance at the labour centres. I. Introduction Unemployment Insurance (UI) is a financial compensation mechanism, offering qualified workers a subsistent income replacement in case of income loss due to unemployment shocks, and is prevalent in many countries around the world. It forms part of the wider spectrum of welfare policies, and operates by pooling the unemployment risk of employees. UI helps to smooth the consumption patterns of recipients and their dependents if they become unemployed. More importantly, the UI system’s goal is to improve the transition process of employees from unemployment to employment. Although UI programs are aimed at empowering employees to search for new jobs and provides them with protection against consumption shocks in case of job losses, the system imposes costs as well. By raising the reservation wages of unemployed, the UI system introduces the potential loss of worker’s willingness to work and increases wage pressures for the employers. Solutions to solve these disincentive effects involve up-scaling monitoring and disciplining efforts, as well as imposing more stringent requirements in order to qualify for benefits. However, solutions such as benefit sanctions and work criteria put even more cost pressures on both employees and employers. On occasion, positive measures to promote job-search and skills development like retraining and up-skilling programs for the unemployed have also been tried, thus attempting to prevent the possibility of moral hazard problems from occurring. In trying to determine the extent of moral hazard problems in the behavioural context, traditionally, researchers would focus on the duration of unemployment spells and the subsequent employment destinations after the spells. This paper, however, adopts a new approach, in that instead of examining the duration of claiming the UI benefits, it focuses on what we term, the claim-waiting period or take-up rate3. More specifically, the paper considers the take-up rate of the unemployed, or the time taken for the unemployed who are eligible for UIF benefits to claim these benefits. Put differently, the paper attempts to describe and understand the determinants of the question: the waiting period of people who are just out of work, prior to their formal application for unemployment benefits. This waiting period is the first critical stage of individual’s post-employment decisions as they choose whether to re-enter the job market or to stay on the insurance benefits. It contains the important information about the behaviour of the recently unemployed. By analysing the length of this period, we shed light on the extent of the moral hazard problem through behaviours such as sporadic employment episodes, low number of accumulated credits and so on. Since this paper is the first known attempt in South Africa tackling the behaviour of the unemployed with regard to UI, Section 2 provides a detailed literary and empirical review of the UI’s influence on the unemployed. In Section 3, we narrow the focus on the local Unemployment Insurance Fund (UIF) system and the new-claimants data, which forms the backbone of this study. Section 4 and 5 present detailed, descriptive, and econometric overview of the take-up rate of the claimants. In section 6, using the results provided in the previous sections, we make a policy proposition that by using the claim-waiting period as a decision rule, the wage subsidy proposal of National Treasury can be more effectively fed through the UIF 3 The terms ‘take-up’ rate and ‘claim-waiting period rate’ shall be used interchangeably throughout the paper, as both terms essentially are referring to the same transition period of the unemployed applying to claim UIF benefits (see Section IV later for detail). 1|Page system in order to achieve full administrative efficiency as well as retaining employment to curb rising unemployment. Section 7 concludes. II. Literature Overview The classical argument against the establishment of unemployment insurance is the resource argument. It argues that insurance benefits will raise the post-unemployment reservation wage (Burgess & Kingston, (1976), Hoelen (1977), and Barron & Mellow (1979)), thereby prolonging the duration of unemployment and deepening the level of structural unemployment in the economy. It has also been argued however that unemployment insurance has a positive impact on the decisions of the unemployed. For example, unemployment benefits could improve the transition process by shifting and smoothening the budget constraint of the individual, giving that agent more time and resources to look for better, future employment opportunities. In addition, workers who are eligible for unemployment insurance in case of unemployment have a stronger bargaining position, thus facilitating a more successful and optimal job-matching process. However, evidence to support the benefits of unemployment insurance is weak, and dependent on individual country’s labour markets and unemployment insurance policies. In turn, the literature identifies the moral hazard problem (through substitution) as the most important negative impact of the UI system. It potentially depresses job search intensity, has an impact on the quality of labour inputs, and may result in loss of human capital or skills. The moral hazard problem may also cause rising wage pressures for employers and an increase in voluntary unemployment (see Classen (1977), Blau & Robins (1986), Kiefer & Neumann (1985), and Addison & Blackburn (2000)). Policy-makers are thus challenged with creating an unemployment insurance system, which best deals with the financial constraint and moral hazard effects endemic to all UI systems, and ultimately find an optimal equilibrium between the labour market efficiency gains and the adverse incentive effects. Mortenson (1977) was the first to seriously model the impact of an unemployment insurance program on search and other outcomes of the unemployment. By utilizing the dynamic search model technique, Mortenson acknowledges that UI’s impact on the labour supply is theoretically ambiguous due to a wide spectrum of parameters in a UI scheme, which determines the individual’s eligibility and consequently, the person’s response to the scheme. This includes variations in replacement ratios, tax exemptions and the relative costs of unemployment on both the workers as well as their employers (Feldsten (1978) and Topel (1983)). In 1997, Hopenhayn & Nicolini designed an optimal unemployment insurance system by solving a repeated principal agent problem, involving risk-averse agents and a risk-neutral principal. They found that if principals have limited foresight on the agents’ search efforts, then the optimal long -term contract must consist of a replacement ratio, which decreases over period of unemployment. This is to ensure positive job-search incentives of employees. Hasen & Imrohoroglu (1992) and Acemoglu & Shimer (1998), by incorporating the element of risk-aversion into the tractable general equilibrium model of job search, show that an increase in employees’ risk-aversion reduces wages, unemployment and investment. Despite this, they also note that UI has a reverse effect generated by the moral hazard, as the insured workers become more risk-loving and susceptible to higher unemployment risks due to seeking high-wage jobs. Hence, given a market with risk-adverse participants, a moderate UI benefit program can not only reduce uncertainty of the claimants through risk sharing but also increase aggregate output. Holmlund (1997) investigates the nature of this market imperfection relative to the appropriate design of UI policies. He finds that if workers can self-insure through saving and borrowing, the case then for a (generous) public UI is not worth considering. Engen & Gruber (1995) finds that when 2|Page households are faced with higher levels of uncertainty in terms of income, they will begin to hold more assets than otherwise. Empirically, the evidence for either a resource or substitution effects is mixed: Ehrenberg & Oaxaca (1976) with much specification difficulties, find no significant unemployment spell duration impacts in their analysis of National Longitudinal Sample (NLS) in the United States. Moff & Nicholson in 1982, successfully found a significant, positive correlation between the length of the unemployment spell and the amount of the UI benefits by using a job search model, and conceded that measurement error and specification problems are significant in altering the results of this analysis. Cross-country regressions, like those of Layard, Nickell and Jackman (1991), also found that amount of benefits has a strong, positive effect on long-term averages of national unemployment rates. In their treatment of different benefit breakdowns, they also found that the level of spending relative to GDP does not reflect the true picture of the benefit received by the individual recipient. Put differently, depending on an individual country’s population size, one may have a high-spending ratio but not a generous social security program - alluding to the fact that the true impact of any UI system depends on the climate of the labour market as well as the design of the UI program. Concerning the income smoothening effect of UI systems, Gruber (1994) in a panel study finds a small but significant role for UI in consumption smoothing during periods of joblessness. He found that poor in general are less capable of smoothening transitory income shocks relative to permanent income, as they have extremely low and limited savings. As a result, they exhibit excess sensitivity of consumption towards cash-on-hand. Gruber then studied the individual behaviour during the weeks before benefits lapse and found that the probability of leaving unemployment rises dramatically just before the expiration of the benefits. In other words, employees are more sensitive to claiming rights than to benefit amounts. In the difference-in- difference study of the same year, he found that when employees’ rights of claiming the UI are extended, the probability of an unemployment spell ending becomes substantially higher. This suggests that overly generous UI systems could have a serious moral hazard cost attached to it, in subsidizing unproductive leisure and creating job disincentives. Chetty (2008) confirms this finding in a later study. In developing countries, Cunningham (1997) examines the impact of Brazil’s new unemployment insurance program on job transitions. The results suggest that the probability of workers remaining in the formal sector does not significantly increase with their eligibility for benefits. Using the Danish micro data, Lentz (2007) successfully developed a U-shaped relationship between unemployment duration and the income level of the worker and proved that the curvature of the utility of individual’s consumption functions (i.e. risk-aversion) is crucial in determining which effect dominates the outcome. Van Ours & Vodopivec (2006) in a difference-in-difference investigation; find that shortening the duration of benefits does not affect the quality of post-unemployment job under the Slovenian Insurance Scheme. Nor were there any changes in wage levels before or after the system reform. Krueger & Mueller (2009) note that workers who expect to be recalled by their former employers have considerably less incentive to search for a job than the average unemployed workers do. They also find that job search is inversely related to the level of generosity prescribed in terms of the unemployment benefits. 3|Page III. A Brief Overview of the Unemployment Insurance System in South Africa The Unemployment Insurance Fund (UIF) is an integral part of the South African welfare system, and is designed to serve as a safety net for workers in the formal, private sector in South Africa. South Africa has one of the highest unemployment rates in the world, standing at roughly 23% in 2010, thereby strengthening the case for implementing an unemployment insurance system in South Africa in 2002. . According to the Unemployment Insurance Act of 2002, all employers and employees are required to contribute on a monthly basis to the risk-sharing fund. In exchange, the contributor or the dependent (in case of a contributor’s death) earns a weekly credit, which entitles them to claim unemployment insurance benefits. In addition, the reason for claiming the UIF must be involuntary, and may include illness, maternity and so on. Voluntary unemployment due to resignation and disciplinary dismissals disqualify employees from claiming UIF benefits. When compared to other unemployment insurance systems around the world, the UIF system in South Africa is arguably fairly stringent and does not provide generous benefits. Firstly, the system provides benefits exclusively to workers who have worked for no less than 24 hours per month. The eligibility of benefits is determined by the time worked by employees – employees receive one credit (day) for every six days on the job, and the accumulated credits may not exceed 238 days. Put differently, an employee who has been continuously working for more than four years is still limited to only roughly one-year’s amount of credits for replacement benefits. Secondly, the ‘raw’ income replacement rate (IRR) is relatively low compared to international estimates4. It ranges from 38-60% in a convex fashion, and is inversely related to the contributor’s income level. Furthermore, the benefit level is invariant to the duration of the unemployment spell. As a welfare system, the UIF system is unique in comparison to other social welfare systems in South Africa in that it operates without any government subsidies (National Treasury, Budget Review 2011). This is partly due to the stringent requirements and restrictions of the UIF system, which has ensured that it is purely contributer-funded and has adequate cash reserves. In the latest fiscal year ending 31st March 2010, the fund paid out R4 536 million in benefits with 628 595 approved claims. At the end of March 2010, there were about 4.2 million unemployed individuals in South Africa (QLFS 2010 1st Quarter, StatSA), meaning that in the financial year ending March 2010, less than 15% of the unemployed received unemployment benefits. Some may argue that this may be the result of factors such as a large informal sector, and the lack of administrative capacity, as in most developing countries. However, unlike other developing nations, South Africa has a small, informal sector, and in addition, the UIF was able to approve nearly 97% (779 604 out of 801 110) of all new claims in the latest financial year. The low number of claims then is readily attributed to the fact that the majority of the unemployed have a long history of unemployment or no prior employment history. Thus, individuals who have never worked before or have not worked for a long time (and exhausted their claiming credits) – on average constituting 85% of the unemployed in South Africa would not qualify for UI benefits. Progressivity in the Income Replacement Rates 4 IRR in Slovenia is 80% and 65% in Czech Republic etc. These raw rates exclude any specific conditions of the claiming period. 4|Page As noted above, the manner in which the UIF determines income replacement rates is rather unique compared to unemployment insurance systems in other economies around the world. In other countries such as Slovenia and Chile, income replacement rates are generally designed so that they are variant to the duration of unemployment, but proportional to income (Vodopivec (2008)). These income replacement systems are thus intentionally designed to promote incentives for workers to return to productive employment, as well as to prevent workers from becoming reliant on the insurance benefits, thus hindering job search. The IRR in the South African case however is determined in the opposite manner: It is progressive in income and invariant to the duration of unemployment spell. In addition, while the claim period in many countries is set to a specific period, in South Africa the claim period is determined by the number of credit-days earned through prior productive employment. Figure 1: UIF Income Replacement rate by Deductible Incomes Income Replacement Rate Unemployment Insurance Fund - South Africa .55 .5 .45 .4 0 5000 10000 15000 Income above contribution ceiling. Source: Unemployment Insurance Fund Act 2002 and own Calculation Figure 1 above clearly shows that there is an inverse relationship between the IRR and income in South Africa, thus ensuring that the replacement rates are progressive, that is, the IRRs for those with higher incomes is lower than for those with lower incomes. Unemployed persons can claim at the calculated IRR rate for up to a maximum of 283 credits (or approximately 14 months), depending on the number of days worked and thus the number of credits they have accumulated. For example, an employee who has continuously worked for more than 4 years, earning about R10 000 per month will be eligible for 38% constant replacement rate for as long as 14 months. In other words, a claimant will earn the same benefit for the period of eligibility, with the only limiting factor being the time the claimant can claim for, and this is dependent on the number of credits accumulated by time worked prior to unemployment. In contrast, unemployed individuals in many developing countries can claim income replacement benefits for a pre-determined period of time (which does not differ across unemployed individuals) at a much higher income replacement rate. In these countries, after some time, the IRR drops to create incentives for the unemployed to search of a job quickly. The UIF system in South Africa thus, in its calculation of the IRR, seems to be devoid of efforts to create incentives for workers to search for work, though for example, instead offering a lower IRR as the number of days of benefits progresses. 5|Page These distinctions between South Africa and other countries’ unemployment insurance systems are fundamental in understanding the true impact the UIF has on the behaviour of the unemployed as they transit unemployment to other employment destinations. For one, it is clear that UIF has no influence over the job-search behaviour of the new entrants to the labour market, which are also the leading cause and concern for South Africa’s persistently high and rising levels of unemployment. Secondly, the UIF’s IRR is progressive with regard to income, so ensuring that the system provides (relatively) more support to more vulnerable workers. IV. A Descriptive Overview of New Claimants Data Description and Methodological Approach The dataset used in this paper includes information on all new claimants between April and August 2009. During this period, there were a total of 348 311 new UIF claims, 80% (or 275 586) of which were related to unemployment specific benefits. We dwell mainly on the 275 586 unemployment-related insurance claims for the remainder of the analysis. Whilst there can be no doubt that this data period is very short, we would argue that the data and the subsequent analysis remains immensely useful for three reasons. Firstly, this is the first study, since 1994, on the raw micro-data of the UIF and as such even a basic overview of claimants is useful. Secondly, there is no reason to believe that a longer time series would necessarily negate or devalue the results found here. Finally, we would argue that data and analysis on the IRR, claimants’ characteristics and of course the claim-waiting period - is immensely useful in and of itself. For new UI claimants, the dataset contains personal information, as well as two crucial date variables that form the core of the claim waiting period analysis in Section 4. These variables are the termination date of employment and the application date for UIF benefits. The termination date variable records the date on which claimants were terminated from employment ( ), while the application date variable notes the date on which a claimant applied to claim UIF benefits ( ) for instance, at a labour centre. Essentially, one could think of these dates as points where individuals transit from one state to the next: The termination date indicates the point at which an individual transits from state of employment to unemployment (without UIF benefits). The application date is the point at which the unemployed individual transits from the state of being unemployed without UIF benefits to finally applying for the benefits. We define this period – the time between the termination date and the application date – as the claim-waiting period ( ), measured in units of days where: ( ) Using this claim-waiting period ( ) as our variable of interest, we are able to analyse the behaviour of the recently unemployed. Essentially, we are looking at how employees respond to the period during which they do not receive benefits while unemployed. If we rank the claim- waiting period ( ) by time and take the proportions, we can determine the take-up rate of unemployment insurance over the period. The data from the UIF, which we illustrate below will indicate a heterogeneity in take-up rates or claim waiting periods amongst those individuals who lose their jobs, and are registered with the UIF. This heterogeneity, in one respect is reflective of differing observable and unobservable individual characteristics. In particular, though we would expect for example on average that household wealth, skill levels relative to labour demand needs and savings to vary positively with the claim-waiting period. Individuals who have savings, are better skilled or indeed have higher household wealth (inclusive of secondary wage earners 6|Page within the household) – should be more likely to wait longer before registering their unemployment status with the UIF, in order to claim benefits. Hence, one can think of the claim-waiting period, as being determined jointly by the following: ( ) ( ) Where apart from individual characteristics , take-up rates should vary positively with household wealth , skills and Savings (so ; ). We note that according to the UIF Act, employees must make claims within six months after they have stopped working. We therefore, expect a convergence in the take-up rate of the UIF since all claimants are required to apply for UIF at or before six months. This convergence in the take-up rate of the UIF has a profound impact on our semi-parametric estimates of the covariates, which will be discussed in further detail in the later section, but it does mean that the take-up rate is for all applicants. New UIF Claims by Individual Characteristics Table 1 below presents a basic descriptive overview of new claims by gender and age group during the period April to August 2009. During this period a total of 275 586 new claims were made, with the average growth rate in new claims for the period standing at 34 percent. The results thus suggest that the recession in South Africa in 2009 may have had a significant impact on the number of people accessing unemployment insurance benefits between April and August 2009. Males made almost double the number of new claims compared to females. Thus, almost 66% of the new claims made were by males claimants compared with 34% for females (a ratio of 2:1). Comparing this ratio to male:female employment ratios in the Quarterly Labour Force Survey (QLFS) 2009:Q1 (7:6) it is clear that new UI claimants were disproportionally male. Considering the average growth rate of new claims, we find that growth in female claimants (42%) outstripped growth in male claimants (30%), in the period. Thus, while new claimants in the period were predominantly male, the growth in claims by females was higher than for males. Importantly though, the number of new male and female claimants rose significantly from 26 thousand and 13 thousand in April 2009 to 34 thousand and 19 thousand in August 2009. It is clear then that the recession had a significant impact on both the number of males and females accessing unemployment insurance in the period. Table 1: Number of New Claimants by Gender and Age Cohort: April – August 2009 % of total Cumulative % change April May Jun Jul Aug new Total Apr-Aug claims Gender Female 13 267 18 882 19 419 22 807 18 899 93 274 42.45% 33.86% Male 26 450 37 327 38 422 45 686 34 427 182 312 30.16% 66.14% Age Cohort 15 to 24 3 752 5 189 5 274 6 471 5 388 26 074 43.60% 9.47% 25 to 34 14 229 19 858 20 104 24 492 18 804 97 487 32.15% 35.39% 35 to 44 10 294 14 679 15 214 17 757 13 860 71 804 34.64% 26.05% 45 to 54 6 561 9 818 10 259 11 658 9 331 47 627 42.22% 17.25% 55 to 65 4 881 6 665 6 990 8 115 5 943 32 594 21.76% 11.84% 7|Page Total 39 717 56 209 57 841 68 493 53 326 275 586 34.26% 100.00% Source: Unemployment Insurance Fund 2009 By age, individuals between the age of 25 and 34 experienced the highest number of total new claims (97 487) followed by those aged 35 to 44 (71 804) and 45 to 54 (47 627). This result could suggest that younger workers (25 to 34) were more likely to lose their jobs during the economic downturn. Older workers with more experience are generally viewed as more productive than younger workers are and were therefore possibly less likely to be dismissed. These new-claim results by age-cohort are broadly consistent with QLFS data, which also shows a marked increase in youth unemployment rates during the recession, relative to older age cohorts. Table 2: Number of New Claimants by Termination Reason: April – August 2009 % change Share of total Reason April May Jun Jul Aug Total Apr-Aug new claims Bus. Close 1 075 1 550 2 069 2 471 1 776 8 941 65.21% 3.20% Cont.expired 15 513 21 694 22 025 26 235 20 910 106 377 34.79% 38.65% Dismissed 8 907 12 084 12 243 15 326 12 017 60 577 34.92% 22.00% Insolvency 741 2 464 2 083 1 562 1 097 7 947 48.04% 2.84% Retrenched 11 167 15 407 16 070 19 209 15 009 76 862 34.40% 27.90% Other 2312 3009 3346 3690 2517 14 874 8.87% 5.41% Total 39 715 56 208 57 836 68 493 53 326 275 578 34.30% 100.00% Source: Unemployment Insurance Fund 2009 Table 2 above shows that during the five month period between April and August 2009, the UIF processed 275 586 new claims that were specifically for unemployment benefits. 39% of these total new unemployment-related claims (106 377) were due to expired contracts, followed by retrenchments at 27.89% (76,862 new claims) and dismissals at 21.98% (60,577 new claims). The growth rates in new claims were however dominated by business closures and insolvencies. Unsurprisingly then, during the height of the financial crisis, business closures and insolvencies as reasons for claiming unemployment benefits had one of the highest average growth rates in number of new claims, albeit from exceptionally low bases. From the above table, the evidence points to the fact that, in the main, the typical unemployment insurance claimant over this period under review was a young, male worker with a high probability of possessing either incomplete schooling or minimal FET training. In addition, the results show that 6.5 out of 10 all new claimants in this period had either been retrenched or their contract had ended. V. Claim Waiting Periods and the Unemployed Noting that we consider the claim-waiting period , to be measured as the difference in the between when the jobs is lost ( ) and the arrival at the UIF office ( ), we provide below an overview of some of the unemployed individuals characteristics and their variance across . The Claim Waiting Period ( ) by Gender, Age and Replacement Rate Every employee in the country who recently became unemployed and wants to claim UIF benefits must go through the process of being assessed by a UIF claims officer, to ensure eligibility of the employee for receipt of insurance benefits. Voluntary resignation and dismissal due to disciplinary punishments disqualify employees from claiming the UIF. In this section, a shorter claim-waiting period means that the claimant is desperately in need of subsistent income 8|Page relief in order to smooth their consumptions schedules, although this will vary across individuals. The latter variance is unobservable in our data. Table 3: Mean Claim-Waiting Period by Gender and Age Group Female Male Share of female new Share of male new Waiting period Waiting period claims claims 15-24 43.2 17.70% 38 10.40% 25-34 59.3 31.40% 41.3 38.90% 35-44 27.5 22.90% 25.4 24.30% 45-54 36.8 9.30% 25.7 12.50% 55-65 27.2 18.70% 29.5 13.90% Total 41.1 100.00% 33.6 100.00% Source: Unemployment Insurance Fund 2009 Note: Waiting period measured in days Table 3 above presents the average claim-waiting period by gender and age. The first striking feature is that male claimants in general have a shorter waiting period than females before they claim, and this is true across all age groups. This is perhaps due to the fact that in many households males are the primary income earners, thus forcing them to apply for insurance benefits earlier in order to help them supplement their income and swiftly re-enter the labour market. From the results above, one could say that the claimants take on average, just more than a month before claiming the UI benefits. The implication is that UI claimants in South Africa are on average able to supplement their lost income for a month, from the time of unemployment to applying for unemployment benefits. This fact, as we illustrate below could be an important decision rule when considering a wage subsidy for the unemployed. Interestingly, the waiting period for claiming UI benefits seems to decline from young to older age groups; while claimants between the ages of 25 and 34 have the longest waiting period of around 60 days, seniors between the ages of 55 and 65, have the shortest waiting periods (just 27 days for females and 30 days for males). A likely explanation for shorter waiting periods among the oldest age cohort is the fact that these individuals are most likely preparing for their retirements. These results thus suggest that younger workers (between 15 and 34 years of age) may either be supplementing their income in some way, or may have other reasons for taking longer than older workers to claim UIF benefits. It is possible, for instance, that younger workers may be more driven than older workers to find a job and may thus immediately attempt to re-enter the labour market. If this fails, and they are unable to find another job, they may then only apply for UIF benefits. Whatever the reason for the time taken to apply for UIF benefits, these results suggest that there is little sign of credible moral hazard problems in so far as younger age groups are concerned, since these workers take much longer than a month before making use of the UI system to supplement their income once they have lost their jobs. Table 4 below presents the length of employees’ claim waiting periods by replacement (of benefit) amounts. The benefit amounts are disaggregated into quartiles while the accumulated credits are sorted into years employees worked. Almost 61% of claimants in the fourth quartile of benefits (high-income bracket of approximately more than R12 000 per month) have accumulated near maximum claiming-credits or have worked for more than four years. In contrast, claimants in the first quartile (lowest benefit amounts) have, for the most part, worked 9|Page for short periods and, are therefore claiming with few benefit credits. This result no surprisingly suggests that high-income workers have better employment security than low-income workers. In terms of , low-income earners (1st quartile benefit amount) with a long employment history (worked for more than four years) wait for 48 days - or more than double the length of time that high-income earners (4th quartile benefit amounts) - with an equally long employment history wait (21 days) before claiming benefits. This then suggests that low-income earners (with associated low benefit amounts) are not incentivized to claim UI benefits more quickly than high-income earners, despite the fact that the IRR is progressive in income. It appears then that the benefit amount of R739.77 per month could be simply too low to incentivize the more vulnerable amongst the recently unemployed to quickly apply for benefits. This seems to be true particularly of those with more than four years of work history and those with less than one year of work history. Table 4a: Claim Waiting Period by Replacement amount and Employment spell quartiles Benefit or Replacement Amounts per credit by Quartile Normalised (R 739.77) 1st 2nd (R 1094.76) 3rd (R 1649.46) 4th (R 3209.05) gap in Length of Employment %total %total %total %total waiting- Waiting Waiting Waiting Waiting spell new new new new period period period period period claims claims claims claims < 1 year 43 6.80% 37 9.30% 58 6.20% 42 2.30% -0.02 1 - 2 years 33 6.90% 48 6.50% 40 2.80% 42 1.50% 0.21 2 – 3 years 28 3.10% 25 3.20% 27 3.60% 54 2.80% 0.48 3 – 4 years 23 2.40% 38 1.40% 40 5.90% 27 2.90% 0.15 > 4 years 48 6.30% 24 4.10% 33 6.80% 21 15.20% -1.29 Note: waiting period measured in days. Source: Unemployment Insurance Fund 2009 Table 5b: Gap in Claim-waiting Period by Replacement Rate and Employment Spell Gap in Claim-Waiting Period Unemployment Insurance Fund - South Africa .5 0 -.5 -1 -1.5 0 1 2 3 4 5 Length of Employment Spell Source: Unemployment Insurance Fund Act 2002 and own Calculation Note: waiting period measured in days. Source: Unemployment Insurance Fund 2009 10 | P a g e For high-income earners, on the other hand, the replacement amount of R3209.05 seems to incentivize employees, particularly those with longer work histories, to claim UI benefits: Claimants with more than four years of work history prior to unemployment claim within just three weeks or 21 days, while workers with three to four years of work history claim within 27 days. Looking more closely at those with less than two years of work history across all the quartiles of benefit amounts shows that these individuals generally claim in more than five weeks, with those in the third quartile with less than one year of work history waiting for an average of eight weeks prior to claiming unemployment benefits. This suggests that the amount of credits accumulated is important in determining how long unemployed people wait prior to claiming UIF benefits. In turn, those with longer work histories (2 years or more), in general, tend to apply for unemployment benefits more quickly. The exception to this is those with more than four years of work history in the lowest quartile of benefits. For this cohort, the benefit amount itself may be just too low to incentivize this cohort to apply more quickly. In summary, the results suggest that the time taken to apply for benefits is dependent on both the amount of credits accumulated as well as the benefit amounts. In particular, although IRRs are progressive in income, those in the lowest quartile of benefits do not apply for benefits more quickly – benefit amounts appear to be just too low for those in the lowest quartiles. On the other hand, high-income employees with short employment episodes are also apparently incentivized to claim UIF benefits quickly. Once high-income employees accumulate sufficient credits though, they resort to claiming UIF in the shortest timeframe. The Claim Waiting Period and Unemployment History The basic approach to nonparametric analysis is estimating the shape of the survival function, or for the purpose of this paper, the escape function by using the Kaplan-Meier survival estimate. Essentially, we are estimating the probabilistic function of remaining unemployed and not applying for the UI benefits at time t. It is worth noting that in the conventional Survival model, the escape function here is referred to as the survival function: S(t) = 1- ∫h(t)∂t, or if in discrete time: St = Π(1-ht) for all t from starting time until the time of transition. However, the same logic can be applied to analyse the period between and , except for the hazard rate (ht) becomes the take-up rate ( t), and the Survival function (St) becomes the escape function (Et). Basically, the conventional terminologies used for Survival analysis: the ‘survival’ and the ‘hazard rate’ functions on either employment or unemployment spells are substituted by ‘escape’ and ‘take -up rate’ functions respectively, to reflect that it is the precisely the time taken by the recently unemployed to apply for UI benefits that we are interested in. The estimates presented here are separated into male and female groups as well as by sub- groups. As mentioned earlier, due to the fact that the unemployed must claim benefits within six- months of becoming unemployed to ensure that their entitlements to UI do not lapse, the survival rate for claimants will tend to converge at or before roughly 180 days. Figure A1 attached in the appendix shows that female claimants with a history of claiming UI benefits have a significantly lower rate of failure than females without. Male claimants, on the other hand, interestingly show the opposite result: Males with a history of claiming UI have a higher rate of failure than males without a history. The gap between the survival functions among females with and without a history is also noticeably wider than is between the male claimants; although males in general have a higher rate of failure than females. The distinctive difference between the gaps suggests that claimants by gender have decidedly different claiming-rates given a prior history of claiming from the UIF. We speculate once again that this may be due to mal es’ responsibilities as primary income earners in traditional households. However, with limited information on 11 | P a g e claimants’ household dependents and other household characteristics, we cannot confirm this hypothesis. In terms of results by location of claimants, Figure A2 in the Appendix shows the survival functions of claimants in metropolitan areas versus claimants in non-metropolitan areas. For both males and females, the survival functions for claimants in metropolitan and non- metropolitan areas are quite similar in (roughly) the one-month period following unemployment. After a period of roughly one month however, claimants in non-metropolitan areas have a higher take-up rate than claimants in metropolitan areas. This may be due to the fact that claimants in non-metropolitan areas find it harder to supplement incomes compared to those in metropolitan areas. An alternative explanation may be that potential claimants in metropolitan areas feel that there is a greater likelihood of finding jobs compared to their counterparts in non-metropolitan or rural areas, with the result that they are less anxious to seek UI benefits. By gender, the survival functions show that the gap between claimants in urban and rural areas is wider for females than for males. The Log-rank test (attached in the appendix) suggests that there is significant spatial difference between waiting periods for both males and females. The Claim Waiting Period and Benefit Values From the data analysis earlier, we identified two main hypothetical sources of incentives that may lead to moral hazard problems. The first source of moral hazard is the benefit amount of the UI claim. The intuition is that an excessively generous benefit amounts may incentivize claimants to become reliant on UIF benefits, and therefore render them less willing to find work. One would observe this moral hazard problem if the differences in survival rates across quartiles of benefit amounts is significant, and more specifically, if higher benefit amounts are associated with lower survival rates. This would then suggest that higher benefit amounts create disincentives for claimants in terms of how long they remain in unemployment. The second potential source of moral hazard is the amount of days claimants can claim UI for. In cases where claimants have a large number of credit days, claimants may be incentivized to remain in unemployment and exhaust their credits, where possible. In turn, those with few accumulated credits would perhaps have more sporadic employment episodes and low-survival rates, since they cannot rely on UI benefits for long periods of time. In summary, we are interested in analysing survival rates keeping in mind incentive effects associated with benefit amounts and credit days. Indeed, the ideal design for the UI policy is to have as little influence as possible on people’s claiming behaviour while providing a cushion to the unemployed so they can supplement incomes and search for employment. In terms of our analysis, we would therefore like to see survival functions by sub-groups (benefit amounts and credit days) that are to one another. Figure 2 below presents the survival functions by quartiles of replacement (or benefit) amounts. Firstly, it appears from the graph that for the male cohort there are no significant differences in the rates of failure of male claimants based on their benefit quartiles. . More specifically, the survival functions for males by benefit quartiles are not distinctly separate and parallel to each other (particularly prior to roughly 35 days), thus suggesting that benefit amounts are relatively insignificant in determining the take-up rate of claimants applying for UI benefits. Female claimants, on the other hand, have more differentiated survival functions with regard to benefit amounts. Interestingly though, female claimants in the lowest quartile of benefit amounts do not have the highest failure rate, suggesting that there is little indication of a moral hazard problem. Finally, as mentioned above, due to the six-month period for eligibility of claiming benefits post-employment, the survival functions converge at roughly 184 days. 12 | P a g e Figure 2: Claim-Waiting Period ( ) Estimates by Benefit Amount female male 1.00 1.00 0.75 0.75 0.50 0.50 0.25 0.25 0.00 0.00 0 50 100 150 200 0 50 100 150 200 analysis time analysis time benefit quartile 1 quartile 2 benefit quartile 1 quartile 2 quartile 3 quartile 4 quartile 3 quartile 4 Source: Unemployment Insurance Fund New Claimants April-August 2009 Figure 3 below presents survival estimates by accumulated credits. We expect that the group of claimants that are most likely and quickest to claim UIF benefits are those with the largest accumulated credit days. The data bears this out – both female and male claimants in the fourth quartile of accumulated credit days all claim within one month or two months at the maximum. Put differently, those who have been working for four years or more are quickest to claim UIF benefits. Figure 3: Claim-waiting Period ( ) Estimates by Accumulated Credit female male 1.00 1.00 0.75 0.75 0.50 0.50 0.25 0.25 0.00 0.00 0 50 100 150 200 0 50 100 150 200 analysis time analysis time credit quartile 1 quratile 2 credit quartile 1 quartile 2 quartile 3 quartile 4 quartile 3 quartile 4 Source: Unemployment Insurance Fund New Claimants April-August 2009 In summary, there is therefore no evidence of moral hazard problems as far as benefits amounts are concerned, but those with larger ‘stocks’ of accumulated credit days claim UI much more quickly than those with very few accumulated days. This latter result then suggests that it is much more worthwhile for those who have become unemployed after a long employment spell to claim UI quickly rather than those with short employment spells. This results may, for instance, 13 | P a g e suggest that those with long employment spells may have larger and more long-term financial commitments and are therefore driven to seek a cushion to their unemployment much more quickly. Determinants of the Take-Up Rate: A Multivariate Analysis The model we use to analyse the probability of claiming UIF is Cox’s proportional hazard (PH) model. It is a maximum partial-likelihood model, which means that no assumption is needed for the nature or shape of the hazard function. In essence, Cox's regression model may be considered a nonparametric or semi-parametric method. While no assumptions are required for the shape of the underlying hazard function, the model does require two properties. First, the Cox’s model assumes a time-only base model and secondly, it predicts a multiplicative log-linear, functional relationship between the underlying hazard function and the covariates. These assumptions together are also referred to as the “proportionality assumption�. Again, it must be noted that we measure here the relationship between and as a representative of the “escape rate�, although this is of course not a standard approach in the unemployment insurance literature. It assumes that the hazard rate is consistent throughout time with the given covariates. Put differently, it assumes that the gap between hazard functions is solely the attributes of the variations of the covariates. The severity of violation of this assumption has a direct relationship with the biases of the parameter estimates. As discussed earlier during the overview of the data, there is a serious concern about the violation of this fundamental assumption, as the legislature requires that all claimants apply for a claim within six months before the entitlement lapses. As a result, regardless of covariate attributes, the survival functions will converge at or before the analysis time of 184 days (approximately 3 months). Hence, while there is no concern for right- censored data in the sample (due to the certain failure within six months), there is the violation of key assumption of proportionality. This means that the parameter estimates will be free of censored data bias, but vulnerable to proportionality bias. The estimates will only be applicable within the analysis time of six months and statistically unreliable as well as legally meaningless after this time period. Ultimately, the peril of this proportionality assumption is that the effects of the covariates are over-estimated and exaggerated over time. Despite the violation of the proportionality assumption, Cox’s proportional hazard model is still preferred to the normal, logistic regression for two key reasons. Firstly, we would like to measure the probability of claiming, and not the variance between variables. Secondly, the distribution of the duration data is fundamentally different from that assumed for logistic regressions. Econometric Approach and Results The central question is: What drives the unemployed apply for UIF benefits at different rates? In other words, what affects the probability of applying for UIF benefits at time t (the claim waiting period) conditional on a system of covariates x and unobserved characteristics uj? Cox’s proportional hazard model can be formally written as: ( ) ( ) (( ) ) where x contains the system of covariates used to describe the survival function θ or the resultant hazard, B is the vector of parameters associating the system of covariates and the probability of claiming function or the take-up rate. Ω represents the claim-waiting period dependence function in terms of time. Put simply, Cox’s Proportional Hazard model is assuming 14 | P a g e a time-only base model Ω(t) called the baseline hazard on which the function of covariates builds in order to estimate the probability of claiming ( ). The baseline hazard or the probability of claiming the UIF is the probability for the respective individual to claim the UIF when all independent variable values are equal to zero. The base line take-up function represents the effect of the time variable alone. When all covariates are equal to zero, the hazard or the probability ratio tells the odds of an event occurring faster or slower given some covariates. The standardized parameter coefficients of the covariates are a measure of the relative importance of the covariate to the take-up function, while controlling for time. In other words, it is measuring relative risk and not absolute risk. To identify the behavioural patterns of individuals during post-employment, but prior to applying for UIF benefits, we set the event of “failure� in the study as the instant at which claimants arrive at the nearest local labour centres and apply for UI benefits. In other words, our dependent variable is the probability of the unemployed resorting to claiming the UIF. The parameters are estimated separately for male and female claimants, presented in the Table 5 below. If the covariate’s coefficient is negative, it means that the higher the covariate, the lower the probability of claiming (or the lower the take-up rate). In turn, a positive coefficient means that the greater the covariate, the greater the claiming probability (or higher the take-up rate). Given the legally inferred six month maximum claim-waiting period, we model the hazard function for those six months when individuals have been unemployed and living without unemployment benefits, and when they eventually apply to claim UIF. We may expect that employees with high wages and, therefore, high benefit amounts have higher hazard rates than employees with low wages and therefore low -benefit amounts. In the main, however, given our descriptive data above, we do not expect the benefit amount to exert a significant influence on claimant in terms of seeking UI benefits. We anticipate claimants to accumulate claiming credits, as opposed to exhausting it. In other words, most claimants would rather work than being unemployed, suggesting that the UIF system is devoid of moral hazard problems. Table 5 below presents the parametric results using Cox’s proportional hazard model with time- dependent covariates. The time-dependent covariates are all significant suggesting that there is an unmistakable violation of the proportionality assumption required for duration analysis. Due to this violation, we will not reflect on the quantitative values of the coefficients as they are biased. However, we will interpret the signs for the variables that can help describe the take-up rate of the unemployed. The covariates in the model consist of the following continuous variables: claimants’ age; replacement values per credits and available credits to claim. The model also contains the following discrete or dummy variables: a history of claiming the UI; reasons for claiming the UI; location dummy for whether claimant resides in a metropolitan area, and finally the education variable for if the claimant is a high school certificate holder or a formal degree holder. Results in Table 5 indicate that female claimants have higher claim waiting period or take-up rate period as their age goes up, controlling for all other covariates. The male claimants, on the other hand, are exactly the opposite: The take-up rate decreases as their age increases, ceteris paribus. Put differently, older male claimants are more reluctant to claim than older females, possibly indicating differences in family responsibilities between the sexes. It is unfortunate that, in this data set, there is no information about claimants’ household characteristics to explain the claimant’s response time to claim. 15 | P a g e Table 6: Male and Female Take-up Rate Equations Female Male VARIABLES5 F1 F2 F3 F4 M1 M2 M3 M4 Benefit amount -0.000*** -0.000*** -0.000*** 0.000*** 0.000*** 0.000*** Available credits -0.002*** 0.003*** 0.001*** 0.005*** 0.005*** 0.005*** Claim history -0.862*** -0.585*** -0.813*** -0.819*** 1.601*** 1.423*** 1.126*** 1.608*** Dismissal -0.548*** -0.519*** -0.419*** -0.008 0.048 -0.001 Contract Expired 0.268*** 0.033* 0.387*** 0.647*** 0.672*** 0.644*** Dismissed 0.476*** 0.433*** 0.687*** 0.444*** 0.626*** 0.456*** Insolvency 0.594*** 0.570*** 0.837*** 0.740*** 0.975*** 0.751*** Retired 0.982*** 0.963*** 1.394*** 0.907*** 1.094*** 0.873*** Staff Reduction 1.488*** 1.419*** 1.774*** 1.433*** 1.717*** 1.451*** Severance Package 1.655*** 1.635*** 2.009*** 1.292*** 1.645*** 1.308*** Urban 0.715*** 0.764*** 0.696*** 0.495*** 0.497*** 0.493*** Certificate -0.636*** -0.582*** -0.606*** -0.221*** -0.310*** -0.213*** Degree -1.210*** -1.001*** -0.952*** -0.694*** -0.916*** -0.678*** Age 0.008*** -0.008*** 0.013*** 0.003*** -0.015*** -0.015*** -0.009*** -0.023*** Age*benefit amount 0.000*** 0.000*** 0.000*** 0.000*** Age*avail. credits -0.000*** -0.000*** 0.000*** -0.000* Observations 87,691 87,691 87,691 87,691 189,498 189,498 189,504 189,498 Note: Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Source: Unemployment Insurance Fund 2009 The covariate benefit value suggests that there is a significant difference, but miniscule amount of impact of the benefit amount on the take-up rate of the UIF for both female and male claimants. This suggests that the incentive effect created by the replacement amount on the claimant’s take-up rates, if any, is negligible. The negative coefficient (for females) suggests that the higher the benefit amount, as well as its functional independent variable (income), the less likelihood of claiming early in the period. Therefore, the evidence suggests that the UIF is not an overly generous benefit system, and does not increase individuals’ claiming-rates, but rather depresses it for those with high incomes. Put simply, when controlling for a range of factors, agents with higher wages would prefer work to claiming benefits. In relatives to the impact of available credits on , the coefficient is positive for both female and males, suggesting that the take-up rate is higher as workers’ accumulated credits increase, ceteris paribus. In other words, people tend to claim only when they have accumulated enough credits, as workers try to avoid sporadic employment episodes, and they would rather remain in employment than being out of jobs. These findings together are an early suggestion that there may be no apparent sign of moral hazard in people’s behaviour towards claiming the UIF as far as the two main sources of work disincentive is concerned: the claiming credit and replacement amounts. The dummy variables (history and city variables) confirm the nonparametric estimates earlier. The coefficient for the dummy variable ‘history’ is negative for female and positive for male. In other words, female claimants with a history of claiming UI benefit before have a greater waiting period and lower probability of claiming than females without a claim history. Male claimants, on the other hand, are the opposite: Male claimants with a history of claiming the UI benefit have a higher likelihood of claiming than males that did not have histories. The city dummy variable indicates that claimants of both sexes are equality effected by the locational significance of where they made the claims. If claimants were residing in metropolitan areas, then they have a higher take-up rather than those do not in the metropolitan cities. This finding contradicts the nonparametric estimates earlier. 5 16 | P a g e The education dummy variables indicate a very interesting pattern of behaviour for the claimants to claim UIF. Individuals with a higher qualification have the lowest probability to claim benefits and have the longest waiting period than those without higher education. If a female individual without education passed secondary schooling has the hazard rate of one, then a degree holder would have only the relative claim-waiting period of 0.363 and individual with a secondary certificate will suffer a hazard rate of 0.542. The educational gap between males is slightly more moderate than the females. If a male individual without education passed secondary schooling has the hazard rate of one, then a degree holder would have the hazard rate of 0.554 and individual with a secondary certificate will suffer a hazard rate of 0.003. This may be the result of two factors: first, the employment probability for the degree and certificate holders is high relative to the non-holders. They are, therefore, reluctant to claim since they are expecting to be re-employed soon in any case. Secondly, due to higher incomes levels for these skilled claimants, they possess a much more even and well-protected consumption pattern than the unskilled or the poor. Hence, the need and the probability of claiming the UI for the educated is relatively lower than for the unskilled. Given the above research evidence on the behavioural tendency of the new-claimants (or the recently unemployed), we can effectively improve the targeting mechanism of other welfare policies to one that is aimed at those who are desperately in need of welfare assistance. In particular, the conventional wage subsidy mechanism can be improved to be more allocatively efficient by incorporating the claim-waiting period as a determinant into the decision rule on the condition of re-employment through the UIF. In this manner, not only will the individual heterogeneity which affects workers’ reservation wage levels be taken into account, but it could potentially minimize the likelihood of job losses in the economy. In the next section, we discuss and present the effectiveness and impact of this employment-retention focused, wage subsidy policy proposal. VI. Take-Up Rates and the Allocation of the Wage Subsidy: The Minister of Finance announcement during the 2011 Budget Speech in Parliament stressed that one element of the state’s strategy to create employment was the offering, through the tax system of a wage subsidy to employers who hired young workers. In its original and current conception (National Treasury, 2010) the wage subsidy is conceived of as a marginal subsidy designed to subsidise net new employment, although this remains unclear in the current documentation. Secondly, the route proposed to intervening is via employers, and through the tax system (specifically the PAYE system of the SARS). The reasoning here is that it would be administratively more efficient as well as better targeted at creating employment (as opposed to reducing welfare) if the subsidy was administered through employers, as opposed to potential employees. It may effectively also reduce leakages if administered via the tax system. The specifics of the wage subsidy proposal as currently presented by National Treasury are:  For a wage less than R15,000 per annum, the subsidy is equal to one-third of the wage  For a wage of R15,000 a year, the subsidy equals R5,000 per annum (maximum subsidy)  For a wage between R15,000 and R45,000 per annum, the annual subsidy equals R7,500 minus one-sixth of the wage  For wages greater than R45,000 per annum there is no subsidy 17 | P a g e Given that the subsidy accrues to those earning less than R45 000 per annum, is it ex ante, targeted at those individuals who are vulnerable and marginalised. Given the above then, and by most accounts in terms of international experience with such schemes, this is a well-conceived of policy idea. However, it is currently not a policy which has been implemented. This wage subsidy proposal has now had a policy lifespan of three years, as it has successively been stalled and delayed at various stages of the policy process. At the time of writing, the proposal is currently stalled within NEDLAC, with a key blockage apparently emanating from the labour constituency, as they view the proposal as:  A policy intervention which will only increase profits and the profit margin to employers, given that the subsidy is managed through firms.  A policy which would displace existing (unionised and permanent ) workers and be used as a strategy to move to increased non-standard employment  An intervention designed to encourage and facilitate a ‘race to the bottom’ in terms of lower wages in particular and a deterioration in the conditions of employment and labour standards in general. Given the above, we would, based on the above evidence, propose that the wage subsidy intervention could also potentially be implemented through employees (rather than employers), using the UIF system and the notion of take-up rates as the anchor for such a wage subsidy. We would argue that this approach would effectively deal with the union movement’s current concerns around the wage subsidy proposal as an employer-friendly and employer-targeted intervention. The proposed intervention here, it must be noted, should we think co-exist with the current proposal of implementing through employers and the tax system. What we provide below then is firstly, a broad schematic design of an employee-based wage subsidy intervention run through the UIF and based on take-up rate data. Secondly, we provide rough estimates of both the cost and reach of such an intervention, were it to be applied on the available pool of registered unemployed individuals. A Wage Subsidy for the Registered Unemployed: A Proposed Decision Rule The policy intervention being proposed here is that is observable, and could potentially be used as an employment retention scheme, through the offer of a wage subsidy. We propose therefore that , can be utilised as a decision rule to allocate the wage subsidy to individuals. Hence, the proposal here is that for those individuals who have come more quickly than others to claim their benefit have (and hence for whom savings, wealth and skill levels are low), instead of a benefit, will be given a wage subsidy directly to their own account, but tied to their employer re-hiring them. The intervention will target, as with the original National Treasury wage subsidy proposal, only those individuals earning R45 000 per annum or less. Hence, in our conception here it would be targeted, co-jointly at those who are the working poor and, with this nuanced proposal, at those who arrive very rapidly at the UIF offices, and hence, we observe low claim- waiting periods. Low claim waiting periods, means that these individuals need income urgently. The fact that when this pre-selected group are offered a tied wage subsidy, means that the employees are targeted (not the employers) at the point of entry of the subsidy. Whilst they would have their own account in which to manage this wage subsidy, it is only activated once the 18 | P a g e employer offers to re-hire the individual6. In that sense, it is an employment-creating (or employment-retaining) wage subsidy. The employer being registered with the UIF, makes administration relatively easy and as efficient as if the system was run through the SARS. The key advantage is that the subsidy is delivered to the workers at the point of the first contact, and could then deal with COSATU’s concerns around employers gaining from the wage subsidy. For this employment-retaining, wage subsidy scheme to work effectively though, there needs to be information symmetry across all parties concerned: the institution, the employer, and the employee. Otherwise, moral hazard problems may occur. For instance, the employer and the employee may collude in agreement and shorten the claim-waiting period to inflate the state of urgency for wage subsidies and exploit the wage subsidy system. In order to deal with this problem of collusion between employers and employees, a cost of a mandatory waiting period for the wage subsidy to be operational for a pre-specified period (two months for example) could be instituted. Hence an employee faces no wage income for two months and an employer no workers for two months. There would of course also be a penalty cost on the colluding employers for cheating. This way, the likelihood for moral hazard problem to occur will be minimized while the institution can gain full administrative control in job creation and retention and provide income assistant to those willing workers that are most in need of wage assistance and secured employment. We now turn briefly to examining the empirical content of instituting such a wage subsidy proposal through the UIF system, based on the take-up rates and earnings figures for the unemployed presented above. A Wage Subsidy through the UIF: Estimates of Outcomes and Impact We begin by applying the wage subsidy to those who earn below R45 000 per annum (R3 750 per month), irrespective of their claim-waiting periods, to test the impact of the most generous and unilateral wage subsidy outcome. We then apply stronger claim-waiting restrictions at which the unemployed become viable for wage subsidies and hence, employment. Given the penalty period of two months, we assume that those who would apply to this system are those who would otherwise be waiting for less than two months before arriving at the UIF for unemployment insurance. Essentially, we wish to gauge the effective number of jobs that could be retained, given those who are willing to work and urgently in need of the financial assistance for wage subsidies. Table 6 below shows the number of jobs that would have been saved if the wage subsidies were directed and managed through the UIF and with a decision rule. From Table 6, it is clear that under the most generous wage subsidy and re-employment system, 181 050 number of jobs would have saved been saved during the period under review. That is, 181 050 individual who were earning below R45 000 per annum over the period April-August 2009, irrespective of their , would be provided with the possibility of retaining their employment via the subsidy on their wage. In all our estimates here we assume a maximum employment retention offer from employers. In practice though, it is likely that the offer of a wage subsidy may not be necessary nor sufficient a condition for employers to re-hire unemployed workers. This number decreases sharply once we lower the income threshold to R15 000 per annum (R1 250 per month), where only 22 959 individuals will be eligible. 6 Indeed, this system would form the start of an ‘own -account’ UI and savings system that has been so successful in Chile (See Vodopivec (2008)) 19 | P a g e Table 7: Number of Eligible Employees for Wage Subsidies and MaximumRe-Employment by Claim- Waiting Period and Wage Average Salary per month < R1 250 chi2 0 Pr>chi2 0 24 | P a g e city 42786 35715.09 100436 94499.03 44905 51975.91 89068 95004.97 chi2(1) 2739.71 chi2(1) 816.81 Pr>chi2 0 Pr>chi2 0 quartpmamo~t 1 21925 16595.4 48732 64144.79 2 22706 22155.93 46017 41143.96 3 21137 32865.38 47376 45609.56 4 21923 16074.29 47373 38599.68 chi2(3) 9881.49 chi2(3) 7109.12 Pr>chi2 0 Pr>chi2 0 quartpmamo~t 1 21925 16595.4 48732 64144.79 2 22706 22155.93 46017 41143.96 3 21137 32865.38 47376 45609.56 4 21923 16074.29 47373 38599.68 chi2(3) 9881.49 chi2(3) 7109.12 Pr>chi2 0 Pr>chi2 0 quartacred~s 1 25013 37560 47620 48947.85 2 18922 15622.67 49690 55699.77 3 24593 26675.29 45407 59044.24 4 19163 7833.03 46787 25812.14 chi2(3) 26022.66 chi2(3) 23369.82 Pr>chi2 0 Pr>chi2 0 Female Male Regions Eastern Cape 8022 9999.83 19251 22922.7 Free State 1919 1847.07 8414 8416.46 Gauteng North 6526 5679.52 13611 29788.16 Gauteng South 11313 7346.65 40699 33547.38 Kwazulu Natal 17571 10690.34 39993 30500.58 Limpopo 2607 2363.52 9881 10821.56 Mpumalanga 10018 7861.66 11733 9650.3 North West 2154 2274.15 17952 12736.06 Northern Cape 1553 1623.28 2958 3243.33 Western Cape 26008 38004.98 25012 27877.47 chi2(9) 13431.83 chi2(9) 19901.75 Pr>chi2 0 Pr>chi2 0 reasons Business Closed 5770 3970.99 2121 2365.43 Constructive Dismissal 84 96.76 99 138.72 25 | P a g e Contract Expired 37568 49548.71 68201 66334.36 Dismissed 19627 17681.05 43689 53878.58 Insolvency/Liquidation 1372 1208.17 3374 3462.71 Retired 6447 4798.11 5382 4876.65 Retrenched/Staff Reduction 16597 10260.02 66147 58071.68 Voluntary Severance Package 226 127.21 491 375.87 chi2(7) 9800.32 chi2(7) 3521.77 Pr>chi2 0 Pr>chi2 0 Continuous variables _t Coef. P>z [95% Conf. Interval] age 0.013982 0 0.013458 0.014505 pmamount 9.59E-05 0 8.96E-05 0.000102 availablec~s 0.003934 0 0.00385 0.004018 26 | P a g e