A longitudinal study of outcomes from the New Enterprise Incentive Scheme

Transcription

1 A longitudinal study of outcomes from the New Enterprise Incentive Scheme Evaluation and Program Performance Branch Research and Evaluation Group Department of Education, Employment and Workplace Relations June

2 Commonwealth of Australia 2008 This work is copyright. Apart from any use as permitted under the Copyright Act 1968, no part may be reproduced by any process without prior written permission from the Commonwealth available from AusInfo. Requests and inquiries concerning reproduction and rights should be addressed to the Manager, Legislative Services, AusInfo, GPO Box 1920, Canberra ACT Research Team Sarah Crooks Michael Cameron Maryam Asgari For further information about this report Department of Education, Employment and Workplace Relations 2

3 Contents Executive Summary 4 1. Introduction Background and research objectives Methodology Previous research NEIS program background 8 2. Labour market outcomes Employment outcomes Gender outcomes Age outcomes Education outcomes Unemployment duration outcomes Culturally and Linguistically Diverse (CALD) outcomes Sole parent outcomes Hours Earnings Average earnings by gender Average weekly earnings by other demographics Employment conditions Competition : Performance against the NEIS business plan Off-benefit outcomes Conclusion 29 References 30 APPENDIX 33 3

4 Executive summary Assistance provided by the New Enterprise Incentive Scheme (NEIS) is regularly monitored by the ongoing Post Program Monitoring survey of job seekers, conducted three months after job seekers have participated in NEIS. While the 3 month post assistance survey provides an assessment of how well NEIS is helping job seekers to find employment, it is unable to provide insights into how NEIS is assisting job seekers in the longer term. The overall aim of this paper is to assess the quality of NEIS outcomes focusing on the sustainability of outcomes and the employment conditions associated with job outcomes. A random sample of job seekers who participated in NEIS and who responded to their 3 month Post Program Monitoring survey were followed up around 16 months after they exited assistance. The main findings of the study were: 1. Employment outcomes were sustainable between the 3 and 16 month mark. There was, however, a general shift from self employment to other employment over the period. The employability of NEIS participants whose businesses did not survive probably reflects the relatively high skill level of the participants involved. 2. NEIS businesses in the property and business service area had relatively high outcomes at both the 3 and 16 month mark while some business types, such as cultural and recreational services and wholesale trade, performed less well. While agriculture, forestry and fishing had relatively poor outcome levels at the 3 month mark there was a large increase in outcomes to the 16 month mark. 3. Surviving businesses were found to have created an additional 0.66 employment opportunities at the 16 month mark. Of these, 0.10 were full-time, 0.17 were part-time, 0.14 were for business partners and 0.25 were for spouses. Given that most businesses opened under NEIS are micro businesses, these results are encouraging. Moreover, additional employment creation was up from the 3 month mark, indicating that as time passes NEIS businesses will probably continue to create more employment opportunities. 4. Outcomes for males were slightly more sustainable than for females, who were generally more successful in creating secondary employment. 5. Culturally and Linguistically Diverse (CALD) participants were somewhat more likely to be in self employment at the 3 month post program mark. Their outcomes, however, were less sustainable with more leaving self employment and a lower proportion of those who left self employment moving into other employment. 6. Outcomes for sole parents were consistently lower and less sustainable than for non-sole parents. With 81% employed at the 16 month mark, however, outcomes are still high especially considering their work and family balance difficulties. 7. While the average number of hours worked per week fell over the period, there was a one percent increase in the proportion of respondents who were working full time. While this is only a small change, it indicates that the decline in the average hours worked was not a result of a move towards part-time employment. 8. For NEIS participants who were employed after exiting assistance, there was a general increase in earnings from $550 at the 3 month mark to $617 at the 16 month mark, a growth of 12%. 4

5 9. There was evidence that businesses opened under NEIS operate in competition with other businesses, with 50% of respondents indicating that they face lots of competition and 38% indicating that they face some competition. This level of competition may partially explain the low level of earnings of those still running a NEIS business. 10. Off benefit outcomes for NEIS are also sustained in the longer term, with almost 80% offbenefits 5 years after exiting NEIS. These results vary somewhat with the type of business opened under NEIS. The remainder of the paper examines these and other findings in more detail. 5

6 1. Introduction 1.1 Background and research objectives During May 1998 the Job Network was introduced as part of a new framework for the delivery of labour market assistance which consisted of Job Matching, Job Search Training and Intensive Assistance as the three main components. Further restructuring occurred with the introduction of the Active Participation Model (APM) in July NEIS is a program which complements other assistance received under the APM, and helps eligible unemployed people to start and run a viable business of their own. NEIS assistance includes three months of accredited training and business advice and mentoring, at the end of which participants submit a business plan. These business plans are then scrutinised closely to ensure that only those of the highest quality are accepted as eligible for NEIS assistance. NEIS assistance then takes the form of ongoing mentoring and a NEIS wage payable for the 12 months following commencement. The NEIS wage is set at a rate equivalent to NewStart Allowance. Assistance provided by NEIS is regularly monitored by an ongoing Post Program Monitoring survey of job seekers conducted 3 months after they have participated in NEIS. While this 3 month post assistance survey provides an assessment of how well NEIS is helping job seekers to find employment (self employment or other), it is unable to provide insight into how it is assisting job seekers in the longer term. The overall aim of this paper is, therefore, to assess the quality of NEIS outcomes focusing on the sustainability of outcomes and the employment conditions associated with job outcomes. Using survey and administrative data, this study looks closely at the following issues: o employment outcomes, to examine how outcomes levels change over time; o employment conditions, such as hours and earnings to look at the quality of job outcomes and how they change over time; and o off benefit outcomes to assess whether participants remain off benefits up to 5 years after exiting assistance. 1.2 Methodology A random sample of job seekers who exited NEIS assistance during the period May to October 2004 and who responded to their 3 month Post Program Monitoring survey were followed up around 16 months after they exited assistance. Some 1079 job seekers were surveyed 16 months after they exited NEIS assistance, with 695 responding giving a response rate of 64%. As with all surveys, estimates from the survey will reflect some degree of sampling error. In general, however, estimates from the survey have a relative standard error of less than 2% at the national level. At higher levels of disaggregation, the sampling error can be expected to be higher. The data collection method for the surveys was a self-completion questionnaire. In both the 3 and 16 month post assistance surveys, participants were sent a survey package including a letter of introduction, a questionnaire and a reply paid envelope. Copies of the 3 and 16 month 6

7 questionnaires can be found in Appendix A and B, respectively. For both surveys, a reminder questionnaire was sent to those who had not responded to the initial questionnaire within 3 weeks. After another 2 weeks a telephone interview was attempted for those job seekers where a response had still not been received. Data on NEIS commencements, business industry classification and client demographics were also gathered from DEWR administrative databases. The demographics of respondents were broadly similar to that of those participating in the NEIS program as a whole, with only small differences. Due to this, post survey stratification was not considered necessary. 1.3 Previous research Longitudinal analysis of Australian labour market program outcomes sustainability has been rare due to the limited availability of longitudinal data. The department has conducted two studies in the past to look at this issue. The first was a study of Job Matching, A Stepping Stone to better Jobs (2001) and examined the progress of those placed in jobs by Job Network providers under the Job Matching service. This study supported the stepping stone hypothesis that even taking up low paid, low skilled jobs can lead to better jobs in terms of hours, skills and wages for many job seekers. The second study was The Sustainability of Outcomes (2004) which examined outcomes from Job Search Training, Intensive Assistance and Work for the Dole for up to 12 months after assistance. This study reported improvements in employment conditions over time in terms of factors such as earnings, permanency, occupation classification levels and employment outcomes. A number of studies have also looked at the progress of those who achieve employment outcomes. Research based on the Australian Bureau of Statistics Survey of Employment and Unemployment Patterns (SEUP) has provided valuable insights into this issue. Carino-Abello, Pederson and King (2001) examined the change in earnings of Australians aged years over the period 1995 to Dunlop (2000) looked at the dynamics of the labour market and the impact of labour market programs, focusing on outcomes achieved by low paid adult workers. These studies found that: o a substantial number of low paid job seekers do move to higher paying jobs over time; o movement from low pay to higher pay is often associated with transitions from part-time to full-time work and increases in skill level; o educational attainment is an important factor in the prediction of increases in earnings; and o movement out of employment is more likely for low paid job seekers. Flatau and Dockery 2001, utilising the Department of Family and Community Services Longitudinal Data Set 1% Sample, have examined how income support recipients interact with the labour market. They concluded that: o part-time earnings have a significant positive effect on the rate of moving from income support to employment and strongly support the stepping stone hypothesis that part-time employment leads to full-time employment; o the take-up of employment options among income support recipients aids the movement off income support and into longer-hours employment for those recipients who prefer such jobs. In 1992 and 1993 two sustainability studies were conducted on NEIS by the department. o The 1992 study surveyed NEIS participants at 12 and 24 months following assistance and found that 12 months after exiting NEIS assistance, 42% of respondents were still operating their business and 91% were in some kind of employment. Only 29% of the 24 month sample were still operating their NEIS business, however, employment outcomes still remained high at 85%. o The 1993 study surveyed NEIS participants at 3 and 12 months following assistance. It 7

8 found that 64% of participants from the 3 month group were self employed and a further 9% were in other employment, giving an overall employment rate of 73%. Of participants in the 12 month group, 54% were in self employment and 9% were in other employment. Outcomes for the 1993 study were lower than in other studies due to their assumption that non-response was an indication that they were not in self employment. These findings are, however, quite dated and this study updates results from these earlier studies. A study conducted by the Centre for Labour Market Research in 2002 looked at NEIS outcomes 18 and 24 months after participating in NEIS. It found that around 56% of NEIS participants were still in self employment 2 years after NEIS participation. 1.4 NEIS program background The NEIS program helps unemployed people start and run their own business. NEIS has operated since 1985 and currently does so through a network of providers who train and support participants to become self supporting and independent. Before receiving NEIS assistance, potential participants undertake three months of accredited training, at the end of which they are required to submit a business plan. If this business plan is accepted they then qualify for a full year of income support at a rate equivalent to Newstart Allowance. During the year NEIS participants also receive business advice and mentoring. During the year ending December 2007, around 6,500 businesses were approved and commenced under the NEIS program. The majority of participants were between the ages of 25 and 49 with 71% of commencements in that group. Around 50% had been unemployed for less than 6 months and 57% had post secondary education. This emphasis towards highly educated and less disadvantaged job seekers is strong as compared to other programs and may be attributed to the strict selection process before a NEIS project is approved. Around 52% of participants were male. Equity groups such as Culturally and Linguistically Diverse (CALD), sole parents and mature age participants received a relatively high share of commencements with proportions comparable to those of other labour market programs. Characteristics of job seekers who commenced in NEIS during the year ending December 2007 are detailed in Appendix C (Table 1). Over the year ending 31 December 2007, almost 20 percent of all businesses opened under NEIS were in the property and business services sector. Personal and other services also accounted for around 20 percent of businesses. Comparatively, the finance and insurance, accommodation cafes and restaurants, communication services, and transport and storage industries had relatively few NEIS businesses. 8

9 Table 1: NEIS businesses by industry classification Industry Classification % Transport and Storage 1 Accommodation, cafes and restruants 2 Agriculture, forestry and fishing 2 Wholesale trade 2 Education 2 Construction 5 Health and community services 6 Manufacturing 10 Retail trade 11 Cultural and recreation services 14 Property and business services 19 Personal and other services 21 Other 3 To compare NEIS industry shares to that of Australia as a whole, Figure 2 shows the industries in which NEIS participants opened businesses compared to the industry share of businesses in the Australian economy as a whole, and to Australian small businesses. About 11% of total NEIS commencements were in the retail trade area, for example, while just under 15% of all businesses in Australia are in the retail trade area. This indicates that as compared to the Australian economy as a whole, retail trade businesses are under-represented in terms of NEIS participation. The business areas on the left in Figure 2 are over-represented in NEIS, while the ones to the right are under-represented. Businesses such as construction are probably under-represented due to the high amount of start-up capital or skills required. Business areas which are over-represented are likely to require less highly developed skills and less start-up capital and include personal services and cultural and recreational services. Figure 2: Industry share of NEIS and Australian businesses 25% 20% 15% 10% 5% 0% Personal and other services Cultural and Recreational services Manufacturing Property and Business Services Education Communication services Health and Community Services Finance and Insurance Accommodation Cafes and Restaurants Wholesale Trade Retail trade Transport and Storage Construction NEIS Australia Australia - small business 9

10 The number of NEIS commencements has been trending downward slightly over time due to strong labour market conditions and the decline in unemployment. As shown in Figure 3, the mix of businesses commencing under NEIS has also changed slightly. Personal and other service businesses have increased as a proportion of NEIS businesses over the period while manufacturing and agriculture businesses have fallen. These changes tend to reflect movements in the economy as a whole including the rapid growth in the services sector. Overall, however, there has been little change in the mix of businesses opened and property and business services has remained the largest sector in terms of NEIS businesses. Figure 3: NEIS Commencements over time by industry 100% 80% 60% 40% 20% Other Personal,Other Cultural,Recreation Health,Community Services Education Property,Business Services Transport/Storage Accomm.,Cafes,Rest Retail Trade Wholesale Trade Construction Manufacturing Agr.forest/fishing 0% - 98 Jul - 99 Jan- 99 Jul - 00 Jan- 00 Jul - 01 Jan- 01 Jul - 02 Jan- 02 Jul - 03 Jan- 03 Jul - 04 Jan- 04 Jul JanJul - 06 Jan - 06 Jul JanJul Jan - 10

11 2. Labour market outcomes 2.1 Employment outcomes A key measure of NEIS program effectiveness is sustained self employment in the business opened under NEIS. While self employment outcomes are the central aspect of NEIS program performance, it is also important to take into account other employment outcomes. Even if a participant s NEIS business fails, the program may have still contributed to their human capital development, increasing the chance of the participant finding alternative employment. The following sections look at both self employment and other employment outcomes from the NEIS program at the 3 and 16 month mark. Employment outcomes from NEIS have historically always been relatively high as compared to those of other labour market programs. This in part reflects the careful selection of viable businesses by NEIS providers prior to commencement in assistance, as well as the characteristics of the job seekers involved, who are generally less disadvantaged. High outcomes may also be a result of self selection; as participation in NEIS is not compulsory it is likely that those who choose to enter the NEIS program are more motivated than those participating in other labour market assistance programs. Three months after exiting assistance around 83% of respondents were in some kind of employment (Table 4). NEIS self employment outcomes 1 comprised the majority of these with just over 58% of exits, with a further 6% indicating that they were running a business which was partly their NEIS business. By the time respondents reached 16 months following assistance, however, only around 46% remained in their original NEIS business. Some of those who were no longer operating their NEIS business opened up another business. Most, however, had moved into other employment. Around 52% of those whose NEIS business did not survive reported that it was due to not having enough capital or not being profitable. Of the remainder, 28% left their NEIS business due to another job offer and 9% due to a medical reason. This indicates that the majority of movement from NEIS self employment to other employment can be explained by their NEIS business not being able to sustain itself. Most participants whose NEIS business did not survive, however, were able to find alternative employment with overall employment outcomes at the 16 month remaining at 83%. NEIS employment outcomes at the 16 month mark remain high compared to other labour market assistance programs. Table 4: Employment outcomes 3 month outcomes (%) 16 month outcomes (%) Self employed in NEIS business Self employed non-neis business Other employed Total employed NEIS self employment outcomes where respondents indicated that they were running a business which was mainly their NEIS business. 2 Respondents that were operating a business which was mainly the NEIS business 11

12 Figure 5 shows how outcomes changed over the 15 month period. NEIS self employment outcomes rose slightly between the respondent s 3 month PPM survey and the 4 month post program mark and then fell consistently until the 9 month post program mark, before flattening out at around 50%. At the same time other employment outcomes rose with employment outcomes as a whole remaining relatively constant over the period. Figure 6 shows the strong correlation between 3 and 15 month outcomes, with most outcomes sustained over that period. Those businesses which survive to the 12 month mark following assistance had a good chance of surviving, while many people whose business did not survive until the 12 month mark were able to quickly find alternative employment. While these results are encouraging, the employability of NEIS participants whose businesses do not survive probably reflects the types of participants involved. Figure 5: Employment outcomes over time 100% 80% 60% 40% 20% 0% Months after program completion Self Employed in NEIS Self Employed in other Other Employed Unemployed NILF * 3 and 15 month results are point in time, while results between 4 and 14 months are for what respondents were doing mostly in that month. Respondents were surveyed at both 3 and 15 months after program completion, so the results from 4 to 14 months relate to client recall of their activities during that period gathered in the 15 month survey. 12

13 Figure 6: NEIS 3 and 16 month outcomes 3 month 16 month Self emp. 68.6% Self emp. 55.4% Other emp. 8.9% UE/NILF 4.3% NEIS participants 100% Other emp. 15.5% Self emp. 1.2% Other emp. 11.8% UE/NILF 2.6% UE/NILF 15.9% Self emp. 2.1% Other emp. 4.4% UE/NILF 9.4% Some variation in employment outcome levels is apparent when looking at the type of business disaggregated by industry (Figure 7). Property and business service businesses had one of the highest levels of employment outcomes with 92% of participants in some kind of employment at the 3 month mark. Self employment outcomes dropped by around 8 percentage points to the 16 month mark. Most of these moved into other employment with only a 1 percentage point drop in employment outcomes overall. Employment outcomes also fell for businesses in the areas of personal services, health and community services and retail, all driven largely by a fall in self employment outcomes. Clients who NEIS business was in the construction industry experienced a rise in employment outcomes over the period despite an 8 percentage point fall in self employment outcomes. This large movement into other employment may be due to the fact that construction skills are highly transferable between jobs so while some small construction businesses opened under NEIS are not sustainable, they are quickly able to use their experience to get a job elsewhere. Agriculture, forestry and fishing had the lowest level of employment outcomes at the 3 month mark with around 78% in some kind of employment. They, however, experienced an increase over the period and were one of the higher performers at 16 months with 86% in employment. Cultural and recreation services and wholesale trade industries also performed relatively poorly at the 3 month mark before rising over the period to the 16 month mark. 13

14 Figure 7: Employment outcomes by business type % Respondents Cultural & Recreational - 3 Month Cultural & Recreation - 16 Month Ag Forestry & Fish - 3 Month Ag Forestry & Fish - 16 Month Wholesale Trade - 3 Month Wholesale Trade - 16 Month Construction - 3 Month Construction - 16 Month Manufacturing - 3 Month Manufacturing - 16 Month Retail trade - 3 Month Retail trade - 16 Month Property & Business - 3 Month Property & Business - 16 Month Health & Community - 3 Month Health & Community - 16 Month Self Employed Other employed It should be noted that past research has shown a greater degree of outcomes variation when looking at business types disaggregated further. The sample sizes available for this study, however, means that an analysis of sustainability could only be carried out at the main industry level of Anzsic coding. Some evidence exists that surviving businesses have also created secondary employment. On average, 3 months after exiting NEIS, each business 3 generated around 0.56 of an additional employment opportunity (Table 8). This increased to 0.66 at the 16 month mark, suggesting that surviving businesses are generally able to generate additional employment opportunities as they grow over time. Of that extra employment created by the 16 month post assistance mark, 0.25 are spouses and 0.14 are partners in the business. For those who are not a spouse or partner, most are employed part-time with only 0.10 employed full-time. While these figures may seem low, it should be kept in mind that the businesses opened under NEIS are generally micro businesses and are not expected to hire substantial numbers of additional staff. 3 For the sake of comparison, businesses here have been limited to those which are still in operation 16 months after exiting assistance 14

15 Table 8: Additional employment creation 4 3 Month additional employment (persons) 16 Month additional employment (persons) Full-time employees Part-time employees Business partner Spouse employment Total additional employment Of course it should be noted that these estimates do not take into account the fact that had the NEIS business not existed some of the people employed in these businesses would have found jobs elsewhere and that other competing businesses may have opened or expanded to deliver that service Gender At the 3 month mark, males were 3 percentage points more likely to be in self employment, but only 1 percentage point more likely to be in employment overall (Figure 9). Towards the 16 month mark, females and males were just as likely to move out of self employment. Males were more likely to move into other employment while females were more likely to become unemployed or leave the labour force, giving males a self employment rate of 5 percentage points higher and an employment rate of 7 percentage points higher than females at the 16 month mark. Figure 9: Employment outcomes by gender % Respondents Male - 3 Months Male - 16 Months Female - 3 Months Female - 16 Months Self Employed Other employed While female NEIS participants were more likely to have their spouses working in the business, on average their businesses employed fewer other employees at the 16 month mark. In general, therefore, outcomes from NEIS were more sustainable for males who were also more successful in creating secondary employment. 4 Respondents who reported that they had employed more than 5 additional part-time or full-time workers were censored to 5. There were 3 cases where this occurred, and in each case their gross weekly revenues were $1000 per week or less indicating that they would not have been able to employ more than 5 workers. 15

16 2.1.2 Age The NEIS businesses were generally more sustainable if the participant was older with around a 19 percentage point fall in the self employment rate at the 16 month mark for those aged 24 or less compared to a maximum of 11 percentage points for the other groups (Figure 10). However, a greater proportion of younger participants moved into other employment, leaving employment outcomes relatively similar between the 3 and 16 month mark. Figure 10: Employment outcomes by age % Respondents Months 16 Months 3 Months 16 Months 3 Months 16 Months 3 Months 16 Months <24 years old years old years old 50+ years old Self Employed Other employed Additional employment creation varied by age group with participants in the age range of 25 to 49 employing, on average, a greater number of additional employees (Table 11). Those aged 24 or less had the lowest secondary employment generation with each surviving business only generating an additional 0.30 jobs on average at the 16 month mark. Older participants were more likely to have a spouse employed in the business, but less likely to have a business partner. Table 11: Secondary employment by age group Age group Full-time employees Part-time employees Business partner Spouse employment Total additional employment 24 or less Total

17 2.1.3 Education Employment outcomes also vary according to educational attainment (Figure 12), with those with less than year 10 being less likely to be in self employment than most other groups but more likely to sustain their self employment between the 3 and 16 month marks. Employment outcomes were higher for those with a degree, TAFE or a year 10 education qualification. The high level of employment outcomes for those who only completed a year 10 education may be due to their ability to easily find entry-level employment opportunities. Their self employment was, however, not very sustainable with around a 12 percentage point reduction between the 3 and 16 month mark. Businesses opened by participants with a degree were the least sustainable, with self employment outcomes for this group falling by around 15 percentage points. With most of these people moving into other employment, the decline in self employment outcomes can be explained by their ability to find higher paying employment elsewhere. Figure 12: Employment outcomes by educational attainment Months 16 Months 3 Months 16 Months 3 Months 16 Months 3 Months 16 Months 3 Months 16 Months < year 10 Year 10 Year 12 TAFE Degree Self Employed Other employed Respondents with a degree tended to generate a low level of secondary employment at the 16 month mark with each business on average employing only 0.45 additional employees (Figure 13). While this may seem contrary to expectations, it can be explained by the low number of participants in this group who are employing a spouse. While those with a year 12 education had low employment outcomes, those who remained in self employment at the 16 month mark were much more likely to be generating secondary employment than those in other groups. On average, those whose highest educational attainment was year 12 were employing one additional person 16 months later, driven largely by higher numbers of full and part time employees rather than employment of a spouse or business partner. 17

18 Figure 13: Secondary employment by educational attainment NEIS longer-term outcomes report Educational attainment Full-time employees Part-time employees Business partner Spouse employment Total additional employment < Year Year Year Tafe Degree Total Unemployment duration 5 Not surprisingly, employment outcomes were somewhat less positive for participants with longer durations of unemployment. Three months after assistance, around 85% of short term unemployed participants are in some kind of employment as compared to 74% of those who were long term unemployed (Figure 14). The difference in employment outcomes increases slightly towards the 16 month mark, with employment outcomes rising for the short term unemployed and falling for the long term unemployed. At the 16 month mark, 86% of short term participants were in some kind of employment, compared to 72% of long term unemployed participants. While outcomes for the long term unemployed are lower than the total population, the difference is not as large as in other programs. Figure 14: Employment outcomes by unemployment duration % Respondents Months 16 Months 3 Months 16 Months 3 Months 16 Months 0-6 months 6-11 months 12+ months Unemployment duration Self employed Other employed 5 Based on unemployment durations of job seekers at the time they commenced in the NEIS program. 18

19 Long term unemployed participants were also less likely to have created secondary employment at the 16 month mark than their short term unemployed counterparts, with each business employing an average of 0.5 additional employees (Table 15). Table 15: Secondary employment by unemployment duration Full-time employees Part-time employees Business partner Spouse employment Total additional employment 0-6 months unemployed month unemployed >12 months unemployed Culturally and Linguistically Diverse (CALD) participants CALD participants were more likely to be in self employment at both the 3 and 16 month mark than other participants. Their outcomes, however, were less sustainable with more leaving self employment and a lower proportion of those leaving self employment moving into other employment. CALD participants were more likely to have employed additional staff with an average of 0.75 additional employees at the 16 month mark compared to 0.62 for non-cald participants. This was largely driven by a higher level of full-time employees. Overall outcomes from NEIS for CALD clients were quite encouraging in terms of employment outcomes, sustainability and employment generation indicating that these clients may benefit more fully from the program Sole parent participants Self employment outcomes for sole parents 6 were consistently lower and less sustainable than for non-sole parents, with 65% of respondents self employed at the 3 month mark and 47% self employed at the 16 month mark. Most of those who left self employment moved into other employment with employment outcomes as a whole falling by only 3 percentage points to 81% at the 16 month mark. None of the sole parents who were still running a business at the 16 month mark were employing any full time staff and had, on average, created a substantially lower level of secondary employment than the average. At the 16 month mark, on average each surviving sole parent participant business had created jobs for an additional 0.53 employees. While outcomes for sole parents are not as encouraging as those for the general population they are still reasonably high considering their work and family balance difficulties. 6 Sole parents who were registered with the Job Network as job seekers 19

20 2.2 Hours Three months after participating in NEIS, those who were employed were working on average 35 hours per week while at the 16 month mark 7, the average was 34 hours per week. As shown in Table 16, the change in hours is dependent on type of employment. Respondents who were not self employed experienced an increase in the average number of hours worked, with casually employed respondents increasing their hours by 14% and permanently employed respondents working on average 2% more hours. This is compared to those who were still operating a business which was mainly their NEIS business who experienced an average fall in hours worked of 5%. This probably reflects the more mature businesses requiring slightly less effort after they become established. Table 16: Change in average hours per week Average hours 3 months Average hours 16 months Change (%) Employed casual Employed permanent Self employed NEIS Other self employed Total While average hours fell over the period, there was an increase in the proportion of respondents who were working full-time hours by around 1 percentage point. While this is quite a small change it indicates that even though the average number of hours has decreased over time this has not led to a move towards part-time employment. Females on average worked fewer hours at both the 3 and 16 month mark than their male counterparts, in all types of employment. This includes casually employed participants where females were working on average 14 hours less per week. Average hours vary by the age of the participant with the younger participants (less than 24 years) working on average around 6% fewer hours than the average participant and those aged between 25 and 49 working more hours than the average. Mature aged participants had on average slightly fewer hours, though the influence is minimal. Average hours also vary little between the other demographic groups, including CALD and sole parent participants. 7 Looks at those who were employed at the 3 and 16 month mark. 20

21 2.3 Earnings For NEIS participants who were employed 3 months after exiting assistance, there was a general increase in earnings from the 3 to 16 month mark, with average earnings growing by 12% from $550 to $617 a week (Figure 17). There was a great deal of variation when looking at earnings by employment type. At the 3 month mark, those who were in self employment were earning around $530 a week on average, significantly less than those who were employed elsewhere ($610). This was especially so for those self employed in a business which is not their NEIS business, who earned less than $460 a week on average. Towards the 16 month mark the difference in average earnings between the self employed and other employed is even greater, with average earnings for self employed rising to around $567 and with other employment earnings increasing to average around $720. Earnings for the self employed groups were less varied, however, with non-neis businesses earning approximately the same as NEIS businesses, driven by a large increase in the average earnings of participants running a non- NEIS business. It should be kept in mind that at the 3 month mark the non-neis businesses were likely to be in an earlier stage of their development, as compared to those still running their original NEIS business. Respondents who were working in a permanent position were earning the most at both the 3 and 16 month mark, and experienced the greatest rise over the period. At the 16 month mark permanently employed respondents were earning around $800 per week on average, up by 20% from the 3 month mark. Figure 17: Average earnings by employment type Average weekly earnings ($) Other self employed Self employed NEIS Total Employed casual Employed permanent 3-Month mark 16-Month mark 21

22 Average earnings and the change over the period, varied dramatically by the type of NEIS business opened. Participants who opened businesses in the areas of retail trade, personal and other services and wholesale trade had the lowest average earnings at the 3 month mark, earning on average less than $430 per week (Figure 18). Over the period to the 16 month mark, however, they experienced a large growth in average earnings, with earnings growing by 41% for retail trade, 31% for personal and other services and by 43% for wholesale trade. On the other hand, participants who opened up a business in the construction area earned the most at the 3 month mark with earnings of over $880 a week, but experienced a 16% decline in average earnings between 3 and 16 months following the program. Average earnings rose for all other business types. Overall, earnings between the various business areas were much more variable at the 3 month mark than at the 16 month mark. Figure 18: Average earnings by business type Average weekly earnings ($) Retail trade Personal and other services Wholesale Trade Cultural and Recreational services Health and Community Services Manufacturing Total Property and Business Services Construction 3-Month mark 16-Month mark 22

23 2.3.1 Average earnings by gender Average weekly earnings were quite varied between males and females, as shown by Figure 19. At the 3 month mark females were earning around $445 on average as compared to $630 for males. While the earnings gap closed to some extent towards the 16 month mark, males were still earning $160 more on average per week than females with an average weekly earning of $680. Females on average earned less than males within all employment types, however, the earnings gap varied according to the type of employment they were in. The relative size of the earnings gap for different types of employment is also shown to vary between 3 and 16 months. Figure 19: Variation in earnings by Gender Average weekly earnings Self employed NEIS Other self employed Employed casual Employed permanent Total Female 3 month mark Female 16 month mark Male 3 month mark Male 16 month mark 23

24 2.3.2 Average weekly earnings by other demographics Figure 20 breaks down earnings into the various demographic groups. While those in the 25 to 34 age range were earning the most on average at the 3 month mark, over the period they experienced a 9% drop in average earnings. All other age groups experienced a rise in average earnings, placing those in the 25 to 34 age range below average in terms of earnings. At the 3 month mark there was little difference between earnings for the long term unemployed (LTU) and the short term unemployed (STU), however, towards the 16 month mark LTU participants experienced a 6% drop in average earnings, while STU participants experience an 11% rise. There was considerable variation in earnings by educational attainment. Those who had less than year 10 education had the lowest level of average earnings at both points. Surprisingly, respondents with a degree or TAFE level qualification on average earned less than those with year 10 or year 12 as their highest educational attainment. This may be due to the types of industries in which the higher educated participants are opening businesses. Sole parents earned around $90 less per week than the average at the 3 month mark. The earnings gap here reduces dramatically towards the 16 month mark, however, with sole parents experiencing a 33% rise in average earnings. Average earnings for CALD participants were not significantly different from the average at both the 3 and 16 month marks. Figure 20: Average weekly earnings by demographic group Average earnings 3 months Average earnings 16 months Change (%) Age Less than 24 years $482 $ years $645 $ years $539 $ years $546 $ Unemployment duration <=12 months on benefits (STU) $573 $ >12 months on benefits (LTU) $571 $ Educational attainment Less than year 10 $451 $477 6 Year 10 $539 $ Year 12 $628 $679 8 TAFE $581 $604 4 Degree $565 $605 7 Sole parent participants $450 $ CALD participants $549 $ Total $550 $

25 2.4 Employment conditions This section examines factors that contribute towards employment outcomes including competition, business growth and prospects for the future. Competition Program eligibility criteria are designed to reduce the chances of NEIS businesses replicating existing local businesses. The types of businesses which are opened under NEIS, however, suggest that competition with existing businesses is likely, and this is confirmed by survey results. Of the 58% of respondents still running their own business at the 16 month mark, 50% indicated that they experience lots of competition and 38% indicated that they face some competition (Figure 21). So, rather than filling a niche in the market, the majority of NEIS businesses faced a reasonable level of competition. This high level of competition may partly explain the low level of earnings for those who are still running a business, as 16 months down the track they are still likely to be in the process of establishing their businesses. Business earnings may increase as they learn more about the market and what consumers are looking for, in which case they may be better equipped to differentiate their product and create a niche for their business. In the medium term, however, it would seem that NEIS businesses compete against other small businesses and this may limit the ability of those businesses to survive or grow, somewhat defeating the goals of the NEIS program. Figure 21: Competition faced by surviving businesses 100% 80% 60% 40% 20% 0% Retail trade Construction Personal and other services Total Cultural and recreational services Manufacturing Property and business services Agriculture forestry and fishing Little or no competition Some competition Lots of competition 25

26 2.4.2: Performance against the NEIS business plan At the 16 month mark, around 50% of respondents who were still running their own business indicated that the revenue of their business had increased over time, while 15% indicated that revenue was about the same. While most businesses had not experienced a fall in their revenue over time, some 54% reported that revenue was less than that which was set out in their NEIS business plan. This was the case whether they were running their original NEIS business or another business. It should be noted, however, that non-neis businesses are likely to be in an earlier stage of their life than NEIS businesses and may experience growth in earnings later on. Respondents whose business focus had changed since their business plan were more likely to have had less revenue than planned. Only around 30% of participants reported that the revenue of their business was around that set out in their NEIS business plan. This suggests that NEIS participants and providers are being too optimistic in their business plans and more training or care may be required when NEIS applicants are creating such plans so that they can more accurately forecast future revenue. Furthermore, 44% of respondents were not earning enough from their business to live on, reinforcing the likelihood that more care needed to be taken in the preparation stage of the NEIS business plan. If these participants were not earning enough income to live off because they were putting it back into the business, it would not be an issue. Those who were earning enough to live on, however, had on average a much larger revenue than those who did not have enough. Only 20% of those who indicated that they did not earn enough to support themselves had businesses with revenue of $1000 or more per week as compared to 55% of those who responded that they earned enough. While the majority of respondents at the 16 month mark indicated that they could not have survived without NEIS assistance, a third of respondents indicated that they could have survived without the assistance, representing a dead-weight loss for the program. Some 40% of respondents who were self employed at the 16 month mark indicated that they required more capital when establishing their business. Since they are still in operation at the 16 month mark, these respondents are likely to require the extra capital to better establish their business earlier or to provide the resources to differentiate their product, rather than to remain in operation. 26

27 3. Off-benefit outcomes 8 Off-benefit outcomes provide a useful assessment of longer term outcomes. As can be seen in Figure 22, off-benefit outcomes remain around 80% for the first 5 years following NEIS. This high level of long term outcomes for the program is very encouraging. Figure 22: NEIS off-benefit outcomes over time Off-benefit outcomes (%) Years after program 5 There is some variation when looking at 3 year off-benefit outcomes by the type of business opened under NEIS. Participants who opened a NEIS business in the agriculture, forestry and fishing area had the lowest outcomes, while businesses in the insurance and finance area had the highest outcomes levels (Figure 23). 8 Data for off-benefit outcomes are taken from administrative datasets and include all NEIS participants. 27

28 Figure 23: NEIS 3 year off-benefit outcomes Outcomes (%) Accomm.,Restaurant Agric.,Forest,Fish Communication Construction Cultural,Recreation Education Finance,Insurance Health,Community Manufacturing Mining Personal,Other Property,Business Retail Trade Transport,Storage Wholesale Trade 28

29 4. Conclusion NEIS employment outcomes remain high, partially reflecting the careful selection of viable businesses by NEIS providers prior to commencement in assistance, as well as the characteristics of the job seekers involved. In addition to this, NEIS businesses generate a small number of employment opportunities for other job seekers, with each surviving business creating an additional 0.66 employment opportunities on average 16 months after exiting assistance. There would appear to be scope to target NEIS to more disadvantaged job seekers given that the outcomes for these job seekers are only marginally lower than average. The sustainability of outcomes was found to be very high, with off-benefit outcomes sustained at around 80 percent for up to 5 years following assistance. The average number of hours worked per week fell over the period. There was not, however, a corresponding fall in the number of participants working full-time hours. While there was a slight fall in average hours worked, there was an increase in the average earnings of participants in all employment types, but particularly for those who were employed permanently. This suggests that as time passes, surviving businesses are probably becoming more established and as a result, earning more. At the same time, however, it was found that, on average, participants had higher incomes where they found other jobs rather than remaining self employed. While employment and off-benefit outcomes are encouraging, the study identified some areas of concern. While the majority of respondents at the 16 month mark indicated that their businesses could not have survived without NEIS assistance, a third of respondents indicated that they could have survived without NEIS assistance, representing a dead-weight loss for the program. In addition to this, there is evidence of overly optimistic revenue projections in business plans and that in the majority of cases NEIS businesses represent competition for local businesses which has the potential to result in economic displacement (ie, where a business that is not receiving government assistance closes down, forgoes expansion or does not start up because of competition from a supported business). This is consistent with international evidence, but whether the deadweight costs and displacement effects of NEIS are higher than those of other labour market interventions is not clear. It is likely, however, that the job displacement effects of NEIS are lower than for most other labour market programs as a significant proportion of the jobs created by NEIS are new jobs. However, it remains of concern that despite the program s eligibility rules, most NEIS businesses operate in competition with other unsubsidised businesses. To gauge the full impacts of the dead-weight loss associated with the NEIS program, a net impact study would be required, and, while difficult, there is some scope for a future study aimed at determining the net impact of NEIS. 29