Investigating accident compensation spells using linked employer-employee data

Transcription

1 Investigating accident compensation spells using linked employer-employee data Tas Papadopoulos and Menaka Saravanaperumal Statistics New Zealand October 2009

2 Notes on the data We thank Sarah Crichton, Jackie Fawcett, Dean Hyslop, and Rodney Jer for their help. Access to the data used in this study was provided by Statistics New Zealand under conditions designed to give effect to the security and confidentiality provisions of the Statistics Act Only people authorised by the Statistics Act 1975 are allowed to see data about a particular person or firm. The tables in this paper contain information about groups of people so that the confidentiality of individuals is protected. The results are based in part on tax data supplied by Inland Revenue to Statistics NZ under the Tax Administration Act. These tax data must be used only for statistical purposes, and no individual information is published or disclosed in any other form, or provided back to Inland Revenue for administrative or regulatory purposes. Any discussion of data limitations or weaknesses is in the context of using the Linked Employer-Employee Database (LEED) for statistical purposes, and is not related to the ability of the data to support Inland Revenue's core operational requirements. Careful consideration has been given to the privacy, security, and confidentiality issues associated with using tax data in this project. Any person who had access to the unit record data has certified that they have been shown, have read, and have understood section 87 (relating to privacy and confidentiality) of the Tax Administration Act. A full discussion can be found in the Linked Employer-Employee Data Project Privacy Impact Assessment paper (Statistics NZ, 2003). Any table or other material published in this report may be reproduced and published without further licence, provided that it does not purport to be published under government authority, and that acknowledgement is made of this source. Published in October 2009 by Statistics New Zealand Tatauranga Aotearoa Wellington, New Zealand ISBN (online) 2

3 Contents 1. Background Introduction The Accident Compensation Corporation A conceptual framework for injury data Injury and labour market outcomes Available data The Linked Employer-Employee Dataset How LEED is constructed The limitations of LEED The injured population in LEED Variables What role can LEED play? Descriptive Statistics using LEED All new ACC spells (includes the self-employed) New ACC spells with prior employment (wage and salary only) New ACC spells with prior employment (wage and salary only) and prior benefit receipt Prior LEED research Introduction Calculating counterfactual outcomes for injured workers Characteristics of the matched samples Simple matching estimates Regression estimates Summary Future work References

4 1. Background 1.1 Introduction This project was motivated by the Accident Compensation Corporation s (ACC s) interest in better understanding the Linked Employer-Employee Dataset (LEED). In particular, they were interested in its usefulness in answering policy questions on the performance of the ACC scheme. The following is an example of such a policy related question. ACC investigations suggest that the time taken for people to rehabilitate after an injury and return to employment has increased over the past decade, despite increased effort to get injured people back into work. A possible explanation for this is that this period has seen the entry into employment of individuals that were, previously, marginally attached to the labour market. Are these new workers a possible explanation for the increased time taken for rehabilitation? This paper provides insight into how LEED can be used to answer questions relating to the labour market histories and outcomes of the injured. In doing so, it sets the scene for ACC to use LEED to carry out future work in examining this issue and other policy questions. The paper is in five sections. The rest of this section provides background information from Crichton et al (2004) and other sources on the ACC scheme, its interaction with the labour market, and data measurement issues. Section 2 provides an introduction to LEED, with a particular emphasis on areas of interest to ACC. Section 3 provides some descriptive summary statistics from LEED. Section 4 summarises previous research in this area using LEED. Section 5 provides examples of future work that can be undertaken with this dataset. 1.2 The Accident Compensation Corporation ACC has administered New Zealand s accident compensation scheme since This provides comprehensive, 24-hour, no-fault personal injury cover and entitlements for everyone in New Zealand. 1 Crichton et al (2004) provides a very good summary of this scheme. Some pertinent points for this paper are: The scheme provides personal injury cover for all New Zealand citizens, residents, and temporary visitors to New Zealand. ACC pays or contributes to the direct medical and rehabilitative costs of all injuries, whether incurred at the workplace or not, and whether incurred by workers or not. ACC also compensates, on a weekly basis, all employees and self-employed individuals who are unable to work for 80 percent of their total earnings, after the first week post-injury. 2 Around 40 percent of claims involving earnings compensation stem from workplace injuries. Around 95 percent of claims are treatment claims those that only involve direct payments from ACC to medical professionals. The other 5 percent of claims, called 1 From Accident Compensation Corporation (2008), which also summarises the wider responsibilities of ACC. 2 Employers are obliged to cover the first week of work missed following the injury. 4

5 entitlement claims, also involve income maintenance, as well as death benefits, rehabilitation, or support for independence. ACC payments are generally made within seven working days, though in cases where cover is initially declined by ACC and later accepted, payments may be delayed. In some cases, the injured party may receive earnings compensation for several months in a single payment. Individuals assessed to be capable of working at least 35 hours per week cease to receive earnings compensation. 1.3 A conceptual framework for injury data In 2002, a review of data on New Zealand injuries was undertaken (Department of Labour and Statistics NZ, 2002). This review led to Statistics NZ being appointed as the Information Manager for injury statistics (this role is summarised in section 1.5). As part of that review, a conceptual framework for injury data was constructed. The key elements in this framework are listed below, along with the ability of LEED to measure each element. Individual These are the key characteristics of the person who was injured. They include their age, sex, ethnicity, region, labour force status, and occupation. LEED contains information on all of these variables, with the exception of occupation. Ethnicity is only available for a subset of the population (recent beneficiaries), and is measured inconsistently across time. 3 The longitudinal nature of LEED means it also contains historical income and labour market information. Environs and locale This is the location and scene of the injury. LEED can only provide information for the subset of injuries that occur at the workplace (eg region of employment, industry, firm size). Activity This is the action being carried out by the person prior to injury. LEED does not capture this. Event/accident This is the incident that results in injury. LEED does not capture this. Injury This is the damage to the person that results from the event. The length of ACC spell, which can be estimated in LEED, can be used as an indication of the severity of the injury. It is an imperfect proxy however, as other factors (eg psycho-social or pre-existing conditions) may also affect duration of ACC receipt. Outcome This is the effect of the injury on the individual, and on society as a whole. LEED is able to capture the subsequent income receipt of the injured (ie investigate whether they go onto receiving wages and salaries, benefit etc, and the amount received). 3 This information is from the Ministry of Social Development s Benefit Dynamics Dataset (BDD) that was integrated with LEED in If the Employment Outcomes of Education integration proceeds, ethnicity information would also be available for most recent participants in tertiary education. 5

6 1.4 Injury and labour market outcomes Crichton et al (2004) contains a discussion of the ways that injury can affect labour market participation. These channels include: Injuries directly affecting an individual s productivity by making tasks difficult to perform. Time spent away from the workplace due to treatment or recuperation, resulting in the reduction of general or firm-specific human capital. Injuries having psychological effects that lead to the employment loss or earnings reductions. For example, an employee may be uncomfortable returning to the same job. This may lead to reduced earnings, through the acceptance of a less optimal job, or because the individual has to start over. Under the current Act, rehabilitation to work or independence is the primary focus of ACC. Crichton et al (2004) also discusses factors that mitigate the effect of injuries on labour market outcomes. These factors include: The level of rehabilitation received. The level of assistance that firms offer injured workers on their return to work. Local labour market conditions. When the economy is strong, firms may be more willing to accommodate injured workers, and injured workers may find it easier to find a job with pay prospects equal to their old job. There may be more to the last point. International evidence 4 shows that the frequency of accident compensation receipt declines in periods of recession. This may be due to recessions either reducing the average risk of injury at work 5, or increasing the fear of job loss in employees. International evidence on the effect of recession on duration of accident compensation receipt is not as strong, but it seems to suggest that recessions are unlikely to increase average duration. 1.5 Available data Using survey data to investigate the impact of injury on labour market outcomes has a number of limitations. Four of these are described in Crichton et al (2004): 1. Few surveys ask about injuries. 2. Surveys which do usually have limited labour market information. 3. There are no standards for measuring injury; therefore, these surveys are unlikely to capture a consistent measure overtime. 4. Only a small percentage of individuals suffer relatively serious injuries. Hence, the sample populations are small in datasets that also have suitable labour market information. 4 Summarised in Institute for Work and Health (2009). 5 Reasons for this may include less experienced employees or more risky equipment being removed from production. 6

7 Statistics NZ is responsible for coordinating the production, collection, and dissemination of official injury statistics. The key output is the annual publication on work-related claims. This publication is based on information collected by ACC. The reliance on administrative data eliminates the sampling error associated with surveys, and allows for a consistent measure of injury to be used overtime. Analysis of the number of claimants can be carried out by age, ethic group, occupation, injury type, bodily location of injury, sex, and territorial authority. However, a limitation of the dataset is that it does not have any labour market information prior to and post injury. LEED is another dataset based on administrative data. It records taxable ACC compensation paid out to individuals, and also allows the labour market outcomes of individuals to be tracked over time. Hence it is a useful dataset to investigate the impact of injury on subsequent labour market outcomes. LEED is discussed in more detail in the next section. 7

8 2. The Linked Employer-Employee Dataset 2.1 How LEED is constructed LEED is produced by combining Inland Revenue (IRD) data on individual incomes from Employer Monthly Schedules (EMS), and business data from Statistics NZ s Business Frame (BF). EMS forms are submitted by organisations registered with IRD each month, and contain all individuals receiving earnings from which tax is deducted at source. These include those who pay Pay-As-You Earn (PAYE) and withholding payments. LEED defines those who pay PAYE as employees, while those who pay withholding tax are a subset of self-employment. 6 Each EMS lists all the employees working for the filing employer during the month. It provides details on the amount paid to employees, the amount of tax deducted at source during a particular month, and, where applicable, job start and end dates. 7 Transfer payments that are taxed at source are also reported to IRD through the EMS. These are income-tested benefits, New Zealand Superannuation, student allowances, paid parental leave, and ACC payments. Each of these types of transfer payments is made using unique IRD numbers. This means that recipients of each of these types of transfer payments can be identified in LEED. 8 The BF is a database of private and public sector organisations engaged in the production of goods and services in New Zealand. Information from the BF includes; the industry and sector the business belongs to, the number of geographical units (physical locations) and their addresses, and the ownership structure of the business. The link between the two data sources is the employer IRD number, a unique identifier (usually) common to both data sources. The IRD number of a business entity is associated with an enterprise unit on the BF. This is represented by link 1 in figure 1. Figure 1 also summarises the links formed over time within each data source and between each data source. Links within the tax data are: between a person (ie employee) and organisation (ie employer) at a given time period for the same person over time for the same organisation over time. Links within the BF data are: for the same organisation over time at the enterprise level for the same organisation over time at the geographic unit level between an enterprise and its geographic unit(s) at a given time period. 6 Around three-quarters of self-employed individuals do not appear in EMS forms, as they do not pay tax at source. Instead they file annual returns. These annual self-employment returns are integrated into LEED, and are used in the LEED Annual statistics. 7 Completion of job start and end dates in the EMS (to signify the start or end of an employment relationship) are not compulsory for employers. Because of this they are usually not completed. Statistics NZ imputes start and end dates when it appears an employment relationship began or ended in the month. This allows job counts to be produced as at specific dates. 8 Since the end of 2008, the Ministry of Social Development's Benefit Dynamics Dataset (BDD) has been integrated with LEED. The BDD is a longitudinal dataset assembled by cleaning and linking benefit administration records for the same individual. This allows the income tested benefit category in LEED to be broken down by type of benefit. 8

9 Finally, employees linked to their employers via the EMS, are then linked to an enterprise and geographic unit on the BF through LEED (this is explored further in the next section). These links allow LEED to provide valuable information on the dynamics of the New Zealand labour market. In addition, being effectively a census means that small groups of interest can be examined without introducing sample error. 2.2 The limitations of LEED However, given the administrative nature of the dataset, and the complexity of the methods needed to construct it, there are a number of quality issues that introduce non-sample error, or otherwise limit LEED s usefulness. Issues that may pertain to the analysis of ACC receipt are discussed below. Missing and incorrectly coded employee IRD numbers The employee IRD number is an important variable in LEED, as it allows individuals to be identified across employers and across time. It is maintained to a high standard by IRD. However, around 2 percent of all EMS job record returns contain missing or incorrectly coded employee IRD numbers. Where this occurs, the individual will not appear to be employed at that organisation in that month. This will affect the individual s employment history, potentially creating false transitions between employment and non-employment. Some, but not all, of these job records are corrected through LEED repair processes. Incorrect industry or region allocated As shown in figure 1, employees are linked to geographic units on the business frame. This is done because the geographic units have more detailed and accurate industrial and regional information. The majority of employers have only a single geographic location, so assigning the employee is automatic. Around 53 percent of all jobs are at such single location employers. For the other jobs, at multi-location employers, LEED needs a method to allocate them to a single geographic unit. This is done using an algorithm which does two things simultaneously. First it allocates employees to geographic units they reside closest to. Second, it maintains job counts at each geographic unit in the same proportion as those previously provided by the employers in business survey returns to Statistics NZ. 9

10 This is an imperfect system, as in some cases the employee does not actually work at the closest employer location. In addition, there can be significant lags before employee address changes are updated with IRD, so that the employee is allocated using an out of date address. Both these factors can lead to employees being assigned to the wrong industry or region. 9 Administrative job churn The longitudinal nature of LEED allows labour market variables, such as worker turnover and job tenure, to be calculated. However, because the data underpinning LEED was not collected for statistical purposes, administrative job churn or artificial worker turnover can be generated, making it difficult to estimate these variables accurately. There are two processes that can cause administrative churn. 1. Administrative births and deaths on the BF These occur when the BF incorrectly ceases a business and births a new one, when in reality the business is an ongoing economic concern. This can occur when an existing business starts using a new IRD number to file its EMS form for administrative reasons, or when a business is purchased by, or merged with, another business. Without any action in LEED, it would appear that all the employees at the businesses ceased employment with one employer and began employment with another. To minimise this, LEED has repair processes that are applied at the geographic unit level. 2. Allocations LEED geographic units at multi-location employers are affected by another kind of administrative job churn. This is caused by the allocations process described in the previous section. Some situations 10 require all jobs at an employer to be reallocated across geographic locations. This causes many employees to be artificially moved from one geographic unit to another. This artificial churn can be removed by measuring job change at both the geographic unit and enterprise level. 11 Monthly structure This poses some issues in the measurement of injury and employment (Crichton et al, 2005): If person starts (or leaves) their work part way through the month, then the earnings they receive will reflect only part-month earnings, not what they would have received if they worked for the entire month. Individuals may receive both ACC and wages and salaries in a given month. It is not possible to determine exactly which part of the month the earnings relate to. 12 Following on from the last point, months where individuals receive income from more than one source (eg multiple employers, benefit transfers, or ACC), it is not possible to confirm whether the income was received concurrently or during different times of the month. 9 Around 30 percent of all jobs are at employers that have geographic locations in multiple (1 digit ANZSIC 2006) industries. Around 35 percent of all jobs are at employers that have geographic locations in multiple regional councils. Some fraction of these jobs will have incorrect industries or regions. 10 There are two such situations. The first is when there are significant changes to the geographic units of an employer. These are often caused by mergers with other businesses or significant restructurings. The second is when the allocated employees at each geographic location become significantly out of alignment with the proportion previously provided by employers in business survey returns. 11 See Papadopoulos (2008) for more detail. 12 Most job start and end dates in LEED have been randomly imputed. 10

11 Activity and income receipt may not occur in the same month. For example, earnings receipt in a particular month can be for work undertaken in the past. Similarly, LEED ACC and benefit payment spells may not correspond with actual spells of eligibility. LEED spells of one month include injuries that result in only a single day or a few days of compensation, while many two-month spells may cover only a week or two of compensation, over two consecutive calendar months. Hence, gaps in income receipt of less than a full calendar month cannot be identified in LEED, and the spell will therefore be seen as continuous. This makes continuous months on ACC an imperfect measure of the actual duration of the injury. Lack of variables As previously mentioned, analysis of ACC receipt using LEED is limited by the absence of important demographic variables such as education, ethnicity, and occupation. This limits explanatory power. In particular, occupation is important in explaining an individual s risk of workplace injury. Additionally, although information on the self-employed is available in LEED, it is not of as high a quality as that for wage and salary earners. In particular, most self-employment income is reported annually to IRD. This limits its use in transition analysis, which is why the self-employed are excluded from most of the statistics in this paper. 2.3 The injured population in LEED Although injured individuals cannot be directly identified in LEED, LEED does capture all payments made to individuals from ACC that have tax deducted at source. This is a subset of the official injured population. LEED cannot measure less serious injuries that result in treatment claims those that only involve direct payments from ACC to medical professionals. LEED also does not capture any injuries to non-earners, because they do not receive any taxable payments from ACC. ACC claims that progress beyond treatment claims are called entitlement claims. LEED does capture most entitlement claims that affect earners. Specifically, LEED contains weekly worker compensation (ie taxable payment made to cover loss of earnings, loss of potential earnings, and accidental death 13 ) that are paid to individuals by ACC. However, it does not capture all entitlement claim payments. This is because: Other payments paid out under entitlement claims to individuals do not have tax deducted from source, and so do not appear in LEED. These include payments for home help, child care, travel costs, and permanent impairment, as well as survivor s grants. These individuals will still be identifiable in LEED, as long as they are also receiving weekly workers compensation from ACC. Large employers may elect to join the ACC Partnership Programme. By doing this, the employer receives discounts on ACC levies, and in return manages claims for injuries at their workplace(s) and pays the resulting workers compensation. Approximately 25 percent of all employees are covered through these arrangements. Workers compensation for any work-related injuries that these employees may have 13 Loss of earnings payments are paid to employees and the self employed when they are unable to work because of an injury covered by ACC. Loss of potential earnings payments are made to those either under 18 years of age, or over 18 years and in full-time study or training, for work that would have been started had the injury not occurred. Accidental death payments are made to dependents that are reliant on financial support from someone who has died from an injury. 11

12 will not be identifiable in LEED. 14 Instead it will appear as wage and salary payments from the employer. The exclusion of a significant proportion of workplace injuries at large employers may introduce bias to injury analysis using LEED. In addition, ACC has an employer re-imbursement programme with around 1,800 employers, where employers continue to pay employees while they are off work due to injury (either work or non-work related). The employers are later reimbursed by ACC. This means that these injured individuals can also not be identified in LEED, instead appearing as employees. Crichton et al (2004) stated that ACC figures indicated that approximately 4 percent of all claims for earnings compensation are associated with individuals that are employed by firms participating in the reimbursement programme. Between July 1999 and June 2000, most new workplace claims were not identifiable in LEED. Over that period, employers were required to take out accident insurance cover with private insurers. These claims can therefore not be identified in LEED. 2.4 Variables Table A Individual level variables Variable Source data Quality Sex Derived from sex-specific title (eg Mr, Ms etc.) for 98 percent No significant data issues. of employees. Imputed from first name for the remaining 2 percent. Age Over 97 percent of dates of birth are obtained from IRD No significant data issues. records. Around 2.5 percent of people who receive taxable income each year have their age imputed using income source. 15 Higher proportions of older people have their age imputed (their age can be ascertained from their receipt of NZ Superannuation). The imputation rate is around 2.0 percent of people aged 15 to 64 years of age who received taxable income over the tax year. Region of residence LEED regional information is available at the regional council or territorial authority level. Moderate quality. - Address change without (or with delayed) IRD notification common. IRD address information was only available for 2001 on. - Addresses from 2001 were backdated to the Earnings / income A fraction of records (0.002 percent) have a detectable error when compared with PAYE amount and earnings history. All detected errors have been replaced with statistically consistent values. Monthly data may include lump sum or part-month payments. beginning of LEED (1999). No significant data issues. As previously mentioned, LEED contains only some information on individuals. However, it has more complete firm information. The tables A and B summarise these variables and their data quality. 14 Employers in this scheme accept liability for either a nominated management period or the duration of the claim. Each year, from employers in the first group, the management of around 200 injured people is transferred back to ACC. These people will then appear in LEED. 15 The sources of incomes that can be identified in LEED are wages and salaries, self-employment, ACC, income-tested benefits, NZ superannuation, student allowances and paid parental leave. 12

13 Table B Firm level variables Variable Source data Quality Region of employment Industry Institutional sector Firm size LEED regional information is available at the regional council or territorial authority level. This is obtained from the BF, which contains information on the geographic unit an employee is allocated to. Industry of the employer is classified according to both the 1996 and 2006 ANZSIC standard industry classifications. This is obtained from the BF, which contains information on the geographic unit an employee is allocated to. Whether the employer is privately or publicly owned. This is obtained from the BF. The firm size dimension refers to the size of the employer at the enterprise level (not at the geographic unit level). Generally high quality. - May be incorrect if region of residence is out of date and work at multi-location employer. Generally high quality. - Some employees of multilocation employers, who do not work at the location nearest their residential address, may have an incorrect industry. No significant data issues. No significant data issues. 2.5 What role can LEED play? To understand how LEED can be used in answering policy questions on the performance of the ACC scheme, we should quickly outline the data that ACC already has at its disposal. In most ways, this information is superior to that contained in LEED, as shown in table C. Table C ACC data versus that in LEED Comparison ACC LEED Population All injury claims made. Captures all ACC payments that have tax deducted at source. This is a subset of all injury claims. Excludes less serious treatment claims. Includes most entitlement claims that result in weekly worker compensation. There may be bias in LEED from the exclusion of entitlement claims occurring at larger employers. Individual characteristics Employer characteristics Injury characteristics Age Sex Geographic location Earnings at injury Ethnicity Occupation Industry Location Size based on payroll Scene of accident Activity prior to injury Injury diagnosis Treatment received Cost of treatment Compensation amount Duration of spell Rehabilitation outcome Age Sex Geographic location Earnings at injury Earnings pre- and post-injury Benefits taxed at source pre- and post-injury Ethnicity (for some populations) Industry Location Size based on payroll or jobs Worker compensation amount Duration worker compensation paid Given this situation, most policy questions can be better answered using ACC s own data. Examples of these policy questions include: How many people are injured? What is the total cost of injury? How do injuries and rehabilitation outcomes vary by individual and employer characteristics? 13

14 Where LEED provides value is through its longitudinal nature. This allows both the employment and benefit histories prior to a spell on ACC, and following a spell, to be looked at, as shown in table D. Table D The history and future outcomes of a spell on ACC History Outcomes Injured population (prior to transition on ACC) (after transition off ACC) Employment Employment employment in a month employment in a month average months in employment over a specified period average months in employment over a specified period continuous months in employment Individuals who started an ACC spell. continuous months in employment employer transitions employer transitions Benefit Either in a month, a year, or Benefit over several years. benefit receipt in a month benefit receipt in a month average number of months average number of months Need enough LEED months receiving benefit over a receiving benefit over a before and after ACC spell to specified period specified period measure history and outcomes. continuous months continuous months receiving benefit receiving benefit Earnings average monthly earnings over months pre transition Earnings average monthly earnings over months post transition Dimensions of analysis Age Sex Region of residence Industry Firm size Region of employment Institutional sector Type of benefit received Number of months receiving ACC (ie spell duration) Income / earnings This means that LEED is useful in answering policy-related questions, such as: Do injured workers return to work? What proportion of injured individuals returns to work within a certain period? Does having marginal attachment to the labour market prior to injury affect the duration of workers compensation receipt? Crichton et al (2004, 2005) focused on the first two points (this is looked at in section 4). The next part of this paper looks at the third point. 14

15 3. Descriptive Statistics using LEED This section provides some summary statistics using up to date LEED data. The LEED series is available from 1 April 1999 to September However, ACC payments are not identifiable in LEED during the period July 1999 June 2000 when the ACC scheme was privatised. The data will be used to investigate a policy-related question raised in the first section of this paper: Does the entry of marginally attached workers since 2001 explain the increased time taken for people to rehabilitate after an injury and return to employment? For this to be true, one of the two following conditions should be apparent in the data: 1. That the previously marginally attached make up an increasing share of new ACC spells, and that they have longer duration ACC spells than average, or 2. That the average ACC spell duration for the previously marginally attached has increased more than for all new ACC spells. 3.1 All new ACC spells (including self-employed) The injured population in this section is defined as individuals who started an ACC spell (whether they were employed previously or not) over the period 1 April 2001 March 2007, and were aged between 15 and 69 years when the spell ended. An ACC spell is defined as a series of consecutive months receiving ACC. Over the six-year period, there were 383,700 new ACC spells. The number of new ACC spells by tax year, together with the total number of individuals who received wages and salaries, or self-employment income, as their main income source over the tax year, is shown in table 1 below. Table 1 All new spells (including self-employed) and total employment Tax year Number of new ACC spells Number of individuals who started an ACC spell (A) Number of individuals receiving wages and salaries or self-employment as their main income source (B) Individuals who started ACC spell / Individuals receiving wages and salaries or selfemployment as main income source (A/B) (percent) ,720 52,500 1,938, ,880 56,440 2,006, ,020 59,350 2,079, ,910 59,360 2,157, ,870 62,860 2,224, ,300 65,400 2,268, The number of new ACC spells, and the number of individuals starting ACC spells (some individuals start more than one spell each year), has increased steadily over the years. However, the increase in both these numbers as proportions of the total number of individuals employed has been less strong. This suggests that the ACC up-take has increased by slightly more than the growth in employment over the period. 15

16 Table 2 shows that the number of new claims in LEED during March years (B) is around 5 percent less than those recorded by ACC. 16 This difference should probably be larger (around 10 percent) given the differences in coverage outlined in section 2.3, especially claims under the ACC Partnership Programme, which are measured in the ACC statistics, but not in LEED. This may be because LEED cannot identify when new spells by the same individual are actually for the same injury. This would lead to double counting in column (B). A way to adjust for this, is to look at the number the number of individuals who started spells in LEED (C). These numbers are around 10 percent less than those from the ACC system (A). The difference between the ACC and LEED sourced numbers in table 2 have increased over time. This is probably due to the increased take up of the ACC Partnership Programme. This may mean that growth in claims in LEED over time, as discussed in the rest of this section, will be understated. This will be particularly true for variables that are affected by size of employer, such as industry. Table 2 All new spells (including self-employed) in LEED and ACC system Year New weekly compensation claims as recorded in ACC system June year (A) Number of new ACC spells in LEED March year (B) Number of individuals who started an ACC spell (C) ,770 56,720 52, ,190 60,880 56, ,260 64,020 59, ,570 63,910 59, ,070 67,870 62, ,220 70,300 65,400 Table 3 shows that the majority of ACC spells have duration of one month. However, over the years, the proportion of spells with one-month duration has decreased, while the proportion of spells with duration of four months or more have increased. Table 3 Proportion of all new spells (including self-employed), by spell duration Continuous months on ACC (percent) Grand Tax year Total New ACC spells (wage and salary only) A key benefit of LEED, is the ability to compare the situation prior to injury with that postinjury. One policy related question that LEED can address, is how the impact of injury differs for different types of employees. To do this, this section focuses on a subset of new ACC spells that were the basis of the last section. New ACC spells are restricted from now on to

17 those belonging to individuals who had prior employment in LEED (in general, taxable worker compensation is payable to those in employment). As per Crichton et al (2005), we define this population as those who received wages and salaries 17 in either the month prior to, or the first month of, the ACC spell. The number of new ACC spells where the individual had prior wage and salary employment is shown in table 4 below. This, as a proportion of all individuals who had wages and salaries as their main source of income over the tax year, seems to have increased slightly over the period. Table 4 New spells Tax year Number of new ACC spells (wage and salary only) As a proportion of all new spells (including self-employed) (percent) As a proportion of all who received wages and salaries as their main income source (percent) , , , , , , The number of new ACC spells with wage and salary employment before transitioning onto ACC, is only around 80 percent of all new ACC spells from table 1 (though this proportion has increased slightly over the period). This difference can be explained by: 1) Our definition of employed. We have restricted employment to wage and salaried employment. Hence, table 4 excludes individuals who were in receipt of selfemployment income prior to injury. 18 2) In some cases, ACC is paid out to cover loss of potential earnings, or is paid to dependents of deceased persons (see footnote 12). In these cases, it is not necessary for the individual to have prior employment. Table 5 Proportion of new spells by spell duration Tax year Continuous months on ACC (percent) Grand Total Table 5 again looks at ACC spell duration, this time only for those new ACC spells with prior wage and salary employment. These spells have less spell length on average. This suggests that those transitioning onto ACC from self-employment are more likely to stay on ACC longer. 17 Most self-employment income is not available monthly, so is excluded. 18 Of the new ACC spells excluded from table 5, but included in table 1, around 75 percent belonged to individuals who received self-employment income in either the tax year the ACC spell began in, or the previous tax year. 17

18 Table 6 shows that males constitute fewer than 70 percent of all new claimants each year, and that this proportion has not changed much over the six-year period. Table 6 Proportion of new spells by sex Sex (percent) Female Male However, there does seem to have been a change in the ages of those beginning a new ACC spell. Table 7 shows that the average age of those beginning a new ACC spell has increased. However, the average age of all employed has also increased, and at a similar rate. Table 7 Average ages for new ACC recipients and the employed Tax year Average age of individuals who started a new ACC spells Average age of all wage and salary recipients Difference Table 8 shows how prior monthly earnings vary by ACC spell length. As Crichton et al (2005) state (p9); Individuals with longer duration spells are also more likely to have higher average earnings in months employed prior to injury however, these differences are not large and the earnings difference may be caused by the increased age of the individuals with longer duration injuries. Table 9 also seems to show a slight decrease in average prior monthly earnings for new ACC spells with very long durations. Table 8 Average prior monthly earnings, by spell duration Spell duration Average prior monthly earnings ($) ,190 2,250 2,350 2,460 2,560 2, ,300 2,370 2,440 2,570 2,640 2, ,320 2,430 2,500 2,590 2,750 2, ,360 2,400 2,520 2,660 2,770 2, ,230 2,360 2,490 2,590 2,780 2, ,110 2,200 2,370 2,880 2,660 2, ,160 2,500 2,340 2,570 2,610 2,730 Total new ACC spells 2,250 2,330 2,410 2,540 2,640 2,790 Total all wage and salaries 2,610 2,690 2,810 2,930 3,070 3,210 Table 8 also shows that average prior earnings for new ACC spells are lower than average earnings for all wage and salary earners. 19 Note that the prior earnings of some individuals have been interrupted by the occurrence of the injury. 19 Who are aged between 15 and 69 years, as per the population for the new ACC spells. 18

19 Table 9 looks at how new ACC spells are spread across regions; while table 10 does the same for all those whose main income source was wages and salaries. By comparing the two tables, it can be seen that Auckland and Wellington are underrepresented in new ACC spells, while the more rural regions are overrepresented. This is probably due to differing industrial compositions across regions, leading to differing injury risk profiles. Table 9 Proportion of new spells by regional council Regional council Proportion of new ACC spells (percent) Auckland Bay of Plenty Canterbury Gisborne Hawke's Bay Manawatu-Wanganui Marlborough Nelson Northland Otago Southland Taranaki Tasman Waikato Wellington West Coast Grand total In general, changes in the proportion of new ACC spells by region across time are related to changes in the proportions of wage and salary earners. Table 11 looks at new ACC spells by industry. 20 The most notable change over the six years, is that the proportion of injured who were previously engaged in the construction industry has increased from 10.8 percent to 15.2 percent. This is partly a reflection of the strong growth in employment in this industry over this period. 20 Some individuals were employed at multiple employers in the month due to multiple job holding or job changes. In these cases, the individual was assigned to the industry of the employer who paid the highest wages and salaries in the month. 19

20 Table 10 Proportion of all wage and salary earners, by regional council Regional council Proportion of new ACC spells (percent) Auckland Bay of Plenty Canterbury Gisborne Hawke's Bay Manawatu-Wanganui Marlborough Nelson Northland Otago Southland Taranaki Tasman Waikato Wellington West Coast Grand total Table 11 New spells by industry Proportion of ACC spells by industry of Industry employment (percent) Agriculture, forestry, and fishing Mining Manufacturing Electricity, gas, and water supply Construction Wholesale trade Retail trade Accommodation, cafes, and restaurants Transport and storage Communication services Finance and insurance Property and business services Government administration and defence Education Health and community services Cultural and recreational services Personal and other services Grand total However, growth in new ACC spells from employees in construction is even higher than this growth. This is shown in figure 2 (and table 12) which displays the number of new ACC spells in each industry, as a proportion of total wage and salaried employment 21 in that industry. The number of ACC spells in the construction industry has increased by more than 21 Age is also restricted to year olds, as for new ACC spells. 20

21 overall growth in employment in this industry. Figure 2 shows that for most other industries, ACC spells have increased in line with overall employment growth. Figure 2 New spells as a proportion of employment in industry Table 12 New spells as proportion of employment in industry (percent) Industry Agriculture, forestry, and fishing Mining Manufacturing Electricity, gas, and water supply Construction Wholesale trade Retail trade Accommodation, cafes, and restaurants Transport and storage Communication services Finance and insurance Property and business services Government administration and defence Education Health and community services Cultural and recreational services Personal and other services All industries Figure 2 (and table 12) also gives some indication of the relative injury rates per industry. There are four distinct groups of injury rates. Alone in the top group is construction. The next riskiest group consists of; agriculture, forestry and fishing, mining, manufacturing, and 21

22 transport and storage. The group with the lowest injury rates consists of; education, finance and insurance, and government administration and defence. Table 13 summarises the average months 22 on ACC for new spells by prior industry. What is surprising is that there is such a small variation between industries. In almost all industries, and all years, the average duration is between 2.5 months and 3.5 months. It should also be noted that, in almost all industries, average duration increased over the six-year period. Table 13 Average months of duration of new spell by industry Industry Agriculture, forestry, and fishing Mining Manufacturing Electricity, gas, and water supply Construction Wholesale trade Retail trade Accommodation, cafes, and restaurants Transport and storage Communication services Finance and insurance Property and business services Government administration and defence Education Health and community services Cultural and recreational services Personal and other services All industries New ACC spells (wage and salary only) with prior benefit receipt LEED can also provide information on prior benefit receipt for those starting new ACC spells. If prior benefit receipt is an adequate proxy for signalling marginal labour force attachment, then it can be used to indicate whether the attachment of those entering ACC spells has varied over the six-year period, and whether attachment is related to the duration of the ACC spell. This section therefore focuses on those who began new ACC spells, and had prior wage and salary employment (ie the population for the last section), but who also received an incometested benefit in any of the 12 months prior to the start of the ACC spell. Their numbers are shown in table 14. The number of new ACC spells with prior benefit receipt has been declining since And as a proportion of all new ACC spells with prior wage and salary employment, they have been declining since It seems that if there had been an increase in ACC spells with prior benefit receipt during this labour market upswing, it occurred before The maximum spell length was capped at 13 months to compensate for the effect of right censored spells (ie 2007 only has a window of around 13 months in the future to measure spell length). Therefore, this means that duration is underestimated. 23 Most new workplace claims were not identifiable in LEED between July 1999 and June 2000 due to privatisation. 22

23 Table 14 New spells with prior benefit receipt (12 months) Tax year Number of new ACC spells with prior benefit receipt Number of new ACC spells As a proportion of all new spells (percent) ,610 44, ,820 48, ,380 51, ,600 50, ,970 54, ,480 56, To check the robustness of this result, table 15 replicates table 14, this time with prior benefit receipt measured over the preceding 24 months. Other than a level difference, the same trend is observed. For the rest of this section, benefit receipt is measured over the preceding 12 months. Tables with benefit receipt measured over the preceding 24 months are generally similar. 24 Table 15 New spells with prior benefit receipt (24 months) Tax year Number of new Number of new ACC As a proportion of new ACC spells with spells spells (%) prior benefit receipt ,400 44, ,840 48, ,430 51, ,430 50, ,700 54, ,770 56, Table 16 looks at the duration for new ACC spells with prior benefit receipt. The differences, when compared to all new ACC spells (table 5), are small. Both populations have seen an increase in the proportion of spells with durations of 10 months or more, but this increase has been stronger among those with prior benefit receipt. Table 16 Proportion of new spells with prior benefit receipt, by spell duration Tax year Proportion of spells Continuous months on ACC (%) Grand Total Comparing table 17 with table 6, shows that, among individuals starting new ACC spells, those with prior benefit receipt were more likely to be female. This is not surprising given that more females receive income-tested benefits than males. 24 They tend to be between those for the prior benefit receipt (12 months) population and those from the previous section. This suggests that the further back the window for measuring prior benefit receipt is, the more like the general employed population your selection becomes. 23

24 Table 17 Proportion of new spells with prior benefit receipt, by sex Sex (percent) Female Male Table 17 also shows that females increased their share of new ACC spells with prior benefit receipt over the six-year period. This is probably due to domestic purpose benefit recipients having an increasing share of total beneficiaries, as the number of unemployment beneficiaries fell over this period. Table 18 shows that, whereas individuals starting new ACC spells have similar ages to all employed individuals, those who have prior benefit receipt are younger on average. This gap closed over the six-year period however, as the average age of those with prior benefit receipt increased at a faster rate. Table 18 Average ages for new spell recipients with prior benefit receipt and the employed Tax year New ACC spells with prior benefit receipt New ACC spells All employed Table 19 shows that prior monthly earnings increase with length of ACC spell for those with previous benefit receipt (as in table 8). It also shows that average prior earnings for new ACC spells with prior benefit receipt are, as would be expected, lower than those either for all new ACC spells or all wage and salary earners. Table 19 Average prior monthly earnings, by cumulative months in spell Cumulative months in spell Average prior monthly earnings ($) ,550 1,570 1,640 1,730 1,720 1, ,660 1,710 1,770 1,960 1,820 1, ,670 1,720 1,770 1,790 1,950 1, ,710 1,740 1,780 1,830 1,990 2, ,600 1,770 1,770 1,830 1,910 1, ,620 1,690 1,780 1,840 1,720 1, ,600 1,810 1,740 1,920 1,950 1,900 Total prior benefit 1,610 1,660 1,710 1,820 1,820 1,860 Total new ACC spells 2,250 2,330 2,410 2,540 2,640 2,790 Total all wage and salaries 2,610 2,690 2,810 2,930 3,070 3,210 Table 20 looks at new ACC spells with prior benefit receipt by regional council area. It can be seen that Auckland has an even smaller share of these ACC spells than all new ACC spells (table 9, which again has a smaller share than that of total wage and salary earners, 24

25 shown in table 10). This is partly explained by Auckland having a below-average ratio of income tested benefit recipients to wage and salary earners. 25 Table 20 Proportion of new spells with prior benefit receipt, by regional council Regional council Proportion of new ACC spells (percent) Auckland Bay of Plenty Canterbury Gisborne Hawke's Bay Manawatu-Wanganui Marlborough Nelson Northland Otago Southland Taranaki Tasman Waikato Wellington West Coast Grand Total Table 21 looks at new ACC spells with prior benefit receipt by industry. The differences with table 11, which contains the distribution of all new ACC spells, seem to reflect the differing likelihoods of industries hiring beneficiaries. Agriculture, forestry and fishing, accommodation, cafes and restaurants, and property and business services 26 have higher shares. Finance and insurance, and government administration and defence, have lower shares. Table 21 also shows that the growth in share of new ACC spells with prior benefit receipt by construction is not as strong as it was when all new ACC spells were considered. 27 Figure 3 (and table 22) displays the number of new ACC spells with prior benefit receipt as a proportion of total wage and salaried employment 28 in the industry. Table 15 previously showed that new ACC spells with prior benefit receipt have fallen strongly as a percentage of total wage and salary employees. Figure 3 shows that this is also true across most industries. Agriculture, forestry and fishing, and construction have the highest proportion of prior benefit receipt spells, but they also showed the steepest decline over the six-year period. 25 LEED annual table builder, table Temping agencies are classified in this industry. 27 Using LEED data, Saravanaperumal (2008) found that there was a reduced reliance on former benefit recipients for new employees in the construction industry over this period. This was likely due to the reduction in total beneficiary numbers during this period. 28 Age is also restricted to year olds, as for new ACC spells. 25

26 Table 21 New spells with prior benefit receipt, by industry Industry ACC spells by industry of employment (percent) Agriculture, forestry, and fishing Mining Manufacturing Electricity, gas, and water supply Construction Wholesale trade Retail trade Accommodation, cafes, and restaurants Transport and storage Communication services Finance and insurance Property and business services Government administration and defence Education Health and community services Cultural and recreational services Personal and other services Grand total Figure 3 New spells with prior benefit receipt as a proportion of employment in industry 26

27 Table 22 New spells with prior benefit receipt as a proportion employment in industry (percent) Industry Agriculture, forestry, and fishing Mining Manufacturing Electricity, gas, and water Supply Construction Wholesale trade Retail trade Accommodation, cafes, and restaurants Transport and storage Communication services Finance and insurance Property and business services Government administration and defence Education Health and community services Cultural and recreational services Personal and other services All industries Table 23 summarises the average months 29 on ACC for new spells with prior benefit receipt, by prior industry. The results are very similar to those for all ACC spells in table 13. The rate of increase in average duration for spells with prior benefit receipt has been slightly steeper, from 2.6 months in 2002 to 3.1 months in 2007 (compared with 2.7 months to 3.0 months for all ACC spells). Industries contributing to this steeper increase were; construction, transport and storage, property and business services, and education. This increase in duration, for those with prior benefit receipt, seems too small to account for the overall increase in spell duration over this period. And given that it was shown earlier that the proportion of new ACC spells that had prior benefit receipt had actually fallen over this period, it seems that neither of the conditions given at the start of section 3 holds. The next section sees if this result holds if marginal attachment is defined in another way. 29 The maximum spell length was capped at 13 months to compensate for the effect of right censored spells (ie 2007 only has a window of around 13 months in the future to measure spell length). Therefore, this mean that duration is underestimated. 27

28 Table 23 Average months of duration of new spells with prior benefit receipt, by industry Industry Agriculture, forestry, and fishing Mining Manufacturing Electricity, gas, and water supply Construction Wholesale trade Retail trade Accommodation, cafes, and restaurants Transport and storage Communication services Finance and insurance Property and business services Government administration and defence Education Health and community services Cultural and recreational services Personal and other services All industries New ACC spells with marginal prior employment Another way to identify those with marginal labour force attachment, other than prior benefit receipt, is to look at the number of months prior to injury that individuals are employed. Prior research (summarised in the next section) suggests that using such an indicator may give different results than using prior benefit receipt. This section once again looks at those who began a new ACC spell, and had received wages and salaries in either the month prior to, or in the first month of, the ACC spell; but this time restricts the population to those who received wages and salaries for less than nine of the 12 months prior to the start of the ACC spell. Their numbers are shown in table 24. Although less drastic than for those with prior benefit receipt (table 14), this definition of marginal attachment also saw a decline in share of new ACC spells over this period. Table 24 New spells with wages and salaries received in less than nine of the preceding 12 months Tax year Number of new ACC spells with W&S in < 9 of prior 12 months Number of new ACC spells As a proportion of all new spells (percent) ,167 44, ,574 48, ,986 51, ,034 50, ,241 54, ,477 56,

29 To check the robustness of this result, table 25 replicates table 24, this time with a stricter restriction of less than five of the preceding 12 months. This group shows a similar decline in share to the less than nine of the preceding 12 months group. For the rest of this section, the less than 9 months group is used. Results for the less than 5 months group are generally similar. Table 25 New spells with wages and salaries received in less than five of the preceding 12 months Tax year Number of new ACC spells with W&S in < five of prior 12 months Number of new ACC spells As a proportion of all new spells (percent) ,697 44, ,807 48, ,959 51, ,011 50, ,006 54, ,088 56, Table 26 looks at the duration for new ACC spells for this new group. It is very similar to the same table for the prior benefit receipt group (table 16), and the all new ACC spells group (table 5). Table 26 Proportion of new spells with wages and salaries received in less than 9 of the preceding 12 months, by spell duration Tax year Proportion of spells Continuous months on ACC (percent) Grand total Finally, table 27 shows the average months 30 on ACC by prior industry, for new ACC spells where wages and salaries were received in less than nine of the preceding 12 months. Again this table is very similar to table 23 (those with prior benefit receipt) and table 13 (all new ACC spells). 30 The maximum spell length was capped at 13 months to compensate for the effect of right censored spells (ie 2007 only has a window of around 13 months in the future to measure spell length). Therefore, this mean that duration is underestimated. 29

30 Table 27 Average months of duration of new spell with wages and salaries received in less than nine of the preceding 12 months, by industry Industry Agriculture, forestry, and fishing Mining Manufacturing Electricity, gas, and water Supply Construction Wholesale trade Retail trade Accommodation, cafes, and restaurants Transport and storage Communication services Finance and insurance Property and business services Government administration and defence Education Health and community services Cultural and recreational services Personal and other services All industries Summary The descriptive statistics from LEED presented in this section confirm some key findings from ACC. They show that new ACC spells have increased since 2001, in both absolute terms and relative to total employment. They also show that the average duration of these spells has also increased. Sections 3.2 and 3.3 shed light on the question of whether the entry of workers marginally attached to the labour force, since 2001, explains the increased time taken for people to rehabilitate after an injury and return to employment. Section 3.1 gave two conditions for this to be true, and we repeat them below: 1. That the previously marginally attached make up an increasing share of new ACC spells, and that they have longer duration ACC spells than average, or 2. That the average ACC spell duration for the previously marginally attached has increased more than for all new ACC spells. Two indicators of marginal attachment were used prior benefit receipt and previous months employed. Neither indicator supported either condition. The proportion of new ACC spells belonging to marginally attached people appears to have actually fallen over this period. It appears that the entry of the marginally attached into employment during this economic upturn may have peaked in 2002 or earlier. In addition, the increase in ACC spell duration for the marginally attached seems too similar to the overall increase to explain it. 30

31 4. Prior LEED research 4.1 Introduction More detailed and rigorous investigations into the effect of injury on labour market outcomes were carried out by Crichton et al (2004, 2005). This part of the report contains a summary of their work, and of the results that pertain to the policy related questions that were raised in section 1.1. Crichton et al (2005) sought to examine the effects of injury on future employment, benefit rates, and income. It used LEED data from the five-year period from April 1999 to March The receipt of ACC worker compensation was used as a proxy for being injured. A series of consecutive months receiving ACC was termed an ACC spell. As information on the severity of the injury is not available in LEED, the duration of the ACC spell was used as a proxy for severity of the injury. 31 The study population was, all individuals whose first observed ACC spell was over the period October 2000 to March and who were employees and aged years in the month prior to injury. Table 2 in Crichton et al (2005) presents characteristics of this injured population, and compares them to the rest of the population. It shows that, as commented upon in the paper, the injured population is more likely to have spent more time in receipt of welfare benefits prior to injury (shown below in table 28). Table 28 Characteristics of the injured and non-injured Population (from Crichton et al, 2005 table 2) Sample Noninjured Total Length in months of first ACC spell characteristics injured Mean age (years) % months employed prior to injury % months on benefit prior to injury % months not observed prior to injury Number of individuals 582, ,980 52,640 29,590 12,340 10,730 2,620 1,130 1,290 This table also shows (our interpretation) that as the length of their ACC spell increased, people were more likely to have spent time in receipt of benefit income prior to injury. They were, however, also more likely to have spent more time in receipt of wages and salaries prior to injury as well. This apparent anomaly seems to be explained to us by the older average ages of those in longer ACC spells. Older individuals are more likely to have been previously observed in LEED by receiving a taxable income. Later analysis will control for the effects of age. 31 Crichton et al (2005) explain why this is an imperfect measure. 32 Individuals who received ACC in the period prior to October 2000 were excluded for two reasons. First, having a sufficient back series prior to the injury provided details on which a matched sample could be derived so as to control for differences between injured and non-injured population. Second, most workplace injury claims were not identifiable in LEED during the period July 1999 to June 2000 when the ACC scheme was privatised. 31

32 4.2 Calculating counterfactual outcomes for injured workers The previous example shows, as the paper makes clear, that descriptive statistics can be potentially misleading. In their figures 1 and 2, Crichton et al (2005) present simple employment and benefit histories, and outcomes for the injured. They state that this type of analysis has shortcomings. In particular: 1. Without knowing the equivalent history or future for a similar non-injured worker during the time period, it is not possible to judge whether, say, a 10 percent decline in employment rate post-injury is a good or bad outcome. 2. Comparing people by severity of injury (say, by using length of ACC spell as proxy) can be problematic. It can be difficult to ascertain whether individuals with minor injuries are actually unaffected by the injury. Therefore, it is not possible to judge whether they are an appropriate comparison group. As Crichton et al (2005) explain, to calculate the effect of injury on labour market outcomes, they need to be compared to an estimate of what injured individuals labour market outcome would have been in the absence of the injury. To do this, a non-injured comparison group was obtained by matching injured population to a random sample of individuals with similar observed characteristics in the month before the individuals were injured. Two match criteria were used; individual match and firm match. 4.3 Characteristics of the matched samples The individual match used individual level information for matching, such as sex, age, region, months employed and earnings levels prior to injury, firm size, and industry. The firm level match matched injured individuals exclusively to their co-workers on region and level of prior earnings. Neither match was explicitly on benefit history. Table 3 in Crichton et al (2005) (shown in table 29 below) shows there was little difference in benefit receipt in the six months prior to injury for the matched populations. However, some differences in prior benefit receipt were evident during the 6 48 months before injury. These are explored further in section 4.4. Table 29 Characteristics of the matched injured population (from Crichton et al, 2005 table 3) Sample characteristics Percent that received no benefits in the six months prior to injury Percent that received benefits one to five months in the six months prior to injury Percent that received benefits in all six months prior to injury Injured population Individual match Firm match Number of individuals 110, ,960 85,390 Crichton et al (2005) states that LEED offered three advantages in terms of methodology: Its comprehensive nature allowed high quality of matches. It could identify and therefore match workers within the same firm. Its longitudinal nature allowed differences-in-differences methodology to be applied, to control for permanent unobserved individual heterogeneity (see section 4.5). 32

33 4.4 Simple matching estimates Figure 4 of Crichton et al (2005) compared the employment and benefit receipt rates of injured and matched non-injured workers prior to and after injury. 33 The top two lines are employment rates, while the bottom two lines represent benefit rates. Month t-1 is the month immediately prior to the injury, month t+1 is the first month after the ACC spell ends, while the vertical line (at month 0) indicates the injury period. By definition all workers are employed at month t-1. Figure 4 Matched comparison of employment and benefit receipt rates (from Crichton et al, 2005 figure 4) (a) spell length=1 (c) spell length= Proportion Proportion Months prior to ACC spell Months after ACC spell (d) spell length=4 6 1 Months prior to ACC spell Months after ACC spell (f) spell length= Proportion Proportion Months prior to ACC spell Months after ACC spell (g) spell length= Months prior to ACC spell Months after ACC spell (h) spell length= Proportion Proportion Months prior to ACC spell Months after ACC spell Months prior to ACC spell Months after ACC spell Matched injured population _ Matched non-injured sample From these graphs come the following points: Crichton et al (2005) state that post-injury outcomes of injured workers who receive ACC for more than three months are considerably worse than the non-injured 33 These results are from the individual match. The firm match results are very similar. 33

34 comparison group. This can be seen in both employment and benefit rates. Pre-injury differences are much smaller than post-injury differences. Our interpretation of this result is that though the injured were more likely to be on benefit in the past than the matched population, they were even more likely to be on benefit post-injury. As Crichton et al (2005) state, differences in benefit receipt and employment rates prior to injury suggest the presence of unobserved differences (such as occupation and/or individual propensity to experience injury) between the two groups. Injured workers who receive ACC for three months or less have slightly lower employment rates, and slightly higher benefit rates post injury, than the non-injured matched population. However, they also have similar differences in employment and benefit rates prior to injury. Again, as Crichton et al (2005) state, this suggests the presence of unobserved differences (such as occupation and/or individual propensity to experience injury) between the two groups. An additional observation we would make, is that as the length of ACC spell itself increases, the differences in prior benefit receipt rates between the injured population and the matched non-injured sample increases. This suggests to us, that either duration of ACC spell is related to prior benefit receipt, and/or that unobserved differences between the injured and the matched comparison group increase with ACC spell length. 4.5 Regression estimates The graphical analysis in the previous section neatly summarises the main findings of the paper. Crichton et al (2005) also presented results from regression analysis. This was undertaken to control for variables, such as recent prior benefit receipt, not used in the matchings. This estimated the impact of injuries on future employment, benefit, and earnings experiences. This was looked at by duration of compensation and the characteristics of the individual. The regression specification used in the paper was as follows: Y Duration( l) *[ injury * duration( l)] X (1) i Y is the outcome of interest (ie employment or benefit receipt). α is the model intercept. Injury is a dummy variable indicating whether they are from the injured or matched control group. Duration is a dummy variable indicating the length of the individual s ACC spell. X is a vector of variables to control for other factors influencing the outcome. These are age, sex, region, months employed and earnings levels prior to injury, benefit receipt prior to injury, firm size, industry, and month of injury. μ is an error term to capture unobserved effects. The key coefficient of interest for Crichton et al (2005) is Duration(l) which represents the effects of injury spells of varying duration on outcome Y. All results are based on ordinary least squares (OLS) regressions. Even controlling for these variables, Crichton et al (2005) found higher rates of benefit receipt among the injured during the 6 48 months before injury, and lower rates of 34

35 employment. It seems likely that these differences would remain post injury. To control for the differences in prior benefit receipt and employment rates between the injured and noninjured populations, a difference-in-difference estimator was used. This compares the difference in, say, benefit receipt rates for the two groups prior to injury, with the difference post injury (ie the difference between two differences). Table 4 in Crichton et al (2005) summarises the regression estimates for the effect of injury on employment rates, for variety of specifications. Those given below are those that control for the covariates and include the difference-in-difference estimator. They show the effects of injury on employment six months and 18 months after leaving ACC, compared to employment rates 18 months prior to being injured. Table 30 Regression estimates of effect of injuries on employment Individual match with covariates (from Crichton et al, 2005 table 4) Effect of ACC spell of: Six months after vs. 18 months before 18 months after vs. 18 months before (7) (11) 1 month duration month duration month duration month duration month duration month duration month duration month duration month duration month duration Observations 730, ,080 As Crichton et al (2005) state, injuries with one to two months of compensation now have almost no impact on employment six months after compensation ends (column 7). There is still strong evidence that longer duration injuries have negative impact on the employment rates (though this effect has been dampened by using the difference-in-difference estimator). For example, an ACC spell of more than seven months reduces the likelihood of employment six months after compensation ends, compared with 18 months before, by around 10 percent. Column 11 captures the change in employment status 18 months after leaving ACC versus eighteen months prior to injury. The estimated impacts are similar to those after six months. Crichton et al (2005) state that this suggests that injuries have long-term effects on individual labour market prospects. Similarly to table 30, the effects of injury on benefit receipt six months and 18 months after leaving ACC, compared with 18 months prior, are shown in the table 31. The results suggest, as Crichton et al (2005) state, that longer duration injuries increase the likelihood of receiving benefits six months after leaving the ACC spell, compared to 18 months prior to injury. For example, a six month spell on benefit increases the likelihood by 4 percent, and longer spells by around 5 percent. These are large increases compared with the benefit receipt rate of only 7 percent for the non-injured population. Column 9 shows there is no evidence that these impacts decline over time. 35

36 Table 31 Regression estimates of effect of injuries on benefit receipt Individual match with covariates (from Crichton et al, 2005 table 5) Effect of ACC spell of: Six months after vs. 18 months before 18 months after vs. 18 months before (3) (9) 1 month duration month duration month duration month duration month duration month duration month duration month duration month duration month duration Observations 730, ,080 The paper also tests the hypothesis that the impact of injuries differs for different groups of individuals. Table 32 below summaries the effects of injury on changes in employment 12 months after leaving ACC, compared with 18 months prior to injury. The results are classified by sex, age, prior industry, prior earnings, prior benefit receipt, and prior employment. Table 32 Regression estimates of effect of injuries on employment 12 months after versus 18 months before (from Crichton et al, 2005 table 6) Duration on ACC 1 2 months 3 4 months 5 7 months 8 24 months (1) (2) (3) (4) Overall Male Female Age Age Age Agriculture, et al Manufacturing Transport, et al Construction Wholesale trade Retail trade Accommodation, restaurants Finance, business, property Other services Education Health, community services mth prior earnings < mth prior earnings mth prior earnings mth prior earnings >= No benefits in 6mth prior Benefits in 6mth prior Employed in 1 2 of prior 6mth Employed in 3 4 of prior 6mth Employed in 5 6 of prior 6mth

37 As Crichton et al (2005) state, there is very little difference in the impact of short-duration injuries (ie 1 2 and 3 4 months) across the different groups of workers. However, there are noticeable differences in the impact of longer-duration injuries. For example, having an ACC spell of between eight and 24 months reduces employment 12 months after (as opposed to employment 18 months before) the spell by: 11 percent for males; 14 percent for females. 9 percent for those whose previous job was in construction; 20 percent for accommodation and restaurants. 33 percent for those that were employed in 1 2 months out of the preceding 6 months; 12 percent for those employed in 5 6 months. However, the authors found no difference in the impact of longer duration injuries (on subsequent benefit receipt, employment, and income) between those that had received income-tested benefits in the six months prior to the ACC spell, and those who had not. This result controls for differences in industry, age, gender, and benefit receipt in the preceding six months, as well benefit receipt in the previous 18 months through the use of the difference-in-difference estimator. Those who had only been employed for 1-2 months of the preceding six months had worse employment outcomes than those that had been employed for 3-6 months. Table 7 in Crichton et al (2005) presented similar estimates for different groups of individuals, this time looking at the impact on total income 12 months after leaving ACC, compared with 18 months prior to injury. The results in this table are less consistent than the previous one, but generally tell a similar story, with differences arising as ACC spell tenure lengthens Summary The abstract to Crichton et al (2005) states the purpose of their paper as follows: We estimate the effect of injuries on employment and benefit rates, and total income, by comparing the observed changes in outcomes for the injured population to those of a matched control group of non-injured individuals who have similar observed characteristics. We allow the magnitude of these effects to differ by the duration of the ACC spell, and other key characteristics, including age, sex, industry, and prior earnings and benefit receipt. Their main findings were: Injuries that result in more than two months of ACC receipt have negative effects on future labour market outcomes (including benefit receipt). The magnitudes of these negative effects increase with length of ACC spell. Longer-duration ACC spells, have a greater impact on women, older workers, and workers with lower earnings or with less stable employment histories. 35 From our reading of the paper the following additional points can also be made: 34 Most of the impact on total income is caused by the large reduction in the employment of injured workers. 35 See abstract, Crichton et al (2005). 37

38 Injured workers had lower employment rates and higher benefit rates before injury than the non-injured matched population. It is not known why this is, but it may be that the jobs that these people have are inherently more risky (after controlling for industry and the demographic variables available in LEED). As the length of ACC spell increases, the differences in prior benefit receipt between the injured population and the matched non-injured sample increases. This suggests that either duration of ACC spell is related to prior benefit receipt, and/or that unobserved differences between the injured and the matched comparison group increase with ACC spell length. There was no difference in the impact of longer duration injuries between those that had received income-tested benefits in the six months prior to the ACC spell, and those who had not. This result controls for differences in industry, age, gender and benefit receipt in the preceding six months, as well benefit receipt in the previous 18 months through the use of the difference-in-difference estimator. Those who had only been employed for 1-2 months of the preceding six months had worse outcomes than those that had been employed for 3-6 months. 38

39 5. Future work One motivation for this report was ACC s interest in the impact of the recent strong labour market, and the resulting increase in the number of new entrants, on accident compensation claims. In particular, they were interested in the interrelationship between benefit and ACC receipt. In this regard, there is not much more insight that LEED can provide beyond that in Crichton et al (2005) and this paper. Two potential areas for future work: In 2008, LEED was integrated with data from the Ministry of Social Development s (MSD s) Benefit Dynamics Dataset. This is a cleaned longitudinal dataset assembled for research purposes. This means the income-tested benefits that can be already be indentified in LEED can be broken down by benefit type 36. Repeating analysis with this new information would allow the relationship between accident compensation receipt and receipt of different types of benefit to be investigated. ACC currently integrate their data with similar data from MSD. The need for this integration may be removed by making use of the integrated LEED-BDD dataset. How do the results of analysis change over the business cycle? Both Crichton et al (2005) and this paper examine a period of strong economic growth. Results may vary in recessionary conditions. The ability to do such work may be some time away. If the current recession ends in 2010, we would need to add a year for the data to be become available in LEED, and another one to two years to allow outcomes post injury to be analysed. Another motivation for this report was to provide ACC a better understanding of where LEED could add insight to the rich dataset that ACC already has available to it. Potential work that could be undertaken in the future includes: Durability of return to work and continuity of employer 37 were areas that were not looked at in detail in Crichton et al (2005). On an annual basis, Statistics NZ produces a selection of descriptive statistics on the receipt of accident compensation from LEED. This includes information on average duration of receipt and transitions. ACC s improved understanding of LEED, and our improved understanding of the ACC scheme, may lead to improvements to these statistics. Integrating ACC information into LEED. As section 2.5 showed, there is much information on the characteristics of the injury that ACC has in its data store. Bringing this information together with LEED s longitudinal labour market information would produce a potentially powerful research dataset. ACC variables of particular usefulness would be injury diagnosis, scene of accident (workplace or not), duration of spell, and rehabilitation outcome. The integration may also improve some of the coverage issues in the LEED ACC statistics mentioned in section 2.3. An Employment Outcomes of Tertiary Education feasibility study, which explored integrating administrative data on tertiary enrolments and completions with LEED, was recently released. If this integration goes ahead, it would provide qualification information for cohorts as they move through the tertiary education system. This may be useful in future accident compensation research. 36 Such as the unemployment, sickness, invalid s and domestic purposes benefits. 37 As defined in Campbell Research and Consulting (2008). Crichton et al (2005) did investigate the continuity of employer, but concluded it was not particularly informative, as it was not possible to identify whether an injury was work-related or not. 39

40 References Accident Compensation Corporation (2008). Statement of Intent , Wellington. Campbell Research and Consulting (2008). 2007/08 Australia & New Zealand Return to Work Monitor, Heads of Workers Compensation Authorities, Lisarow, Australia. Crichton S, Stillman S and Hyslop D (2004). Returning to Work from Injury: Longitudinal Evidence on Employment and Earnings, Statistics New Zealand, Wellington. Crichton S, Stillman S and Hyslop D (2005). Returning to Work from Injury: Longitudinal Evidence on Employment and Earnings (Update), Statistics New Zealand, Wellington. Department of Labour and Statistics New Zealand (2002). New Zealand Injury Data Review, Department of Labour, Wellington. Institute for Work and Health (2009). Workers compensation and the business cycle, Issue Briefing, March Papadopoulos T (2008). Measuring Job Tenure Using Linked Employer-Employee Data, Statistics New Zealand, Wellington. Saravanaperumal M (2008). Labour Market Adjustment in the Construction Industry , Statistics New Zealand, Wellington. 40