Melanie K Jones
email: m.k.jones@swan.ac.uk.

Disability, Employment and Earnings: An Examination of Heterogeneity

Keywords: Disability, heterogeneity, labour market JEL Classification: I10, J20, J30 Disability, heterogeneity, labour market

This paper uses information from an ad-hoc module on disability in the 2002 UK Labour Force Survey to identify the heterogeneity that exists within the disabled group and examine its impact on labour market outcomes. After controlling for a range of personal characteristics, the type, severity, duration and cause of the disability are found to be important determinants of employment, but there is less evidence to support the influence of within group heterogeneity on earnings.

Introduction

Whilst international evidence that compares labour market outcomes between disabled and non-disabled individuals has grown rapidly (see Kidd et al., 2000, Madden, 2004 and Jones et al., 2006 for UK evidence), less attention has been paid to heterogeneity within the disabled group and its implications for labour market outcomes. However, the features of disability that differentiate it from empirical analysis of gender and ethnicity, such as it being a limitation rather than a characteristic and its fluid nature give rise to potential dramatic heterogeneity within the disabled group. Even the most obvious within group differences, such as in the type and severity of an impairment may be expected to impact work productivity, non-work income, the disutility of work and discrimination. Thus, it is likely that these features are fundamental in the analysis of labour market outcomes of the disabled. Indeed, there has been a longstanding recognition of the policy importance of this issue, with Baldwin and Johnson (1994), stating 'the success of the Americans with Disabilities Act may depend on the extent to which the implementation of its policies recognises the differences among persons with disabilities and among types of impairments' p14. Moreover, this issue has been more recently emphasized again in the UK by Berthoud (2003).

Despite this, studies consistently split the population into two or more sub groups and identify the disadvantage associated with disability as if it were homogeneous. 1 This is, in part, a result of restrictions imposed by data availability and the widespread application of standard decomposition techniques. Therefore, most studies have provided limited information with respect to questions such as which features of the disability give rise to the disadvantage? This is not only an important question in its own right, but may provide additional insights into the processes that determine the disadvantage of the entire group. Furthermore, as Silverstein et al. (2005) note, the failure to account for within group diversity may lead to misleading inferences in comparisons between groups. A limited number of recent UK studies have begun to consider individual aspects of heterogeneity including the effect of the severity of the disability (Berthoud, 2003), the type of disability (Kidd et al., 2000 and Jones et al., 2006) and, using longitudinal data, the duration of the disability (Jenkins and Rigg, 2004 and Burchardt, 2003).

However, restrictions on data availability have limited a more comprehensive analysis of heterogeneity. This paper, by exploiting additional questions introduced in the UK Labour Force Survey (LFS) as part of an ad hoc module on the employment of disabled people, is able to consider several forms of heterogeneity of a disability simultaneously and examine their implications for both employment and labour market earnings. 2 Importantly, these data also contain information, including the cause of the disability, which has not been previously examined in the UK. Whether the disability stems from an accident or illness, if it is work-related or if an individual was born with their disability has potentially important labour market implications. For example, an individual may receive compensation from an injury at work which is likely to raise his/her reservation wage. Moreover, onset at birth or in childhood will affect prelabour market experiences, entry to the labour market and an individual's entire labour market history (see Baldwin and Johnson, 2001).

The remainder of the paper is structured as follows. A concise review of previous evidence relating to different forms of heterogeneity is given in Section 2. Section 3 then considers the advantages of using this data and outlines the econometric methodology. Section 4 presents the key results before the final section briefly concludes. 2 The module was introduced in the European Labour Force Survey to provide comparable information on the labour market situation of people with disabilities in the EU in preparation for the 2003 European Year of People with Disabilities (see Dupre and Karjalainen, 2003). The ad hoc module on disability considered here also contains information relating to the cause of disability, which has received less consideration. However, the nature of onset may also have important labour market implications. Baldwin and Johnson (2001) highlight that individuals who are disabled at birth may be limited in terms of education, face pre labour market discrimination and their disability may affect their entire labour market experience. Individuals who experience age onset disability will face a different set of labour market issues, including retaining employment and promotion opportunities. In contrast, Loprest and Maag (2007) and Wilkins (2004), in the only known international studies to examine this issue, find, using cross sectional data from the US and Australia respectively, that early disability onset has a positive effect on employment relative to older disability onset. Both studies suggest that this is a result of adaptation. Wilkins (2004) argues that disabled youths will have more time and greater incentives to adapt to the disability and Loprest and Maag (2007) add that disabled children can choose careers in which their disability can be more easily accommodated.

It is not only the heterogeneity of the disability itself that has potentially important labour market implications; the impact of the disability will depend on the environment in which an individual is situated (Silverstein et al. 2005). Important influences may include the availability of medical care, the attitudes of others and government policies and legislation. Probably the most significant influence in this respect is the availability of an alternative source of income through the benefit system. Since one quarter of the UK population with a long-term health problem is in receipt of some type of disability or sickness benefit, this creates a crucial difference within the disabled group, with obvious implications for work incentives. Another significant change, that arises from the introduction of the Disability Discrimination Act (DDA) in 1995, is the obligation on employers to make reasonable adjustment to their premises and employment arrangements to facilitate and enhance the access to employment for the disabled. The effects on employment and earnings are not clear.

While access to employment and productivity in work should increase, the additional cost induced by these accommodations may reduce the employment of disabled workers (Acemoglu and Angrist, 2001) and/or result in employers passing these costs (Baldwin and Johnson, 2001). 3 In the UK, however, the Access to Work scheme provides financial support for employers when making such modifications, which should limit the negative impact of this requirement of the legislation.

Data and Methodology

The Data

This study uses additional questions introduced in an ad hoc EU module on disability in the Spring 2002 Quarter of the UK LFS. Whilst the data are limited to a single cross section, they contain important retrospective information on the duration and cause of disability; this is in addition to questions relating to the severity and type of the disability that are included every quarter. Importantly, therefore, this additional information, combined with the scale of the LFS, means multiple aspects of heterogeneity can be examined simultaneously. The retrospective nature of the additional information means, however, that it may suffer from recall error. While the bounded nature of the responses to duration, particularly the upper bound group of '10 years or more', should reduce this effect it also limits the accuracy of this control.

Fortunately, information about the cause of the disability includes a control for 'at birth'; however, hereditary conditions which may only become limiting in later life are also included in this group.

Employment

The econometric methodology modifies previous analysis of the impact of disability on employment and earnings (Kidd et al., 2000, Madden, 2004 and Jones et al., 2006) to focus on within rather than between group differences. This emphasis means the sample is restricted to individuals of working age who self-report a long-term health problem 4 . Full-time students are also excluded from the analysis. Employment 3 Gunderson and Hyatt (1996) consider the impact on earnings using a specialized dataset from Ontario in the early 1980s. They find that the proportion of the cost of the accommodation passed on to workers is higher if they are injured at another firm. 4 The additional questions relating to the duration, cause and severity of the disability were only asked to those with a long-term health problem. Those with a long-term health problem represent 28% of the male working age sample and 27% of the respective female sample. Whilst this definition does not coincide with more standard work limiting or DDA definitions, it maximises the number of observations for the analysis and considers the entire heterogeneity within the broadest definition of

i i i i X Y E µ + + = * (1)
where the observed variable i E is related to the latent variable * i E as follows:

5 i E = > ise otherw if E * i 0 0 1 i Y contains
personal and household related characteristics including age, qualifications, ethnicity, marital status, the presence of dependent children, region of residence and housing tenure (see Table 1 for variable definitions). However, these variables are supplemented with a range of controls relating to the nature of the disability, i X . Conditional on there being correlation between the included and omitted variables, the significance of ˆwould indicate that estimates based on a model without controls for within group differences will suffer from omitted variable bias.

Variables that should be included in i X are less well established, but can be separated into cross sectional and more dynamic influences. In this model, controls for cross sectional forms of heterogeneity include four classifications for the type of main health problem, namely, limbs, sight and hearing, chest and breathing and mental health (the base group is other), variables that capture self-reported measures of severity (restrictions on the kind and amount of work, and mobility restrictions) and a control for individuals with multiple health problems. 6 An additional specification is disability. It is reassuring to note that the main conclusions are robust to restricting the sample to alternative (work-limiting or DDA) definitions of disability. 5 Employment is defined using ILO definitions in the LFS and therefore includes, employees, the self employed, those on government training schemes and unpaid family workers. The non-employed include both the unemployed and the inactive. 6 These variables all relate to the current period and therefore no information is available on how the severity or type of disability has changed over time. The impact of the duration of disability may depend on the severity at onset and how severity changes over time, however, no information is available to control for this. A series of dummy variables relating to the duration of the disability are included, ranging from durations of 'less than one year' to 'ten years or more'. A continuous measure of duration would capture the difference in the reward to experience in the presence of a disability. A lower return may be expected if the presence of a disability limits labour market experience and training, relative to a period without a disability.

Hence, it is anticipated that those at long durations may face the most labour market disadvantage. However, controlling only for duration assumes that the impact of a disability is constant across the lifecycle. As discussed above, the impact of disability may also depend on the source and timing of onset. A series of dummy variables are included to capture the cause of the disability, which includes a control for 'at birth'.

Whilst differences in observable characteristics that result from the cause of disability (for example, education) will be captured by the controls in i Y , the variables relating to the cause will capture the direct influence on labour market outcomes. For example, differences in the ability to adapt on the basis of age of onset will be captured by the control for 'at birth'.

There are some limitations in introducing dynamic concepts in cross sectional analysis, which have been emphasised in evidence relating to the assimilation of immigrants. 7 Borjas (1985) argues that cross sectional estimates of the impact of the duration of residence in the home country on earnings will be affected by selection effects and will include the influence of changes in cohort quality.

i i i v T D + = * (2)
However, only the binary indicator is observed:

> = , 0 0 1 * i i D otherwise if D
And, at the second stage, the employment equation ( 1) is estimated conditional on disability being observed ( 1 = i D

). Given the nature of the information available in the LFS, the determinants of disability, i T , are restricted to personal and household characteristics. As such, the characteristics included in i T are largely the same as the characteristics in i Y . While identification can be achieved in this model through functional form alone, following Madden (2004) between the two errors is given by . If unobservables affect both equations, the correlation will be non-zero ( 0) j and, in this situation, the results from a simple probit model will be biased.

Endogeneity Bound (1991) argues that self-reported information on health is subject to two sources of bias. The individual subjective nature of reporting will mean that responses may not be comparable across individuals, giving rise to measurement error. Some individuals may also have more incentive to misreport disability to justify nonemployment, which gives rise to justification bias. 10 The same concerns may extend to questions involving whether disability affects the kind or amount of work among the disabled population, which are included as controls for heterogeneity. That is, conditional on disability, the non-employed may also have an incentive to over-report that their disability affects the type or amount of work they can do. Theoretically, these two sources of bias will influence the estimates of severity on labour market outcomes in opposite directions; measurement error will give rise to a downward bias, whereas justification bias will result in an overestimation of the impact on disability.

It is difficult to find suitable instruments for the severity measures of disability in the LFS and, as such, it is not possible to test the exogeneity of these variables or examine the issue further here. 11

Earnings

For a subset of employed disabled individuals, the log of hourly earnings ( ) i W is also modelled as follows:

identification. However, this information is only available for a subset of respondents. The results were not sensitive to the change in identification strategy. 10 The evidence, however, is mixed. Several studies find that the non-employed tend to over-report illhealth or disability (see, for example, Kerkhofs and Lindeboom, 1995 and Kreider and Pepper, 2007) whilst others find that self-reported information is an unbiased measure of true health or disability (see, for example, Dwyer and Mitchell 1999 and Benitez-Silva et al. 2004). 11 The variables included in i Y and i Z are typical in the literature and are assumed to be exogenous. However, when examining the labour market impact of epilepsy, Famulari (1992) considers the endogeneity of education in an earnings equation and finds that failing to account for this endogeneity underestimates the impact of severity on earnings.

11 i W i i i X Z W + + = (3)
where i Z includes productivity related characteristics and a set of controls for the type of employment, such as industry, occupation and sector. The controls for heterogeneity, outlined previously, are supplemented with controls for working in sheltered employment and the receipt of assistance to help work in order to form W i X ;

however, the control for benefit income is excluded.

There 12 Information about earnings is only asked to employees in waves 1 and 5 of the LFS. The results from the selection equations are qualitatively similar to the employment equations above, but are not presented here. None of the identifying variables is significant in the simple earnings equation. 13 For identification, these variables need to have a significant impact in their respective equation but no influence on earnings. All these variables are insignificant when included in a simple earnings equation. Unemployment 12 months ago is not significant at the 5% level in a simple probit model of disability status. Moreover, the key results are not sensitive to modifications of the exclusion restrictions, when the selection terms are created from two independent probit models or to estimation

Descriptive statistics

Table 1 presents the mean values for the variables used in the analysis for males and females respectively. Amongst those who are disabled, as defined by the presence of a long-term health problem, about 55% report they are limited in the kind of work they can do, about 45% find it affects the amount of work they can do and a third report difficulties getting to work due to their disability. In accordance with the worklimiting definition of disability, 59% of men and 57% of women report a disability that affects either the type or amount of work they can do. Just less than half of those with a long-term health problem report more than one health problem. The most common type of main health problem for men is associated with back or neck (17%)

or heart, blood pressure and circulation (17%), both slightly higher than chest or breathing (13%) or legs and feet (12%).

Disability also varies in its permanency; about 45% of the disabled population have a disability that has lasted more than 10 years and an additional 20% have a disability lasting between 5 and 10 years. In terms of the cause of disability, 49% of men and 63% of women have a disability caused by a non-work related disease or illness, but those whose disability occurred at birth comprise nearly 20% of the disabled population. About 3% of the disabled workers are employed in sheltered employment and, more surprisingly, even after several years of the DDA only 7% of those who need assistance in work actually receive it.

Employment

Tables 2 and 3 display the coefficients from the models of the determinants of employment for males and females respectively. In each case, the first set of estimates is from a simple probit model and the second set of estimates is from a bivariate probit model with sample selection. In both cases the initial specification, denoted (1), is then supplemented with disability benefit income in (2). The estimates from the probit and bivariate probit are qualitatively similar and is not significantly different without the selection correction. Variables included in the wage but not the employment or disability equations (for example, industry) are defined only for the employed. 13 from zero at the 5% level in any specification. 14 Therefore, the discussion largely focuses on the results from the probit models.

The results of standard productivity related variables are in accordance with expectations and, therefore, the focus of the discussion relates to the controls for heterogeneity within the disabled group. A likelihood ratio test indicates the controls for within group differences are significant at the 1% level for both males and females. Indeed, for males the pseudo R squared in the probit model increases from 0.26 to 0.50 with the inclusion of the within group controls, confirming their importance. Moreover, these features of disability are significant after the inclusion of a control for receipt of disability benefit income, which, consistent with the rules governing incapacity benefit receipt, has a strong negative effect on employment.

Consistent with previous evidence in the UK (Berthoud, 2003) and elsewhere (Wilkins, 2004 and Loprest and Maag, 2007), information relating to the severity of the disability has a negative effect on the probability of employment. This influence remains important even after controlling for the type, duration and cause of the disability. The marginal effect of a disability which greatly affects mobility is particularly strong at 53% and 46% for males and females respectively, and is greater than the effect of limitations on either the kind or amount of work. In addition, for both genders, the number of recorded health problems has a significant negative effect on employment; one additional problem reduces the probability of employment by nearly 4.7% and 3.4% for males and females respectively, confirming the additional difficulty experienced by those with multiple health problems. The type of health problem is also important and, relative to the base group, individuals with mental health problems have a 22% lower probability of employment, which is consistent with previous UK evidence (Kidd et al., 2000 and Jones et al. 2006). The reasons for this, however, are more difficult to distinguish. Consistent with evidence based on longitudinal data in the UK (Jenkins and Rigg, 2004), shorter durations of the disability are associated with higher employment probabilities. The effect, which is measured relative to the base group of more than 10 years, is significant amongst males who have had their disability for less than one year and this extends to two years for females. This is consistent with individuals remaining in work until they are more aware of the permanency of their condition and, possibly, using sick leave in the period immediately after disability onset to remain employed. The cause of the disability also has a significant effect on the probability of employment. Of particular interest is the positive influence of onset at birth, particularly for men. After controlling for type, severity and duration, males who are born with their disability are 5% more likely to be employed than those with a non-work related disease or illness. 15 Thus, there is no evidence to suggest that onset prior to labour market entry increases employment disadvantage, consistent with the arguments of adaptation by Wilkins ( 2004) and Loprest and Maag (2007). 16 For men, having a disability which results from a traffic accident is also associated with significantly higher employment rates. Interestingly, for females, employment is positively associated with work related causes of disability either from an accident or progressive illness. These controls may, however, capture some prior commitment to work.

Earnings

Table 4 presents the results for the earnings equations for males and females respectively. 17 In each case the estimates are presented for the earnings equation (3), then with controls for selection into employment and, finally, with controls for selection into both employment and disability. Surprisingly, both selection correction terms are insignificant in the earnings equation; this is, however, consistent with evidence from Madden (2004). Overall, within group heterogeneity appears to be a less important determinant of earnings than of employment, consistent with disability having a larger effect on employment than earnings (Blackaby et al. 1999 and Kidd et al. 2000). After controlling for the type of work, the measures of severity are not an important influence on male earnings, while for women restrictions on the amount of work and mobility are negatively associated with earnings. In a similar manner to employment, mental health problems have the most negative influence on earnings, particularly for women.

The duration of the disability has no consistent influence on earnings for men, whilst for women the results appear counterintuitive, with shorter durations of disability being associated with lower earnings. 18 These results may, however, be in part a result of an additional selection effect; that is, it may be those with the worst economic prospects that exit the labour market as the duration of the disability lengthens. As such, some individuals who remain employed at long durations of disability may be a self-selected group of higher earners.

For men, at least, the cause of the disability appears to be an important determinant of earnings. Consistent with the positive influence of disability at birth on employment, this group also earn significantly more than other disabled workers. Even after controlling for industry and occupation, men with disability onset relating to a work related accident have significantly lower earnings. This may, in part, reflect a concentration of accidents among particularly low paid workers.

The measures that control for being in sheltered employment or having assistance to help with work have no significant effect on earnings. Therefore, there is no evidence (after controlling for the type of employment) to suggest disabled workers pay for taking opportunities created to help their entry into employment. 18 The negative influence of short durations would be consistent with the effect of adaptation through time outweighing the negative influence of time out of the labour market or reductions in human capital investment.

Conclusion

There is a well-established and internationally consistent literature that documents the labour market disadvantage faced by disabled individuals relative to their nondisabled counterparts. The evidence presented here for the UK confirms that within group differences, on the basis of the type, severity and duration of the disability, are important determinants of employment for both men and women, despite the range of controls for personal and household characteristics, including receipt of disability benefits. The significance of these variables suggests that analysis which fails to control for characteristics of the disability potentially suffers from omitted variable bias and, therefore, inferences may be misleading. This paper also presents evidence which establishes the influence of the cause of disability onset on labour market outcomes in the UK. For males, onset of disability at birth is positively associated with employment and earnings in later life, and there is no evidence to suggest this group are more disadvantaged in the labour market than the rest of the disabled.

Given the importance of the heterogeneity of the disability, it may be expected that general policies, which aim to increase employment amongst the disabled, will be more effective for certain sub-groups that are 'nearer to the labour market'. Indeed, interventions may be better targeted if they relate to the specific needs of sub-groups of the disabled and, thus, acknowledge the existing differences in labour market performance within the group. Future research should also consider the influence of within group heterogeneity on other outcomes, such as hours and the nature of employment, and in the evaluation of changes in legislation, such as the introduction of the DDA.

Whilst the information collected as part of this ad hoc module on disability provides additional insights into the processes involved, the data are not without their limitations and the features of heterogeneity are certainly not exhaustive. Indeed, this paper has highlighted the difficulties involved in using the currently available cross sectional or longitudinal evidence, given the multiple forms heterogeneity may take. If Since the selection effects are not significant all results relate to the basic probit or OLS specifications.

a
1. Exclude duration and cause of disability.

Employment Probit

Males

  workers in terms of a pay penalty

  empirically using probit models and are estimated separately by gender:

  a control for the receipt of incapacity or any other sickness related benefit, since, otherwise, the controls for heterogeneity may partially capture this influence.

  are, potentially, two sources of sample selection bias in the above equation, since individuals are both in employment and disabled. Three specifications of the earnings equation are estimated. Firstly, equation (3) is estimated by OLS. Secondly, estimates from an employment probit model are used to construct a selectivity correction term which is included in the earnings equation, following Heckman(1976). For identification, controls for the presence of dependent children, another income earner in the household and unemployment status 12 months ago are included in the employment equation but not the earnings equation.12 In the final specification, both sources of selection bias are modelled simultaneously. The estimates from the bivariate probit model outlined above are used to create two selection terms, as suggested byTunali (1986), and these are included in the earnings equation. In this case, identification requires that there is at least one variable in each selection equation which is excluded from both the earnings equation and the other selection equation. As discussed above, the presence of a disabled household member is only included in the disability equation and, thus, fulfils this role. In the case of employment, the controls for dependent children and another earner in the household are significant determinants of disability and, therefore, it is the indicator of unemployment status 12 months ago that is included in the employment equation, not in the disability or earnings equations.13

Table 2 . The influence of within group heterogeneity on the employment of the disabled males

 2 performance, and sufficient observations for the examination of within group differences. It is this type of evidence that will aid policymakers who seek to encourage the disabled into employment, in a country with one of the highest rates of working age disability. Examples include special adaptations or equipment, support in getting to and from work, understanding by superiors or colleagues, assistance in the kind of work, assistance in the amount of work and assistance in getting around at work. This question is only asked to those who state they have problems with the kind, amount or getting to work. It is assumed that the disabled who are not asked this question do not need any form of assistance and are therefore included in the zero category. Those who need, but do not get assistance are not separated from those who do not need assistance since the latter variable is a function of severity and therefore would be highly correlated with variables relating to difficulties with the kind and amount of work.Notes to table: Specification includes a full set of regional dummies not reported here. Absolute T statistics reported in parenthesis. ***, ** and * denote significance at the 1%, 5% and 10% level respectively. Significance of tested using a likelihood ratio test for independent equations.

	F o r
	P
	e
	e r
	R e v
	i e
	w

disability survey is commissioned in the UK (see,

Purdon 2005

, for a feasibility study) it is essential that it contains retrospective questions relating to disability onset, a longitudinal element which traces changes in both the disability and labour market

Table 3 . The influence of within group heterogeneity on the employment of the disabled females

 3 Notes to table: See notes to Table2.

		Probit		Bivariate Probit
		(1)	(2)	(1)		(2)
				Employ	Disability	Employ	Disability
	Constant	-0.103	-0.071	-0.237	0.066	-0.194	0.064
		(0.65)	(0.44)	(1.36)	(1.03)	(1.09)	(0.99)
	Age 16-24	0.175*	0.135	0.064	-0.832***	0.035	-0.831***
		(1.83)	(1.39)	(0.56)	(20.36)	(0.30)	(20.34)
	Age 25-34	0.322***	0.333***	0.224**	-0.634***	0.243***	-0.634***
		(4.48)	(4.54)	(2.45)	(19.42)	(2.61)	(19.40)
	Age 35-44	0.454***	0.489***	0.379***	-0.456***	0.420***	-0.457***
		(7.26)	(7.63)	(5.02)	(15.20)	(5.44)	(15.22)
	Age 45-54	0.306***	0.332***	0.273***	-0.233***	0.303***	-0.233***
		(6.08)	(6.46)	(5.06)	(8.90)	(5.47)	(8.89)
	Duration <1	0.335***	0.246***	0.335***		0.248***
		(4.46)	(3.27)	(4.50)		(3.32)
	Duration <2	0.155**	0.123*	0.153**		0.122*
		(2.27)	(1.78)	(2.27)		(1.78)
	Duration <3	0.083	0.060	0.080		0.058
		(1.28)	(0.91)	(1.25)		(0.89)
	Duration <5	0.008	-0.016	0.005		-0.018
		(0.14)	(0.30)	(0.10)		(0.34)
	Duration <10	0.001	0.001	0.001		0.000
		(0.02)	(0.01)	(0.02)		(0.01)
	Birth	0.066	0.077	0.069		0.079*
		(1.41)	(1.62)	(1.50)		(1.69)
	Work acc	0.266***	0.357***	0.267***		0.356***
		(2.86)	(3.69)	(2.91)		(3.72)
	Traffic acc	0.101	0.115	0.099		0.113
		(1.01)	(1.12)	(1.00)		(1.11)
	Household	0.067	0.052	0.068		0.052
		(0.70)	(0.53)	(0.72)		(0.54)
	Work illness	0.166**	0.184***	0.165**		0.183***
		(2.39)	(2.58)	(2.40)		(2.59)
	Kind	-0.362***	-0.332***	-0.357***		-0.329***
		(7.42)	(6.70)	(7.33)		(6.64)
	Amount	-0.502***	-0.387***	-0.494***		-0.383***
		(10.09)	(7.59)	(9.95)		(7.54)
	Mobility1	-1.268***	-0.949***	-1.250***		-0.938***
		(20.07)	(14.04)	(19.25)		(13.81)
	Mobility2	-0.566***	-0.467***	-0.562***		-0.465***
		(11.68)	(9.34)	(11.59)		(9.34)
	Number health	-0.087***	-0.061***	-0.085***		-0.059***
		(6.61)	(4.47)	(6.55)		(4.43)
	Limbs	0.036	0.003	0.036		0.005
		(0.66)	(0.05)	(0.66)		(0.08)
	Sight/hearing	-0.149	-0.138	-0.147		-0.135
		(1.47)	(1.33)	(1.46)		(1.31)
	Breathing/heart	-0.012	-0.062	-0.012		-0.061
		(0.23)	(1.20)	(0.23)		(1.18)
	Mental health	-0.553***	-0.497***	-0.546***		-0.491***
		(7.30)	(6.43)	(7.22)		(6.36)
	White	0.314***	0.349***	0.307***	0.004	0.341***	0.004

Table 4 . The influence of within group heterogeneity on the earnings of disabled workers.

 4 Notes to table: See notes to Table2. Specification also included controls for industry and occupation of employment, but these are not reported here.

			Males			Females
		OLS	Employment	Double	OLS	Employment	Double
			selection	selection		selection	selection
	Constant	1.777***	1.741***	1.844***	1.908***	1.933***	1.888***
		(12.73)	(11.80)	(9.41)	(14.96)	(13.97)	(10.91)
	Sheltered	-0.068	-0.074	-0.075	-0.086	-0.083	-0.082
		(1.01)	(1.10)	(1.09)	(1.56)	(1.51)	(1.47)
	Assistance	-0.073	-0.098	-0.099	0.077	0.058	0.059
		(1.05)	(1.41)	(1.39)	(1.29)	(0.97)	(0.96)
	Experience	0.025***	0.026***	0.025***	0.018***	0.019***	0.020***
		(6.25)	(6.24)	(5.72)	(4.82)	(5.18)	(5.06)
	Experience	-0.045***	-0.048***	-0.049*** -0.032***	-0.035***	-0.034***
	Sq/100	(6.36)	(6.21)	(6.08)	(4.22)	(4.62)	(4.32)
	Tenure	0.001***	0.001***	0.001***	0.001***	0.001***	0.001***
		(4.48)	(4.69)	(4.57)	(3.14)	(3.25)	(3.18)
	Tenure Sq/100	-0.000**	-0.000**	-0.000**	-0.000	-0.000	-0.000
		(2.22)	(2.36)	(2.29)	(0.84)	(0.99)	(0.99)
	Birth	0.058**	0.054*	0.054*	0.018	0.017	0.018
		(2.08)	(1.93)	(1.88)	(0.73)	(0.69)	(0.69)
	Work acc	-0.089**	-0.081*	-0.081*	0.030	0.032	0.031
		(2.06)	(1.90)	(1.85)	(0.56)	(0.59)	(0.55)
	Traffic acc	-0.095	-0.098*	-0.101*	0.060	0.070	0.072
		(1.63)	(1.70)	(1.72)	(1.11)	(1.30)	(1.29)
	Household acc	-0.054	-0.063	-0.064	0.010	0.011	0.011
		(1.17)	(1.35)	(1.35)	(0.20)	(0.21)	(0.20)
	Work illness	-0.028	-0.022	-0.024	0.003	0.006	0.005
		(0.86)	(0.68)	(0.70)	(0.08)	(0.15)	(0.13)
	Duration <1	0.022	0.020	0.019	-0.074*	-0.092**	-0.093**
		(0.50)	(0.46)	(0.44)	(1.85)	(2.27)	(2.25)
	Duration <2	0.076*	0.074*	0.073*	-0.001	-0.007	-0.008
		(1.90)	(1.87)	(1.80)	(0.03)	(0.20)	(0.22)
	Duration <3	0.050	0.045	0.044	-0.068*	-0.080**	-0.082**
		(1.31)	(1.16)	(1.13)	(1.80)	(2.13)	(2.14)
	Duration <5	0.061*	0.052	0.052	-0.005	-0.013	-0.014
		(1.76)	(1.51)	(1.48)	(0.16)	(0.42)	(0.43)
	Duration <10	0.010	0.006	0.008	-0.010	-0.016	-0.017
		(0.33)	(0.21)	(0.27)	(0.39)	(0.60)	(0.60)
	Kind	-0.035	-0.041	-0.041	0.006	-0.002	-0.001
		(1.32)	(1.52)	(1.46)	(0.22)	(0.06)	(0.03)
	Amount	-0.014	-0.028	-0.023	-0.056*	-0.064*	-0.061*
		(0.43)	(0.74)	(0.58)	(1.89)	(1.78)	(1.67)
	Mobility1	-0.089	-0.071	-0.055	-0.106*	-0.144*	-0.138*
		(1.14)	(0.72)	(0.53)	(1.66)	(1.83)	(1.71)
	Mobility2	0.040	0.033	0.039	-0.126***	-0.146***	-0.144***
		(1.00)	(0.73)	(0.84)	(3.58)	(3.71)	(3.57)
	Number health	-0.018*	-0.018*	-0.018	0.006	0.007	0.007
		(1.72)	(1.68)	(1.62)	(0.62)	(0.73)	(0.75)

Exclusion of controls for the nature of employment. OLS Earnings

  All specifications are the same as in the paper, however since the work-limited have a health problem which effects either the kind or amount of work interpretation of these variables is quite different.

	Females 0.168* (1.80) 0.304*** (4.32) 0.443*** (7.28) 0.312*** (6.36) -0.360*** (7.54) -0.496*** (10.23) -1.259*** (20.67) -0.550*** (11.71) -0.095*** (7.42) 0.074 (1.45) -0.143 (1.44) -0.025 (0.50) -0.545*** (7.44) 0.316*** (4.50) -0.098 (1.63) -0.339*** (6.97) 0.881*** (12.41) 0.643*** (9.87) 0.499*** (8.70) 0.447*** (9.17) 0.342*** (6.50) 0.449*** (10.80) -0.240*** (11.65) -0.535*** (6.83) -0.202*** (2.97) 0.007 (0.10) 0.382*** (5.83) -0.246** (2.47) -0.056 (0.37) 8893 Female -0.113** (2.13) 0.067 (1.17) 0.017*** (4.69) -0.000*** (4.12) 0.001*** (3.25) -0.000 (0.94) 0.009 (0.36) -0.064** (2.23) -0.125** (1.99) -0.117*** (3.43) 0.008 (0.88) -0.003 (0.09) -0.097* (1.69) -0.024 (0.90) -0.115** (2.19) -0.029 (0.64) 0.005 (0.15) -0.040 (1.51) 0.326*** (7.06) 0.242*** (5.46) 0.094** (2.34) 0.058* (1.67) -0.011 (0.30) 0.127*** (2.60) -0.119*** (2.78) -0.297*** (7.44) -0.534*** (6.89) -0.494*** (10.92) -0.418*** (8.78) -0.484*** (7.17) -0.531*** (11.34) 0.263** (2.19) 0.592*** (3.72) 0.076 (1.34) 0.163* (1.76) -0.072 (1.45) 0.096 (1.52) 0.124** (2.50) 0.039 (0.86) -0.040* (1.82) -0.098*** (4.72) 0.046 (1.09) -0.089** (2.05) -0.066 (1.50) -0.005 (0.13) 1.906*** (15.43) 1577 0.53 DDA disabled Female 0.373*** e 0.252*** (2.69) 0.684*** (9.28) 0.646*** (10.41) 0.570*** (11.52) -0.359*** (6.96) -0.598*** (11.69) -1.429*** (23.98) -0.607*** (12.08) -0.126*** (9.12) 0.056 (0.87) 0.071 (0.73) 0.019 (0.29) -0.593*** (7.01) 0.306*** (3.88) -0.097 (1.43) -0.029 (0.50) 0.272*** (3.92) 0.176** (2.23) 0.286*** (5.66) 0.171*** (2.79) R e P o F r e r e v i w 0.308*** (5.27) 0.452*** (11.65) 0.003 (0.14) 0.126 (1.20) -0.261*** (3.59) 0.077 (1.06) 0.510*** (7.30) -0.864*** (11.17) 0.059 (0.36) 9598 F o r P e e r R e v i e w OLS Earnings Equation Age 16-24 Age 25-34 Age 35-44 Age 45-54 Kind Amount Mobility1 Mobility2 Number of health Limbs Sight/hearing Chest/heart Mental health White Single Married Degree Other higher A level O level Other educ Other earner Child 19 Child 2 Social Owned Mortgaged Unemploy12 Constant Observations Male Sheltered -0.055 (0.85) Assistance -0.073 (1.05) Experience 0.023*** (5.84) Experience Sq -0.000*** (5.96) Tenure 0.001*** (4.93) Tenure Sq -0.000** (2.57) Kind -0.042 (1.63) Amount -0.024 (0.76) Mobility1 -0.032 (0.42) Mobility2 0.043 (1.08) Number of health -0.018* (1.72) Limbs 0.013 (0.35) Sight/hearing -0.006 (0.11) Chest/heart 0.013 (0.35) Mental health -0.091 (1.48) White 0.067 (1.23) Single -0.026 (0.62) Married 0.035 (1.03) Degree 0.347*** (7.34) Other high educ 0.140*** (2.80) A level 0.077** (2.03) O level 0.098** (2.35) Other educ 0.004 (0.09) Professional occupations -0.020 (0.50) Associate professional and -0.169*** F o r P e e r R e v i e w technical (4.46) Administrative and secretarial -0.416*** (8.75) Skilled trades occupations -0.413*** (10.75) Personal service occupations -0.542*** (7.75) Sales and customer service occupation -0.420*** (6.85) Process, plant and machine operatives -0.530*** (13.49) Elementary occupations -0.597*** (14.68) Agriculture & fishing dummy 0.022 (0.21) Energy & water dummy 0.349*** (3.90) Manufacturing dummy 0.228*** (4.18) Construction dummy 0.252*** (4.02) Distribution, hotels & restaurants dummy 0.101* (1.80) Transport & communication dummy 0.238*** (3.98) Banking, finance & insurance dummy 0.284*** (5.05) Public admin, education & health dummy 0.134** (2.43) Part-time -0.032 (0.74) Small firm -0.111*** (4.75) Temporary 0.018 (0.32) Social -0.146*** (2.95) Owned -0.032 (0.68) Mortgaged -0.000 (0.01) Constant 1.818*** (13.41) Observations 1592 R-squared 0.53 F o r P e e r R e v i e w 2. Definition of disability Employment Probit Work-limited disabled Male Female Male Age 16-24 0.256** 0.324** 0.363*** (2.04) (2.49) (2.73) (2.60) Age 25-34 0.682*** 0.387*** 0.670*** 0.341*** (7.27) (3.96) (6.72) (3.58) Age 35-44 0.593*** 0.423*** 0.628*** 0.438*** (7.56) (5.18) (7.74) (5.57) Age 45-54 0.477*** 0.266*** 0.580*** 0.247*** (7.61) (4.03) (9.04) (3.90) Duration <1 0.541*** 0.518*** 0.510*** 0.558*** (5.05) (5.09) (4.71) (5.29) Duration <2 0.174* 0.216** 0.219** 0.180** (1.78) (2.33) (2.20) (1.99) Duration <3 0.183** 0.055 0.138 0.056 (2.04) (0.63) (1.47) (0.66) Duration <5 -0.004 -0.014 0.017 0.002 (0.05) (0.20) (0.24) (0.02) Duration <10 -0.044 0.038 -0.028 -0.010 (0.73) (0.62) (0.44) (0.16) Birth 0.243*** 0.116* 0.229*** 0.071 (3.48) (1.79) (3.25) (1.13) Work acc 0.031 0.179* -0.028 0.221** (0.39) (1.77) (0.32) (2.00) Traffic acc 0.328*** 0.074 0.183 0.112 (2.83) (0.62) (1.42) (0.90) Household 0.131 0.008 0.072 -0.028 (1.11) (0.06) (0.52) (0.22) Work illness -0.002 0.187** 0.079 0.113 (0.03) (2.23) (1.14) (1.29) Kind -0.889*** -0.720*** -0.476*** -0.437*** (6.09) (6.98) (6.20) (6.46) Amount -0.638*** -0.592*** -0.571*** -0.508*** (10.94) (10.05) (8.13) (7.87) Mobility1 -1.425*** -1.279*** -1.431*** -1.292*** (21.91) (18.91) (21.08) (18.69) Mobility2 -0.651*** -0.673*** -0.646*** -0.596*** (11.15) (11.76) (10.56) (10.34) Number health -0.148*** -0.089*** -0.120*** -0.085*** (9.27) (5.90) (7.40) (5.68) Limbs 0.084 0.010 0.107 -0.027 (1.03) (0.14) (1.29) (0.39) Sight/hearing 0.051 -0.297** 0.098 -0.325** (0.39) (2.17) (0.67) (1.96) Breathing/heart 0.116 -0.086 0.095 -0.083 (1.43) (1.17) (1.19) (1.28) Mental health -0.559*** -0.600*** -0.562*** -0.686*** (5.56) (6.44) (5.45) (7.45) F o r P e e r R e v i e w White 0.225** 0.390*** 0.370*** 0.386*** (2.27) (3.95) (3.54) (4.07) Single -0.143* -0.115 -0.050 -0.094 (1.65) (1.46) (0.55) (1.18) Married -0.059 -0.290*** -0.032 -0.325*** (0.80) (4.52) (0.43) (5.09) Degree 0.423*** 0.978*** 0.288*** 0.941*** (4.51) (10.02) (2.99) (9.79) Other higher 0.251** 0.536*** 0.242** 0.655*** (2.35) (6.04) (2.31) (7.54) A level 0.352*** 0.437*** 0.333*** 0.453*** (5.58) (5.64) (5.04) (5.99) O level 0.239*** 0.434*** 0.266*** 0.476*** (3.08) (6.56) (3.28) (7.44) Other educ 0.329*** 0.322*** 0.309*** 0.364*** (4.54) (4.54) (4.07) (5.25) Other earner 0.417*** 0.505*** 0.408*** 0.471*** (8.33) (8.98) (7.87) (8.52) Child 19 0.010 -0.194*** 0.007 -0.200*** (0.34) (6.67) (0.23) (7.12) Child 2 0.030 -0.539*** 0.125 -0.701*** (0.23) (4.60) (0.86) (5.94) Social housing -0.227** -0.234** -0.249** -0.130 (2.44) (2.50) (2.46) (1.41) Owned 0.229** -0.039 0.225** 0.038 (2.40) (0.39) (2.20) (0.39) Mortgaged 0.623*** 0.278*** 0.557*** 0.447*** (6.85) (3.02) (5.67) (5.02) Unemploy12 -0.797*** -0.232* -0.646*** -0.204 (8.17) (1.80) (5.82) (1.45) Constant 0.451* 0.245 -0.356 -0.221 (1.77) (1.06) (1.60) (1.10) Observations 5420 4881 5403 5344 Notes: F o r P e e r R e v i e w OLS Earnings Work-limited disabled DDA disabled Male Female Male Female Sheltered -0.135 -0.119 -0.108 -0.033 (1.49) (1.44) (1.26) (0.60) (1.28) (1.45) (2.04) White 0.030 -0.062 0.039 -0.114* (0.32) (0.85) (0.44) (1.78) Single -0.041 0.044 0.015 0.052 (0.58) (0.73) (0.21) (1.01) Temporary -0.110 -0.023 -0.015 0.003 (1.34) (0.31) (0.18) (0.05) Social housing -0.058 -0.011 -0.106 -0.128* (0.72) (0.15) (1.34) (1.84) Owned -0.019 -0.021 -0.035 -0.083 (0.58) (0.83) Mental health -0.193*** -0.136** (2.72) (2.19) White 0.050 -0.002 3. Male (0.81) (0.04) Female (0.45) Assistance -0.105 0.044 -0.057 Married 0.053 -0.101** 0.045 -0.078** (0.24) (0.26) (0.47) (1.18) Sheltered -0.156** Single -0.027 0.002 -0.106* -0.013 (1.45) (0.64) (0.70) (0.95) (2.29) (0.81) (2.02) Mortgage 0.002 0.054 -0.003 -0.017 (2.12) (0.56) (0.04) (1.70) (0.18) Experience 0.019*** 0.011 0.027*** Degree 0.396*** 0.179** 0.478*** 0.250*** (0.03) (0.75) (0.05) (0.27) Assistance -0.031 Married 0.060 -0.074** 0.115* 0.010 (2.87) (1.54) (4.20) (5.07) (2.17) (5.91) (3.62) Constant 1.513*** 2.029*** 1.457*** 2.137*** (0.40) (1.54) (2.44) (1.72) (1.64) Experience Sq -0.000*** -0.000 -0.000*** Other high educ 0.284*** 0.208** 0.267*** 0.174*** (6.69) (9.57) (6.31) (11.31) Experience 0.029*** Degree 0.702*** 0.737*** 0.027*** -0.000 (3.06) (1.39) (4.27) (3.42) (2.55) (3.26) (2.67) Observations 597 560 639 705 (6.67) (14.94) (16.14) (6.43) (1.35) Tenure 0.001 0.001** 0.002*** A level 0.145** 0.136* 0.153** 0.085 R-squared 0.57 0.54 0.57 0.56 Experience Sq -0.001*** Other high educ 0.409*** 0.479*** -0.001*** 0.001*** (1.32) (2.16) (3.64) (2.46) (1.88) (2.47) (1.46) (7.17) (7.56) (10.39) (6.00) (2.94) Tenure Sq 0.000 -0.000 -0.000* O level 0.119* -0.001 0.210*** 0.058 Tenure 0.002*** A level 0.208*** 0.246*** 0.002*** -0.000 (0.47) (0.59) (1.78) (1.79) (0.02) (3.18) (1.18) (5.27) (5.00) (5.61) (4.47) (1.40) Birth 0.102** 0.020 0.094** -0.010 (2.07) (0.40) (2.09) (0.27) Work acc -0.099* 0.055 -0.080 0.036 (1.67) (0.81) (1.23) (0.51) Traffic acc -0.149* 0.003 -0.078 F o r Other educ 0.074 -0.026 0.113* 0.028 (1.14) (0.42) (1.69) (0.53) Professional occupations -0.047 0.185* -0.059 0.190** (0.65) (1.90) (0.92) (2.51) Associate professional and technical -0.147** -0.146* -0.189*** -0.137** F o r F o r Tenure Sq -0.000** -0.000 (2.13) (1.23) Birth 0.050 0.000 (1.60) (0.00) Work acc -0.100** 0.036 O level 0.203*** 0.157*** (4.38) (4.14) F Other educ 0.047 0.010 (1.01) (0.25) o Social housing -0.231*** -0.109** r -0.035 (1.94) (0.04) (0.90) (0.43) Household acc -0.034 -0.046 -0.165* -0.092 (0.51) (0.60) (1.71) (1.05) Work illness -0.080 -0.033 -0.047 0.040 (1.53) (0.57) (0.88) (0.75) Duration <1 0.043 -0.010 -0.029 -0.027 (0.64) (0.13) (0.41) (0.42) Duration <2 0.156** -0.113* 0.108* P e e r (2.20) (1.74) (2.96) (2.04) Administrative and secretarial -0.365*** -0.284*** -0.399*** -0.298*** (4.76) (3.57) (5.38) (4.73) Skilled trades occupations -0.331*** -0.483*** -0.356*** -0.492*** (5.14) (3.51) (5.58) (4.50) Personal service occupations -0.494*** -0.477*** -0.405*** -0.419*** (4.29) (5.51) (3.85) (5.92) Sales and customer service occupation -0.424*** -0.390*** -0.428*** -0.397*** P e e r P e e r (2.08) (4.11) (2.19) (0.61) Traffic acc -0.075 0.062 (1.13) (1.01) Household acc -0.046 0.027 (0.88) (0.47) Work illness -0.016 -0.014 (0.43) (0.31) Duration <1 0.052 Owned -0.055 -0.041 P (1.04) (0.81) Mortgage 0.015 0.057 e (0.32) (1.31) Constant 1.385*** 1.211*** e (9.93) (9.58) r Observations 1553 1531 -0.061 0.045 (2.20) (1.72) (1.72) (0.82) Duration <3 0.083 0.012 0.047 0.022 (1.31) (0.17) (0.76) (0.38) Duration <5 0.060 -0.029 0.075 0.016 (1.07) (0.53) (1.39) (0.34) Duration <10 0.037 -0.035 0.030 -0.024 (0.79) (0.74) (0.65) (0.60) Kind 0.093 -0.060 -0.035 -0.025 (1.19) (0.97) (0.86) (0.68) Amount 0.015 -0.062* -0.029 -0.090** (0.41) (1.66) (0.63) Mobility1 -0.143* -0.176** -0.107 (1.77) (2.27) (1.19) Mobility2 0.054 -0.128*** 0.053 (1.08) (2.62) (0.99) Number health -0.020 0.010 -0.011 (1.46) (0.78) (0.82) Limbs -0.008 0.021 -0.036 (0.13) (0.36) (0.62) Sight/hearing 0.059 -0.059 -0.018 (0.70) (0.60) (0.17) Chest/heart -0.013 0.018 -0.054 (0.23) (0.32) (1.05) Mental health -0.054 -0.110 -0.148 (2.60) (2.79) (3.97) (4.14) -0.153** Small firm -0.098*** -0.106*** -0.153*** -0.131*** Chest/heart -0.024 -0.027 (0.66) (1.09) (1.51) (0.32) (1.65) (0.92) (1.68) -0.025 Part-time -0.066 -0.061 0.021 -0.055* Sight/hearing -0.054 -0.111* (0.46) (2.16) (0.79) (0.75) (1.43) (0.05) (0.91) -0.057 Public admin, education & health dummy 0.188** 0.059 0.069 0.103 Limbs -0.002 -0.034 (0.02) (3.02) (1.49) (3.05) (2.14) (1.66) (0.42) -0.001 Banking, finance & insurance dummy 0.274*** 0.124 0.289*** 0.166** Number health -0.020* 0.004 (1.03) (1.68) (0.01) (1.77) (1.39) (0.36) (2.91) 0.012 Transport & communication dummy 0.165* 0.002 0.176* 0.132 Mobility2 0.016 -0.115*** (2.23) (1.39) (0.66) (0.58) (0.59) (1.55) (1.60) -0.099** Distribution, hotels & restaurants dummy 0.124 -0.055 0.054 -0.046 Mobility1 -0.136 -0.116 (1.28) (2.98) (1.38) (2.78) (1.83) (1.45) (2.69) -0.090 Construction dummy 0.305*** 0.398 0.290*** 0.305* Amount -0.052 -0.089*** (2.33) R e v i e w (4.47) (4.08) (4.45) (5.49) Process, plant and machine operatives -0.462*** -0.501*** -0.504*** -0.546*** (7.00) (4.25) (7.86) (5.64) Elementary occupations -0.588*** -0.544*** -0.570*** -0.535*** (8.79) (6.14) (8.59) (7.45) Agriculture & fishing dummy -0.053 0.265 -0.067 0.197 (0.33) (1.09) (0.43) (1.18) Energy & water dummy 0.423*** 0.401* 0.423*** 0.722*** (2.75) (1.89) (2.84) (3.61) Manufacturing dummy 0.251*** 0.101 0.143 0.136 (2.89) (1.02) (1.56) (1.98) (0.15) (1.53) R e v i e w R e v i e w (1.07) (1.34) Duration <2 0.093** 0.003 (2.09) (0.08) Duration <3 0.038 -0.095** (0.89) (2.21) Duration <5 0.036 (0.94) (0.39) Duration <10 0.004 (0.11) (0.01) Kind -0.058** 0.004 w 0.000 e i 0.014 v e R R-squared 0.41 0.39

All the models are also estimated without the controls for the dynamic influences (duration and cause) but the results are not sensitive to this.

This is consistent with[START_REF] Madden | Labour market discrimination on the basis of health: an application to UK data[END_REF] who finds the controls for selection into disability are insignificant in participation equations. The positive sign on indicates that unobservables that are positively correlated with disability are positively correlated with employment amongst the disabled, which appears counterintuitive. The correlation in a standard bivariate probit model with disability and employment (without selection) is, however, negative and strongly significant.

It may be that the nature of birth onset disability differs from age onset disability; however, a range of controls for the type, severity and duration of the disability are already included in the model to capture these influences.

Onset at birth also has a limited effect on observable characteristics such as education. Indeed, there is no descriptive evidence to suggest those who are disabled at birth are significantly less likely to hold educational qualifications. It should, however, be noted that hereditary conditions which are included in this group may not affect an individual prior to labour market entry.

A more restricted specification was also estimated, which excluded the controls for type of employment, since the within group differences may affect earnings through occupational choice. The results are fairly robust to the inclusion of the additional controls and so they are not presented here, but are available from the author on request.

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript


Philip Richard Lane
email: plane@tcd.ie.

Tara Mcindoe

Christine Dwane

CURRENCY UNIONS AND IRISH EXTERNAL TRADE *

Keywords: EMU, Trade, Ireland F14, F17, F31) F14, F17, F31)

HAL is

Introduction

The impact of currency union on the volume of international trade has been studied intensively in recent years, following the seminal contribution of [START_REF] Rose | One Money, One Market: Estimating the Effect of Common Currencies on Trade[END_REF]. Estimates vary widely across different specifications and samples, with the out-of-sample relevance of estimated coefficients much disputed. In particular, the original Rose estimates have been viewed as having limited relevance for currency unions among high-income countries. This has led to a wave of research that has tried to directly estimate the impact of European Monetary Union (EMU) on trade, rather than rely on estimates obtained from other currency unions. Prominent contributions in this literature include [START_REF] Micco | The Currency Union Effect on Trade: Early Evidence from EMU[END_REF] and the survey by Baldwin (2006).

The 'EMU and trade' literature has recognized that differences in the structural characteristics and initial positions of the individual member countries mean that the impact of EMU is unlikely to be uniform across these countries. For this reason, it is useful to consider studies of individual member countries. In this regard, Ireland is a particularly interesting case, since EMU is not its first experience with currency union -until 1979, it was in a long-standing currency union with the United Kingdom. Accordingly, our goal in this paper is investigate the impact of these two currency unions on Irish trade.

Our paper relates to several recent contributions. The time series evidence on the relation between currency unions and trade has been most extensively explored by Glick and Rose (2002), who find a significant impact: for instance, leaving a currency union implies a decline in trade volume of about 50 percent. In relation to Ireland, [START_REF] Thom | The Effect of a Currency Union on Trade: Lessons from the Irish Experience[END_REF] F o r P e e r R e v i e w investigated the impact of the currency union between Ireland and the United Kingdom that was in place from 1922 until 1979, when Ireland broke link with Sterling and entered the European Monetary System. However, neither of these studies incorporates the EMU period. In terms of methodology, Faruqee (2004) also adopts a cointegration framework and employs the dynamic ordinary least squares estimator. However, his panel only covers 1992-2002 and has twenty-two source countries -in contrast, we focus on a single source country (Ireland) and a much longer time span . Finally, [START_REF] Dwane | EMU and Irish Trade[END_REF] provides a comprehensive review of the literature, data sources and methodologies that have been applied in studying Irish trade.

Data Description and Empirical Specification

We compile a long time series panel of data, running over 1950-2004, for Ireland's twenty one major trading partners that have data over this time period. 1 These countries are the EU-15, plus Australia, Canada, New Zealand, Norway, Switzerland and the United States.

Figures 1a and1b show the evolution of the export shares of the United Kingdom and the EMU member countries over 1950-2004. The figures show a dramatic decline in the United Kingdom share of Irish exports and a substantial increase in the EMU share of Irish imports; there are similar, but less steep, changes in import shares. Our goal is to ask whether the shifts in trading patterns are related to changes in Ireland's participation in currency unions.

1 A detailed data appendix is available from the auhors upon request. This estimator includes leads and lags of the first differences of non-stationary regressors in order to correct for the impact of serial correlation in the residuals. In view of the limited time horizon, we implement a DOLS(-1,1) specification.

Table 1 reports our baseline specification. The estimates show that EMU is not directly associated with an increase in trade -either with other EMU partner countries or with nonmember countries. However, the Sterling dummy is significantly positive -the level of trade between Ireland and the United Kingdom was significantly higher during the period of currency union up to 1979. This effect derives from the behavior of exports -the Sterling dummy is not significant for imports.

In terms of the other regressors, it shows that the time-series evolution of Irish trade is significantly linked to the dynamics of GDP per capita -trade expands with those partner countries that are growing most quickly. In addition, holding fixed GDP per capita, there is a significantly negative time-series association with the level of total GDP. The correct interpretation is that faster population growth in a partner country is associated with a relative decline in trade -we do not dwell on this result, which highlights the difference between cross-sectional analysis (total trade is increasing in country size) and time-series analysis.

2 Unit root and cointegration test results are available upon request from the authors. See also Kao and Chiang (2000) and Faruqee (2004) on the DOLS estimator. In order to assess the robustness of the result that the Sterling currency union boosted trade, we explore an alternative specification in Table 2. In this specification, we allow the level of trade with the United Kingdom to be influenced by two other factors. First, following [START_REF] Thom | The Effect of a Currency Union on Trade: Lessons from the Irish Experience[END_REF], we interact the EU dummy with the United Kingdom country dummy, on the basis that membership of the European Union was another major factor in reducing the dependence of Ireland on the United Kingdom as a trading partner. Second, we also interact the United Kingdom country dummy with a time trend. This is intended to capture the gradual erosion over time of the special status of the United Kingdom as a trading partner, in view of the complex historical and political factors that accounted for the high degree of trade with the United Kingdom at the start of the sample period.3

Indeed, it turns out that the EMU dummies remain insignificant and the Sterling dummy is no longer significant in the alternative specification reported in Table 2. Rather, the dynamics of trade with the United Kingdom is better captured by the country-specific time trend -the table shows that there has been a gradual decline in exports to the United Kingdom, rather than 'step' changes associated with EU membership in 1973 and the breaking of the Sterling link in 1979. Again, the trend is only evident on the export side: the interaction terms are not significant for imports.

Conclusions

We have investigated the impact of currency unions on Irish trade patterns. In contrast to most of the multi-country panel studies, we do not find any impact of EMU on trade. However, this is qualitatively consistent with the pattern noted by Baldwin (2006) that EMU has had a bigger impact on the 'core' member countries than on the peripheral member countries that have weaker economic linkages with the rest of the currency union. That said, we acknowledge that the time period may be too short to pick up the EMU effect on Irish trade -it will important to re-visit this study in a few years. 0.8 0.9 1 1 9 5 0 1 9 5 3 1 9 5 6 1 9 5 9 1 9 6 2 1 9 6 5 1 9 6 8 1 9 7 1 1 9 7 4 1 9 7 7 1 9 8 0 1 9 8 3 1 9 8 6 1 9 8 9 1 9 9 2 1 9 9 5 1 9 9 8 2 0 0 1 2 0 0 4 UK Share of Exports EMU Share of Exports 1 9 5 0 1 9 5 3 1 9 5 6 1 9 5 9 1 9 6 2 1 9 6 5 1 9 6 8 1 9 7 1 1 9 7 4 1 9 7 7 1 9 8 0 1 9 8 3 1 9 8 6 1 9 8 9 1 9 9 2 1 9 9 5 1 9 9 8 2 0 0 1 2 0 0 4 UK Share of Imports EMU Share of Exports

Introduction

The impact of currency union on the volume of international trade has been studied intensively in recent years, following the seminal contribution of [START_REF] Rose | One Money, One Market: Estimating the Effect of Common Currencies on Trade[END_REF]. Estimates vary widely across different specifications and samples, with the out-of-sample relevance of estimated coefficients much disputed. In particular, the original Rose estimates have been viewed as having limited relevance for currency unions among high-income countries. This has led to a wave of research that has tried to directly estimate the impact of European Monetary Union (EMU) on trade, rather than rely on estimates obtained from other currency unions. Prominent contributions in this literature include [START_REF] Micco | The Currency Union Effect on Trade: Early Evidence from EMU[END_REF] and the survey by Baldwin (2006).

The 'EMU and trade' literature has recognized that differences in the structural characteristics and initial positions of the individual member countries mean that the impact of EMU is unlikely to be uniform across these countries. For this reason, it is useful to consider studies of individual member countries. In this regard, Ireland is a particularly interesting case, since EMU is not its first experience with currency union -until 1979, it was in a long-standing currency union with the United Kingdom. Accordingly, our goal in this paper is investigate the impact of these two currency unions on Irish trade. European Monetary System. However, neither of these studies incorporates the EMU period. In terms of methodology, Faruqee ( 2004) also adopts a cointegration framework and employs the dynamic ordinary least squares estimator. However, his panel only covers 1992-2002 and has twenty-two source countries -in contrast, we focus on a single source country (Ireland) and a much longer time span .

Data Description and Empirical Specification

We compile a long time series panel of data, running over 1950-2004, for Ireland's twentyone major trading partners that have data over this time period. 1 These countries are the EU-15, plus Australia, Canada, New Zealand, Norway, Switzerland and the United States.

Figures 1a and1b show the evolution of the export shares of the United Kingdom and the EMU member countries over 1950-2004. The figures show a dramatic decline in the United Kingdom share of Irish exports and a substantial increase in the EMU share of Irish imports; there are similar, but less steep, changes in import shares. Our goal is to ask whether these shifts in trading patterns are related to changes in Ireland's participation in currency unions.

1 A detailed data appendix is available from the authors upon request.

= + + + + + + + (1)
where ijt T is the sum of the log of exports and the log of imports between Ireland and trade partner j in period t , j α is a country dummy, t φ is a time dummy, _ jt EMU IN takes the value 1 if trade partner j is also a member of EMU in period t for 1999 t ≥ and 0 otherwise; _ jt EMU OUT takes the value 1 if trade partner j is not a member of EMU in period t for 1999 t ≥ and 0 otherwise; jt STERLING takes the value 1 for the United Kingdom until 1978 and 0 otherwise; ijt EU takes the value 1 if both Ireland and the trading partner are members of the European Union in period t and 0 otherwise (Ireland joined the European Community in 1973 at the same time as the United Kingdom and Denmark); and ijt Z represent time-varying bilateral factors that influence trade volumes (in particular, the log levels of GDP and GDP per capita).2

The _ jt EMU OUT variable is included to allow for the possibility that EMU boosts trade with all partner countries, for the reasons outlined in Baldwin (2006). The inclusion of country dummies means that the focus is only on the within-country variation in tradedifferences in the level of trade across partner countries is not explored. Similarly, the inclusion of year dummies means that we strip out the impact of global factors that may affect the general level of trade with all partner countries, such as shifts in global trading costs.

Since trade volumes, GDP and GDP per capita are non-stationary but cointegrated variables, dynamic ordinary least squares (DOLS) is an appropriate estimation framework. 3

This estimator includes leads and lags of the first differences of non-stationary regressors in order to correct for the impact of serial correlation in the residuals. In view of the limited time horizon, we implement a DOLS(-1,1) specification.

Results

Table 1 reports our baseline specification. The estimates show that EMU is not directly associated with an increase in trade -either with other EMU partner countries or with nonmember countries. However, the Sterling dummy is significantly positive -the level of trade between Ireland and the United Kingdom was significantly higher during the period of currency union up to 1979. This effect derives from the behavior of exports; the Sterling dummy is not significant for imports.

3 Unit root and cointegration test results are available upon request from the authors. See also Kao and Chiang (2000) and Faruqee (2004) on the DOLS estimator. In terms of the other regressors, it shows that the time-series evolution of Irish trade is significantly linked to the dynamics of GDP per capita -trade expands with those partner countries that are growing most quickly. In addition, holding fixed GDP per capita, there is a significantly negative time-series association with the level of total GDP. 4 The correct interpretation is that faster population growth in a partner country is associated with a relative decline in trade -we do not dwell on this result, which highlights the difference between cross-sectional analysis (total trade is increasing in country size) and time-series analysis.

In order to assess the robustness of the result that the Sterling currency union boosted trade, we explore an alternative specification in Table 2. In this specification, we allow the level of trade with the United Kingdom to be influenced by two other factors. First, following [START_REF] Thom | The Effect of a Currency Union on Trade: Lessons from the Irish Experience[END_REF], we interact the EU dummy with the United Kingdom country dummy, on the basis that membership of the European Union was another major factor in reducing the dependence of Ireland on the United Kingdom as a trading partner. Second, we also interact the United Kingdom country dummy with a time trend. This is intended to capture the gradual erosion over time of the special status of the United Kingdom as a trading partner, in view of the complex historical and political factors that accounted for the high degree of trade with the United Kingdom at the start of the sample period. Again, the trend is only evident on the export side: the interaction terms are not significant for imports. Quantitatively, the size of this effect corresponds to an annual trend decline of 6.9 percent in exports.

Conclusions

We have investigated the impact of currency unions on Irish trade patterns. In contrast to most of the multi-country panel studies, we do not find any impact of EMU on trade.

However, this is qualitatively consistent with the pattern noted by Baldwin (2006) that EMU has had a bigger impact on the 'core' member countries than on the peripheral member countries that have weaker economic linkages with the rest of the currency union. That said, we acknowledge that the time period may be too short to pick up the EMU effect on Irish trade -it will be important to re-visit this study in a few years. It is also possible that EMU has had an impact in particular sectors: this hypothesis could be investigated in future 0.8 0.9 1 1 9 5 0 1 9 5 3 1 9 5 6 1 9 5 9 1 9 6 2 1 9 6 5 1 9 6 8 1 9 7 1 1 9 7 4 1 9 7 7 1 9 8 0 1 9 8 3 1 9 8 6 1 9 8 9 1 9 9 2 1 9 9 5 1 9 9 8 2 0 0 1 2 0 0 4 UK Share of Exports EMU Share of Exports 1 9 5 0 1 9 5 3 1 9 5 6 1 9 5 9 1 9 6 2 1 9 6 5 1 9 6 8 1 9 7 1 1 9 7 4 1 9 7 7 1 9 8 0 1 9 8 3 1 9 8 6 1 9 8 9 1 9 9 2 1 9 9 5 1 9 9 8 2 0 0 1 2 0 0 4

  Our baseline empirical specification postulates a long-run relation between the volume of trade and a set of core regressors, including dummy variables to capture participation in a currency union.

  Figure1a. Ireland: Export Shares, 1950-2004

Figure

  Figure1b. Ireland: Import Shares, 1950-2004

  Our paper relates to several recent contributions. The time series evidence on the relation between currency unions and trade has been most extensively explored byGlick and Rose (2002), who find a significant impact: for instance, leaving a currency union implies a decline in trade volume of about 50 percent. In relation to Ireland,[START_REF] Thom | The Effect of a Currency Union on Trade: Lessons from the Irish Experience[END_REF] of the currency union between Ireland and the United Kingdom that was in place from 1922 until 1979, when Ireland broke link with Sterling and entered the

  Figure1a. Ireland: Export Shares, 1950-2004

Figure

  Figure1b. Ireland: Import Shares, 1950-2004

  This can be written as

	Since trade volumes, GDP and GDP per capita are non-stationary but cointegrated
	variables, dynamic ordinary least squares (DOLS) is an appropriate estimation framework. 2
	log( ) ijt T	1 ijt α φ γ 4 * j t EU γ = + + +	* + EMU IN _ * ijt Z u ijt jt β +	+	γ	2	*	_ EMU OUT	jt	+	γ	3	*	STERLING	jt	(1)
	where α is a F o r
	country dummy, t φ is a time dummy, also a member of EMU in period t for EMU IN takes the value 1 if trade partner j is _ jt 1999 t ≥ and 0 otherwise; _ P e
				e r				t ≥	1999	and 0
	otherwise;	jt STERLING takes the value 1 for the United Kingdom until 1978 and 0 R
	otherwise;	ijt EU takes the value 1 if both Ireland and the trading partner are members of the e
	European Union in period t and 0 otherwise; and ijt v Z represent time-varying bilateral factors that influence trade volumes (in particular, the log levels of GDP and GDP per capita). The _ jt EMU OUT variable is included to allow for the possibility that EMU boosts i e w
	trade with all partner countries, for the reasons outlined in Baldwin (2006). The inclusion of
	country dummies means that the focus is only on the within-country variation in trade -
	differences in the level of trade across partner countries is not explored. Similarly, the
	inclusion of year dummies means that we strip out the impact of global factors that may
	affect the general level of trade with all partner countries, such as shifts in global trading
	costs.

ijt T is the level of trade between Ireland and trade partner j in period t , j jt EMU OUT takes the value 1 if trade partner j is not a member of EMU in period t for

Table 2 .

 2 Finally, in relation to the Sterling currency union, we have emphasized that the apparent significance of the Sterling link on trade is misleading -rather, Irish trade with the United Kingdom has shown a negative trend over many years. Micco, A., E. Stein and G. Ordonez, 2003. "The Currency Union Effect on Trade: Early Evidence from EMU," Economic Policy 37, 313 -56. **, **, * indicates significance at 1, 5, 10 per cent levels. Standard errors given in parentheses. Number of observations is 1040. All specifications include time and country dummies. Arellano-Bond test for AR(1) residuals: H 0 rejected in all specifications. Alternative Specification Dynamic OLS Fixed Effects Estimates, 1950 -2004 **, **, * indicates significance at 1, 5, 10 per cent levels. Standard errors given in parentheses. Number of observations is 1040. All specifications include time and country dummies. Arellano-Bond test for AR(1) residuals: H0 rejected in all specifications.

	Rose, A.K., 2000, "One Money, One Market: Estimating the Effect of Common Currencies
	on Trade," Economic Policy 30, 7-45.
	Thom, R. and B. Walsh (2002), "The Effect of a Currency Union on Trade: Lessons from
	the Irish Experience," European Economic Review 46, 1111-1123.
	F o r
	P e
	e
	r
	R
	e
	v
	i e w

Page 14 of 22 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

  Our baseline empirical specification postulates a long-run relation between the volume of trade and a set of core regressors, including dummy variables to capture participation in a

										Submitted Manuscript
	currency union. This can be written as
	ijt T	j α φ γ t	1	*	_ EMU IN	jt	γ	2	*	_ EMU OUT	jt	γ	3	*	STERLING	jt
		γ	4	*	EU	ijt	β	*	Z	ijt	ijt u
		F o r
								P e
											e
														r
															R
																	e
																	v
																	i e w

  Indeed, it turns out that the EMU dummies remain insignificant and the Sterling dummy is no longer significant in the alternative specification reported in Table2. Rather, the dynamics of trade with the United Kingdom is better captured by the country-specific time trend -the table shows that there has been a gradual decline in exports to the United Kingdom, rather than 'step' changes associated with EU membership in 1973 and the breaking of the Sterling link in 1979.

	F o r
	P e
	e
	r
	R
	e
	v
	5 i e w

Table 2 .

 2 research. Finally, in relation to the Sterling currency union, we have emphasized that the apparent significance of the Sterling link for trade is misleading -rather, Irish trade with the United Kingdom has shown a negative trend over many years. **, **, * indicates significance at 1, 5, 10 per cent levels. Standard errors given in parentheses. Number of observations is 1040. All specifications include time and country dummies. Arellano-Bond test for AR(1) residuals: H 0 rejected in all specifications. Alternative Specification Dynamic OLS Fixed Effects Estimates, 1950 -2004 **, **, * indicates significance at 1, 5, 10 per cent levels. Standard errors given in parentheses. Number of observations is 1040. All specifications include time and country dummies. Arellano-Bond test for AR(1) residuals: H0 rejected in all specifications.

	F o r
	P e
	e
	r
	R
	e
	v
	i e w

Page 22 of 22 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

	UK Share of Imports	EMU Share of Exports

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

See alsoBerger and Nitsch (2006) who argue that trade among EMU partner countries has shown a positive trend for many years.

We followBaldwin (2006) in measuring total trade as the sum of the log of exports and the log of imports, since this is theoretically appropriate in the context of a gravity framework. Trade data are deflated using export and import price deflators.

We note that the magnitudes of the coefficients on GDP and GDP per capita are larger than typically found in gravity studies. One reason is that we are estimating a cointegration equation, where the coefficient captures the permanent impact on trade of a permanent change in the regressors: this will naturally be larger than the short-run impact. A second is that our fixed-effects specification means that only the time-series dimension of the data are examined -the coefficients are much lower if the crosssectional variation is incorporated.

5 See alsoBerger and Nitsch (2006) who argue that trade among EMU partner countries has shown a positive trend for many years.

F14 -Country and

* Lane's work on this paper was supported by the IRCHSS and the HEA-PRTLI grant to the IIIS. We thank Patrick Honohan for helpful suggestions and Luca Ricci for Stata code. * Lane's work on this paper was supported by the IRCHSS and the HEA-PRTLI grant to the IIIS. We thank Patrick Honohan for helpful suggestions and Luca Ricci for Stata code.