Equality and Equity Report

In early 1992, as part of the Review of Employment Equality in Northern Ireland, the Central Community Relations Unit commissioned research to provide a detailed profile of the Catholic and Protestant male unemployed. The research was conducted by Dr Anthony Murphy and Mr David Armstrong, who have produced this report on their findings.

In carrying out the Employment Equality Review, it is the policy of the Central Community Relations Unit to publish commissioned research, with a view to informing wider discussion of the issues. The views expressed in the report are, however, the responsibility of the authors and should not necessarily be regarded as being endorsed by the Central Community Relations Unit.

We are grateful to the ESRC Data Archive, the Department of Economic Development, and the Policy, Planning and Research Unit (PPRU) of the Department of Finance and Personnel for providing the Labour Force Survey and Continuous Household Survey data used in this report. They are not responsible for the content of this report. Any errors or omissions are our responsibility.

Background

Catholic and Protestant men in Northern Ireland have very different unemployment rates (Table 1.1). In the 1991 Census, the unemployment rate for Catholic men is about two and a quarter times higher than the unemployment rate for Protestant men. This unemployment differential has persisted throughout the 1980s. It has provoked a lively debate amongst academics and policy makers about the factors which account for it.

Notes: The 1971 and 1981 Census figures are from Compton (1991). The Protestant figures refer to all non-Catholic denominations. The figures in brackets are obtained by allocating those in the religion 'not stated' category proportionately between Catholics and others.

Explaining the Unemployment Differential

The basic issue is whether or not the difference in unemployment rates reflects differences in labour market opportunities. According to some (eg Compton 1991) it is possible to explain a large part of the unemployment differential in terms of 'structural' factors such as age, number of children, geography, social class and/or industry which, it is argued, have little or nothing to do with differences in opportunities., According to others (Eversley, 1989 or Smith and Chambers, 1991) much of the unemployment differential is explained by religion or factors highly correlated with religion and not by differences in the observed characteristics of the two groups. In this paper we address this question of whether or not the unemployment differential can be largely explained by 'structural factors'.

Research Topics

The paper investigates a number of different aspects of Catholic and Protestant male unemployment. This includes not only the incidence of unemployment but also the following topics:

non-participation (economic inactivity and its links with unemployment;

discouragement amongst non-participants;

duration of unemployment;

job search behaviour;

labour turnover;

flows between employment, unemployment and non-participation;

claimant status of the unemployed.

Use of Econometric Models

Two different household survey datasets are used to look at these topics. These two datasets are larger than those previously used. A wide range of statistical techniques have been used to analyse the data. In addition to examining the data using simple univariate and multivariate techniques, we construct a series of econometric models which help to disentangle the effects of various personal characteristics, including religion, which are correlated with each other.

Such models are very useful. For example our models for the incidence of unemployment throw light on the claim in Compton (1991) that the unemployment difference between Catholic and Protestant men is largely explained by observable factors such as age, geography etc and not by religion or other factors correlated with religion.

Outline of Paper

The outline of this paper is as follows. The findings of previous model-based research on the subject of Catholic and Protestant unemployment are briefly reviewed in Chapter 2. The Labour Force and Continuous Household Survey sample datasets are discussed in Chapter 3. Models of the incidence of unemployment are presented and discussed in Chapter 4. Non-participation and discouragement are considered in Chapter 5. We turn to the duration of unemployment next in Chapter 6. We examine and model the job search behaviour of the unemployed in Chapter 7. In Chapter 8 we consider flows to and from employment and unemployment. Finally, we provide some conclusions in Chapter 9.

Nearly all the 'technical' econometric material is set out in Appendices. In the main text, sections containing technical material are in small type. Some readers may wish to skip over much of the detail.

Introduction

In this Chapter we examine two important studies which attempt to model the incidence or duration of male unemployment by religion in Northern Ireland. The large number of studies which examine, but do not explicitly model, the incidence of unemployment are not reviewed here. These are reviewed in Gallagher (1991) and Whyte (1990).

This chapter concentrates on the work of Compton (1991) and Smith and Chambers (1991) since these are fairly sophisticated studies which' reach very different conclusions. Compton (1991) suggests that over 80% of the difference in unemployment rates between Catholic and Protestant males is explained by a small number of factors apart from religion. Smith and Chambers (1991) suggest that over 60% of the difference in male unemployment rates between Catholics and Protestants in their data is accounted for by religion or factors correlated with religion. The remaining 40% is accounted for by differences in the characteristics of the two groups. This suggests a wide range of disagreement; between 40% and 80% of the difference in unemployment rates between Catholic and Protestant men is attributed to differences in the characteristics of the two groups.

Modelling the Incidence of Unemployment By Religion

The major limitation of much research on male unemployment and religion has been the absence of quantifiable models. Without model results it is very difficult to identify the relative importance of the various factors which contribute to the different unemployment rates amongst Catholic and Protestant males. In consequence there is the risk that research effort will focus on issues which are either not important or else not very important.

Another problem with many studies is the difficulty of simultaneously handling the large number of explanatory variables which are available. These explanatory variables simultaneously contribute to the high male Catholic unemployment rate. However, ft is unlikely that they are uncorrelated and do not interact with each other. More sophisticated methods are required to ensure that the effects of the various explanatory variables are correctly disentangled.

The paper of Smith (1987) was a major advance since he used a logistic regression to model the incidence of unemployment by religion and included a large number of explanatory variables in his model. A logistic regression or logit model is the simplest method of modelling an outcome such as being employed or being unemployed. A range of personal and other characteristics which explain the incidence of unemployment may be included as explanatory variables in a logit model.

Smith and Chambers (1991)

The logit model results in Smith and Chambers (1991) update the results in Smith (1987). They take account of some of the criticisms of the results presented in 1987. However the 1987 and 1990 results are very similar.

Smith and Chambers list common explanations of the unemployment differential between Catholics and Protestants apart from direct or indirect discrimination and the chill factor:

Dual Labour Markets:
Catholics tend to belong the secondary rather than the primary labour market and thus have fewer opportunities.

Geography:
More Catholics tend to live in areas with high unemployment.

Age Structure:
Catholics are younger and the young tend to have higher unemployment rates.

Industry:
More Catholics tend to work in industries with high unemployment rates.

Socio-Economic Group:
More Catholics are in lower socioeconomic groups which tend to have higher unemployment rates.

Qualifications:
Catholics have poorer qualifications.

Family Size:
Catholic families tend to be larger so that they fall into the benefit trap and "choose" unemployment.

Population Growth:
The Catholic population is growing faster so, even if employment opportunities grew at the same rate, Catholic unemployment rates would rise.

Oddly enough Smith and Chambers do not include the following two explanations in their list:

Security Related Jobs:
Catholics tend to avoid jobs in the security forces.

Black Economy Jobs:
More Catholics are engaged in black economy jobs. Howe (1989, 1990) is often quoted to back this up.

Smith and Chambers use CHS data for 1983 to 1985 to construct a logit model of the incidence of unemployment. Their effective sample consist of over 5,500 economically active males aged 16 and over. They include the following explanatory variables:

Age:
They use three age groups: 16-24, 25-441 45 and over.

Number of Children in the Household:
The number of children in the family unit would be better since there may be two or more families in the same household.

Highest Academic or Vocational Qualification:
They use seven groups ranging from a degree to no qualifications.

Socio-Economic Group (SEG):
They use five groups. The intermediate and junior non-manual groups are aggregated. Those with no previous jobs appear to be implicitly combined with those in professional and managerial groups which is rather odd.

Travel to Work Areas (TTWAs):
They use fourteen areas disaggregating the Belfast TTWA into the district council area and inner and outer rings.

The results in Smith and Chambers (1991) confirm Smith's earlier results (Smith, 1987) which were challenged by Compton et al (1988), Cormack and Osborne (1988) and others. Smith and Chambers discuss the criticisms levelled against the results presented in the 1987 papers. Their discussion of the points raised appears reasonable to us. Of course, as Wilson (1989) and others have pointed out, Smith was wrong to claim that he had taken account of all relevant factors.


Criticisms of Smith (1987)

It is worthwhile discussing some of the criticisms of Smith (1987) in some detail since many of the same criticisms may be applied to the findings in this paper.

(i) Sampling Problems

A clustered and stratified sample design was used to collect the 1983 and 1984 CHS data. The primary sampling units were wards selected from each of three strata - Belfast district council, other areas east of the Bann and west of the Bann. Although clustering increases sampling variability and makes modelling more difficult it certainly does not invalidate any analysis below the strata level eg at TTWA level. In support of this it should be noted that when we use LFS data we obtain similar results using clustered 1985/86 data and unclustered 1990/91 data.

(ii) Measures of Goodness of Fit

Smith and Chambers were criticised for not presenting a measure of the goodness of fit of their model. Various measures of fit have been proposed for binary models such as the logit or probit. However, no single measure dominates unlike the use of R 2 in ordinary regressions (Maddala 1983, Dhrymes 1986, Greene 1993). Various pseudo R 2 measures are available but, with a large cross-sectional dataset, high pseudo R 2 values are extremely rare. In these circumstances various measures of fit and mis-specification tests should be presented along with the model estimates.

(iii) Educational Qualifications

Differences between Catholics and Protestants in the mix of subjects studied at school or college may be part of the explanation of the unemployment differential. In the LFS and CHS we have little or no data on subject mix. However, there is no evidence from international studies that subject mix has a large effect on the incidence of unemployment. Accordingly we do not believe that subject mix has anything like as large an effect on the incidence of unemployment as the level of qualifications. The effect of subject mix is likely to be quite small. Miller et al (1990) examine the relationship between degree subject and earnings for graduates. However, as Gallagher (1991) notes this evidence deals with a narrow sector of the labour market and with earnings rather than unemployment. Murphy and Shuttleworth (1994) find that, ceteris paribus, the number and level of qualifications of school leavers and not subject choice affects the incidence of unemployment amongst Catholic and other school leavers.

(iv) Population Growth

There are really two separate stories in the arguments about the effects of population growth on the incidence of unemployment. The micro-economic story has to do with the benefit trap. It is assumed that, ceteris paribus, men with large families have very similar incomes in and out of work and so are more likely to be unemployed. Since Catholic families are larger, Catholics are more likely to be unemployed. Miller and Osborne (1983) reanalysed a large cohort study of the unemployed in Northern Ireland and did not find strong support for large benefit trap effects. In any case our reduced form econometric models include many of the factors leading to high replacement ratios^[1] such as the number of children, qualifications, others unemployed in the household, etc. These variables should pick up benefit trap effects.

The macro-economic story, which is associated with Compton (1981, 1991), has to do with the effect of differences in labour force growth on the unemployment differential. This argument cannot really be dealt with using micro household data. However, it should be noted that Compton's examples are based on unrealistic and rather extreme assumptions. For example, he assumes that existing jobs always remain segregated since there is no turnover in these jobs. Smith (1987), Eversley (1989) and Smith and Chambers (1991) all point out the limitations of his examples.

However, possibly the best approach is to construct other more realistic examples or models of the unemployment differential in the presence of differences in labour force growth. In Appendix 2.1 we present a simple model which is used to examine the effect of differential labour force growth on the unemployment differential. Our model is based on what we consider to be plausible assumptions about, for example, separation and engagement rates. However, our model gives results which are the opposite of Compton's results. In particular, our model suggests that the contribution of differences in labour force growth to the unemployment differential is likely to be quite small.

(v) Security Related and Black Economy Jobs

Very few individuals in security related jobs appear in household surveys such as the Continuous Household and Labour Force Surveys. Of course some individuals in security related jobs may conceal their occupations by stating that they are civil servants. There is little one can do about this. Individuals engaged in the black economy are also very unlikely to respond to household surveys. Thus it is unlikely that security related jobs, which are predominantly held by Protestants, or black economy jobs, which some argue are more common in Catholic areas, bias the modelling results to a large extent. However, care must be taken when applying our model results to aggregate data. Smith and Chambers (1991) discuss the likely effect of security related jobs on the aggregate unemployment differential. Their calculations are very crude and their results, as they correctly point out, are over-estimates of the effects of security related jobs on the unemployment differential. In Appendix 2.2 we examine the contribution of security related jobs to the unemployment differential. Our results suggest that removing the security related jobs effect would reduce the unemployment differential by between 10% and 15%.

Compton (1991)

Compton (1981, 1991) uses Census data and standardization techniques to decompose the difference in unemployment rates between Catholic and Protestant males. For example in Table 3.5 in his 1991 paper, Compton used 1981 Census data to calculate a predicted Catholic male unemployment rate assuming Catholics had the same structure (in terms of age, geography, social class and/or industry) as Protestants. He finds that industry alone explains 65% of the unemployment difference while industry and geography explain 78% of the unemployment difference. Compton finds that age only explains 9% of the unemployment differential. He suggests that this is because the age structure is a poor proxy for labour force growth.

We wish to discuss four important issues regarding Compton's approach:

(i) (ii) (iii) (iv)

Meaning of standardization procedure; Choice of standard (reference) groups; Level of dis-aggregation; Other relevant explanatory variables.

It is unclear what it means to assume that Catholic males have the same structure as Protestant males since social class or industry are hardly structural. They are just as likely to reflect differences in labour market outcomes as to cause such differences.

Compton discusses this issue at length. He points out that, although his results provide statistical insights, as explanations of the unemployment differential "they should be approached with caution".


(ii) Choice of Standard or Reference Groups

However, the real problem with Compton's standardization procedure is that ft is not unique. In Appendix 2.3 we show what is going on. Compton calculates a "predicted" Catholic unemployment rate assuming Catholics have the same characteristics as Protestants. An alternative predicted unemployment rate is obtained when Catholics and Protestants differ in characteristics but, for any given set of characteristics, Catholics are assumed to face the same unemployment rate. This alternative predicted Catholic rate is equally valid yet it "explains" a much lower share of the unemployment differential. Other choices yield different results. Compton's choice of reference base appears to be the one which "explains" the largest share of the unemployed differential.


(iii) Level of Dis-Aggregation

As Compton briefly notes, his procedure is not invariant to the level of dis-aggregation. Under plausible assumptions, greater dis-aggregation always leads to greater explanatory power.


(iv) Other Relevant Explanatory Variables

In principle the multiple standardization procedure used by Compton may be extended to include any number of explanatory variables. In practice only a small number of explanatory variables are used. As a result relevant explanatory variables are omitted. The effect of omitting these variables is not considered. Since the omitted relevant variables are very unlikely to be uncorrelated with the included variables, biased results are very likely.

The basic point about all multiple standardization procedures is that they say nothing about causality; they are merely accounting procedures.


Conclusions

In this chapter we have reviewed two studies which model differences in the incidence or duration of unemployment between Catholic and Protestant men. The two opposing views of the causes of the unemployment differential are represented by the work of Compton (1981, 1990) on the one hand and Smith (1987) and Smith and Chambers (1991) on the other hand.

The results reported in Smith (1987) were an important element of the 1987 Standing Advisory Commission on Human Rights report on fair employment which led to the 1989 Fair Employment Act. It had a high profile and as a result Smith (1987) came in for a lot of criticism. In the case of his logit model results on the incidence of unemployment, much of this criticism was undeserved. Compton's work has come in for a good deal less criticism. We set out some important new criticisms of Compton's methodology. However, to date, no real attempt has been made to nest Compton's and Smith's work in a common framework, in order to understand why they reach different conclusions and to identify the valid elements in the two approaches.


Notes:
[1] The replacement ratio is the ratio of the income which someone could get if they were out of work to the income they could get if they were in work.

Introduction

This chapter describes the two household survey datasets used in the analysis. The first dataset consists of four years Labour Force Survey (LFS) data. The four years are 1985, 19869 1990 and 1991. Religion data were not collected in the LFS between 1987 and 1989. The second dataset consists of four years Continuous Household Survey (CHS) data. The four years are from 1986 to 1989. Since the sample size in these datasets in any one year is quite small, the data from each of the years are pooled.

Labour Force Survey

The LFS is an annual survey carried out each spring by interviewing the adult members of around 4,000 households about their personal circumstances and work. It is the largest regular household survey in Northern Ireland and provides a rich source of information about the labour force using internationally agreed definitions.

The LFS provides a comprehensive picture of the economic activity of the private household population. It does this by classifying all adult survey respondents into three main activities, namely employment, ILO unemployment and economic inactivity, according to their circumstances in the week prior to the interview. In the LFS, the employed, the ILO unemployed and the economically inactive are defined as follows:

Employed:
Any adult who did some paid work or who had a job that they were temporarily away from or who was on a government employment and training scheme.

ILO Unemployed:^[1]
Any adult without a job who was available to start a job within two weeks and who had either looked for work in the previous four weeks or was waiting to start a job already obtained.

Economically Inactive:
Any adult not employed or unemployed. This group includes the retired, the long-term sick and disabled and most full-time students.

The sample used in this report consists of economically active males aged 20 to 59 with a known religion (Catholic, Protestant, other religion and no religion). The age range 20 to 59 was chosen to avoid extensive modelling of participation in education and training schemes and retirement decisions.

The LFS data were recoded to ensure consistency of definitions. The sample data are unweighted and non-respondents to the economic activity and religion questions are excluded. Proxy information was obtained from 54.4% of the sample.

Some details of the LFS sample are set out in Table 3.1. Each year the LFS sample contains about 2,800 co-operating males aged 20-59 with known religion. This gives a total sample of just under 11 300. The religious composition of this sample is 40.2% Catholic, 56.1% Protestant, 1% other religion and 2.7% no religion.

Continuous Household Survey

The Continuous Household Survey (CHS) is similar to the General Household Survey in Britain. The CHS is a continuous survey carried out by interviewing the adult members of around 3,000 households each year about their social and economic conditions.

Since 1985, an unclustered sample design has been used to collect the CHS. This means that the addresses for the CHS are randomly sampled from each of three regional strata (Belfast, East and West). Unlike the LFS, there are separate questionnaires for those interviewed face-to-face and those for whom only proxy information was available. The range of information available for proxies is limited. In particular, no details of highest educational or vocational qualifications are collected. The questions on economic activity in the CHS are generally less comprehensive than those in the LFS. In the CHS, economic activity is classified as follows:

Employed:
Any adult who did some paid work or who had a job that they were temporarily away from or who was on a government scheme.

Unemployed:^[2]
Any adult without a job who was either looking for work, temporarily not looking for work because of sickness or injury or waiting to start a job already obtained.

Economically Inactive:
Any adult not employed or unemployed. This group includes the retired, the long term sick and disabled and most full-time students.

The CHS Sample

Some details of the CHS sample are set out in Table 3.2. The sample contains about 7,600 co-operating males aged 20 to 59 with known religion and economic activity. Data from full or partial interviews are available for 86.6% of these. Only proxy information is available for the remaining 13.4%. Excluding proxies, the sample size is about 6,600. The sample, which excludes proxies, is generally used for the detailed statistical analysis since the proxy responses do not contain information on highest qualifications.

Religion data were missing for some individuals. Where an individual did not answer the religion question or only proxy information was available, religion was imputed using the religion of the head of the household or his/her spouse. The religious composition of the sample is 38% Catholic, 58.9% Protestant, 0.6% other religion and 2.5% no religion.

The Unemployment Differential in the CHS and LFS Samples

In public debate a great deal of attention is paid to the unemployment differential ie the ratio of Catholic to non-Catholic unemployment rates. The average unemployment differential in the LFS and CHS samples is 2.5. The 95% confidence interval for this differential ranges from 2.3 to 2.7. This means that we can be 95% confident that the unemployment differential in the CHS sample lies between 2.3 and 2.7.^[3]

Economic Activity in the CHS and LFS Samples

Tables 3.3 and 3.4 show different aspects of the economic activity of Catholics and non-Catholics in the LFS sample and Tables 3.5 and 3.6 show different aspects of the economic activity of Catholics and non-Catholics in the CHS sample.

In the LFS the Catholic and non-Catholic unemployment rates are 25.7% and 10.4% respectively. The corresponding participation rates are 85.8% and 92.2%. The same pattern shows up in the CHS data. Catholic men are significantly more likely to be both unemployed and inactive. Unemployment rates are significantly lower and participation rates significantly higher in the LFS than in the CHS which reflects the stricter definition of unemployment used in the CHS.

Notes:
[1] The ILO unemployment figures obtained from the LFS are different from the "claimant count" unemployment figures which are published in Northern Ireland every month by the Department of Economic Development. The claimant count is a by-product of the administrative system which is used for paying unemployment-related benefits. This means that some claimants are not unemployed according to the ILO definition, and some of the ILO unemployed are not claiming unemployment-related benefits.

[2] It is not possible to construct a measure of ILO unemployment using the CHS data. However, this is not a major problem since, when we model the two measures of unemployment using LFS data, we obtain fairly similar results.

[3] Details of how the confidence intervals are calculated are given in Appendix 3.2. In the LFS sample, the unemployment differential fell from 2.6 in 19U/86 to 2.3 in 1990/91. However a clustered sample design was used to collect the data in 1985 and 19%. Because of this we cannot say that the fall from 2.6 to 2.3 is significant from a statistical point of view. This is discussed in more detail in Appendix 3.1