Equality and Equity Report

Introduction

This chapter investigates the relationship between religion and unemployment amongst men aged between 20 and 59. It was shown in Chapter 3 that according to the LFS and CHS samples, Catholic men were approximately two and a half times as likely to be unemployed as non-Catholic men. This chapter investigates the extent to which this unemployment differential can be explained in terms of factors such as age structure, geographical location, qualifications, etc. For example, it could be argued that Catholics are more likely to be unemployed than non-Catholics because they are more likely to live in the West of the Province where, for a variety of reasons, unemployment is high. Likewise, it could be argued that Catholics are more likely to be unemployed than non-Catholics because the Catholic population tends to be younger than the non-Catholic population and, for a variety of reasons, younger people are more likely to be unemployed than older people. In this chapter, therefore, we assess how strong the relationship is between religion and unemployment after a range of relevant factors have been taken into account.

Labour Force Survey (LFS) and Continuous Household Survey (CHS) data are used to model the incidence of unemployment by religion for men aged 20 to 59 in Northern Ireland. We present a range of estimates of the effect of religion on the incidence of unemployment. The estimates imply that religion directly accounts for about half of the difference in unemployment rates between Catholics and others. 'Structural factors', ie differences in the personal and other measured characteristics of the two groups, account for the rest of the difference in unemployment rates. These findings are robust and imply that, ceteris paribus, Catholic men are significantly more likely to be unemployed. We discuss the interpretation of these findings at length.

Methodology

In order to measure the contribution of religion and other factors to the incidence of unemployment, a number of quite sophisticated statistical (econometric) models have been estimated. These models estimate the effects of religion and a range of other factors on unemployment simultaneously. The particular type of statistical models which are used are called logit and probit models and they are described in detail in Appendix 4.2.

The models include a range of factors, other than religion, which have been shown in other studies to have an important effect on unemployment^[1]. The factors, which are included are as follows:

• Age;
• Marital Status;
• Number of Children;
• Housing Tenure;
• Health Problems which Limit Economic Activity;
• Others Employed or Unemployed in the Household;
• Highest Academic or vocational Qualifications;
• Claimant Count Unemployment Rates by Travel-to-Work Area (TTWA);
• Socio-Economic Group (SEG);
• Religion

In terms of the present study it is important to not e that these factors affect unemployment and many of them are correlated with religion. For example, it is a standard finding that, all other things being equal, people with large families are more likely to be unemployed than people with small families. This is because the amount of unemployment-related benefit to which a person is entitled increases with family size. This means that the income that some people can get when they are unemployed is similar or even greater than that which they could get if they were in a job, and this can be attributed, at least in part, to family size. For example, more than one-fifth of Catholics have three or more children, compared to slightly more than one-tenth of non-Catholics. Therefore, it might be the case that part of the difference in unemployment rates between Catholics and non-Catholics could be attributed to the fact that Catholic families tend to be larger and people with large families are more likely to be unemployed. The correlation between all of these factors and religion is shown in Appendix 4.5.

Unemployment and Religion in the LFS

Table 4.1 shows the incidence of unemployment by age group in the LFS sample. The unemployment differential is 2.5 in our sample. This means that, in aggregate Catholics are two and a half times more likely to be unemployed than non-Catholics.

As outlined above, one of the main purposes of the present analysis is to estimate how much more likely Catholics are to be unemployed than non-Catholics after a range of factors such as geographical location and the age structure of the population are taken into account. In order to do this, probit models of the incidence of unemployment have been estimated. The results of the basic probit model which has been estimated for the incidence of unemployment are given in Table 4.2. More sophisticated versions of this basic model are given in Appendix 4.5 along with a detailed discussion of the technical issues which have been addressed.

In Table 4.2, variables with positive coefficients increase the probability of unemployment and variables with negative coefficients reduce the probability of unemployment. For example, in Table 4.2 the variable called "Married" has a negative coefficient and this means that married men are less likely to be unemployed than non- married men. The coefficients are generally significant (t statistics of 2 or higher) and correctly signed according to our prior expectations. The effects of all the other variables on unemployment are discussed in detail in Appendix 4.5. However, the main point of interest is that these results show that Catholics are significantly more likely to be unemployed even after taking into account the factors listed in Table 4.2.

This, however, has still not answered the question of how much more likely Catholics are to be unemployed compared to non-Catholics after accounting for the other relevant factors. The results of the econometric models, like those presented in Table 4.2, can be used to derive a number of different estimates of this. These estimates are described and discussed in Appendix 4.5. A priori, there is no particular reason for using one measure instead of another. For this reason all three measures have been estimated and, reassuringly, each of them gives a similar result.

Overall, when the different estimates are compared, it is the case that approximately one-half of the observed unemployment differential can be explained in terms of religion and the other half can be explained in terms of other factors in the model such as geographical location, the age structure of the population etc. It should be noted that this is not the same as saying that half of the unemployment differential can be explained in terms of Northern Ireland employers discriminating against Catholics. A range of interpretations of the 'religion effect' can be given and these are discussed in detail in the final section of this Chapter.

Log Likelihood LL = -3396.91
McFadden's Pseudo R² = .217
Correct Predictions = 85%

Unemployment and Religion in the CHS

All of the analysis reported in the previous section is based on LFS data. This section presents the results of a similar analysis which was conducted using CHS data. Table 4.3 shows the unemployment rates for Catholics and others in different age groups in the CHS sample. The figures show that the unemployment differential does not vary greatly by age group.

As in the analysis of the LFS, one of the main purposes of the present analysis is to estimate how much more likely Catholics are to be unemployed compared to non- Catholics after a range of relevant factors have been taken into account. To do this probit models have been estimated for the incidence of unemployment using a similar set of explanatory variables to those used to model the incidence of unemployment in the LFS sample. All of the explanatory variables, except one, are defined in the same way in the two datasets^[3].

Notes:

Log Likelihood LL = -2070.9 McFadden's Pseudo FR2 = 0.282 Correct Predictions = 83.9% The Catholic and Other unemployment rates are 31.6% and 12.7%.

The results of the basic probit model for the incidence of unemployment are given in Table 4.4. These are very similar to the results obtained using our LFS data which is very reassuring. As in the analysis of the LFS, we have used these results to derive an estimate of how much of the unemployment differential can, be explained directly in terms of religion and how much can be explained in terms of the other observed factors. Again, a number of different estimates of the size of the religion effect have been derived. These are summarised in Appendix 4.6, which also contains the results from some more sophisticated econometric models.

When we exclude socioeconomic group (SEG) from our models, our results suggest that between 55% to 60% of the observed unemployment differential is directly accounted for by religion. The remainder is accounted for by observed differences in the characteristics of Catholics and others. These results, therefore, are broadly similar to the results obtained with the LFS sample. In our LFS sample, we cannot readily include SEG in our models because this information is not available for a large proportion of the unemployed. When we include SEG in our CHS models, our results suggest that between 47% to 54% of the observed unemployment differential is accounted for by religion.

In conclusion, therefore, our model results suggest that about half of the difference in the unemployment rates between Catholic and non-Catholic men is due to differences in the characteristics of the two groups, and the other half is accounted for by religion. This result appears to be robust. Generally speaking, these results provide support for Smith and Chambers' (1990) findings rather than Compton's (1990) findings.


Interpretation of Incidence of Unemployment Results

In our econometric models of the incidence of unemployment religion accounts for about half of the unemployment differential in our two samples; differences in the observed personal and other characteristics of the two groups account for the rest. However the large and significant Catholic effect on the incidence of unemployment must be interpreted carefully and, for example, cannot simply be equated with discrimination against Catholics. When interpreting the findings a number of factors have to be taken into account.

Omitted Variables

A large number of explanatory variables are included in our models. As outlined above, our choice of explanatory variables is based on a standard set of variables which have been used to model the incidence of unemployment in the applied econometrics literature. It is possible, however, that some important variables have been omitted from the models.

However, even if some relevant variables have been omitted from our models, the estimated Catholic effect is not necessarily larger than it should be. On the one hand, some of our explanatory variables such as housing tenure or the presence of other unemployed individuals in the household may be endogenous, ie simultaneously determined with unemployment. If this is the case, these variables will tend to reduce the estimated Catholic effect. On the other hand, our choice of explanatory variables is limited to those for which we have data. For example, there is no data on motivation and little or no data on subject mix at school or college in either of our datasets. If motivation or subject mix are correlated with being Catholic and contribute to the incidence to unemployment then their omission tends to increase the estimated religion effect.

The large and significant Catholic effect on the incidence of unemployment may be explained by factors which are not in our models because they are not measured in our data. These factors must be both correlated with religion and have a large effect on the incidence of unemployment. A number of such factors are commonly mentioned. These include differences in labour force growth, subject mix at school or college, motivation as well as direct or indirect discrimination and the 'chill factor^[4].

Labour Force Growth

As we discussed in Chapter 2, differences in labour force growth between Catholics and others have both micro and macro effects. The micro effect has to do with the benefit trap and the age structure of the population. In our models of the incidence of unemployment we include age, the number of children, qualifications and so on. These variables are significant and it is likely that they are capturing the micro labour force growth effects. The macro story appears to have more to do with segregation in employment.

In order to examine the macro labour force growth effect we construct a simple model of the labour market in Appendix 2.1. In order to focus on the labour force growth issue, Catholics and others are assumed to have the same chances of getting and leaving jobs ie equal engagement and separation rates. However, it is assumed that the Catholic labour force grows at the rate of 1% per annum and that the Protestant labour force is constant. The rate of unemployment is assumed constant in the long- run. Using this model we do not find that labour force growth has a large effect on the unemployment differential.

We have also modified the assumptions, and we find that when we do this we can still not generate a large unemployment differential effect. In fact, some combination of low labour turnover, zero or negative employment growth, high Catholic labour force growth and segregation in employment, appears to be required to obtain large unemployment differential effects. Such assumptions appear to us to be implausible.

Subject Mix

Differences between Catholics and others in the mix of subjects studied at school or college may be part of the explanation of the unemployment differential. However, there is very little evidence in Northern Ireland or elsewhere that differences in subject mix are a major explanation of the unemployment differential. Murphy and Shuttleworth (1994) do not find a subject mix effect for school leavers. Miller et al (1990) examine the relationship between degree subject and earnings for graduates. However, this evidence relates to a very small group in the labour force and to earnings rather than the incidence of unemployment^[5]. Also, Smith and Chambers (1990) note that the difference in unemployment rates between Catholic and Protestant men in their CHS data is highest for those with no qualifications. All of this would tend to suggest that little of the unemployment differential can be attributed to differences in subject mix. In support of this, according to our LFS data, the second largest difference in unemployment rates is for those with no qualifications; the largest difference is for the small category with 'other' qualifications. It is also the case that the difference in unemployment rates is highest for those aged 20 to 24, the age group with the smallest difference in subject mix. However, in these comparisons other factors are not being held constant. On the whole, therefore, there is little evidence that subject mix has a big effect on the incidence of unemployment.

Motivation

There is little or no evidence of differences in motivation, flexibility or attitudes to work between Catholics and Protestants. The limited evidence in Miller (1978) and McWhirter (1984,1989) does not suggest that Catholics are less work orientated. The evidence from the LFS and the Northern Ireland Social Attitudes Survey suggests that Catholics are as flexible as Protestants in terms of the types of job they are looking for and would accept, retraining, moving and so on. In the absence of good Northern Ireland based studies with motivation data, such as the one analyzed by Gallie and Vogler (1990), we cannot say that there are large differences in motivation between Catholics and Protestants.

Discrimination and the 'Chill Factor'

These factors are hard to measure. However, suppose for the sake of argument we believe that some mix of direct or indirect discrimination or the 'chill factor' is important in explaining our results. With the data available, it is not possible to split this effect into direct discrimination, indirect discrimination or 'chill factor' components. In addition, ft is difficult to apportion these effects into current and past components. The reason is that past unemployment increases the risk of current unemployment. This is likely to be due to the loss of human capital or 'stigma' or 'scarring' effects. For example, a long-term unemployed man is far less likely to leave unemployment for a job than an otherwise similar recently unemployed man. This is the case, either because the long-term unemployed man has actually lost job skills or because employers perceive him as being less employable. Thus, the current incidence of unemployment depends, in part, on the past incidence of unemployment. As a result, it is rather difficult to disentangle the effect of past factors from ongoing factors.

However, We may still obtain some indication of whether these factors operated only in the past or continue to operate more recently. We obtain this evidence by examining the incidence of unemployment for young males, say those aged 20 to 24, and by examining flows to and from employment and unemployment. In both cases, as we show in later chapters, we find significant negative Catholic effects. This indicates that some current disadvantage is present for Catholics, in addition to the 'structural' disadvantages discussed above.


Summary

We construct a series of econometric models of the incidence of unemployment by religion and carry out a range of statistical tests to check the robustness of our results. Our models control for a large number of relevant factors including age, number of children, housing tenure, educational and other qualifications and area of residence. We find that religion accounts for about half of the unemployment differential in our two samples. Differences in the personal and other characteristics of the Catholic and non-Catholic populations account for the rest of the unemployment differential. Ceteris paribus, Catholic men are significantly more likely to be unemployed. These findings are robust and are consistent with the results of Smith and Chambers (1991).


Notes:
[1] Examples of such studies are Nickell (1980), McCormick (1988), and Pissarides and Wadsworth (1990), all of which use data from surveys conducted in Great Britain.

[2] One might argue that some of the explanatory variables may be endogenous, eg housing tenure and others employed/unemployed in the household. Individuals with a high probability of unemployment are more likely to be NIHE tenants rather than owner occupiers and to be in households with other unemployed members. These variables are likely to pick up part of any religion effect. We do not find that housing tenure is endogenous when we control for other relevant variables. In general, there is not too much that we can do about the endogeneity of the explanatory variables. In any case, they are standard explanatory variables which are widely used in the literature.

[3] The one exception is the variable which relates to health problems. In the CHS the health problem question refers to a long standing illness which lirni% activity. In the LFS the health problem question refers to a long standing illness which limits economic activity.

[4] We do not have to consider security-related or black economy jobs. The reason is that those employed in security-related jobs or engaged in the black economy are very unlikely to respond to household surveys such as the LFS or CHS.

[5] As Teague (1993) notes in relation to the link between qualifications and earnings, any observed relationship between subject mix and the incidence of unemployment may just be a screening or statistical discrimination' effect.

Introduction

In the previous chapter we show that, ceteris paribus, Catholic men are more likely to be unemployed than non-Catholic men. Are they also more likely to be outside the labour force? In this chapter we examine differences in economic inactivity or non-participation between Catholic and non-Catholic men. Since we are dealing with prime aged men, ie those aged 20 to 59, the vast majority of men in our samples are in the labour force. The main reasons for economic inactivity are long term or permanent illness or disability, full-time study and discouragement. Discouragement, ie the belief that no, presumably suitable, jobs are available is an important reason for not actively searching for a job. We examine the incidence of discouragement since the discouraged are often considered to be disguised unemployed. We also consider the claimant status of the economically inactive.

Differences Between LFS and CHS Economic Inactivity

In the LFS economic activity is more precisely defined than in the CHS. For example, the unemployed must not only be looking for work but (i) have looked for work in the past four weeks or (ii) be waiting to start a job already obtained and (iii) be ready to work in the next two weeks. In our OHS sample, the definition of unemployment is broader. As a result we expect that some individuals classified as inactive in the LFS would be classified as unemployed in the CHS. We find higher economic inactivity rates and discouragement rates in the LFS than in the CHS which supports our conjecture. However, apart from these two differences, the results obtained using the two sources are fairly similar.

Economic Inactivity In the IFS

Table 5.1 presents inactivity or non-participation rates by age group. The figures show that Catholic men are more likely to be economically inactive than non-Catholic men. In particular, Catholic men are on average, nearly twice as likely to be inactive than non-Catholic men.

The reasons for economic inactivity are shown in Table 5.2. The three main reasons are long term/permanent sickness and disability, discouragement and studying. The most striking difference between Catholic and other men is that Catholic men are significantly more likely to be discouraged than other men - the rates are 21.8% and 13.1% respectively. However, the significantly higher rate of Catholic discouragement only accounts for about one third of the overall difference in non-participation rates between Catholic and other men.

Table 5.3 shows that, despite the higher level of discouragement, significantly more inactive Catholic men would like a job. In Table 5.4 significantly more inactive Catholic men claim unemployment-related benefits. However, this is almost wholly explained by the higher level of Catholic discouragement.

Economic Inactivity in the CHS Sample

Table 5.5 shows the rates of economic inactivity or non-participation in our CHS sample. As in the LFS, the Catholic rate of non-participation is significantly higher overall and by age group. The rates of non-participation for both Catholics and others are lower in the CHS data than in the LFS data. As indicated above, this is because a stricter definition of unemployment is used in the LFS.

The composition of the economically inactive is shown in Table 5.6. Those who are permanently unable to work make up over 60% of the total. Fewer Catholic men are studying or have retired early whilst more Catholic men are in the residual 'other' category.

For those interviewed face to face, we know the reasons they are not looking for a job; these are set out in Table 5.7. The most striking difference between Catholic and other men is, again, in the rate of discouragement. The Catholic rate of discouragement is 12.5% as opposed to 5.6% for other men. Table 5.2, based on the LFS, and Table 5.7, based on the OHS, are not directly comparable since different definitions of unemployment and inactivity are used in the two surveys. In addition, the possible responses in the two surveys to the question of why someone is not looking for a job are different. As a result the large differences in discouragement rates between the LFS and OHS, eg 21.8% versus 12.5% for Catholic men, are not too surprising.

For a subsample of the inactive, we know whether they would like a job or not. As Table 5.8 shows, more inactive Catholic men would like a job. The sample numbers are small but we obtain a similar result in our LFS sample.

Table 5.9 shows the claimant status of the unemployed and inactive. Significantly more inactive Catholic men claim unemployment related benefits. However, this higher claimant rate is largely accounted for by the higher rate of discouragement amongst Catholics. This is also found in our LFS sample.

Econometric Analysis of Inactivity and Discouragement

In our LFS and OHS samples there are important differences in the rates of economic inactivity and discouragement between Catholic and other men. In particular, Catholic men are significantly more likely than others to be both economically inactive and discouraged. We have estimated a range of econometric models of the incidence of inactivity and discouragement. Probit model results based on the LFS and OHS are reported in Appendices 5.1 and 5.2 respectively.

The probit results show that there are still important differences in economic inactivity rates between Catholics and others, even after we control for a range of factors such as geographical location, health status and qualifications. For example, the models which were estimated using LFS data suggest that between 40% and 60% of the diffference in economic inactivity rates is accounted for by religion. The remainder is accounted for by the other factors included in the models.

In the econometric models, the effect of religion on the incidence of discouragement is smaller. For example, the models based on LFS data suggest that only between one-quarter and one-third of the religious difference in discouragement rates is accounted for by religion; the rest is accounted for by differences in the other observed characteristics of Catholic and other men. When the incidence of discouragement is modelled using OHS data, we obtain a statistically insignificant religion effect.

Summary

Catholic men are significantly more likely to be economically inactive than non-Catholic men. In our econometric models half of the difference in inactivity rates between Catholics and others is explained by religion; the remainder is explained by differences in other characteristics.

In the raw data significantly more inactive Catholics are discouraged ie they are not actively looking for work because they believe that there are no, presumably suitable, jobs available. However, when we control for a range of relevant factors, we do not find a large religion effect on the incidence of discouragement. When we model the incidence of discouragement a significant, albeit small, religion effect is obtained with the LFS data but not with the OHS data.

More inactive Catholics claim benefits. However, the higher Catholic rate of claiming is largely accounted for by their higher rate of discouragement.

Introduction

This chapter focuses on differences in the duration of unemployment between Catholic and other men. It is important to do this because whether or not someone is unemployed depends on two factors, namely the probability of entering unemployment and how long the person remains unemployed (ie unemployment duration). We look at the raw data and estimate a range of econometric models in order to disentangle the effects of religion and the other explanatory variables which are all correlated with, each other.

The Duration of Unemployment in the LFS Sample

Table 6.1 shows the duration of unemployment in our LFS sample. Catholics are significantly less likely to be unemployed for less than a year and significantly more likely to be unemployed for four years or more. These figures suggest, therefore, that Catholics are more likely to be long-term unemployed than non-Catholics and less likely to be short-term unemployed.

In Table 6.2 the duration data are split up into data for the mid 1980s and for the early 1990s. Overall the pattern is similar in both periods. Sampling fluctuations are probably the reason for the relatively high share of non-Catholics in the three to four year duration band in 1985/86 and in the four to five year duration band in 1990/91.

The duration of unemployment by age group is shown in Table 6.3. The numbers in some of the cells are small. However the difference in the duration of unemployment between Catholics and others aged 20 to 24 is striking.

Table 6.4 shows that the activity of Catholics and non-Catholics before becoming unemployed is very similar. In the LFS we only know the reasons the unemployed left their last job if they left in the past three years. For these individuals Table 6.5 shows that Catholics are significantly more likely to have their last job because a temporary job ended.

The Duration of Unemployment in the CHS Sample

The duration of unemployment in the OHS sample is shown in Table 6.6^[1]. The figures show that non-Catholics are significantly more likely to be in 0-6 months duration band. The figures also show that there is quite a large difference in the proportions of Catholics and others in the five years plus duration band. The differences in the duration of unemployment between Catholics and others are much smaller in our OHS sample than in our larger LFS sample. This is because a stricter definition of unemployment is used in the LFS and so some of the CHS unemployed would be classified as being economically inactive in the LFS.

The duration of unemployment by age group is shown in Table 6.7. The numbers in some of the cells are very small. However, generally speaking the CHS figures, like the LFS figures show that Catholic men are more likely to be long-term unemployed than non-Catholic men and less likely to be short-term unemployed. It should be noted, however, that in our econometric analysis of the CHS data the effect of religion on unemployment duration is found to be statistically insignificant. This is discussed below. Finally, it should be noted that a couple of unusual results are revealed in Table 6.7, eg the relative proportions of Catholics and others aged 45 to 54 in two to five years duration band. These results are probably the result of random sampling variability.

Econometric Analysis of Unemployment Duration

We have estimated a number of econometric models of unemployment duration; these models are discussed in detail in Appendices 6.1-6.4. Some of these models are relatively simple, eg probit models for particular durations, and some of them are quite complex, eg hazard rate models. Using the LFS the model results generally show that even when a range of relevant factors are controlled for, Catholics are still more likely to be long-term unemployed than non-Catholics and less likely to be short-term unemployed. It is also the case that high local unemployment rates, living in Belfast DC area, a large number of children, a health problem, claiming benefit, others unemployed in the household all increase the probability of longer durations.

When the CHS is used to model unemployment duration we do not find any significant religion effects. This is highly surprising and implausible. If true it implies that, ceteris paribus, differences in unemployment rates between Catholic and other men are solely due to differences in entry rates into unemployment. There is no incidence for this in the larger LFS sample or from other data sources such as the Census and the Social Attitudes Surveys. Examination of the data suggests that sampling variability is the reason for these strange results. Certainly there appear to be significant religion effects in the OHS duration data for the years 1983 to 1985.

Summary

Using LES duration data we find significant religion effects. Catholic men are significantly more likely to be long term unemployed than other men, ceteris paribus. Also, their exit rate from unemployment is significantly lower. These effects are found both in the raw data and in the models which control for a range of factors. With CHS duration data no significant religion effects are found either in the raw data or in our models. This is highly surprising and implausible and we suspect that it can be explained in terms of sampling variability.

Notes:

[1] When the CHS data are disaggregated by year, the 1988 duration data appear to be rather different from the data for the other three years in our sample.