Declining Union Density in the 1980s: What Do Panel Data Tell Us?

British Journal of Industrial Relations 32:3 Sept 1994 0007-1080 $3.00

Declining Union Density in the 1980s: What Do Panel Data Tell Us? Martyn Andrews and Robin Na ylor

Abstract

In this paper we analyse data from the 1980-4 WIRS panel for the light they can shed on the causes of the decline in aggregate trade union density in the UK. We argue that, contrary to conventional wisdom, intra-establishment union density did, on average, decline between 1980 and 1984. This suggests that a traditional compositional change story is untenable. We proceed to investigate the correlation of intra-establishment change with variables suggested by the various hypotheses advanced to explain declining density over the period. We find some evidence for an influence of intra-establishment compositional changes, but the most statistically significant variables are wages and unemployment. These take the wrong sign, however, to support the business cycle interpretation of falling aggregate union density.

1. Introduction

The issue of the determinants of union membership in the UK has generated an extensive empirical literature in recent years, much of it appearing in this Journal. Undoubtedly, the chief reason for this interest in unionization behaviour stems from the phenomenal fall in aggregate union membership, together with a consistent fall in union density, in the UK from 1979 onwards. Table 1 shows that density fell almost perfectly linearly, from 57.4 per cent in 1979 to 43.5 per cent in 1990, i.e. at a rate of 1.3 percentage points per year. At the same time, aggregate membership fell by 33.6 per cent. The causes of this decline are the focus of econometric analysis of aggregate membership data by Disney (1990), Freeman and Pelletier (1990) and Carruth and Disney (1988). Other work has focused on what micro-data can tell us about union membership, and from this one can hope to gain insights into macro-behaviour. Bain and Elias (1985), Booth (1986), Green (1990), Cregan and Johnston (1990) and Guest and Dewe (1988) are all studies of union membership based on micro-data at the level of the individual worker. Data on union density at the level of the individual establishment is analysed in Bain and Elsheikh (1980) and Gregg and Naylor (1993). Martyn Andrews is at the University of Manchester, and Robin Naylor is at the University of Wanvick.

414 British Journal of Industrial Relations

The debate surrounding the causes of the dramatic decline in UK union density has centred on the relative explanatory power of three main hypotheses: the ‘business cycle model’, the ‘compositional change’ hypothesis and the ‘legislative change’ argument. Disney (1990) finds in favour of the business cycle model, arguing that such a model estimated by Carruth and Disney (1988) predicts density satisfactorily, at least as recently as 1987. Against this, Freeman and Pelletier (1990) argue that the special feature causing union membership to decline in the UK was the impact of the changed industrial relations climate from 1980 onwards. Both sets of authors find no evidence of compositional effects influencing union density adversely. Conversely, Green (1992) finds that the changing composition of employment - according to gender, full-time or part-time status, establishment size, age occupation, region and industry - accounts for an estimated 30 per cent of the fall in aggregate density. Metcalf (1991) identifies the interaction of these three influences, along with both union and employer activities, as jointly explaining the observed behaviour in aggregate density.

TABLE 1 Aggregate Density*

U N r pw DlogU DlogN DlogD D D

1979 13,289 23,171 57.4 1980 12,947 22,991 56.3 -2.6 -0.8 -1.8 -1.0 1981 12,106 21,892 55.3 -6.7 -4.9 -1.8 -1.0 1982 11,593 21,414 54.1 -4.3 -2.2 -2.1 -1.2 1983 11,236 21,067 53.3 -3.1 -1.6 -1.5 -0.8 1984 10,994 21,238 51.8 -2.2 0.8 -3.0 -1.6 1985 10,821 21,423 50.5 -1.6 0.9 -2.5 -1.3 1986 10,539 21,387 49.3 -2.6 -0.2 -2.5 -1.2 1987 10,475 21,584 48.5 -0.6 0.9 -1.5 -0.7 1988 10,376 22,258 46.6 -0.9 3.1 -4.0 -1.9 1989 10,158 22,661 44.8 -2.1 1.8 -3.9 -1.8 1990 9,947 22,855 43.5 -2.1 0.9 -3.0 -1.3 1980-84 change -16.4 -7.9 -8.4 -4.5 WIRS Panel (manuah) 1980 113 149 79.2 76.4 1984 98 135 76.0 72.5 -14.6 -9.4 -5.2 -3.9

* p is unweighted average density (unobserved in the aggregate); p, is weighted average, or aggregate density ( D = U/N), which is also estimated in the WIRS Panel. Sources: U (total membership at year end in UK, ’OOO): Bird et al. (1992); N (employees in employment in UK, ’OOO): Economic Trends, Annual Supplement, 1992.

There is much to be said for Metcalf‘s approach, not least because it is not at all clear that the rival theories are in fact competing. For example, the business cycle is likely to manifest itself in various ways, including through compositional changes in employment structure - as in the shift towards more flexible part-time employment when demand is low. In such circum-

Declining Union Density in the 1980s 415

stances it is not valid to attempt to portion out explanatory power across these theories. Furthermore, in important aspects the individual theories fail to provide a satisfactory account of the mechanisms through which union membership is affected. Indeed, the direction of causation is not always clear. Thus, as Disney (1990) argues, it is plausible that legislative change was an effect rather than a cause of ‘weaker’ trade unions. But this invites the analogous argument that macroeconomic performance itself could be, at least in part, a consequence of the level of union density. If, for example, one has in mind efficient bargains between unions and firms along positively sloped contract curves, then a reduction in union bargaining power - perhaps as a consequence of legislation reducing union membership - would cause a potential reduction in employment.

The more significant weakness of the different theories, however, and perhaps the major stumbling block to a consensus on the causes of declining density, concerns the black-box characteristic of each of the theories. Through what mechanisms and channels, for instance, does an increase in unemployment bring about a reduction in union density? This vagueness makes testing between the different theories a difficult task. If a depressed state of aggregate demand causes firms to shut down some establishments and these are the ones that - coincidentally or otherwise - tend to be highly unionized, then is the consequent fall in aggregate union density a result of compositional change or of business cycle variables? Similarly, legislative change which reduces union bargaining power is likely to produce compositional changes along with declining membership. Identifying causation may be an impossible task conceptually.

The objective of this paper is to peer inside the ‘black box’ by analysing the changes in union density occurring between 1980 and 1984 in the establishments covered by the WIRS panel for those years. These data enable us to address a number of issues pertinent to the decline in aggregate union density in the UK over this period - a period in which aggregate trade union density fell from 56.3 to 51.8 per cent.

2. Alternative hypotheses

Before we consider the WIRS panel data, we shall attempt to describe the micro-underpinnings to the different hypotheses outlined in the previous section. The traditional business cycle theory predicts that union density will grow in a boom: with tight labour and product markets, firms can pass pay increases on to the consumer and so will be less likely to engage in management opposition to unions, while workers will reward unions for successful wage negotiations by joining. Furthermore, higher price inflation is likely to induce workers to join unions as a defensive reaction. High wage inflation could go either way: on the one hand there is the ‘credit effect’, but on the other hand workers might perceive unions as irrelevant when wages are increasing anyway. This ambiguity leads Carruth and Disney (1988) to


expect a negative sign on real-wage changes. Hence, for them the fall in aggregate union density in the UK is largely a result of increased unemployment coupled with rising real wages.

As we have argued, one might expect the business cycle effects to be manifest partly through compositional changes. Thus, as examples, large organizations might close down their larger-scale establishments, or firms might lay off relatively unskilled workers during a recession (see Oi 1962; Nickel1 1986). Additionally, however, there is no reason to expect intra- establishment union density to remain constant. On the contrary: the business cycle theory is traditionally seen as working through the decisions of both firms and workers. If workers are less likely to join unions when unemployment and real wages are high, as the business cycle model predicts, then this should be discernible at the level of the establishment in the form of lower union density (at least in the absence of closed shop provisions, i.e. where membership is voluntary). Thus, one would expect that a less amenable management attitude associated with higher unemployment and rising real wages would generate falling union density within the establishment. We examine the WIRS panel to consider the issue of constancy of union density within establishments over 1980-4 and are able to examine the influence of variables such as local unemployment and product market conditions on any changes we observe. If we find that intra- establishment density is constant over time, then this will be strong prima facie support for a simple compositional explanation. If, on the other hand, density appears to decline in those establishments where, ceteris paribus, local unemployment rates are high, then this would be supportive of the business cycle theory.

Finally, if there is a constant decline in density for all plants, this would suggest that there is some aggregate phenomenon at work which cannot be easily identified in the data. For example, one would expect the impact of legislative change to affect union density not only through compositional changes across establishments, but also by affecting the level of union density within the establishment. To the extent that firms become more hostile to unions, we might expect fewer workers to join unions. Unlike with the business cycle explanation, it is not obvious that we would observe any systematic pattern associated with the level of unemployment.

Thus, it is possible to generate differing predictions from the different hypotheses and to test these against the panel data. This is what we shall proceed to examine in Section 4 of the paper. First, however, we discuss briefly a ‘stylized fact’ of UK union membership in the 1980s and examine this in the light of the panel data.

3. Stylized fact or stylized myth?

From analysis by Millward and Stevens (1986) of WIRS 1980 and WIRS 1984, it has become a stylized fact that the level of union density within


establishments was constant over this four-year period. They conclude: ‘Our panel data suggested that there was little gross change in whether workplaces had union members present and so the net decline in manufacturing seems [our emphasis] to be structural change, reflecting the higher rate of closure of large manufacturing plants in industries with trade union traditions’ (p. 302). On the other hand, they report that overall union density across all employees was 62 per cent in 1980 and 58 per cent in 1984. One way to resolve the ‘stylized fact’ of constant intra-establishment density with the aggregate decline in union density is to argue that between 1980 and 1984 highly unionized plants lost disproportionately more employment. This is the assertion behind the compositional change hypothesis as traditionally understood. Most commonly, it has been argued that the recession was associated with the disproportionate closure of large establishments (Oulton 1987) and with establishments that undertook - or were ‘shocked into’ - more organizational change (Metcalf 1989; Machin and Wadhwani 1991). Both types of establishment tend to be more highly unionized.’ as the data in Tables 2 and 3 indicate.

TABLE 2 Rate of Change in Employment, 1980-1984, by Organizational

Change (OC) and Union Recognition.

oc No OC Total

Union -0.0689 -0.0312 -0.0484 (515)

0.0066 (280)

No union 0.0307 ‘ (206)

-0.0326 (162)

(279) (442) (721)

O.oo00

-0.020

(235)

(44) Total -0.053

* Numbers of establishments in parentheses. Source: WIRS 1984.

TABLE 3 Average Union Density and Establishment Size

Size of establishment (no. of employees) 25-49 50-99 100-199 200-499 500-999 1ooO+ Average density 26 30 39 47 60 72

Source: Millward and Stevens (1986: Table 3.4).

More formally, the traditional compositional change hypothesis asserts that density did not fall in establishments, at least on average, whereas aggregate density did fall. But aggregate density is simply the weighted average of union density across all plants in the economy, where the weights are employment shares. Put crudely, were one to observe union member-


ship in each and every plant in the economy, then the hypothesis compares the weighted average,

i

with the unweighted average,

where i is indexes plant, N is employment, U is union membership, D = UIN is density and n is the number of plants in the economy. According to the traditional compositional change hypothesis, aggregate density fell because of falling employment shares in highly unionized establishments, while intra-establishment densities remained constant .* This, then, is consistent with the stylized fact of constant intra-plant density.

Data on economy-wide Di are not available. However the WIRS 1984 Panel does record union density, for both 1980 and 1984, in a sample of 148

FIGURE 1 Histogram of change in density

-0.85 -0.8 -0.15 -0.15 -0.1 -0.1 -0.65 4.65 -0.6 -0.6 -0.55 -0.55 -0.5 -0.5 -0.45 -0.45 -0.4 -0.4 -0.35 -0.35 -0.3 -0.3 -0.25 -0.25 -0.2 4 . 2 -0.15 -0.15 4 . 1 4 . 1 4.05 -0.05 0.0 0.0 0.05 0.05 0.1 0.1 0.15 0.15 0.2 0.2 0.25 0.25 0.3 0.3 0.35 0.35 0.4 0.4 0.45 0.45 0.5

1 0 0 0 0 0 1 1 0 2 2 3 1 3 11 15 11 15 12 5 2 0 2 0 0 0 1

Note that the bin range does not contain lower value. For example, one range is -0.05 < x 0. In fact, there are 41 plants who had exactly a zero change in density.

The total number of establishments is 148.


establishments where unions are recognized for bargaining.3 Estimates of the change in both and p can then be computed, and this is what we are able to compare in the present paper. Specifically, we are able to examine whether the fall in aggregate density was associated with constant or with changing densities within establishments.

As has already been noted, aggregate union density fell by 4.5 percentage points between 1980 and 1984 (from 56.3 to 51.8 per cent), comprising an 8.4 per cent fall in employment and a 16.4 per cent fall in membership. The WIRS Panel records similar changes in union density: the weighted average fell by 3.9 percentage points, from 76.4 to 72.5 per cent (see Table 1, lower panel). This comprises a 9.4 per cent fall in employment and a 14.6 per cent fall in union membership. However, the levels of union density are different, suggesting that the sample of 148 plants might not be wholly representative. In fact, the whole point of using panel data is that systematic biases, if they arise for the same reason, net out when measuring the change between years. Note also that Millward and Stevens report an estimate of the change in aggregate density of -4.0 percentage points, and argue that the two WIRS cross-sections are repre~entative.~

The second advantage of the panel is that we can analyse the distribution of the change in density across these establishments. Figure 1 depicts a histogram of the changes and shows that 49 out of 148 establishments experienced a fall in density of between 15 and 0 per cent, while another 47 experienced precisely no change at all, and a further 32 experienced an increase in density between 0 and 15 per cent.5 Even though one-third experienced no change in density, the average change (p) is -3.2 percentage points with a standard deviation of 0.11. Thus, the change is significantly different from zero.6 This change is similar to the change in the weighted average in the sample.

To conclude, this section suggests that a traditional compositional change story is untenable. This represents a point of departure from the conventional wisdom. Our argument is that intra-establishment union density did, on average, decline between 1980 and 1984. In the rest of the paper we are interested in analysing the establishments in the panel in order to determine the reasons for this behaviour as summarized in the histogram in Figure 1.’ We shall attempt to identify the characteristics associated with the changing level of density at the establishment level and to interpret these in the light of the alternative theories of changing union density at the aggregate level.

4. Panel data and methodology

The obvious advantage of using panel data is that one can attempt to model the change in density within establishments rather than looking at the change in average density. All of the existing literature explains union density by examining cross-sectional data (Bain and Elsheikh 1979 using industry-level data, and Bain and Elsheikh 1980 and Gregg and Naylor 1993 using establishment-level data). Finding variables that are associated with


the variation in density across establishments does not imply that the same variables are associated with the change in density, simply because such variables need not have changed themselves. None the less, it is possible that the level may be correlated with the change in density. This can only be interpreted as a short-run, or disequilibrium, effect, as in the longer run density would go to either zero or unity. It is the case that the early 1980ssaw consider- able changes (the deepest postwar recession, much organizational change, further union legislation, increased productivity, etc.), and such short-run effects could well explain, loosely speaking, much of the change in density.

The starting-point for our analysis is two simple cross-section models, described by

yi, = yr + Prwif + uir; t = 1,2; i = 1,. . ., N. (1) The effect of the single variable x on the level of density may be different in the two years ( t = 1 is 1980; t = 2 is 1984) for the reasons discussed. Our dependent variable is the change in density rather than the level.* From (l), it follows that

Yi2 - Y i l = (Y2 - Yl) + p2xi2 - PlXil + E i , (2) with E; = ui2 - uil . Equation (2) is estimated by OLS using the WIRS Panel, and is reported in Table 4. Estimates of P1 and P2 are reported in columns (1) and (2). These estimates can be re-parameterized as follows:

If H,, : PI + P2 = 0 is not rejected and is consequently imposed, the model becomes the change in density associated with the average level in the explanatory variable x , (xil + xi2)/2, which is the short-run effect noted above. This is reported in column (4) of Table 4. Similarly, if H1 : PI = p2 is not rejected, the model becomes the change in density associated with the change in x, which is the effect we need to observe in the data if the levels are related to each other. In fact, it can be interpreted as a cross-section relationship in which unobserved fixed-effects have been removed by differencing. This more conventional effect is reported in column (3) of Table 4. Finally, a test of H2 : P1 = P2 = 0 is reported in column (8).

Table 4 reports estimates for equation (3) for each variable taken on its own; i.e., each row represents a separate regression. This is because it is not obvious that every variable is exogenous. Business cycle variables are a case in point. As we are interested only in detecting associationsbetweendensity and other variables, we examine each variable separately.’ A further reason is that we cannot afford to discard data. Of course, we could still run a regression for all variables for which we have 148 observations, but in fact, the results turn out to be almost identical to those reported.

Before discussing, in the next section, precisely which explanatory variables might be associated with the change in density and why, it is

Declining Union Density in the 1980s 421 TABLE 4

Regression Results

WOREG

WOIND

W

UREG

PML

SIZE

oc

cs PRE

PSK

PFMAN

RENT

LCRNF

CRSSA L

0.0815 0.195

-0.206 0.0644 (1.05) (0.59)

-0.1562 0.0282 (1.77) (0.43) 2.835 1.173

(1.64) (1.14) -0.0316 0.0808 (0.36) (0.89)

(0.25) (1.46)

-0.0217 -0.0249 (0.86) (0.97)

0.0143 (0.63)

-0.0212 (0.94)

-0.0226 (0.69)

(0.95) (0.06)

(0.64) (0.45)

-0.0539 -0.0030

0.0460 -0.0324

-0.oo07

0.0184 - 0 . m

0.1568 0.0936

(0.028)

(0.44) (0.06)

(0.71) (0.40)

0.198 (1.48)

0.226

0.1600 (3.02)

(2.45)

(1.47) (2.01)

(2.76)

2.005 -0.166 0.078

0.1120 0.553

-0.0230 (0.93)

0.581

0.135

0.0490 0.247 (1.03)

(2.19) -0.0784 0.349

-0.0212 (1.53) -0.0702 0.461 (1.00)

0.031 1.16 148 0.135

0.021 4.64 148 0.132

0.064 3.42 118 0.142

0.151 0.073 2.13 148 0.134

0.562 0.009 3.84 148 0.133

IO.3041

[O.l3cJOl

[0.036]

[O. 1151

[0.025]

I0.67) -0.129 0.47 148 0.136

0.419 148 0.135

148 0.135

148 0.135

0.290 0.043 0.69 133 0.136

0.311 -0.038 2.38 138 0.130 [0.550]

[0.0941 0.517 m 0.118

-0.113 1.18 67 0.133 I0.3041

0.418 -0.044 0.65 67 0.133 [0.55]

Notes 1. Cols. (1) and (2) report estimates of equation (2); cols. (3) and (4) report restricted

estimates (see text) of (2). t-ratios are in (). Description of each variable given in Table 5. 2. All data are porportions, whose means are reported in cols. (5) and (6), or logarithms. The

change in the means for all variables is reported in col. (7). 3. Col. (8) reports an F-test of H,:fl, = f12 = 0.p-value in [ 1. 4. Col. (9) reports sample size. If manufacturing establishments only, N = 67. 5. There is little evidence of heteroskedasticity, as the White standard errors are very close to

their OLS counterparts, reported above.

worth noting that very few change variables are significant. As noted, this is either because the variables do not change or because, if they do, they are not correlated with density changes. (The change in the means is reported in column (7).) This is not too surprising as these data are known to be considerably noisy (see Andrews and Harrison 1993). By contrast, many variables in levels form are weakly correlated with density changes. Our discussion below focuses specifically on why such disequilibrium effects are


in these data. Obviously, this represents a substantial departure from the existing literature in which no such effects can be detected.

5. Results

In this section we review the various hypotheses to suggest a set of variables that might exert an influence on the behaviour of the changes in union density within the establishment over time. As noted, the results are summarized in Table 4.

The business cycle hypothesis

As indicated above, some aspects of the business cycle hypothesis are consistent with a compositional change thesis. Thus, the disproportionate incidence of the closure of larger and more highly unionized plants in the early 1980s could be interpreted as initiating the recession of the time. Similarly, the concentration of unemployment among manual workers - a labour demand response to the cyclical downturn - would have tended to reduce trade union membership density. Additionally, however, the business cycle hypothesis would be expected to predict intra-establishment changes in union density, depending on particular characteristics of the establishment. These we now investigate.

We distinguish between two possible channels through which business cycle forces might influence the level of trade union density. The first is through the decisions of individual workers. Typically, the impact of the business cycle is seen as mediated through this channel (see e.g. Carruth and Disney 1988). We list the variables suggested by this argument.

(a) Decisions of workers

Wages Real-wage growth is usually expected to make individual workers less likely to be members of trade unions. This is because the credit effect - by which workers join unions as a reward for unions’ having apparently bargained successfully for higher wages - is expected to be dominated by the belief ‘that in a sellers’ labour market higher wages may be gained without union action and indeed may be hindered by the inertia of union contracts’ (Carruth and Disney 1988: 6). Aggregate time-series results tend to corroborate this a priori conjecture. Consequently, we would expect to find that, if the data are consistent with the usual business cycle model, union density changes will be associated negatively with changes in the establishment wage.

Three measures for wages are available to us. A regional wage variable (WOREG - see Table 5 for definitions of variables) was used and a weakly significant levels effect is detected. This is a conventional correlation in the sense that an establishment located in a high earnings region has a higher

Declining Union Density in the 1980s 423 TABLE 5 Variables

UREG WOREG WOIND PML SIZE oc cs PRE PSK PFMAN W RENT LCRNF CRSSAL

regional unemployment (AH’, DEGb) regional real earnings (AH, DEG) industry real earnings (AH, NES‘) proportion manuals (WIRS~) employment (AH, WIRS) organizational change (WIRS 1984; Machin and Wadhwani 1991) closed shop (AH, WIRS) pre-entry closed shop (AH, WIRS) proportion skilled manuals : manuals (WIRS) proportion female manuals : manuals (WIRS) real earnings, manual males (AH, WIRS) No competitors (WIRS 1984) as defined in Stewart (1990); see text -log (no firms in 4-digit industry) (CF), Mfg only 5-firm conch ratio (sales) for 3-digit industry (CP), Mfg only

a See Andrews and Harrison (1993) for further details. Employment Gazette. New Earnings Survey. WIRS Panel. Census of Production.

level of union density. The estimate has the interpretation that a 10 per cent increase in real wages is associated with 2 percentage points higher density. Since real wages rose, this cannot explain the fall in density between 1980 and 1984. A more disaggregated measure at the level of each establishment is the real earnings variable (W), which delivers a significant short-run or disequilibrium effect only. The estimate implies that an establishment that pays wages 10 per cent higher than another establishment will have a larger change in union density of about 1.5 percentage points. For example, one plant’s change in density might be -3 per cent; the higher-paying plant would be -1.5 per cent. Clearly, these disequilibrium effects are better at explaining the variation in density changes than at explaining why density change is negative on average. The industry wage variable (WOZND) produces very similar results.

These results on the relationship between union density in the establishment and wages, then, run counter to the business cycle prediction that an increase in real wages will cause a reduction in aggregate union density, if, as is usually the case, the business cycle effects are seen as mediated at least in part through the decisions of individual workers influencing intra-establishment union density. The prediction of the business cycle theory with respect to the likely effect of real wages on density is not unambiguous, however. The positive effect that we have identified is supportive of the ‘credit effect’ by which unions are rewarded for perceived success in wage negotiations by an increase in membership.

Local unemployment The usual business cycle prediction is that trade union density is likely to decline when unemployment is high, partly because workers ‘no longer see positive advantages to union membership’ during a


depression, when ‘Employers’ bargaining power is strengthened and they are more likely to resist unions’ demands, both because there is excess supply of labour and excess capacity, and also because they are unable to pass on costs due to low product demand’ (Booth 1983).

The only unemployment variable with which we are able to test this business cycle prediction is regional unemployment (UREG). It is the only variable that exhibits both a conventional levels and a short-run disequilibrium effect. The distinction between the two is as follows. The positive (conventional) effect says that establishments located in high unemployment regions have higher levels of density. The size of the coefficient (significant only at the 15 per cent level) says that a 7.3 percentage point increase in unemployment (the sample mean) is associated with a 14.6 percentage point increase in the level of density, and so, like WOREG earlier, does not explain the fall in density. The disequilibrium effect says that establishments in high- unemployment regions have lower changes in density. Again the effect is strong. Two plants whose regional unemployment rates differ by 1 percentage point have changes in density that differ by 1.6 per cent.

The conventional effect is also consistent with the argument proposed by Blanchflower et al. (1990) that union density will be higher in those establishments located in areas of high local unemployment. The reasoning behind this is that risk-averse workers join unions primarily for protection against unfair dismissal and hence will be more likely to be members of unions where unemployment - which raises the expected costs of dismissal - is high. An alternative interpretation of the cross-section result is that density has tended to be high historically in particular industries and regions where unemployment has been typically high.

Other measures of the business cycle It can be argued that wage and unemployment measures are poor proxies for the business cycle. As is well known, the output of the establishment is not recorded in WIRS, only work-force size. Consequently we employ more general measures, such as shock and activity variables, to try to detect the influence of industry variations in the influence of the business cycle on changes in union density, matched in through the establishment’s 4-digit SIC. These include the net output of its industry, net output per plant, productivity and the change in industry employment between 1979 and 1982. The last is Metcalf‘s (1989) shock variable, included to capture any effects of this particular recession on subsequent changes in workplace organization. Usually, of course, causation is assumed to go the other way, with measures of unionization determining productivity, firm performance and so on. None of these is significant, however, and therefore mostly unreported. The one variable we do report is Machin and Wadhwani‘s (1991) organizational change variable (OC), which suggests that density was not affected within recognized plants. Given what is reported in Table 2, this suggests that some of the change in density between 1980 and 1984 is compositional, but this is something that cannot be examined in our particular sample.


The nature of the bargaining arrangements To the extent that union density in the establishment changes as a result of individual membership decisions by individual workers, this is more likely to occur in the absence of closed- shop bargaining arrangements, i.e. where membership is voluntary. Thus, we would expect to find declining density to be a characteristic pre- dominantly in the ‘open-shop’ establishments. In fact both the closed-shop (CS) and the pre-entry closed-shop (PRE) dummies are insignificant.

(6) Decisions offirms

The second channel through which business cycle forces might influence the level of trade union density concerns the decisions of firms. From the perspective of an establishment-level study, these decisions can either be lumpy, such as the decision to shut down an establishment, or marginal, such as an increase in the proportion of part-time workers in an establishment. The former type of decision is likely to be associated with apparently compositional causes of changing union density which our panel data analysis cannot pick up. The latter are likely to lead to intra-establishment changes in density which, potentially, can be correlated with various characteristics of the establishment. It is not clear, however, that such changes are the consequence of business cycle forces rather than of other influences on work-force composition. For example, the growth in the proportion of part-time working in the early 1980s could be a result of an employer preference for flexibility at a time of low demand and uncertainty. Alternatively, it could be interpreted as a structural change in employment patterns reflecting changed labour supply conditions, organizational structure or technological innovation. We shall simply categorize the characteristics we might expect to affect union density in this way under the heading of compositional factors, while recognizing that this is not necessarily incompatible with the business cycle interpretation.

Compositional Changes

As we argued previously, some aspects of a broader compositional change interpretation of declining union density should be detectable at the level of the individual establishment. We outline the following aspects.

Proportion of workers who are female To the extent that the gender-mix in an establishment changed between 1980 and 1984, we would expect a possible change in union density. Clearly, the argument here is tied closely to that for part-time workers. A rise in the female proportion in employment in the establishment would be expected to have a negative effect on union density. However, as the data consist solely of unionized plants, trade union availability to women is probable and hence Green’s (1990) findings would suggest there might be no gender-mix effect on union membership density.


For the variable measuring the proportion of the manual work-force that is female (PFMAN), a significant disequilibrium effect is estimated as -0.08 per cent. That is, an establishment with 10 percentage points more female manual workers (say 40 per cent compared with 30 per cent) has a greater fall in density (say -3.8 percentage points compared with -3.0 percentage points).

Proportion of workers who are manual Cross-section studies of union membership have found that, for both manual and non-manual workers, union density is influenced by the proportion of manual workers in the work-force. This has been given a social custom interpretation (see Gregg and Naylor 1993). A change in the proportion of manual workers in the work-force would be expected to have consequent effects on union density. Unfortunately, there was very little change in this proportion between 1980 and 1984. In the sample the proportion rose by 0.9 percentage points; in manufacturing industry, by 1.7 percentage points. Therefore it is not surprising to find, as for the female proportion, that the proportion of employees who are manual (PML) has only a disequilibrium effect on union density. The effect is statistically significant with a semi-elasticity of 0.12; i.e., density fell more where there are fewer manuals to non-manuals.

Proportion of workers who are skilled Most cross-section studies of union membership have found that union density is higher among skilled manual workers. A change in the proportion of skilled workers in the work-force would be expected to have consequent effects on union density. However, our finding is that the effect of the skill proportion has a weak disequilibrium effect only, in spite of an increase of some 4 percentage points in their proportion over the four-year period.

Size of the establishment Perhaps the most common finding in individual cross-section analyses of union membership is the importance of the effect of plant-size in union density (see Table 3 above). The most usual interpretation of this positive effect is based on the concept of alienation in large establishments. One could also appeal to the idea of economies of scale in union recruitment. The effect could also be picking up the business cycle, and can be interpreted as a shock effect. In our regressions, this variable (SIZE) is insignificant, in spite of falling some 12.9 percentage points. This result is inconsistent with other findings, e.g. Gregg and Naylor (1993), Bain and Elsheikh (1980) and Booth (1986). Of course in the aggregate, establishment size affects density through changing weights, which is the traditional compositional change hypothesis already discussed in Section 3.

Legislation

Freeman and Pelletier (1990) argue that the major determinant of the decline in UK aggregate trade union membership was the changing legislative framework, which accommodated, if not actually encouraged,


policies of opposition by management towards trade unions. One of the motivations for management opposition to unions was to facilitate organizational change. We discussed the OC variable earlier.

As there has been no attempt in the foregoing literature to develop a thesis concerning the pattern of incidence of the effects of legislation, there is nothing to guide the empirical researcher towards tests of predictions from this hypothesis. One could speculate, however, that in establishments most affected by employment and trade union legislation there will have been a change in the incidence of industrial conflict. Future work should examine this. In the present paper we consider bargaining arrangements and product market conditions.

1980 closed shop To the extent that even by 1984 much of the Govern- ment’s concern was to reduce the powers of the closed shop, we investigate the possible argument that density declined partly because of an under- mining of the bargaining power of unions in establishments in which there were closed-shop bargaining arrangements in 1980. As already reported, however, the pre-entry closed-shop dummy is insignificant.

Product market competition The importance of legislation for an establishment could also be argued to depend upon the nature of competition and concentration in its product market. Thus, we also consider different measures of product market competition. Stewart’s (1990) measure of competition (RENT) works at the level of the establishment and is a categorical variable which reports the number of competitors for the firm’s major product (whether there are none, many or few). Stewart finds that a wage mark-up is won only where some kind of product market power is available. It would be interesting to see whether union density declines in such establishments, but the variable RENT has no significant effect. Second, there may be characteristics of the industry in which the plant is located that determine union density, the most obvious being industry structure. Two standard measures of concentration are used for the 67 plants that are in manufacturing industry: the logarithm of the inverse of the number of firms (LCRNF), and the five-firm concentration ratio (CRSSAL). Both fell appreciablyinoursample. LCRNFhasa weakeffect on thechangeindensity .

6. Conclusions and further remarks

In this paper we have been able to employ the WIRS 1980-4 Panel of establishments to address the competing explanations for the decline in aggregate union density in the UK over this time period. We have found that the data indicate that the traditional compositional change explanation is not sufficient but that intra-establishment changes are also important. This is likely to have policy implications. For example, unions cannot be compla- cent in regarding the fall in aggregate density as a one-off consequence of a shift in employment from large to small establishments. Instead, there appears to have been a steady fall in union density within establishments

428

regardless of size. There is no inevitable brake on this process. Union recruitment strategies may have to be more pro-active to redress the decline. Further work based on the analysis of WIRS3 would serve to illuminate these issues for the latter half of the 1980s.

The fall in union density within establishments has not been uniform over the period 19804. Accordingly, we have regressed the change in density separately against a range of possible explanatory variables. We find that the results do not support the business cycle hypothesis. We argued that, while the business cycle is likely to have had effects not observable in our data (such as through the closure of plants), there should also be intra-establishment manifestations of business cycle effects. Indeed, we have calculated statistically significant effects of both real wages and regional unemployment, but these are of the wrong sign to support a business cycle explanation for the fall in aggregate density. Again, therefore, it follows that an end to recession does not necessarily lead to an upturn in aggregate trade union density.

Important aspects of the compositional hypothesis are not amenable to panel data analysis, as that hypothesis emphasizes such factors as the shift in employment from large- to small-scale plants, regional disparities and the shift from manufacturing to service-sector employment. Nevertheless, some aspects of compositional changes would be expected to have an establishment-level effect and, indeed, we find that the proportions of female and of manual workers in employment appear to be statistically significant influences on union density change.

Our argument can be summed up as follows. Despite the evidence from much aggregate time-series data attaching a business cycle interpretation to the fall in aggregate trade union density in the UK in the 1980s, a number of commentators have emphasized the predominant importance of compositional effects. Previous work has focused on either aggregate time-series or cross-section data and has not used panel data. Yet panel data can be potentially informative as the various hypotheses are likely to have implications for the behaviour of union density within establishments over time. We have explored some of these implications and addressed the WIRS 1984 panel to them. The panel gives information for 1980 and 1984 - an important period in the decline of aggregate union density - and suggests that declining density within establishments was widespread, contradicting the foregoing ‘stylized fact’. We have argued that, while the results are not supportive of a narrow or ‘traditional’ view of the compositional change hypothesis, nor are they consistent with the predictions from the business cycle theory. We are left with the conclusion that the fall in union density, if not explained by disequilibrium effects, is explained by variables not observed in our analysis. The most obvious unobserved variable is legislative change, which is likely to have affected all plants. This is a fixed effect, however, and so this explanation cannot be tested directly within our framework.

British Journal of Industrial Relations

Final version accepted 26 January 1994


Acknowledgements

We thank Daphne Nicolitsas for her excellent research assistance, and Denise Osborn and two referees for comments. The data analysed in this paper are from the Workplace Industrial Relations Survey, Panel Study, 1980-1984, first used in an earlier study by Martyn Andrews and Alan Hamson. We acknowledge the Department of Employment, the Economic and Social Research Council, the Policy Studies Institute and the Advisory, Conciliation and Arbitration Service, as joint depositors of the WIRS data, and the ESRC Data Archive; none of these organizations bears any responsibility for our analysis of the WIRS data. The data for Table 2 and for Machin and Wadhwani’s (1991) organizational change variable were kindly supplied by Steve Machin.

Notes

1. Additionally, larger plants are more productive and had the bigger increases in productivity growth over this period (Dunne and Hughes 1992).

2. Set N = 2, for example. Suppose D1 and D2 are both constant and D1 > D2. The change in aggregate density is given by AD = DIAwl + DzA* where wd = N,JN and Awl + A% = 1. That is AD = Awl (Dl - D2), which is negative if Awl < 0, i.e. if the share of the more unionized plant falls.

3. The part of the WIRS Panel used in this paper was constructed by Andrews and Harrison (1993), where further details can be found. These data refer to manual workers.

4. Of course, the levels in density differ because our plants are ‘recognized only’, whereas Millward and Stevens consider both recognized and non-recognized plants. The latter consist of plants with 25 or more employees, whereas aggregate figures capture all employees. The upward bias in WIRS can be seen from examination of the Labour Force Survey. In 1989, for example, density is 39 per cent for all plants, but 48 per cent for plants with 25 or more employees.

5. That some plants showed an increase in density is not surprising, as the fall in employment was largest in unionized plants where it is likely that non-unionized workers were among the first to be laid off.

6. Assumingnormality, which Figure 1 suggestsis reasonable. Removing the outlier in the left-hand tail does not change this finding.

7. Recall that these are establishments recognized for union bargaining. The objective is to see whether union density changed. Adding a number of plants with zero density, where, by construction, density did not change, simply distorts the story. Naylor and Cripps (1993: Table 1) report that, of 245 plants in W I R W that did not recognize unions for bargaining, 60 had non-zero density among manual workers. (The corresponding figure for non-manuals was 52 out of

8. Which is why we cannot analyse the two cross-sections. See Gregg and Naylor (1993), however.

9. There are two reasons why OLS might not be appropriate in a study of this kind. The first is when the dependent variable is not a drawing from a normal

445.)


distribution, as is the case when estimating single cross-sections where the level of density is both bimodal, at zero and near unit density, and censored; however, because the change in density looks normally distributed, OLS is appropriate. The second is when some of the ‘determinants’ are not exogenous, as is the case here. However, we are interested only in establishing associations rather than in formal modelling of two-way causality (say between density and employment). The latter (with IV or ML, say) requires assumptions about which variables are truly exogenous, which is difficult, and is not in the spirit of our simple analysis.

References

Andrews, M. J. and Harrison, A. H. (1993). ‘Testing for efficient contracts in unionised labour markets’. Mimeo, University of Manchester, July.

Bain, G. S. and Elias, P. (1985). ‘Trade union membership in Great Britain: an individual-level analysis’. British Journal of Industrial Relations, 23: 71-92. - and Elsheikh, F. (1979). ‘An inter-industry analysis of unionisation in Britain’.

British Journal of Industrial Relations, 17: 137-57. __- (1980). ‘Unionisation in Britain: an inter-establishment analysis based on

survey data’. British Journal of Industrial Relations, 18: 169-78. Bird, D., Kirosingh, M. and Stevens, M. (1992). ‘Membership of trade unions in

1990’. Employment Gazette, April: 185-90. Blanchflower, D. G , Crouchley, R., Estrin, S. and Oswald, A. J. (1990). ‘Un-

employment and the demand for unions’. Working Paper no. 3251, National Bureau of Economic Research.

Booth, A. L. (1983). ‘A reconsideration of trade union growth in the UK’. British Journal of Industrial Relations, 21: 379-93. - (1986). ‘Estimating the probability of trade union membership: a study of men

and women in Britain’. Economica, 53: 41-61. Carruth, A. A. and Disney, R. (1988). ‘Where have two million trade union

members gone?’ Economica, 55: 1-19. Cregan, C., and Johnston, S. (1990). ‘An industrial relations approach to the free-

rider problem: young people and trade union membership in the U K . British Journal of Industrial Relations, 28: 84-104.

Disney, R. (1990). ‘Explanations of the decline in trade union density in Britain: an appraisal’. British Journal of Industrial Relations, 28: 165-77.

Dunne, P. and Hughes, A. (1992). ‘The changing structure of competitive industry in the 1980s‘. In C. Driver and P. Dunne (eds.), Structural Change in the UK Economy, Occasional Paper no. 58. University of Cambridge Department of Applied Economics.

Freeman, R. B. and Pelletier, J. (1990). ‘The impact of industrial relations legislation on British union density’. British Journal of Industrial Relations, 28: 141-64.

Green, F. (1990). ‘Trade union availability and trade union membership in Britain’. Manchester School, 53: 378-94. - (1992). ‘Recent trends in British trade union density: how much of a

compositional effect?’ British Journal of Industrial Relations, 30: 445-58. Gregg, P. and Naylor, R. A. (1933). ‘An inter-establishment study of union

recognition and membership in Great Britain’. Manchester School, 61: 367-85. Guest, D. E. and Dewe, P. (1988). ‘Why do workers belong to a trade union? A

social psychological study in the UK electronics industry’. British Journal of

Declining Union Density in the 1980s 43 1

Industrial Relations, 26: 178-201. Machin, S. J. and Wadhwani, S. B. (1991). ‘The effects of unions on organisational

change and employment’. Economic Journal, 101: 835-54. Metcalf, D. (1989). ‘Water notes dry up: the impact of the Donovan reform

proposals and Thatcherism at work on labour productivity in British manufacturing’. British Journal of Industrial Relations, 27: 1-31. - (1991). ‘British unions: dissolution or resurgence?’ Oxford Review of

Economic Policy, 7: 18-32. Millward, N. and Stevens, M. (1986). British Workplace Industrial Relations I98&

1984. London: Gower. Naylor, R. A. and Cripps, M. W. (1993). ‘An economic theory of the open shop

trade union’. European Economic Review, 37: 1599-1620. Nickell, S. J. (1986). ‘Dynamic models of labour demand‘. In 0. Ashenfelter and

P. R. G. Layard (eds.), Handbook of Labour Economics. Amsterdam: North- Holland, Chapter 9.

Oi, W. (1962). ‘Labor as quasi-fixed factor’. Journal of Political Economy, 70: 538-55.

Oulton, N. (1987). ‘Plant closures and the productivity miracle in manufacturing’. National Institute Economic Review, 121: 53-9.

Stewart, M. B. (1990). ‘Union wage differentials, product market influences, and the division of rents’. Economic Journal, 100: 1122-37.

Declining Union Density in the 1980s: What Do Panel Data Tell Us?

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