The Economic Effects of the Introduction of the UK National Minimum Wage

Preliminary: Comments Welcome, Not to be Quoted Without Authors’ Permission

Stephen Machin*, Alan Manning ** and Lupin Rahman***

July 2000 Revised February 2001

* Department of Economics, University College London and Centre for Economic Performance, London School of Economics

** Department of Economics and Centre for Economic Performance, London School of Economics

*** Centre for Economic Performance, London School of Economics

Abstract Between 1993 and April 1999 there was no minimum wage in the UK (except in agriculture). In this paper we study the impact effects of the introduction of a National Minimum Wage (NMW) in April 1999 on one heavily-affected sector, the residential care homes industry. We look at the impact on both wages and employment. Our results suggest that the minimum wage raised the wages of a large number of care homes workers, causing a very big wage compression of the lower end of the wage distribution thereby reducing wage inequality. There is some evidence of employment and hours reductions but no effect on home closure.

Acknowledgements We would like to thank the numerous students who punched the care homes data in to the computer and participants in seminars at the Reserve Bank of Australia, STICERD’s Inequality Conference, Warwick and the EEEG Conference in Southampton.

0 I. Introduction

In April 1999 the UK government introduced a National Minimum Wage (NMW) to the UK labour market. This is the first time that the UK labour market has had an economy wide minimum wage and, as the old industry-based Wages Council system was abolished in 1993, the NMW was introduced into a labour market which had no minimum wage legislation in operation.1 Because of this, the introduction of the UK

NMW provides a very good testing ground for evaluating the economic effects of minimum wages. This is what we undertake in this paper.

Our analysis is based upon a large scale survey that we carried out before and after the introduction of the NMW. This survey focused upon the whole population of residential care homes in Britain, collecting information on all workers in the sampled homes, and on an array of home characteristics. We chose to look at this sector for several reasons. First, it contains many low wage workers (a lot of whom are part-time female workers), so if the minimum wage is likely to have had an important impact on economic outcomes it ought to be in an industry like this one. Second, we chose this sector because it is not unionised. Thirdly, it consists of large numbers of small firms

(average employment being somewhere in the range of 15-20 workers) doing a very homogeneous activity in geographically concentrated markets. The small size of these firms means that monitoring problems are unlikely to be severe because the owner normally also works in the home and also that collection of good quality data on all workers is feasible. Finally, the product market side in this sector is interesting. A substantial fraction of the old people in these homes have their care paid for by the

1 Except in agriculture where the Agricultural Wages Board was not abolished along with the other Wages Councils.

1 Department for Social Security (DSS). But, the amount they will pay is capped and was not increased when the minimum was. Homes whose residents are paid for by the DSS are then in a situation where they are unable to pass any of their cost increases on to prices: this is likely to make the employment effect worse.

We consider what happened to wages and employment in the care homes sector in the period surrounding the introduction of the NMW. The analysis confirms that the choice of sector is a good one for studying the likely impact of minimum wages. Pay is very low, with the average hourly wage being around £4 just before the minimum wage introduction. Before its introduction around 40 percent of workers were paid less than the minimum wage. In April 1999 we see a spike in the wage distribution of around 30 percent at the minimum wage. Clearly the minimum wage ‘bit hard’ in this sector. It therefore provides a very good environment for looking at the impact of the minimum wage on employment.

We look at the employment effects of the minimum wage by considering what is now a standard technique in the empirical literature on minimum wages and employment by relating changes in employment before and after the minimum wage introduction to the fraction of low paid workers in the pre-minimum wage period (see, for example,

Card’s, 1992, state based study of an increase in the US federal minimum wage in the early 1990s). Our results point to a small negative impact of the minimum wage on employment and hours in the care homes sector.

The rest of the paper is structured as follows. Section II presents a brief history of minimum wage legislation in the UK. Section III describes the data and presents some descriptive statistics. Section IV presents our empirical findings on the wage effects of

2 the minimum wage. Section V then moves on to consider the employment and hours effects. Section VI then concludes.

II. Minimum Wages in the UK

Unlike in other countries, minimum wages have not historically had an important role to play in the UK labour market. There used to be an industry-based system of minimum wage floors, the Wages Councils, which operated from the start of the century up to their abolition in 1993. In earlier work we have studied the impact of the Wages Councils, concluding that their activities did not harm employment (Dickens, Machin and Manning,

1999; Dolado et al, 1996)

Following their election in 1997, Tony Blair’s Labour Government was committed to introducing a National Minimum Wage. It set up a Low Pay Commission consisting of academics and representatives of employers and workers to report on a suitable form and level. Eventually, a minimum wage of £3.60 per hour was introduced in April 1999 for those aged 22 or older, with a lower youth rate of £3 per hour for those aged 18-21 inclusive (those aged less than 18 were not covered). This was approximately x% of average hourly earnings and it was expected that x% of workers would be directly affected.

III. Data Issues and Descriptive Statistics

Most existing UK data is either not yet available or not well suited to carrying out a before and after evaluation of the economic impact of the introduction of the minimum

3 wage to the UK labour market.2 For this reason we decided to collect our own data. We wanted to focus on a situation where the minimum wage has the potential to have an important impact and so chose to collect data on employers and workers in residential care homes.

There are several reasons for choosing this sector. First, it is a leading employer of many low wage workers. The principal occupation, care assistants, is one of the lowest paid occupations in the UK. Second, most homes are reasonably small (average employment size < 20; median employment size = 15) and this enables us to collect data on all workers within the homes. Third, there are basically no trade unions to distort wage-setting procedures in this sector.

Sample Design

Our sample design was to sample the population of UK care homes before and after the introduction of the minimum wage. We obtained lists of all homes from the Yellow

Pages Business Database in July 1998 (for the pre-minimum sampling) and in May 1999

(for the post-minimum sampling). There were 11635 care homes in the former and

11036 homes in the latter. As one of the things one might be interested in is the extent to which employers adjusted wages before the minimum wage introduction we sampled

(based on area stratification) one-ninth of the homes in each of the nine months before minimum wage introduction, and then we re-sampled the homes (including new homes), and again one-ninth at a time, in the nine months following the introduction of the wage floor. We also identified home closures that occurred over this time period.

2 For example the survey containing the best wage data, the New Earnings Survey, has several drawbacks. First, it is carried out in April of each year which is unfortunate as the minimum wage was introduced in April 1999 resulting in wages contained in the survey being a mish mash of pre- and post-introduction wages. Second, and even more important, it undersamples low wage part-time workers (as workers below

4 The questionnaire was mailed to the manager of the care homes and asked a range of questions about the home, about the views managers (who are often home owners) had about the minimum wage. For obvious reasons, the precise nature of the attitudinal questions was different for questionnaires sent out before and after the introduction of the minimum wage. Managers were also asked to provide data on job title, sex, age, length of service, possession of a nursing qualification, weekly hours and hourly wages for all workers. The exact questionnaires are reproduced in the Appendix to the paper. For a postal survey we achieved a reasonable response rate (of the order of around 20 percent) and, in terms of number of residents, area and employment size of homes, the responses were representative of the population as a whole. Managers were less likely to complete all the information on worker characteristics. Where there is missing information on hourly wages and/or hours we imputed them using the average for job within that firm.

We report both results including and excluding imputed figures below.

Descriptive Statistics

Some features of the sample are described in Table 1. The first two columns show the characteristics of the sample of workers and homes pre and post minimum wage introduction, for all homes that we obtained some responses on worker characteristics.

It is clear that wages are very low in the sector we have chosen to study. Before the introduction of the minimum wage average wages were about £4 per hour (whether we include imputed results does not make much difference). The choice of sector therefore clearly satisfies one of the criteria we wanted to emphasise, namely that we wanted to look for the economic effects of minimum wages in a situation where the

the National Insurance weekly earnings lower limit are not well picked up). Other surveys may well be characterised by measurement errors in wages as they are employee, rather than employer, surveys.

5 imposition of the minimum wage floor has the potential to affect a reasonably large number of workers. What is also interesting is the fact that average wages rose significantly after the minimum wage was introduced, going up by somewhere around 25 pence per hour (or around 6 percent). We consider this in much more detail in the next

Section where we discuss the ‘bite’ of the minimum wage introduction.

The Table also documents other characteristics of the sample. Average home size is fairly small, both in terms of workers and residents. Mean employment is in the range of 16 to 18 workers, and the average number of residents is just over 20 per home. They both remain fairly constant before and after the minimum wage introduction.

As stated earlier the principal occupation in this sector is care assistants who comprise around 62 percent of the workers in the sample. The workers are typically older workers (average age = 40), overwhelmingly female, working an average of 25 hours per week. Only about 10% have a nursing qualification (other educational qualifications are not relevant in this sector). The average job tenure is 3.5 years.

Finally, we have collected other home level information on the occupancy rate of beds and, since the sector has price regulation operating through local authorities, on the percent of residents who pay local authority prices for beds. This latter feature of the sector is, of course, very interesting in the context of minimum wage effects. One argument sometimes put forward in the literature is that one may not observe employment responses to minimum wages if employers are able to pass minimum wage increases on to consumers in the form of higher prices. This seems unlikely to happen in the care homes sector as prices are, in many cases, controlled by ocall authorities. In this regard, it is useful to note that prices do not seem to go up by much (and certainly by

6 nowhere near as much as average wages) after the minimum wage introduction. We return to this later, after considering the wage and employment effects of the minimum wage introduction.

Because we are interested in comparing employment before and after the introduction of the minimum wage, most of our estimation uses a panel of homes for whom we have a response before and after the introduction. The third and fourth columns present the descriptive statistics for these homes. They are broadly representative of the whole sample. Some of these homes have quite large numbers of imputed hours and hourly wages so the final two columns report the descriptive statistics restricting attention to those homes where less than half of the workers have missing hours or wage information.

IV. Wages and the Minimum Wage

The main aim of this paper is to look at the employment consequences of the minimum wage introduction in the low wage sector that we have sampled. However, before considering this, one clearly needs to establish that the minimum wage has had the effect one expects on wages and the distribution of wages. Confirming that the minimum wage introduction had real ‘bite’ and affected the wages of low wage workers in the expected direction is clearly a prerequisite before one goes on to look at the impact on employment.

The ‘Bite’ of The Minimum Wage Introduction

The UK National Minimum Wage was introduced in April 1999 at £3.60 per hour for workers aged 22 or more, and at £3.00 per hour for 18-21 year olds. When presenting measures of the impact of the minimum wage we sometimes use these age-specific

7 minimum wages and sometimes just the adult minimum: the reason for this is that there is a spike in the youth wage distribution at the adult minimum after the introduction so that one could argue that the adult minimum is the effective minimum (see Katz and Krueger,

1992, for evidence that in the US the youth sub-minimum is rarely used). Table 2 reveals that a large number of care home workers were paid below the minimum wage before

April 1999. Something like one-third of workers were paid less than their age-specific minimum wage and 38% paid below the adult minimum rate.

Table 2 also presents measures of the ‘wage gap’ the percentage increase in wages needed to bring workers up to the minimum. The wage gap in firm i is computed

min åh jimax(W ji -W ji ) j as GAPi = where hji is the weekly hours worked by worker j in åh jiWji j

min firm i, Wji is the hourly wage of worker j in firm i, and Wji is the minimum wage relevant for worker j in firm i (this might be the age-specific or the adult minimum).

Table 2 shows that the wage gap averages 4% using the age-specific minimum and 4.7% using the adult minimum.

After the minimum wage is introduced, there seems to be very little under- payment. One might be concerned that those firms that subsequently pay illegally below the minimum wage do not respond to our survey. However, there is no evidence thst this is the case. If this was true we would expect to see the initial wage levels in our sample as a whole being below those in our balanced panel as the latter group also repsond after the

8 minimum wage is increased: in Table 1 one can see that there is no evidence that this is the case3.

Table 2 shows there is a very noticeable spike at the minimum after April 1999.

Something like 28% of workers are paid the age-specific minimum and 30% the adult minimum. Not surprisingly the minimum wage introduction therefore had a sizable impact on wage dispersion: the gap between the 50th and 10th percentiles of the log(hourly wage) distribution narrowed from 0.21 to 0.09. It is also interesting to notice that the upper half of the distribution was unaffected with the gap between the 90th and

50th percentiles of the distribution not changing. A companion paper (Machin and

Manning, 2001) investigates the impact of the minimum wage on the wage distribution as a whole.

Measuring the Impact on Care Home Wages

The statistics of Table 2 present evidence about the impact of the minimum wage on the overall wage distribution. But, for our empirical analysis, we would also like evidence that the homes that we would most expect to be affected were, indeed, het most effected.

This is the purpose of this section. More specifically we estimate wage change equations of the form:

?ln(Wit ) = a1 +ß1MINi,t-1 + d1?Xit + eit (1.1) where DlnWit is the change in wages for home i in the period surrounding minimum wage introduction (t-1 denotes the period before, t the post-minimum period), MINi, t-1 is a measure of the importance of the minimum wage for home i (defined below), DXit is the change in other home and worker characteristics between -t1 and t and e is an error term.

3 We also investigated whether the sample response rates fell disproportionately in the low-wage regions

9 The parameter b1 is what we want to estimate and can be thought of as measuring the relation between wage changes and the minimum holding constant the other factors we control for through the change in X.

There are several practical concerns with this kind of equation. First, and most important, is how one measures MINi, t-1. We use the two measures of the impac tof the minimum wage already discussed in Table 2: the proportion of workers paid less than the minimum wage in the period just before its introduction, and the wage gap. It is not clear which is the better measure. For example, if the minimum wage caused all workers intially paid below it to lose their jobs, then the headcount might be the better measure, but if it is more difficult to raise the productivity of those a long way below the minimum wage than those near it, then the wage gap measure might be better.

Because the minimum wage introduction in the UK was at national level the variation across homes in the impact of the minimum wage all comes from variation in the initial level of wages. b is then only a true measure of the impact of the minimum wage if, in the absence of the minimum wage, there would be no relationship between the initial level of wages and the change in wages i.e. if wages follow a random walk. This is the implicit identification assumption in using an equation like (1.1) to estimate the impact of the minimum wage.

We test this identifying assumption by also looking at the relationship between wage changes and initial wages using some data we collected in an earlier time period

(1992/93) for care homes on the south coast of England (see Machin, Manning and

wherethe minimum wage had more bite. There was no evidence this was the case.

10 Woodland, 1993, for more detail on this earlier survey).4 This enables us to consider whether one observes a different association in the period surrounding minimum wage introduction as compared to an earlier time period where no such policy was put in place.

Estimates of Wage Equations

Tables 3a and 3b report estimates of care home-level wage change equations for hourly wages (Table 3a) and for weekly wages (Table 3b). The first four rows estimate wage equations including the low-pay and wage-gap measures of MINi, t-1 described above with and without additional controls. These all show evidence of bigger wage increases in homes with more low-wage workers. We will concentrate our discussion on the hourly wage results in Table 3a though the weekly earnings results in Table 3b are very similar.

The associations are very strong in statistical terms and are sizable. For example, according to column (2) of Table 3a a home with one third of its workers paid less than the minimum saw average wage growth of 3.5 percent higher than one which had one tenth of its workers paid less than the minimum. This is large given that average wage growth was around 6 percent in this time period. It is worth nothing that the wage gap measure appears to do a much better job in explaining the change in average wages than the headcount measure.

These results appear to establish an important impact on wages of the minimum wage but, as noted above, it may merely be because there has always been a link between wage growth and initial low pay. So rows (5) and (6) of the Tables compare the relationship between wage growth and initial wages in the 1998/99 and 1992/93 surveys.

4 This 1992/93 survey was carried out for the same reason as the current one, namely to evaluate the impact of minimum wage introduction on care homes. The first wave of the survey was carried out before the April 1992 election as the Labour Party manifesto had committed to introduce a minimum wage if they

11 First, we estimate the more general wage equation of the following form relating wage change to the initial period average wage:

?ln(Wit ) = a 2 + ß2ln(Wi,t-1) +uit where the focus is now on estimating b2 , the association between wage changes and the initial average wage (u is an error term).

The significantly negative coefficient on the initial wage in row (5) of Tables 3a reconfirms that, in the period surrounding minimum wage introduction, wage growth was higher in firms with lower wages in the initial period. Column (6) looks at the situation in 1992/93. There is also a negative coefficient on the initial wage, but it is clear that its magnitude is nowhere near as marked as in 1998/99.

We also report the results of wage growth equations for the 1998/99 data that include both the initial wage and the low-pay and wage-gap measures of the impact of the minimum wage. Even controlling for the initial wage, there is still evidence of a significant impact of the wage gap measure on wage growth: in fact the coefficient on the initial wage is reduced a level similar to that found in the 1992/93 data. It is also worth noting that when we restrict the sample to the ‘clean panel’ in which less than half the worker information is imputed, the coefficient on the wage gap measure increases (rows

(9) and (10).

Some graphs also serve to make the same point. Figure 1a plots the relationship between average wage growth and the initial log wage in 1998/99 and Figure 1b does the same for the 1992/93 data. While it is very clear that there is a negative relationship in

were elected. Their loss of the election meant our plan to carry out a before and after analysis of minimum wage introduction was scuppered.

12 both periods, the strong diagonal effect on homes with initial low wages is very apparent in the 1998/99 data. This is the impact of the minimum wage.

County Level Analysis

So, far we have considered differences in wage growth across firms with different initial levels of wages. But, we might also be interest in the impact across areas with different initial levels of wages. Because of regional differences in wages (largely the result of regional differences in house prices), the impact of the national minimum wage was very different in different parts of the country. In our sample there were no worker initially paid below the minimum wage in Berkshire, Buckinghamshire, Hertfordshire or

Oxfordshire while over 60% of workers were low-paid in Glamorgan, Humberside,

Lincolnshire, Merseyside, Northumberland, South Yorkshire and Tyne and Wear. We would expect to find bigger increases in wages in those counties with the lowest initial level of wages. Here, we supplement our data with data on care assistants from the

Labour Force Survey.5

Table 4 reports county-level hourly and weekly wage equations from aggregated care homes data and from the Labour Force Survey. A reassuring similar pattern emerges. Counties with more low wage workers in the period before minimum wage introduction had faster wage growth (hourly and weekly) and the negative, strongly significant, relationship between wage changes and the initial average wage is again present. The impact of the wage-gap measure seems larger in the county-level than the

5 This exercise is essentially the same as Card’s (1992) analysis of state level wage and employment changes before and after the US 1991 federal minimum wage increase. He relates state-level changes to the initial proportion of workers paid less than the new minimum, arguing that identification comes from the fact that more workers wages were affected in some states than others.

13 firm-level data possibly reflecting spillovers from low-wage to higher-wage firms within an area.

The evidence of this Section has established a clear and important positive effect on wages in the period when the national minimum wage was introduced. We investigate the possible employment effects in the next section.

V. Employment and the Minimum Wage

We analyse the employment consequences of the minimum wage introduction using the same methodology as the wage analysis i.e. we estimate equations of the form:

?ln(Nit)=a4+ß44MINi,t-1++dXitwit

As before, the implicit identification assumption here is that, in the absence of the minimum wage, there would be no relationship between employment growth and the level of wages. It is not obvious this is the case (e.g. homes that are doing less well might pay lower wages and have lower employment growth) so we start by investigating the relationship between employment growth and initial wages in the 1992/93 data.

Changes in Employment and Initial Wages

Table 5 begins the analysis of employment links by considering the associations between changes in care home employment and the initial wage for the period surrounding minimum wage introduction (1998/99) and for the earlier period (1992/93) where no such policy intervention occurred. The following equation is estimated:

14 ?ln(N it) = a3 +ß3ln(W i,t-1) +xit where it is now employment change for home i, Dln(Nit), that we relate to the initial wage

(x is the equation error).

Table 5 presents estimates of this equation for two measures of employment, the number of workers and total workers hours, for the 1998/99 period and for the earlier

1992/93 period. The first thing to note is that the correlations of employment changes with initial wages are much weaker than the correlation of wage growth with initial wages. There is, however, some evidence that the associations between employment change and the initial wage are more positive in 1998/99 than in 1992/93. The average number employed effects are weak but there is more evidence of an effect on total hours.

For example, the t-statistic for the hypothesis that the gap between the coefficients on the initial log hourly wage is zero is 1.43 for the total employment and 1.96 for total hours.

Changes in Employment And Initial Minimum Wage Variables

Figures 2 and 3 plot the basic data used in the regressions that are discussed below. The change in log total employment is plotted against the initial proportion low- paid in Figure 2a and against the initial wage-gap in Figure 2b. Figures 3a and 3b do the same but with the change in log total hours on the left-hand axis. The regression results are reported in Table 6, again for total employment and hours in the upper and lower panels respectively. Columns (1) and (5) report the coefficient on the initial proportion low-paid and the initial wage-gap respectively in a regression where the dependent variable is the change in total log employment and there are no other controls. The estimated impact of the minimum wage is negative though not significantly different from zero. However the addition of controls (columns (2) and (4)) and restricting the

15 sample to those with relatively complete information on worker characteristics makes the coefficients both larger in absolute terms and more significant. When other controls are included and the restricted samples is used, the minimum wage variables are estimated to have a significant negative effect on employment growth. We also report implicit elasticities if the minimum wage was raised by 10p (the adult rate going from £3.60 to

£3.70) and 40p (the adult rate going from £3.60 to £4.00). These elasticities ar ein the range of –0.2 to –0.3.

The bottom half of Table 6 uses the change in the log of total hours worked as the dependent variable. The coefficients become more a little bit more negative and a little bit more significant.

While the employment effects are clearly nowhere near as strong as the wage effects considered, they do suggests that employers cut employment and hours in response to the minimum wage.

County Level Analysis

As before, we also consider whether there is any evidence of difference in employment growth between high- and low-wage counties. The results are reported in Table 7. The minimum wage variables generally have the opposite sign to Table 6 but the standard errors are so large that they are never significantly different from zero. However, these results do suggest there is no evidence at county-level that low-wage areas were particularly badly affected by the minimum wage.

Home Closures and Openings

Thus far we have restricted attention to the balanced panel of continuing homes.

However, the sector does have high turnover of workers and businesses. We have

16 therefore also looked at whether home closure and opening rates are connected to the incidence of low wage workers in the period before minimum wage introduction.

We have carried out two sets of analysis here. The first looks at home closure and initial low pay incidence using the home level data. The second compares county-level closure and entry rates to the low pay structure of the area before the minimum wage came in.

Table 8 contains the home-level closure equations. A variety of specifications are reported, using all three minimum wage variables, and either including or excluding the control variables. A very clear pattern emerges. In none of the specifications is there any evidence that the homes with more low wage workers shut down because of the imposition of the minimum wage. Indeed, several of the coefficients are actually estimated to be negative, rather than the positive that would be predicted if one hypothesised that the rising wage costs ensuing from the minimum wage would cause closure.

The second analysis, now at county-level, is considered in Table 9. Here we report the same kinds of specifications as in Table 8 for county-level closure rates in the upper panel of the Table and for county-level entry rates in the lower panel. Again, none of the associations is significant in statistical terms and in both cases both positive and negative coefficients emerge. It seems that differential entry and exit related to wage levels are unlikely to have contributed to reducing employment in the care homes sector over the minimum wage introduction period.

17 So, to summarise, the results point to a very sizable wage impact of the minimum wage and there is some evidence of an impact on jobs and hours in the firm-level regressions.

Other Outcomes and the Minimum Wage

This section briefly investigates the impact of the minimum wage on other outcomes. It is possible that they may have ‘passed on’ increased wage costs from the minimum wage introduction through higher prices though the extent to which this is possible may be limited by the extensive price regulation. Second, there may have had to have been re-organizations that could raise productivity (e.g. quality of care improvements, or increases in care worker productivity, or simply making people work harder for their higher wages).

We consider these two possibilities in Table 10. The upper panel reports price change equations and the lower two panels consider two productivity measures. The first is changes in residents per worker hour, the second comes from managers responses to a question about whether they think worker effort went up, stayed the same, or fell in the period around minimum wage introduction.

Perhaps not surprisingly given the nature of price regulation in the care homes sector we find no evidence that prices rose by more in the initial low wage firms. Whilst all the estimated coefficients in the upper panel are positive none of the coefficients approach anything near statistical significance (all have t-ratios < 1). As such there seems to be no evidence that minimum wage increases might have been passed on through higher prices in this sector.

18 There is more evidence in line with the re-organization/productivity improvement idea considered in the lower panels of Table 9. The estimated coefficients on the minimum wage variables in both the change in residents per worker hour and subjective effort change equations are all estimated to be positive. The pattern of significance is, however, somewhat mixed but the results are nevertheless suggestive that low wage employers may have been able to sustain the sizable wage increases that occurred by re- organizations to increase worker effort.

VI. Conclusions

In this paper we present empirical work on the wage and employment consequences of the recent introduction of a minimum wage to the UK labour market. We focus on a specific low wage sector, the care homes industry, on which we carried out our own survey before and after the minimum wage was introduced. We find a very important wage compression effect on the bottom half of the wage distribution in this low wage sector. Before its introduction around 40 percent of care homes workers were paid less than the minimum wage. In April 1999 we see a spike in the wage distribution of around

30 percent at exactly the minimum wage. This resulted in wage growth being considerably higher in the period surrounding minimum wage workers in homes who had more low paid workers before the minimum came in. This seems to establish that the minimum wage had considerable ‘bite’ on average wages and wage structure. Turning to the employment effects we find some evidence of employment and hours reductions, but no effect on home closures. Of course, the sector we have examined is particularly vulnerable to the minimum wage as it has very many low-paid workers and is unable to

19 pass wage costs on into prices. Hence, one should be cautious in drawing conclusions from this study about the impact of the introduction of the National Minimum Wage on the UK as a whole.

20 References

Card, David (1992) Using regional variation in wages to measure the effects of the federal minimum wage, Industrial and Labor Relations Review, 46, 22-37.

Card, David and Alan Krueger (1995) Myth and Measurement: The New Economics of the Minimum Wage, Princeton: Princeton University Press.

Dickens, Richard, Stephen Machin and Alan Manning (1999) The Effects of Minimum Wages on Employment: Theory and Evidence From Britain, Journal of Labor Economics, 17, 1-22.

Dickens, Richard and Alan Manning (1995) After Wages Councils, New Economy, 2, 223-27.

Dickens, Richard, Stephen Machin, Alan Manning, David Metcalf, Jonathan Wadsworth and Stephen Woodland (1995) Minimum Wages and UK Agriculture, Journal of Agricultural Economics, 49, 1-19.

Dolado, Juan, Francis Kramarz, Stephen Machin, Alan Manning, David Margolis and Coen Teulings (1995) 'The Economic Impact of Minimum Wages in Europe', Economic Policy, 23, 317-72.

Machin, Stephen, Alan Manning and Stephen Woodland (1993) Are workers paid their marginal product? Evidence from a low wage labour market, Discussion Paper No.93-09, University College London, minor classic that sadly remained unpublished.

21 Table 1: Nature of Survey

All Firms Balanced Panel Balanced Panel (excluding firms with lots of missing worker info) Pre- Post- Pre- Post- Pre- Post- Minimum Minimum Minimum Minimum Minimum Minimum Number of Homes 1865 2144 643 643 617 617 Number of Workers 17.23 17.19 16.43 16.70 16.48 16.90 (12.16) (13.02) (9.71) (11.9) (9.67) (11.9) Hourly Wage 4.03 4.28 4.00 4.25 3.97 4.24 (not imputed) (0.85) (0.81) (0.80) (0.74) (0.74) (0.72) Hourly Wage 4.01 4.26 3.98 4.23 3.98 4.23 (imputed) (0.82) (0.75) (0.75) (0.72) (0.74) (0.70) Weekly Hours 25.6 25.2 24.9 24.9 24.7 24.8 (not imputed) (6.5) (6.1) (6.3) (5.8) (5.9) (5.7) Weekly Hours 25.8 25.4 25.2 25.2 24.9 25.0 (imputed) (6.5) (6.0) (6.2) (5.8) (5.9) (5.6) Weekly Earnings 103.9 108.4 100.4 106.8 99.0 105.9 (not imputed) (38.9) (37.8) (37.8) (35.4) (33.7) (34.6) Weekly Earnings 104.2 108.8 100.6 107.3 99.7 106.3 (not imputed) (37.1) (35.9) (34.5) (34.5) (33.6) (33.6) Proportion of 0.10 0.11 0.06 0.04 0.04 0.04 workers with missing information Proportion Female 0.94 0.95 0.95 0.95 0.95 0.95 Average Age 38.2 39.0 38.1 38.9 38.2 38.8 (7.5) (7.6) (7.4) (7.4) (7.3) (7.3) Average Length of 3.5 3.6 3.5 3.6 3.4 3.6 Service (Months) (2.1) (2.1) (1.9) (2.0) (1.8) (2.0) % Care Assistants 0.93 0.90 0.94 0.93 0.94 0.93 % Nursing 0.086 0.095 0.098 0.094 0.099 0.097 Qualification Number of Beds 20.7 19.7 18.6 19.1 18.6 19.2 (36.6) (16.2) (18.4) (19.8) (18.7) (20.1) Number of Residents 18.5 17.8 16.5 17.1 16.6 17.1 (35.7) (15.0) (17.7) (18.7) (17.9) (19.1) % Occupancy Rate 0.87 0.89 0.88 0.89 0.88 0.89 Average Weekly 252.4 258.2 250.2 257.6 249.7 257.0 Price per Bed (£) (86.1) (92.2) (78.2) (79.2) (78.2) (78.8) % DSS/Local 0.55 0.58 0.52 0.57 0.52 0.57 Authority

Notes: 1) Standard errors in parentheses 2) Pre-Minimum observations refer to responses received before April 1999 and post-minimum to responses received after March 1999. 3) Care Assistants include senior, day and junior carers but exclude night carers and sleep-ins.

22 Table 2: The “Bite” of the MinimumWage Introduction And Subjective Views on Employment Impact

All Firms Balanced Panel Balanced Panel (excluding firms with lots of missing worker info) Pre- Post- Pre- Post- Pre- Post- Minimum Minimum Minimum Minimum Minimum Minimum % Paid Less 32.2 1.0 31.3 0.8 31.7 0.7 Than Minimum Wage % Paid Less 38.2 4.2 37.8 4.3 38.3 4.2 Than Adult Minimum Wage Wage Gap 0.040 0.002 0.041 0.003 0.038 0.002 Adult Wage 0.047 0.006 0.050 0.007 0.048 0.007 Gap % Paid 8.7 27.7 9.3 28.3 9.4 28.6 Exactly Minimum Wage % Paid 8.6 30.0 9.0 30.7 9.2 31.0 Exactly Adult Minimum Wage

Notes: 1) Pre-Minimum observations refer to responses received before April 1999 and post-minimum to responses received after March 1999.

Table 3a: Changes in Average Log Hourly Wages And Initial Period Wage Measures

Dependent Variable: Change in Log Average Hourly Wage Data Low-Pay Wage Initial Controls R2 Number Proportion Gap Log of Homes Wage (1) 98/99 0.145 No 0.19 643 (0.012) (2) 98/99 0.156 Yes 0.36 593 (0.022) (3) 98/99 0.800 No 0.29 643 (0.070) (4) 98/99 0.830 Yes 0.44 593 (0.087) (5) 98/99 -0.360 No 0.29 643 (0.040) (6) 92/93 -0.174 No 0.07 231 (0.057) (7) 98/99 0.039 -0.306 No 0.30 643 (0.019) (0.061)

(8) 98/99 0.567 -0.178 No 0.40 643 (0.064) (0.043) (9) 98/99 0.905 No 0.36 617 clean (0.046) panel (10) 98/99 0.702 -0.126 No 0.39 627 clean (0.058) (0.031) panel

Notes: 1) Sample is balanced panel of homes. 2) Wage-gap is the wagebill shortfall from the minimum wage as a proportion of the total wagebill. 3) Low paid are defined as those aged 18-21 inclusive paid less than £3.00 per hour and those aged 22+ who are paid less than £3.60 per hour. 4) Control variable are the initial proportion female, proportion with nursing qualification, proportion of care assistants and average age (all workers), occupancy rate, proportion of local authority/dss residents, county and month dummies.

Table 3b: Changes in Average Log Weekly Wages And Initial Period Wage Measures

Dependent Variable: Change in Log Average Weekly Wage Data Low-Pay Wage Initial Controls R2 Number Proportion Gap Log of Homes Wage (1) 98/99 0.137 No 0.04 643 (0.025) (2) 98/99 0.156 Yes 0.18 593 (0.022) (3) 98/99 0.669 No 0.07 643 (0.119) (4) 98/99 0.715 Yes 0.21 593 (0.143) (5) 98/99 -0.324 No 0.21 643 (0.031) (6) 92/93 -0.272 No 0.07 231 (0.055) (7) 98/99 0.034 -0.311 No 0.22 643 (0.028) (0.037)

(8) 98/99 0.318 -0.296 No 0.23 643 (0.082) (0.035) (9) 98/99 0.832 No 0.07 617 clean (0.134) panel (10) 98/99 0.386 -0.268 No 0.21 627 clean (0.127) (0.032) panel

Notes: 5) Sample is balanced panel of homes. 6) Wage-gap is the wagebill shortfall from the minimum wage as a proportion of the total wagebill. 7) Low paid are defined as those aged 18-21 inclusive paid less than £3.00 per hour and those aged 22+ who are paid less than £3.60 per hour. 8) Control variable are the initial proportion female, proportion with nursing qualification, proportion of care assistants and average age (all workers), occupancy rate, proportion of local authority/dss residents, county and month dummies.

Table 4: County-Level Analysis of Changes in Average Log Wages And Initial Period Wage Measures, Care Assistants, Aggregated Care Homes Data 1998/99 and Labour Force Survey 1998/99

Dependent Variable: Change in Average Log Hourly Wage (1) (2) (3) (4) (5) (6) Care Homes Labour Force Survey 1998/99 1998/99 Data Data Initial Proportion Paid Less Than 0.215 0.410 Minimum Wage (0.044) (0.153) Initial Wage Gap 1.308 1.609 (0.309) (0.797) Initial Log Hourly Wage -0.419 -0.449 (0.084) (0.184) R-squared 0.38 0.33 0.43 0.08 0.06 0.08 Number of Counties 55 55 55 55 55 55 Dependent Variable: Change in Average Log Weekly Wage (7) (8) (9) (10) (11) (12) Care Homes Labour Force Survey 1998/99 1998/99 Data Data Initial Proportion Paid Less Than 0.187 0.546 Minimum Wage (0.088) (0.205) Initial Wage Gap 1.114 2.618 (0.497) (0.847) Initial Log Hourly Wage -0.385 -0.702 (0.098) (0.204) R-squared 0.10 0.09 0.22 0.09 0.11 0.22 Number of Counties 55 55 55 55 55 55

Notes: 1) Weekly gap is the wagebill shortfall from the minimum wage as a proportion of the total wagebill 2) Low paid are defined as those aged 18-21 inclusive paid less than £3.00 per hour and those aged 22+ who are paid less than £3.60 per hour. 3) Robust standard errors in parentheses.

Table 5: Changes in Log Employment and Hours And Initial Period Wage Measures, All Workers, Care Homes Data 1998/99 and 1992/93

Change in Log Number Employed (1) (2) 1998/99 1992/93 Data Data Initial Log Hourly Wage 0.108 -0.133 (0.082) (0.147) R-squared 0.002 0.002 Observations 643 237 Change in Log Total Hours (3) (4) 1998/99 1992/93 Data Data Initial Log Hourly Wage 0.152 -0.220 (0.093) (0.165) R-squared 0.004 0.009 Observations 643 231

Notes: 1) Robust standard errors in parentheses.

Table 6: Changes in Log Employment and Hours And Initial Period Minimum Wage Measures, All Workers, Care Homes Data 1998/99

Dependent Variable: Change in Log Number Employed (3) (4) (5) (6) (7) (8) (1) (2) clean clean clean clean panel panel panel panel Initial Proportion Paid -0.050 -0.152 -0.077 -0.168 Less Than Minimum (0.042) (0.054) (0.041) (0.054) Wage -0.162 -0.334 -0.252 -0.565 Initial Wage Gap (0.109) (0.138) (0.165) (0.251) Demographic Variables Yes Yes Yes Yes Firm Characteristics Yes Yes Yes Yes Variables Response Month Yes Yes Yes Yes Dummies Average Elasticity -0.205 -0.624 -0.316 -0.690 -0.064 -0.133 -0.100 -0.224 (3.70-3.60) Average Elasticity -0.115 -0.350 -0.177 -0.387 -0.079 -0.162 -0.122 -0.275 (4.00-3.60) Observations 643 593 617 571 643 593 617 571 R-squared 0.002 0.158 0.005 0.155 0.001 0.151 0.002 0.146 Dependent Variable: Change in Log Total Hours (3) (4) (5) (6) (7) (8) (1) (2) clean clean clean clean panel panel panel panel Initial Proportion Paid -0.061 -0.166 -0.082 -0.176 Less Than Minimum (0.046) (0064) (0.045) (0.064) Wage Initial Wage Gap -0.307 -0.450 -0.338 -0.502 (0.138) (0.157) (0.206) (0.312) Demographic Variables Yes Yes Yes Yes Firm Characteristics Yes Yes Yes Yes Variables Response Month Yes Yes Yes Yes Dummies Average Elasticity -0.250 -0.681 -0.337 -0.722 -0.122 -0.179 -0.134 -0.199 (3.70-3.60) Average Elasticity -0.140 -0.382 -0.189 -0.405 -0.149 -0.219 -0.164 -0.244 (4.00-3.60) Observations 643 593 617 571 643 593 617 571 R-squared 0.002 0.145 0.005 0.154 0.003 0.132 0.003 0.136

Notes: 1) Demographic variables: initial proportion female, proportion with nursing qualification, and average age (care assistants). 2) Home characteristics variables: changes in occupancy rate and proportion of local authority/dss residents, county and month controls. 3) Standard errors in parentheses.

Table 7: County-Level Analysis of Changes in Log Employment and Hours And Initial Period Wage Measures, Care Assistants, Aggregated Care Homes Data 1998/99 and Labour Force Survey 1998/99

Change Log Number Employed (1) (2) (3) (4) (5) (6) (7) (8) Care Homes Labour Force Survey 1998/99 Data 1998/99 Data Initial Proportion Paid Less Than 0.032 0.117 -0.072 -0.075 Minimum Wage (0.148) (0.304) (0.280) (0.366) Initial Log Hourly Wage Gap 0.166 0.708 0.984 1.471 (1.310) (1.660) (1.375) (1.441) Demographic Variables Yes Yes Yes Yes R-squared 0.005 0.04 0.000 0.038 0.000 0.058 0.007 0.071 Number of Counties 55 55 55 55 55 55 55 55 Change Log Total Hours (9) (10) (11) (12) (13) (14) (15) (16) Care Homes Labour Force Survey 1998/99 Data 1998/99 Data Initial Proportion Paid Less Than 0.004 0.055 0.064 0.012 Minimum Wage (0.267) (0.332) (0.292) (0.353) Initial Log Weekly Wage Gap -0.028 0.474 1.994 2.300 (1.43) (1.80) (1.332) (1.495) R-squared 0.000 0.050 0.000 0.050 0.001 0.002 0.032 0.052 Number of Counties 55 55 55 55 55 55 55 55

Notes: 1) Demographic variables: changes in proportion female, proportion with nursing qualification and average age (care assistants). 2) Weekly gap is the sum of the wagebill shortfall from the minimum wage as a proportion of the total wagebill 3) Hourly gap is the sum of the hourly shortfall from the minimum wage as a proportion of the total hourly wagebill 4) Low paid are defined as those aged 18-21 inclusive paid less than £3.00 per hour and those aged 22+ who are paid less than £3.60 per hour. 5) All regressions control for county-level growth in male employment for male employees aged 25-64. 6) Standard errors in parentheses.

29 Table 8: Home Closures and the Minimum Wage

Home Closures (1) (2) (3) (4) Initial Proportion Paid Less Than Minimum 0.017 0.044 Wage (0.178) (0.222) [0.004] [0.009] Initial Wage Gap 1.381 1.473 (0.889) (0.994) [ 0.294] [0.292] Demographic Varaibles Yes Yes Home Characteristics Variables Yes Yes Response Month Dummies Yes Yes Log-Likelihood -266.05 -237.36 -264.86 -236.29 Number of Homes 682 647 682 647 Number of Closures 90 85 90 85

Notes: 1) Sample is drawn from balanced panel including crossover homes and 105 closed homes who replied in the pre- minimum period. 2) Probit coefficient estimates, standard errors in parentheses 3) Marginal effects in square brackets 4) Demographic variables: changes in proportion female, proportion with nursing qualification, proportion of care assistants and average age (all workers). 5) Home characteristics variables: changes in occupancy rate and proportion of local authority/dss residents. 6) Wage gap is the sum of the wagebill shortfall from the minimum wage as a proportion of the total wagebill. 7) Low paid are defined as those aged 18-21 inclusive paid less than £3.00 per hour and those aged 22+ who are paid less than £3.60 per hour.

Table 9: County-level Closures and Entrants and the Minimum Wage

County Closure Rate (1) (2) (3) (4) Initial Proportion Paid Less Than 0.074 0.285 Minimum Wage (0.242) (0.268) [0.007] [0.025] Initial Wage Gap 0.458 1.490 (1.347) (1.430) [0.040] [0.132] Demographic Variables Yes Yes Home Characteristics Variables Yes Yes R-Squared 0.03 0.18 0.03 0.18 Number of Counties 55 55 55 55 County Entry Rate (5) (6) (7) (8) Initial Proportion Paid Less Than -0.418 -0.265 Minimum Wage (0.430) (0.463) [-0.022] [0.014] Initial Wage Gap -1.080 0.254 (2.409) (2.481) [-0.056] [0.013] Demographic Variables Yes Yes Home Characteristics Variables Yes Yes R-Squared 0.02 0.22 0.00 0.21 Number of Counties 55 55 55 55

Notes: 1) Sample is county level panel data compiled from the unbalanced panel of care homes. 2) Closure rate refers to proportion of estimated home closures of all homes by county; dependent variable is the logistic transformation log [p(1-p)]. 3) Entrant rate refers to proportion of estimated new homes of all homes by county; dependent variable is log ((p+1/(2n))/(1-p+1/(2n))), with the correction included due to zero entry rates in 2 counties. 4) Demographic variables: changes in proportion female, proportion with nursing qualification, proportion of care assistants and average age (all workers). 5) Firm characteristics variables: changes in occupancy rate and proportion of local authority/dss residents. 6) Wage gap is the sum of the wagebill shortfall from the minimum wage as a proportion of the total wagebill. 7) Low paid are defined as those aged 18-21 inclusive paid less than £3.00 per hour and those aged 22+ who are paid less than £3.60 per hour. 8) All regressions control for county-level growth in male employment for male employees aged 25-64. 9) Standard errors in parentheses. 10) Marginal effects in square brackets.

31 Table 10: Prices, Productivity and the Minimum Wage

Change in Log Average Price (1) (2) (3) (4) Initial Proportion Paid Less Than Minimum Wage -0.001 -0.013 (0.024) (0.027) Initial Wage Gap 0.116 0.048 (0.145) (0.155) Demographic Variables Yes Yes Home Characteristics Variables Yes Yes Response Month Dummies Yes Yes R-Squared 0.00 0.11 0.00 0.11 Number of Homes 572 501 572 501 Change in Log Residents Per Worker Hour (5) (6) (7) (8) Initial Proportion Paid Less Than Minimum Wage 0.103 0.068 (0.044) (0.047) Initial Wage Gap 0.334 0.111 (0.275) (0.273) Demographic Variables Yes Yes Home Characteristics Variables Yes Yes Response Month Dummies Yes Yes R-Squared 0.01 0.21 0.00 0.20 Number of Homes 586 514 586 514 Subjective Responses on Change in Worker Effort (9) (10) (11) (12) Initial Proportion Paid Less Than Minimum Wage 0.063 0.100 (0.183) (0.201) Initial Wage Gap 0.530 0.922 (1.085) (1.166) Demographic Variables Yes Yes Home Characteristics Variables Yes Yes Response Month Dummies Yes Yes Log-Likelihood -245.24 -210.91 -245.18 -210.72 Number of Homes 561 486 561 486

Notes: 1) As for Table 6. 2) Effort variable coded as an ordered response based on answers to the question “Has the minimum wage had an impact on work effort in your business? No/Yes – Decrease/Yes-Increase”. It is ordered from 0 (decrease), 1 (no change), to 2 (increase). Ordered probit coefficients (and associated standard errors) reported for this variable.

32 Figure 1a The Relationship between Wage Growth and Initial Wages: 1998/99

Figure 1b The Relationship between Wage Growth and Initial Wages: 1992/93

33 Figure 2a The Relationship between the Change in Log Total Employment and Initial Proportion Low-Paid

Figure 2b The Relationship between the Change in Log Total Employment and Initial Wage Gap

34 Figure 3a The Relationship between the Change in Log Total Hours and Initial Proportion Low-Paid

Figure 3b The Relationship between the Change in Log Total Hours and Initial Wage Gap

35 Appendix