Crime and the Minimum Wage 1

Transcription

1 Crime and the Minimum Wage 1 Kirstine Hansen * and Stephen Machin ** August 2001 December Revised * Department of Sociology, London School of Economics ** Department of Economics, University College London and Centre for Economic Performance, London School of Economics Abstract This paper considers the connection between crime and the labour market in a different way to existing work. We focus on a situation where the introduction of a minimum wage floor to a labour market previously unregulated by minimum wage legislation provides substantial pay increases for low wage workers. We argue that this has the potential to alter peoples incentives to participate in crime. We formulate empirical tests, based upon area-level data in England and Wales, which look at what happened to crime rates before and after the introduction of the national minimum wage to the UK labour market in April The minimum wage introduction yielded sizable pay increases to low wage workers. Comparing police force area-level crime rates before and after the minimum wage introduction produces evidence in line with the notion that changing economic incentives for low wage workers can influence crime. 1 We would like to thank Nigel Beaumont, Judith Cotton and David Povey at the Home Office for kindly providing us with some of the data we use in this paper. We would like to thank David Downes, Richard Harries, Marco Manacorda, Steve Pischke, Paul Rock, Jonathan Wadsworth and participants in an LSE criminology seminar, the MIT labor lunch, the Centre for Economic Performance labour markets workshop, the LSE research laboratory opening conference, the British Society of Criminology conference at Leicester and the American Society of Criminology meetings at San Francisco for a number of helpful comments and suggestions. The Editors of this issue of the journal (Tim Hope, Susanne Karstedt and Alan Trickett) also provided us with helpful comments that improved the paper. 0

2 1. Introduction The way in which the volume of criminal activity moves over time, and what factors lie behind its evolution, has been an important research and public policy question for many years. Some of the academic research on this subject studies the role of government in affecting crime (e.g. the way in which changes to the criminal justice system and its governance mechanisms impact upon crime). Other work tries to identify why crime rates fluctuate and why different kinds of individuals are more or less likely to participate in crime. In this paper we are principally interested in issues of the latter kind. In particular we consider the way in which one possible determinant of criminal behaviour, the state of the labour market, impinges upon crime. We approach this question in a rather different way to existing research on crime and the labour market. We study what happened to crime before and after a big regulatory change was made to the UK labour market, namely when a National Minimum Wage (NMW) was introduced in April 1999 (previously there had been no minimum wage in operation). If labour market conditions are related in an important way to crime, or individual s propensities to commit criminal acts are altered by changing labour market opportunities, then one may well see changes in crime occur in the time period surrounding minimum wage introduction. In this paper we test this hypothesis using police force area level data from England and Wales. We start the paper by setting the scene in terms of a review of the relevant accumulated research literature on crime and the labour market. We then move on to consider theoretical arguments as to why a link between crime and the measure of the labour market we focus on in this paper, low wages, might exist. This theoretical discussion is very much lined up to motivate the test of the relation between crime and low wages that we implement in the empirical part of the paper. 1

3 How can one test for a link between crime and low wages? We look at crime and wages but, as already noted above, in a rather different way to that of earlier work. If one thinks that differential wage opportunities matter for crime then presumably the best way of testing for the existence of a crime-wage link is to look at a situation where people on the margins of criminal participation receive a (potentially large) wage increase. Such a situation is clearly offered when a binding minimum wage floor is introduced to a labour market that previously was not regulated by minimum wage legislation. We report a set of findings which rest well with the notion that giving low wage workers sizable wage increases displays an association with crime rates. Changes in crime rates before and after the minimum wage introduction in April 1999 are seen to be lower in areas with more workers affected by the introduction of the NMW. Perhaps not surprisingly, the negative association is seen to be much stronger statistically for property and vehicle crimes than for violent crimes. Moreover, when we look back and benchmark against periods before the minimum wage introduction we find the negative association between crime and the incidence of low wages to be much stronger in the period surrounding the minimum wage introduction. In fact there is a weak negative association between changes in crime and low pay in the earlier non-minimum wage period (between 1995 and 1998), and a very strong negative one in the year around minimum wage introduction. As such the introduction of the minimum wage to the UK labour market does seem to have been associated with less crime in low wage vis-à-vis high wage areas. 2. Empirical Work On Crime And The Labour Market Early empirical work in this area tended to focus heavily on links between crime and unemployment. Surveys of this work by Freeman (1999), Box (1987) and Chiricos 2

4 (1987) report that the relationship between crime and unemployment appears fragile at best. Some studies have detected a positive relationship between crime and unemployment (Land, McCall and Cohen 1990; Levitt 1996, 1998), but this is often more easily found in studies using individual longitudinal data (see, for example, Thornberry and Christenson 1984; West 1982 for the UK), The same is true if specific 2, rather than aggregate, unemployment rates are examined. However, other studies have found that, in data where there exists a statistically significant unconditional correlation between crime and unemployment, once other variables are taken into account the relationship between crime and unemployment disappears (examples are Butcher and Piehl 1998 for the US and Machin and Meghir 1999 for England and Wales). Even stronger than this, others have found there to be no relationship between crime and unemployment at all (Cullen and Levitt 1999). This weak pattern of results is not so surprising when one realises that there are a number of conceptual reasons why unemployment may not be the most appropriate labour market variable to examine in relation to crime. Because criminal participation is unlikely to be something that most individuals enter into lightly crime may well be more responsive to long term labour market measures than to short run ones such as contemporaneous unemployment (Gould et al, 2002). Indeed, there is a much larger body of individuals who, although in employment, are in insecure low paid low skill jobs, or in part time or temporary work, who are economically and socially marginalized. Moreover, by the very fact that they are employed and socially connected these people may be in a 2 Usually the unemployment rate of young males in explaining crime amongst young men (as in Freeman and Rogers 1999 and Allan and Steffensmeier 1989 for the US and Reilly and Witt 1996 for the UK). 3

5 better position to commit crimes than the unemployed (see Box 1987; Fagan and Freeman 1999; Grogger 1998). 3 Because of this a number of studies have looked at broader measures of crime and the labour market. Sampson and Laub (1993), using the 1939 Boston Cohort, found that job stability was negatively related to subsequent criminal behaviour. Crutchfield (1989), looking specifically at violent offences, found that labour instability was a significant predictor of overall violence, murder, assault and robbery. The link between job stability and crime has also been highlighted in UK research by West and Farrington (1977) and Farrington (1986). Along the same lines, Allan and Steffensmeier (1989) found the quality of work is important in relation to crime, and for young adults there is a strong association between individuals who work less hours than they would like to and crime. Hale (1998), in his UK study, found that changes in the structure of employment are related to crime, in particular shifts from manufacturing to the service sector, increasing part time and temporary jobs and changes between male and female employment. Similarly, and again based on UK data, Farrington et al (1986) found that individuals were more likely to offend if they worked in low status jobs. Related to these studies a number of researchers have looked at the role of economic incentives. The US evidence of Fowles and Merva (1996) and Hsieh and Pugh (1983) found poverty to be positively related to crime (the latter looked at violent crimes only). Other US studies link the rise in crime to widening wage inequality, which has been witnessed since the 1970s as a result of a decline in both relative and absolute wages at the bottom end of the market (Fowles and Merva 1996; Blau and Blau 1982; Hsieh and 3 In the 1980 wave of the National Longitudinal Study of Youths (NLSY) over half of those working reported that they had committed some crime and one fifth of those working had committed at least 1 4

6 Pugh 1983). Similarly, Witt, Clarke and Fielding (1999) have looked at police force area data in England and Wales from 1988 to 1996, finding that wage inequality changes are positively correlated with changes in crime. 4 A more recent body of work has, by looking at the wage rates of low skilled workers, concentrated on those at the bottom of the wage structure rather than looking at the gap between the top and the bottom of the wage or income distribution. Gould et al (2002) look at the relationship between changes in crime and changes in wages across areas in the US between 1979 and 1995 and report that the falling wages of unskilled men in this period led to a rise in burglary of nearly 14%, a rise in larceny/theft of around 7%, a 9% increase in aggravated assault and an 18% rise in robbery. From data on the police force areas of England and Wales between the mid 1970s and mid 1990s, Machin and Meghir (1999) looked at cross-area changes in crime in relation to changes in the 25 th percentile of the area wage distribution. They found a negative correlation between the types of crime they examine (theft and handling, burglary, vehicle crime and total property crime) and low wages, even after controlling for other variables including demographic change and measures of deterrence. Finally, Grogger (1998) uses data from the US National Longitudinal Survey of Youth to look at the relationship between wages and property crimes for young people. He reports results which show falling real wages not only offer an explanation of the rise in youth crime in the 1970s and 1980s but also of the differences in criminal involvement between age and ethnic groups. These latter findings are clearly in line with the idea that economic incentives are important for crime. Moreover, they also suggest that wage measures, especially income producing crime (Fagan and Freeman, 1999; Grogger, 1998). In Fagan s (1992) study more than 25% of drug dealers were also working. 4 A smaller body of work has looked at other measures of economic activity. For example, Witt and Witte (2000) consider the relation between crime and female labour supply, reporting results based on US time series showing common trends in crime and female labour force participation. 5

7 measures for workers towards the lower end of the wage distribution, may provide better measures of the state of the labour market for people on the margins of crime than unemployment. We also look at crime and wages in this paper, but by adopting a different methodological approach compared to other work. We consider what happened to crime before and after the introduction of the National Minimum Wage (NMW) to the UK labour market in April This provides a good testing ground for looking at the impact of a wage change for people deciding whether to participate in crime as the wage increases received by low wage workers were sizable. Metcalf (1999) estimates that about 2 million workers would receive wage gains from the imposition of the NMW. Moreover, the average wage gain for workers paid less than the NMW of 3.60 per hour ( 3.00 for year olds) before its introduction was estimated to be of the order of 30 percent. 3. Why Should There Be A Link Between Crime and Low Wages? Theoretically there are a number of reasons for thinking that low wages should be related to crime and how the introduction of a minimum wage would affect this relationship. Firstly, simple choice theoretic models of crime (e.g. Becker 1968 or Ehrlich 1973) formulate that individuals have a choice between crime and work, or more generally they choose to allocate their time across crime-work space. These decisions are a function of a number of factors, including expected earnings from crime, expected earnings from the labour market, and perceptions of the severity of the punishment if one gets caught. Seen as a simple work/crime decision this explains why people with no work may decide to partake in crime. But on a more complex level this can also shed light on how individuals who are employed may also decide to commit crimes and the extent to which they allocate their time between work and crime (for, as already noted above, we 6

8 know that many people do both). Thus, an increase in legal wages brought about by the introduction of the national minimum wage should reduce the incentive to participate in illegal activities thus bringing the crime rate down. Also by raising wages workers now have more to lose by getting caught, which should also act to discourage criminal activity and reduce crime. 5 Of course, these simple choice based models of crime have themselves been called into serious question for their relatively simplistic assumptions about criminal behaviour. But other theoretical approaches generate a relation between crime and low wages. Strain theory, for example, predicts that people with low wages are likely to suffer financial hardship, sometimes in similar ways to those who are unemployed (Merton 1957; Cohen 1955; Cloward and Ohlin 1960). This financial strain may well encourage individuals to commit acquisitive crimes either for themselves or to sell for cash in order to obtain the goods they cannot afford. Financial strain may also lead to feelings of frustration or anger, which may well manifest themselves in violence. Thus, we would expect low wages to be associated with relatively high rates of both property and violent crimes. An increase in wages brought about by the introduction of the minimum wage may ease financial strain, which may well lead to a reduction of both types of crime. Over and above the financial strain faced by the low paid, their situation is worsened by the fact that most of them will be in jobs where promotion or career advancement is hard (if not impossible). Thus their opportunities to have money and status may be blocked. Unable to achieve success legally these individuals may be forced to resort to illegal methods. Moreover, such individuals are more likely to live in poorer areas where it is possible illegal opportunities to achieve goals are more abundant than 5 It is something of an unanswered question as to whether the economic model is only relevant to nonviolent crimes for which monetary incentives may alter behaviour or whether it can also be extended to the 7

9 legal opportunities (Cloward 1959). In these areas there may also be peer pressure to get involved in crime (Cloward and Ohlin 1960) or increased opportunity for learning criminal behaviour through associations and interactions with other criminals (Sutherland 1924; Akers 1977). As noted above the introduction of the minimum wage in the UK raised low wage workers wages by a sizable amount and, in doing so, may well have reduced the need to turn to crime to achieve success or status. It may even eventually give people the power to migrate to better areas where there is less criminal peer pressure, but this would be much more long term. Finally, as employment is one of the major institutions through which social bonds are formed between individuals and society, social control theory predicts that employees with low paid jobs may be less attached to society (Hirshi 1969; Box 1971). Thus, crime rates may be high amongst those in low paid jobs as social controls will be less able to deter them from breaking the law. If wages are increased due to the implementation of the National Minimum Wage, this may act as a mechanism for strengthening the social bonds between the low paid and society. More tied to society and therefore more constrained by social controls this group will be less likely to commit crimes. Thus, there are a number of potential explanations as to why the introduction of the minimum wage may influence crime and help us try and pin down a link between crime and the low wage labour market. It is on the basis of these ideas that we developed our hypothesis that the introduction of the minimum wage may have the potential to reduce crime. We next turn to the methodology we utilize to formulate tests of this hypothesis. case of violent crime. Various researchers have taken different stances upon this (though see Grogger 2000 for an interesting attempt to apply the economic model to violent crime). 8

10 4. Methodology Our empirical methodology involves comparing what happened to crime rates before and after the minimum wage introduction in the police force areas of England and Wales. We relate changes in various crime rates before and after minimum wage introduction to the initial proportion of low wage workers (i.e. those paid less than the minimum wage prior to its introduction) in those areas. This is much the same methodology as that adopted in some US work (notably Card, 1992) to look at the relationship between employment and minimum wages in US states before and after the large federal minimum wage increase of April Identification of the minimum wage effect comes from the fact that there are more low wage workers in some areas than other and therefore the minimum wage should be thought of having more of an effect there than in areas where there are fewer low wage employees. As Card (1992 p.22) puts it: From an evaluation perspective a uniform minimum wage is an underappreciated asset. A rise in the federal minimum wage will typically affect a larger fraction of workers in some states than in others. This variation provides a simple natural experiment for measuring the effect of legislated wage floors, with a treatment effect that varies across states depending on the fraction of workers initially earning less than the new minimum. This approach to looking at crime and the labour market is founded upon the idea that a sizable change in labour market opportunities has the potential to alter an individual s incentive to participate in crime. The theoretical approaches outlined in the previous section highlight that an individual s propensity to commit crime, say C 6, will depend on a number of factors such that, in general terms, C = C(W c, p, S, W, Z) where 6 C may reflect a discrete 0-1 choice between work and crime or could reflect the allocation of hours per week between formal labour market activity and criminal actions (see, for example, Ehrlich, 1973). As we are interested in wages and crime the latter is probably more appropriate. 9

11 W c is the earnings from a successful crime, p is the probability of being caught, S is the punishment, W is the earnings available on the legitimate job market and Z are other factors relevant for crime. According to the theoretical approaches the C(.) function depends positively on W c and negatively on p, S and W. It therefore reveals a clear trade off between perceived earnings from crime and formal labour market activities. One can aggregate the C(.) function to area-level so that C(.) becomes the area-specific crime rate (= the number of people engaging in crime divided by the population). Our empirical approach, based on looking at the differential impact of the minimum wage introduction across areas, can be thought of as providing a positive (and sizable) increase in W. As long as its impact is not offset by coincidental changes in W c, p, S or Z (which it must be said seems highly unlikely in the short time period we consider) one should see crime fall in areas where W has the potential to rise by more. The main factors in Z, the other determinants of crime, are likely to be those other factors that influence both the supply and demand for crime. In a simple supply-demand framework, the demand side can be thought of as being characterised by an inverse relation between crime and criminal earnings, while the supply side is driven by the wage and criminal justice system variables. The demand for crime is likely to be shaped by demographics (e.g. if there are more rich consumers perhaps the pickings from crime may be more lucrative) and so we also control for demographic changes over the short time period we consider. 7 So, to briefly recap, our empirical approach will be to compare changes in areaspecific crime rates before and after the introduction of the NMW in April The quasi natural experiment created by the fact that some areas have more low wage 7 These are: change in average age, change in the population share of young (<25) men, change in proportion black, change in population share with no educational qualifications, change in proportion female, change in share of public sector jobs. 10

12 workers than others will be exploited to see if the minimum wage had the potential to reduce crime in the time period surrounding the minimum wage introduction. 5. Data Crime Data The crime data we use are notifiable offences reported and recorded by the police force areas of England and Wales. Crime rates were obtained for 41 police force areas 8 in the six-month period (April-September 1999) immediately following the introduction of the NMW. For most of our analysis we compare and contrast crime in this six-month period with crime in the six-month period before (October 1998-March 1999) or with the same six-month period in the preceding year (April-September 1998). In what follows the choice of comparison group is not that important, though intuitively the analysis of the same six-month (April-September) periods across years is probably slightly more attractive. We focus upon three different crime rates: property crime (defined as burglary plus theft and handling); vehicle crime (theft of a vehicle, theft from a vehicle, aggravated vehicle taking, vehicle interference and criminal damage to a vehicle); and a measure of violent crime (violence against the person). We consider the latter, even though much of the modelling of crime and the labour market is largely concerned with the way in which altering economic opportunities affects crime and we would probably think there is more potential for this to affect non-violent crimes. 9 8 There are actually 43 police force areas in England and Wales: in our analysis we aggregate the City of London and Metropolitan police forces and the Gwent and South Wales police forces. The reason for joining together the City and Metropolitan forces is because the small number of residents living in the City produces artificially high crime rates. Gwent and South Wales have been aggregated as a result of a police force boundary change that occurred. As a result we consider 41 consistently defined areas in our empirical work. 9 Indeed, this is what Gould et al. (2002), Machin and Meghir (1999) and May (2000) find in their arealevel analyses of crime and wages. Both find strong, negative associations between non-violent crime and 11

13 One should note that use of official crime statistics may mean our analysis perhaps overlooks those crimes that are not reported to or recorded by the police, referred to as the dark figure of crime (see McDonald 2001). However, insurance requirements together with increased communication and public awareness of crime has meant that a large number of crimes do now appear in the official statistics and that a large percentage of those that do not are minor or trivial offences. Our focus on very recent time periods is very much helped by this. Moreover, because we are looking at cross-area patterns of crime change, if this dark figure of unknown crime varies randomly across the areas we are looking at then it should not bias our results. 10 Similarly the fact that most of our analysis is based upon changes over short time periods means that our results are unlikely to be contaminated by reporting biases of this kind. 11 Labour Market Data The area-level labour market data is obtained by aggregating the UK Labour Force Survey (LFS), an individual-level survey, to police force area. The LFS is a quarterly survey of around 60,000 households in Britain. The data has been set up to match the six-month crime data, as the LFS reports the month in which people are interviewed. The LFS contains information on hourly earnings (derived from separate questions on weekly wages and hours) and these are used to define our low pay variables. The NMW was introduced in April 1999 at 3.60 per hour for people aged 22 or higher, and at 3.00 per hour for those aged (inclusive), so we look at the proportion of low wages, but much less of a link with violent crime. Although others have found a relationship between violent crime and the inequality of wages (see Hsieh and Pugh, 1983 or Land et al, 1990). 10 Furthermore, in England and Wales the official statistics provide the only source of data on crimes by police force area. The British Crime Survey (which as a victim survey some argue captures, at least partially, the dark figure of crime) does not have (publicly available) information on areas. 11 Indeed, McDonald (2001) makes the very point that time series analyses may suffer from bias problems because the under-reporting of crime varies systematically with the economic cycle. Our short time period of study is a clear advantage in this regard. 12

14 workers aged 18 or higher below the appropriate level by area. To consider our key hypothesis, we are interested in whether the change in crime rates before and after minimum wage introduction were seen to vary with the initial number of low paid people. The LFS contains a great deal of information regarding demographics and job structure, which has allowed us to additionally set up a number of other variables for our analysis. One variable of particular interest is the area unemployment rate, a variable to which we devote some attention later for two reasons. First, as noted earlier, a lot of work on crime and the labour market has looked at crime-unemployment correlations. As such we will be interested in exploring whether such a correlation exists here. Second, and more important, is the possibility that the imposition of the minimum wage has an effect on unemployment and that this then impinges on crime. We discuss this in more detail later. We have also assembled other LFS variables to enter into our crime equations as, despite the fact that we consider a short time period, we do not want to confound any minimum wage effects with shifts in demographic structure. These variables, which theory and past empirical work inform us may be important when examining the correlates of crime, include variables related to age, education, race, gender and so on (they are detailed in full in the notes to the Tables and in footnote 5 above). Finally, to capture any shifts in the probability of detection we consider changes in the crime clear up rate across the relevant comparison periods. Descriptive Statistics Table I presents descriptive statistics on average wages, wage inequality and average crime rates for three groups of areas, delineated by the proportion low paid into areas with a lot of low wage workers in the period prior to minimum wage introduction 13

15 (Most Low Pay), a middle group of areas (Middle Low Pay) and the areas with the fewest low wage workers (Least Low Pay). 12 For each of these groups of areas the Table reports the mean log(hourly wage), a measure of wage inequality for the bottom half of the wage distribution (the log(hourly wage) differential) and mean crime rates (defined per 1000 population). These are reported for the three six month periods of interest, along with the change before and after the NMW introduction (with associated standard errors) calculated for the two possible six month pre-minimum wage periods. Differences in the change between areas with the most and least low pay are also reported, with the changes over time in the period surrounding minimum wage introduction given in bold typeface. The numbers in the Table show that, in terms of wages and wage dispersion, the introduction of the minimum wage operated largely as one would expect. Average wage growth was not so different across the groups of areas but wage dispersion at the bottom end of the wage distribution fell by more in low wage areas. In fact the differential rose by 3 percent in the areas with most low pay, by 2 percent in the middle group of areas and was more-or-less unchanged in the areas with the least amount of low pay. The gaps between the areas with most and least low pay are seen to be statistically significant. As such the imposition of the minimum wage seems to have significantly improved the relative earnings position of the low paid (abstracting away from any impact on jobs, an issue we return to later). Over the pre- and post-minimum wage periods average crime rates do not alter much for the non-violent crimes, with property crime rising from 30.8 to 31.1 crimes per 1000 population and vehicle crime falling from 14.5 to 14.3 crimes per Violent 12 The exact cutoffs were found by ordering the data on the proportion of workers paid less than the NMW in the year preceding its introduction. High wage areas had less then 9.7 percent of below NMW workers, 14

16 crime, on the other hand, shows more of an increase, going from 4.9 to 5.9 crimes per 1000 people. It is, however, of considerable interest that there are differential patterns of change for the different area groupings. Specifically, property and vehicle crimes decline in low pay areas, but actually increase in high pay areas compared with either one year or six months earlier. Violent crime appears to go up in all three groups, but by less in the low pay areas. There is therefore clear evidence in the Table that wage inequality fell and crime fell (or rose by less) in the areas with more low wage workers in the period surrounding minimum wage introduction. All six of the Most Low Pay Vs. Least Low Pay comparisons of changes in crime, given in bold, prove to be negative and five of the six are statistically significant. Thus the descriptive findings appear to be in line with the hypothesis that crime is likely to have fallen where the minimum wage has had more impact. This finding is further illustrated in Figure I which plots changes in the three crime rates against the initial low pay proportion across police force areas. The graphs seem to show that crime went up by less in the areas with more low paid workers in the period before minimum wage introduction. 13 This is borne out by the regression lines fitted through the data points, all of which show a negative relationship between crime change and the initial low pay proportion. We subject this finding to a more rigorous analysis in the following Section of the paper. middle wage areas between 9.7 and 11.7 percent, and low wage areas had over 11.7 percent of workers paid less than the NMW. 13 The violent crime Figure shows a large change in violent crime in the West Midlands over this time period. We checked the Home Office data and contacted the West Midland police to ensure that this is not an error in the data. We are very grateful to Dylan Harthill of the performance review section of the West Midlands police force for confirming that this is a genuine rise and not a data error. 15

17 6. Statistical Results Basic Regression Results Table II reports a set of statistical regressions that relate changes in crime to the proportion of workers paid less than the NMW in the initial period. Six sets of specifications are reported for each crime measure. In the first three columns of the Table the change is defined as the change over the six-month post-minimum wage period April- September 1999 as compared to the same six-month period in the preceding year. In the final three columns the April-September 1999 six month period is compared to October 1998-March For each pre-minimum wage benchmark group, the three reported specifications differ in terms of variables included. The first is a simple regression of the change in crime on the initial period proportion of workers paid beneath the minimum. The second adds in the demographic controls and the change in the crime clear up rate variable discussed above. The third additionally adds in the change in the unemployment rate between the pre- and post-minimum time periods. Considering first the basic specifications, a negative relationship between the three crime measures and the initial proportion low paid emerges. This is true irrespective of which benchmark period is used to fix the pre-minimum wage reference time period. This just re-confirms the raw pattern suggested by the numbers in Table I, namely that crime has gone up by less in the lower wage areas more affected by the introduction of the minimum wage. Adding in the variables measuring changes in the demographic structure 14 of areas and the change in the clear up rate pre- and post-minimum wage introduction tends to reduce the estimated coefficient on the initial period low pay proportion. This is 14 The estimated coefficients on these variables are not reported as our main concern is with the initial proportion low paid variable. It is, however, worth noting that the coefficient on the change in the clear up rate was usually negative and significant. 16

18 particularly true for the comparisons based on the same six-month April-September periods. In these models, in the property and vehicle crime equations the negative coefficient on the initial proportion remains sizable and strongly significant in statistical terms. For violent crimes the estimated coefficient is, however, driven to statistical insignificance. This is probably not that surprising: one would think that shifts in demographics are more likely to be important for violent crimes. 15 As noted above a critical question surrounding the introduction of the NMW is its likely impact on unemployment. There was much speculation on this before the introduction of the wage floor as opponents of minimum wage legislation argued that minimum wages tend to hurt those they set out to initially help as the imposition of a minimum wage prices workers out of jobs. 16 Were this to be true there would be another mechanism we would need to consider here, namely that there would be more unemployed workers who could not get jobs who may then turn to crime. In this case the minimum wage may raise crime rates. For this reason it is important that we also control for changes in unemployment that may have occurred differentially across areas, in case we are biasing the coefficient on the low pay proportion by neglecting another route in which crime may be affected by the labour market. The final specifications therefore add in changes in the log(unemployment rate). In almost all cases this has little impact. There is no statistically significant association between changes in crime and changes in unemployment, and the estimated coefficient 15 See Land et al (1990) for a discussion of the importance of demographic characteristics in predicting violent crime. 16 Of course there has been a lot of (sometimes acrimonious) debate about the economic effects of minimum wages, especially their impact on unemployment. This is not of major concern to us here, but see Card and Krueger s (1995) book and the symposium in the Industrial and Labor Relations Review of July 1995 for a flavour of the strong views held in the US debate or Metcalf s (1999) discussion of the UK debate. 17

19 on the low paid proportion does not shift much at all on its inclusion. 17 The only possible exception to this is the change in the violent crime rate for the April-September year-onyear comparison. Here the coefficient on the unemployment rate is estimated to be positive and is right on the margins of significance at the 10 percent level with a p-value of.106. Using Other Wage Measures To Gauge The Initial Proportion Variable The results so far point to a negative association between changes in property and vehicle crime rates over the period of minimum wage introduction and the incidence of low pay. We take this as evidence that shifts in the nature of low wage labour markets have the potential to affect crime. However, there are some issues that should be addressed about the wage variables we use to compute the initial period low pay proportion. The first is that so far we have only considered a simple head count measure of low pay. How far workers wages are beneath the minimum may also differ across areas so one may wish to look at wage gap measures as well. Second we have thus far looked at all workers in the Labour Force Survey, but men commit most crime. Further, one may think that it is workers in dead end jobs going nowhere, or who are stuck in certain permanent low paid jobs, that may be more movable on the crime-work choice. For these reasons we have also estimated regression models that refine the nature of the initial low pay variable. The first three results columns in Table III use a wage gap measure of low pay, in place of the headcount measure considered earlier. This wage gap basically measures what share of the area wage bill would need to be paid to bring the 17 This is not a consequence of looking at unemployment rates (see Chamlin and Cochran, 2000, who argue that use of conventional unemployment rates can obscure crime unemployment relations due to a priori measurement choices and that use of other measures may uncover a link with crime). If, instead, one entered the change in the employment rate or the inactivity rate in the area into the equations similar results emerged. For example, for the change in property crimes, the estimated coefficient (standard error) on the initial low pay proportion was (.289) if the change in the employment rate was added (the employment rate itself had a coefficient with an associated standard error of.832); if the area-specific 18

20 low wage workers in the area up to the minimum. The pattern of results using the headcount in Table II is very much reconfirmed for the wage gap measure specifications in the Table. 18 We have also considered a wage measure for specific low wage workers across areas. The results using this are given in the remainder of Table III which uses an area initial low pay measure defined for men working in low skill occupations (defined as those with an average wage beneath the 25 th percentile of the male wage distribution). We think this is useful as it is low wage men who we would probably think are those on the margins of crime and who, if the hypothesis advanced about big wage boosts lowering crime are correct, we should therefore focus upon. The results very much confirm that property and vehicle crimes went up by less in areas with more low skill men who were paid beneath the minimum in the period before it was introduced. Again there is little link with violent crime. We view these findings as strongly supportive of the notion that the minimum wage gave a sizable boost to people on the margins of crime thereby shifting them away from crime. The final issue to do with the wage measures concerns possible measurement errors. It may be that measurement errors in hourly earnings taken from the Labour Force Survey do not necessarily produce accurate measures of the size of the area-specific low wage labour market. Of course if such measurement errors do not vary systematically by area then this would not be a concern. We have investigated this question by computing measures of the area low pay proportion from another data source, the employer reported New Earnings Survey, which is a large sample (1 percent of the working population) of change in the inactivity rate was included the coefficient (standard error) on the low pay proportion was (.281) and the coefficient (standard error) on the change in the inactivity rate was.098 (.202). 18 Notice that the scale of the coefficients is sizable. This is because the average wage bill share needed to raise workers to the minimum is a fairly small number (the mean is.0085 when expressed as a proportion, or 0.85 percent of the wage bill). 19

21 workers carried out in April each year. There is a concern about this data source, namely that it does tend to undersample low wage workers (as one needs to have weekly earnings above the National Insurance lower earnings limit to be in the survey). Nevertheless we also computed the low pay proportion for April 1998 from these data. The measure is strongly correlated with the LFS measure (correlation coefficient =.89). This gives us confidence that our LFS based measure is likely to be a good measure of the state of the low wage labour market in the areas we consider. As we now have two measures of the initial low pay proportion this opens up the possibility of using instrumental variable techniques to assess whether measurement error is a problem. The following Table shows what happens when we instrument the LFS low pay proportion using the NES low pay proportion as an instrumental variable (IV). The IV and OLS results are similar but, if anything, there appears to be a stronger link with changes in crime, for all three crime categories, when we use the IV techniques in an attempt to purge measurement error. OLS IV Change in Log(Property Crime Rate) LFS Proportion Paid Less Than The Minimum Wage in Period (.307)*** (.328)*** Change in Log(Vehicle Crime Rate) LFS Proportion Paid Less Than The Minimum Wage in Period (.316)*** (.431)*** Change in Log(Violent Crime Rate) LFS Proportion Paid Less Than The Minimum Wage in Period (.655)* (.821)** Notes: Estimated coefficients (standard errors) on LFS initial proportion variables based on specification using (April 1999 September 1999) (April 1998 September 1998) change in crime models as reported in Table II. From full models containing all controls and the change in log(unemployment rate). NES initial low pay wage survey proportion as instrument for LFS initial proportion. *** denotes statistically significant at 1% significance level or better, ** 5%, * 10%. Benchmarking Against Earlier Time Periods A potentially very important concern that emerges from considering the results presented so far is whether we are really identifying any change resulting from studying the minimum wage period. For example, it might be that crime rates have not been rising 20

22 as fast in low wage areas in time periods when the minimum wage was not present. Were this to be the case our results may be spurious. We have explored this possibility by looking at econometric models specified in the same way as those considered to date for earlier time periods. In the simplest specification reported before (in column (1) of Table II) the regression relationship between changes in property crime and the proportion below the minimum wage in the initial period for the periods around minimum wage introduction was as follows (standard error in parentheses): Change in Log(Property Crime) = Proportion Paid Less Than The Minimum Wage in Period 1 (.349) For earlier periods of change [(April 1996 September 1996) (April 1995 September 1995)] and [(March 1998 October 1997) (March 1997 October 1996)] 19 the regression relationship is: Change in Log(Property Crime) = Proportion Paid Less Than The Minimum Wage in Period 1 (.181) So, in this earlier time period there is a (weak) negative association between changes in property crime and the initial low pay proportion, but it is nowhere near as marked as around the minimum wage introduction period. Indeed, the regression line fit through the points has a slope four times as large (in absolute terms) in the period surrounding minimum wage introduction. 20 This shows a tilting of the crime low pay 19 This sample period is dictated by the availability of the county-level data in the LFS which does not allow us to go back any earlier. Notice also that two possible comparison periods are left out. This is because the Home Office changed their definitions for data collection and recording of crime from the police force areas of England and Wales in April Any changes that span this period therefore had to be dropped from the analysis. So we omitted the [(March 1999 October 1998) (March 1998 October 1997)] and [(April 1998 September 1998) (April 1997 September 1997)] time periods. For more on the nature of the recording change (which affected violent crime definitions by substantially more than property crime definitions) see Home Office (1999). 20 Of course, as the periods not surrounding minimum wage introduction are pooled, the regression slope is the average slope across all periods. However, if each period is taken individually, the slope is always markedly steeper in the period surrounding minimum wage introduction. 21

23 relationship such that the relationship between changes in crime and low pay becomes stronger in the period when the minimum wage was introduced. A more formal way of thinking about this is to explicitly couch the modelling approach in a difference-in-differences framework. Our analysis covers two distinct time periods, one where the minimum wage raised wages by more in low wage areas (which we can call period M), and one where no minimum wage legislation was in place (period NM). We can therefore benchmark our measures of the change in crime from the period surrounding minimum wage introduction C M against our measure of the change in crime from the non-minimum wage period C NM. Looking at the relationship between C M - C NM and the initial period low paid proportion then provides a difference-indifferences estimator of the change in crime-low wage relation. For the case of changes in property crime the estimator is simply the gap between the coefficients on the initial low pay proportion variable across the two specifications (for the example considered this is {-.276} = -.750). As column (1) of the upper panel of Table IV shows, despite this being a stiff test of our hypothesis, this differencein-difference estimate remains statistically significant and shows a marked shift in the relationship between changes in property crime and the initial low pay proportion when benchmarked against earlier time periods. The rest of the upper panel of Table IV is devoted to presenting more detailed specifications for property crimes and the middle and lower panel of the Table report the same models for the other crime measures. In all cases there seems to have been a shift in the relationship between changes in crime and the initial low pay proportion across the time periods considered as the effects are estimated to be much more negative in the 22

24 period surrounding minimum wage introduction than in the periods before. 21 This is true for all three specifications of property and vehicle crime equations, though not for violent crime. 22 The nature of the data, on the same areas followed through time, means that one can also adopt an even more stringent test by including area-specific trends in the estimating equation. The final column of the Table therefore additionally includes 41 area trend variables. The results are very robust to this. The estimated coefficient on the minimum wage variable is slightly reduced in absolute terms for property and vehicle crimes and, of course as one would expect, the standard errors rise. But the coefficient remains significant at better than the 2 percent level (p-value =.014) for property crimes and at better than the 12 percent level (p-value =.114) for vehicle crimes. The violent crime results, probably not surprisingly, become extremely imprecise as the standard error almost doubles. But the fact that the non-violent crime results remain very resilient to this strong test seems very reassuring. Our reading is therefore that our results are strongly supportive of the idea that changes in non-violent crime were significantly lower in areas where workers wages were more affected by the introduction of the NMW. Discussion of the Size of the Estimated Link Between Crime and Minimum Wages An important question concerns the magnitude of the estimated links between the minimum wage introduction and crime. Our results clearly show that areas that were likely to have been more affected by minimum wage introduction saw lower changes in crime in the period surrounding the policy introduction. One could think about assessing 21 If the comparison is restricted to April-September comparisons the same pattern of a statistically significant negative relationship between changes in property and vehicle crime and low pay incidence in the minimum wage period remains. For the column (3) models the estimated coefficients (standard errors) on the initial low pay proportion were: property crime (.330); vehicle crime (.333). Again the relationship with changes in violent crime was statistically insignificant (coefficient = -.766, standard error =.753). 23

25 the magnitude of this relationship in a number of ways. One option is to compute the elasticity of crime changes with respect to the initial low pay proportion so as gauge how sensitive crime changes were to variations in the proportion of low wage workers across areas. As the dependent variable is the change in the log(crime rate) and the independent variable of interest is the level of the initial low pay proportion, then this elasticity can be computed as the estimated coefficient on the low pay variable multiplied by its mean. 23 For property crimes the range of estimates of the coefficient on the initial low pay proportion variable is from around -.65 in the most stringent difference-in-difference estimates up to around -.9 in the earlier specifications with control variables included. The mean of the initial low pay proportion is.10 so this gives an elasticity in the range of around to So an area with a 50 percent higher initial low pay proportion (say.15 rather than.10) is predicted to have a change in property crime around 3.3 to 4.5 percentage points lower in the period surrounding minimum wage introduction. The comparable numbers for vehicle crime are much the same as the estimated coefficients are in the -.6 to -.9 range, corresponding to changes in vehicle crime being around 3 to 4.5 percentage points lower in an area with a 50 percent higher initial low pay proportion. These predicted changes would shift an area something around one-half to two-thirds of a standard deviation down the change in property crime distribution (which has a standard deviation of 6.8 percentage points in the period surrounding minimum wage introduction), and a little less down the change in vehicle crime distribution (whose standard deviation is slightly higher at 8.2 percentage points). 22 As in the simple cross-section (single difference) models when one uses the initial share of the low paid in the total wage bill or the low skill males initial low pay proportion instead of the head count initial low pay proportion measure the pattern of estimated effects is very similar. 23 Denote the change in log(crime rate) as logc and the initial low pay proportion as L, and the estimated regression as logc = βl + other variables, then the elasticity is (dc/dl).(l/c) = (dlogc/dl).l = βl. 24

26 7. Concluding Remarks In this paper we consider possible links between crime and the labour market by using the introduction of the UK National Minimum Wage to the UK as a means of asking what happens to crime when the wages of low wage workers are given a sizable boost. Our results support the idea that crime and the low wage labour market are significantly related. In an empirical analysis across the police force areas of England and Wales, we find that in the period surrounding minimum wage introduction changes in crime were markedly lower in areas with more low paid workers before the imposition of the minimum wage. A variety of different empirical approaches confirm this finding. For example, the estimated effects are unaffected by the possible unemployment effects of the minimum wage. Nor are we picking up a relationship that was operating in the same way in earlier periods when a minimum wage was not introduced. These findings provide an interesting counter-angle to other work in this area which is increasingly tending to find that wage opportunities matter for crime. By adopting a rather different methodology we also reach this conclusion. Furthermore, it seems that the introduction of the minimum wage to the UK labour market went hand in hand with reductions in crime in areas with low wage workers. Of course, unless the minimum wage is increased by a sizable amount in future, then this is very much likely to be a one-off change associated with the sizable wage increases that workers received in the period when the minimum wage was introduced. 25

27 REFERENCES Akers, Ronald L Deviant Behaviour. Wadsworth:Belmont, California. Allan, Emilie Andersen and Darrell J. Steffensmeier Youth Underemployment and Property Crime: Differential Effects of Job Availability and Job Quality on Juvenile and Young Adult Arrest Rates. American Sociological Review 54: Becker, Gary Crime and punishment: An economic approach. Journal of Political Economy 76: Box, Steven Deviance, Reality and Society. London: The Macmillan Press. Box, Steven Recession, Crime and Punishment. London: The Macmillan Press. Butcher, Kristen F. and Anne Morrison Piehl Cross City Evidence on the Relationship between Immigration and Crime Journal of Policy Analysis and Management, 17: Card, David Using Regional Variation in Wages to Measure the Effects of the Federal Minimum Wage Industrial and Labor Relations Review 46: Card, David and Alan Krueger Myth and Measurement: The New Economics of the Minimum Wage. Princeton: Princeton University Press. Chamlin, Mitchell and John Cochran Unemployment, Economic Theory and Property Crime: A Note on Measurement Journal of Quantitative Criminology 16: Chiricos, Theodore Rates of Crime and Unemployment: An Analysis of Aggregate Research Evidence. Social Problems 34: Cloward, Richard A Illegitimate Means, Anomie and Deviant Behaviour. American Sociological Review. 24: Cloward, Richard A and Lloyd E. Ohlin Delinquency and Opportunity. New York: Free Press. Cohen, Albert K Delinquent Boys. New York:Macmillan. Crutchfield, Robert Labour Stratification and Violent Crime Social Forces 68: Cullen, Julie Berry and Steven D Levitt Crime, Urban Flight and the Consequences for Cities, Working Paper No 5737 (NBER, Cambridge MA). Ehrlich, Isaac Participation in Illegitimate Activities: A Theoretical and Empirical Investigation. Journal of Political Economy 81:

28 Farrington, David P, Bernard Gallagher, Lynda Morely, Raymond J. St. Ledger and Donald J. West Unemployment, School Leaving and Crime. British Journal of Criminology 26: Farrington, David P Stepping Stones to Adult Criminal Careers In D. Olweus, J Block and MR Yarrow (eds), Development of Antisocial and Prosocial Behaviour. New York: Academic Press. Fagan, Jeffrey Drug Selling and Illicit Income in Distressed Neighbourhoods: the Economic Lives of Street-Level Drug Users and Dealers, in George E Peterson and Adele V Harrell (eds) Drugs, Crime and Social Isolation: Barriers to Urban Opportunity. Urban Institute Press: Washington DC. Fagan, Jeffrey and Richard Freeman Crime, Work and Unemployment, in M. Tonry (ed.) Crime and Justice: A Review of Research, Volume 25, University of Chicago Press: Chicago. Freeman, Richard The Economics of Crime in O. Ashenfelter and D. Card (eds) Handbook of Labor Economics, Elsevier Science: Amsterdam. Freeman, Richard B, and William M Rodgers III Area Economic Conditions and Labor Market Outcomes of Young Men in the 1990s Expansion. Working Paper 7073 (NBER, Cambridge MA). Fowles, Richard and Mary Merva Wage Inequality and Criminal Activity: An Extreme Bounds Analysis For The United States, Criminology 34: Gould, Eric, Bruce Weinberg and David Mustard Crime Rates and Local Labor Market Opportunities in the United States: Review of Economics and Statistics, forthcoming. Grogger, Jeffrey Market Wages and Youth Crime. Journal of Labor Economics 16: Grogger, Jeffrey An Economic Model of Recent Trends in Violent Crime, in Alfred Blumstein and Joel Wallman (eds.) The Crime Drop in America, Cambridge University Press. Hale, Chris Crime and the Business Cycle in Post War Britain Revisited British Journal of Criminology, 38: Hirshi, Travis Cause of Delinquency. California University Press. Home Office Recorded Crime Statistics England and Wales, April 1998 to March 1999 Home Office Statistical Bulletin 18/99. Hseigh, Ching-Chi and MD Pugh Poverty, Income Inequality and Violent Crime: A Meta-Analysis of Recent Aggregate Data Studies. Criminal Justice Review 18:

29 Land, Kenneth C, Patricia L McCall and Lawrence E. Cohen Structural Covariates of Homicide Rates: Are There Any Invariances across Time and Social Space? American Journal of Sociology 95: Levitt, Steven D The Effect of Prison Population Size on Crime Rates: Evidence from Prison Overcrowding Litigation Quarterly Journal of Economics 111: Juvenile Crime and Punishment Journal of Political Economy, 106: Machin, Stephen and Costas Meghir Crime and economic incentives. University College London mimeo. May, Sean Wages and youth arrests. Chapter 1 of MIT PhD thesis Essays on the Economics of Crime and Econometric Methodology. McDonald, Ziggy Revisiting the Dark Figure A Microeconometric Analysis of the Under-reporting of Property Crime and Its Implications. British Journal of Criminology, 41: Merton, Robert K Social Theory and Social Structure. New York: Free Press. Metcalf, David The Low Pay Commission And The National Minimum Wage. Economic Journal Features 109: F46-F66. Reilly Barry and Robert Witt Crime, Deterrence and Unemployment in England and Wales: An Empirical Analysis. Bulletin of Economic Research 48: Sampson, Robert J and John H Laub Crime in the Making: Path Ways and Turning Points Through Life. Harvard University Press: Cambridge MA. Sutherland, Edwin H Criminology. Philadelphia: Lippincott. Thornberry, Terence and RL Christenson Unemployment and Criminal Involvement: An Investigation of Reciprocal Causal Structures. American Sociological Review 56: West, Donald J and David P Farrington The Delinquent Way of Life. London: Heinemann. West, Donald J Delinquency: Its Roots, Careers and Prospects. London: Heinemann. Witt, Robert, Alan Clarke and Nigel Fielding Crime and Economic Activity, British Journal of Criminology 39: Witt, Robert and Ann Witte Crime, Prison and Female Labor Supply, Journal of Quantitative Criminology 16:

30 Figure I: Changes in Property, Vehicle and Violent Crime And The Proportion Low Paid, Between April 1998 September 1998 And April 1999-September 1999 Change log(pc) = Prop Below Min One Year Before Change log(vc) = Prop Below Min One Year Before.2.2 BEDFORDS WEST MID Change in Log(Property Crime Rate LONDON THAMES V WILTSHIR ESSEX SURREY GLOUCEST SUSSEX WARWICKS CAMBRIDG LINCOLNS AVON AND MERSEYSI GWENT SW NOTTINGH DERBYSHI SUFFOLK NORTH WA NORFOLK SOUTH YO HERTFORD WEST MER GREATER NORTHAMP NORTH YO WEST YOR DORSET STAFFORD HAMPSHIR LEICESTE CHESHIRE DURHAM CUMBRIA HUMBERSI KENT WEST MID LANCASHI NORTHUMB DEVON AN CLEVELAN DYFED-PO Change in Log(Vehicle Crime Rate WILTSHIR BEDFORDS LONDON SUFFOLK.1 THAMES V MERSEYSI NORTH WA SUSSEX GWENT SW SURREY WARWICKS ESSEX CAMBRIDG GREATER LINCOLNS 0 AVON AND NORTHAMP NORFOLK HERTFORD DERBYSHI WEST STAFFORD MER HAMPSHIR NORTH YO SOUTH YO NOTTINGH GLOUCEST KENT CHESHIRE LANCASHI CUMBRIA LEICESTE HUMBERSI -.1 WEST YOR DORSET NORTHUMB DURHAM DEVON AN DYFED-PO CLEVELAN Prop Below Min One Year Before Prop Below Min One Year Before Change log(vioc) = Prop Below Min One Year Before.5 WEST MID Change in Log(Violent Crime Rate HERTFORD NORTHAMP GLOUCEST THAMES V SURREY NORTH WA LONDON ESSEX SUFFOLK WEST MER NOTTINGH NORFOLK HAMPSHIR MERSEYSI SOUTH YO SUSSEX LEICESTE DORSET DERBYSHI STAFFORD DURHAM WILTSHIR BEDFORDS GREATER GWENT SW CAMBRIDG NORTH YO WARWICKS WEST YOR KENT AVON AND CHESHIRE LANCASHI NORTHUMB CUMBRIA LINCOLNS HUMBERSI DEVON AN CLEVELAN DYFED-PO Prop Below Min One Year Before Notes: Population weighted regression line fit through data points 29

31 Table I: Mean Log Wages, Inequality and Crime Rates Broken Down Across Areas By The Number of Low Wage Workers in the Year Before Minimum Wage Introduction (1) (2) (3) (4) (5) October 1998 March 1999 April 1998 September 1998 April 1999 September 1999 Change Relative to Same Six Month Period A Year Earlier (3) (1) Change Relative to Previous Six Month Period (3) (2) Log(Hourly Earnings) Most Low Pay (.01)***.04 (.01)*** Middle Low Pay (.01)***.02 (.01)** Least Low Pay (.01)***.03 (.02)** Most Low Pay -.25 (.04)*** -.27 (.03)*** -.25 (.04)***.00 (.01).01 (.01) Least Low Pay Log(Hourly Wage) Differential Most Low Pay (.01)***.03 (.01)** Middle Low Pay (.01)**.02 (.01)** Least Low Pay (.02).01 (.01) Most Low Pay.07 (.02)***.09 (.02)***.11 (.02)***.04 (.01)***.02 (.01)** Least Low Pay Property Crimes (Per 1000) Most Low Pay (1.27) -.46 (1.17) Middle Low Pay (1.08) -.49 (1.15) Least Low Pay (.93) 1.67 (.91)* Most Low Pay 1.55 (3.25).72 (3.77) (3.66) (.88)*** (.67)*** Least Low Pay Vehicle Crimes (Per 1000) Most Low Pay (.65) (.77) Middle Low Pay (.74) -.86 (.91) Least Low Pay (.74).25 (.87) Most Low Pay.01 (1.24) -.11 (1.58) (1.42) (.41)*** (.46)*** Least Low Pay Violent Crimes (Per 1000) Most Low Pay (.36).48 (.35) Middle Low Pay (.30)***.87 (.29)*** Least Low Pay (.51)** 1.21 (.51)** Most Low Pay Least Low Pay (1.42) (1.46) (1.86) -.74 (.53) -.73 (.12)*** Notes: Areas are split into three equal sized groups of police force areas (14 in each, with Gwent and South Wales kept separate here as the boundary change discussed in footnote 7 of the main paper took place before the period considered in this Table). The groupings are based upon the proportion of workers paid less than the minimum wage in the period April 1998 to March Areas in the Most Low Pay group have over 11.7 percent of workers beneath the minimum wage (average = 14.2 percent). Areas in the Middle Low Pay group have between 9.7 and 11.7 percent of workers beneath the minimum (average = 10.9 percent). Areas in the Least Low Pay group have less than 9.7 percent of workers beneath the minimum wage (average = 7.1 percent). *** denotes statistically significant at 1% significance level or better, ** 5%, * 10%. Standard errors in parentheses. 30

32 Table II: Estimates of Area-Level Crime Equations (Proportion Low Paid in Labour Force Survey) Time Period For Change: (April 1999 September 1999) (April 1998 September 1998) Proportion Paid Less Than The *** -.839*** -.928*** Minimum Wage in Period 1 (.349) (.274) (.307) Change in Log(Unemployment Rate) Change in Log(Property Crime Rate) Time Period For Change: (April 1999 September 1999) (October 1998 March 1999) -.787*** (.333) (.387) (.397) (.084) (.090) Demographic Changes No Yes Yes No Yes Yes Change in Clear Up Rate No Yes Yes No Yes Yes R-squared Sample Size Change in Log(Vehicle Crime Rate) Time Period For Change: (April 1999 September 1999) (April 1998 September 1998) Time Period For Change: (April 1999 September 1999) (October 1998 March 1999) Proportion Paid Less Than The Minimum Wage in Period *** (.348) -.833*** (.283) -.964*** (.316) *** (.333) -.966** (.416) Change in Log(Unemployment Rate) (.127) (.121) Demographic Changes No Yes Yes No Yes Yes Change in Clear Up Rate No Yes Yes No Yes Yes R-squared Sample Size Change in Log(Violent Crime Rate) Time Period For Change: (April 1999 September 1999) (April 1998 September 1998) -.977*** (.422) Time Period For Change: (April 1999 September 1999) (October 1998 March 1999) Proportion Paid Less Than The Minimum Wage in Period * (.449) (.597) * (.655) *** (.239) *** (.432) Change in Log(Unemployment Rate) (.206) (.102) Demographic Changes No Yes Yes No Yes Yes Change in Clear Up Rate No Yes Yes No Yes Yes R-squared Sample Size Notes: Coefficients (heteroskedastic consistent standard errors) reported; The demographic controls entered were change in average age, change in the population share of young (<25) men, change in proportion black, change in population share with no educational qualifications, change in proportion female, change in share of public sector jobs. *** denotes statistically significant at 1% significance level or better, ** 5%, * 10% ** (.466) 31

33 Table III: Estimates of Area-Level Crime Equations (Wage Gap And Proportion Low Paid Males in Low Skill Occupations in Labour Force Survey) Change in Log(Property Crime Rate) Time Period For Change: (April 1999 September 1999) (April 1998 September 1998) Share of Wage Bill Measure Low Skill Males Measure Period 1 Less Than The *** *** *** *** -.948*** *** Minimum Wage Measure (3.346) (2.526) (2.728) (.430) (.355) (.390) Change in Log(Unemployment Rate) (.088) (.091) Demographic Changes No Yes Yes No Yes Yes Change in Clear Up Rate No Yes Yes No Yes Yes R-squared Sample Size Change in Log(Vehicle Crime Rate) Time Period For Change: (April 1999 September 1999) (April 1998 September 1998) Share of Wage Bill Measure Low Skill Males Measure Period 1 Less Than The *** *** *** *** -.946*** ** Minimum Wage Measure (3.280) (2.534) (2.832) (.434) (.374) (.382) Change in Log(Unemployment Rate) (.125) (.126) Demographic Changes No Yes Yes No Yes Yes Change in Clear Up Rate No Yes Yes No Yes Yes R-squared Sample Size Change in Log(Violent Crime Rate) Time Period For Change: (April 1999 September 1999) (April 1998 September 1998) Share of Wage Bill Measure Low Skill Males Measure Period 1 Less Than The ** * -.989* ** Minimum Wage Measure (4.278) (5.888) (6.585) (.581) (.743) (.758) Change in Log(Unemployment Rate) (.206) (.204) Demographic Changes No Yes Yes No Yes Yes Change in Clear Up Rate No Yes Yes No Yes Yes R-squared Sample Size Notes: Coefficients (heteroskedastic consistent standard errors) reported; The demographic controls entered were change in average age, change in the population share of young (<25) men, change in proportion black, change in population share with no educational qualifications, change in proportion female, change in share of public sector jobs. *** denotes statistically significant at 1% significance level or better, ** 5%, * 10%. 32

34 Table IV: Difference-in-Difference Estimates of Area-Level Crime Equations Change in Crime Models Estimated With Data Pooled Over Six Month Time Periods Period Surrounding Minimum Wage Introduction: [(April 1999 September 1999) - (April 1998 September 1998)] Earlier Time Periods: [ (March 1998 October 1997) (March 1997 October 1996 )], [ (April 1997 September 1997) (April 1996 September 1996)], [ (March 1997 October 1996) (March 1996 October 1995 )], [(April 1996 September 1996) (April 1995 September 1995)] Change in Log(Property Crime Rate) Proportion Paid Less Than The Minimum Wage in Period 1 X [(April 1999 September 1999) (April 1998 September 1998)] -.750* (.391) -.635** (.296) -.685** (.291) -.651** (.279) Change in Log(Unemployment Rate) (.028).033 (.028) Demographic Changes No Yes Yes Yes Change in Clear Up Rate No Yes Yes Yes Area-Specific Trends No No No Yes R-squared Sample Size Change in Log(Vehicle Crime Rate) Proportion Paid Less Than The Minimum Wage in Period 1 X [(April 1999 September 1999) (April 1998 September 1998)] -.875** (.415) -.566* (.313) -.604** (.307) (.373) Change in Log(Unemployment Rate) (.040).026 (.042) Demographic Changes No Yes Yes Yes Change in Clear Up Rate No Yes Yes Yes Area-Specific Trends No No No Yes R-squared Sample Size Change in Log(Violent Crime Rate) Proportion Paid Less Than The Minimum Wage in Period 1 X [(April 1999 September 1999) (April 1998 September 1998)] (.684) (.694) (.683) Change in Log(Unemployment Rate) (.083) Demographic Changes No Yes Yes Yes Change in Clear Up Rate No Yes Yes Yes Area-Specific Trends No No No Yes R-squared Sample Size (.833).093 (.091) Notes: Coefficients (heteroskedastic consistent standard errors) reported; The demographic controls entered were change in average age, change in the population share of young (<25) men, change in proportion black, change in population share with no educational qualifications, change in proportion female, change in share of public sector job. All equations include dummy variables for time period and the proportion low paid variable. *** denotes statistically significant at 1% significance level or better, ** 5%, * 10%. 33