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The conclusions regarding the negative effects of minimum wage increases on .... series data set of the employment rate of young adults and the impact of.
Labour Market Bulletin 1997:2 Pages 25–50

Do minimum wages have an adverse impact on employment? Evidence from New Zealand 1 S IMON C HAPPLE This paper presents the results of research into the effects of minimum wages on employment in New Zealand. Two different approaches are taken – one based on changes in the relative employment rates of young adults, and the other on differences in employment changes across industries. Both approaches produce a range of results. The conclusions regarding the negative effects of minimum wage increases on employment are strikingly non-robust.

1 Introduction

N

to a 1990 survey of New Zealand economists generally agreed with the proposition that a minimum wage increases unemployment amongst the young and unskilled. Another quarter agreed but had reservations (Coleman 1992, p 66). However, despite growing recent international interest in both the United States and Europe in the employment impact of minimum wages (see Brown 1988; Neumark and Wascher 1994; Card and Krueger 1995; Kennan 1995; Dolado et al 1996; Fernie and Metcalf 1996), researchers and analysts have completed only a small amount of empirical work on the impact of minimum wages on employment in New Zealand. The only econometric work, undertaken by Tim Maloney, uses time series data to address the issue of the impact of minimum wage changes on the relative employment of teenagers and young adults over the period from 1985 to the mid-1990s (Maloney 1995; 1997). Maloney finds significant negative impacts of minimum wages on the young adult labour market. He calculates a demand elasticity of -0.35 and, when estimated over a longer time period and with a different specification, -0.38.2 EARLY HALF O F THE RESPONDENTS

Thanks to David Rea, Sylvia Dixon, Dave Maré and the three anonymous referees for their helpful input. The opinions and judgments expressed in this paper are not necessarily those of the Department of Labour. 2 It is important to keep in mind that this elasticity is for a particular small sub-section of the labour market, rather than for the labour market as a whole. Given this and gross substitution between different sorts of labour, the economy-wide impact is likely to be very much smaller. For example, given that there are about 180,000 young adults currently employed, the partial equilibrium effect of a 10 percent rise in real minimum wages would be to cut their employment by about 6,500. Given that employment for other groups would probably expand, the effect on total economy-wide employment is likely to be much smaller. 1

© 1997 Department of Labour, New Zealand, unless otherwise specified

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This study focuses on the economic impact of minimum wages on one particular group whose employment is often perceived as vulnerable to changes in the real minimum wage – young adult workers. Using quarterly time series data, it examines the employment impact of minimum wages on young workers in the 20–24 year age group. The study addresses the impact of minimum wages on employment in two ways. First, it re-visits Maloney’s time series study. It uses a longer time series, a more theoretically satisfactory specification including the maximum available range of relative prices suggested by standard production theory, and a methodology emphasising residual-based diagnostic tests. The aim is to test the robustness of Maloney’s conclusions regarding the impact of minimum wages on the employment rate of young adults. Secondly, the study checks Maloney’s results by cutting up the data in a different way. It examines the impact of minimum wages on employment in a panel data set of 29 New Zealand industries over the 1980–97 period, and develops a specification which is as consistent as possible in using the full suite of relative prices with the time series study. Using this information takes advantage of the substantial variations in minimum wages during the mid-1980s to obtain estimates of the industry employment response to changes in minimum wages. It also allows the study to consider whether higher minimum wages have stronger dis-employment effects in low-wage industries. In common with much of the recent overseas work (eg Card and Krueger 1995; Schmitt and Bernstein 1997), this study finds that, on the basis of observed variations of real minimum wages, it is difficult to isolate a robust significant negative impact of minimum wages on employment. This finding can be rationalised by a wide variety of alternative hypotheses. It is consistent with any or all of the following (the list is not comprehensive): low coverage of the minimum wage, low compliance with minimum wage regulations, minimal substitution possibilities for workers covered by minimum wages, shock effects on productivity of minimum wage increases, offsetting cuts in non-wage parts of the remuneration package and monopsony power in labour markets. Unfortunately, this study does not have the ability to discriminate between these or other alternative explanations of the results.

2 Recent history of New Zealand minimum wage changes There have been a number of changes to the nominal minimum wage over the period 1980–97. Changes have been both large and small and are summarised in Table 1. These discrete exogenous jumps in minimum wages in the economy offer variation through which to examine the employment impact of minimum wage changes.

Simon Chapple

There is little evidence that the big changes in nominal and real minimum wages through the 1985–87 period compressed the income distribution along observable dimensions. Dixon’s (1995) study of inter-industry distribution shows no evidence of compression in the wage distribution through this period (see in particular Dixon 1995, p 49, Figure 1). Nor does her later study of individual earnings indicate any evidence of compression (Dixon 1996). Easton’s (1996) survey of income distribution in New Zealand through the 1984–96 reform period also shows little evidence of any compression across a wider number of distributional dimensions. The failure to observe strong evidence of wage compression may be because very few workers were bound by the minimum, employers’ compliance with the minimum was low or other changes swamped any minimum wage effect. The extent to which minimum wages were binding may well have changed over the period 1980–97. Prior to 1991, various versions of the old labour market framework set legal floors on market wages in all industries covered by an Arbitration Court award. Under the Minimum Wage Act 1983, the minimum wage set by an Order in Council placed a floor under all wages for workers over the age of twenty. 3 Prior to the abolition of blanket award coverage, it could be plausibly argued that, because of award minima, statutory minimum wages were likely to be binding for fewer workers. As long as minimum wages fell short of award wages and most workers (especially low-paid workers) were covered by awards, they remained essentially irrelevant for employment issues. However, following the abolition of the industrial conciliation and arbitration system and the introduction of the Employment Contracts Act 1991 ( ECA), the award system disappeared. Given these institutional changes, it is possible that the statutory minimum wage is now a more effective floor than under the award system. For the pre-1991 period, establishing the proportion of the workforce not covered by awards is an aspect of calculating the impact of minimum wages. Award coverage in 1985 was probably around half the workforce. Many of those not covered by awards would have been higher-income earners. Thus only a minority of workers could fall below award rates and were potentially subject to the minimum wage constraint. According to Harbridge (no date) the 1985 rise in the minimum wage pushed minimum wages above adult award minimum rates in five awards. In the 1986/87 wage round, 20 percent of the 583 settlements set the statutory minimum wage above fixed minimum award wages (Harbridge and McCaw 1993, p 151; footnote 1, p 158). Thus it is also possible that minimum wages became more important as a floor following the 1985 minimum wage rises. The Minimum Wage Act 1983 allowed employers to opt out of having to pay the minimum wage by applying for a permit from the Inspector of Awards (see Cumming 1988, footnote 7, p 4).

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TABLE 1: Recent adult minimum wage rises (hourly) Date of rise

Rise from ($)

11 June 1981 5 February 1985 2 September 1985 9 February 1987 8 February 1988 15 May 1989 17 September 1990 22 March 1995 18 March 1996 1 March 1997

2.00 to 2.10 to 2.50 to 4.25 to 5.25 to 5.63 to 5.88 to 6.13 to 6.25 to 6.38 to

Percentage rise

2.10 2.50 4.25 5.25 5.63 5.88 6.13 6.25 6.38 7.00

5.0 18.8 70.0 23.5 7.1 4.4 4.3 2.0 2.0 9.8

The real hourly adult minimum wage, deflated by the producer price (output) index, is shown in Figure 1. The period 1980–88 has the most variation in both the nominal and the real minimum wage. Examining real minimum wages over the period 1980–97 reveals a number of interesting episodes: • a substantial erosion in the real minimum wage as a consequence of a stable nominal minimum and rising prices (1980–84); • a large nominal minimum wage increase from a position of a relatively low real minimum (1985); • a large minimum wage increase from a position of a relatively high real minimum (1987);

4.00 3.50 3.00 2.50 2.00

Sources: Statistics New Zealand; Department of Labour

Mar 97

Mar 96

Mar 95

Mar 94

Mar 93

Mar 92

Mar 91

Mar 90

Mar 89

Mar 88

Mar 87

Mar 86

Mar 85

Mar 84

Mar 83

Mar 82

Mar 81

1.50 Mar 80

Real minimum wage (December 1982 $)

FIGURE 1: Adult minimum wages deflated by real producer price

Simon Chapple

• a small minimum wage increase from a position of a relatively high real minimum (1988); and • gradual erosion of the real minimum wage until 1997.

3 The connection between theory and evidence of the employment impact of minimum wages This paper uses the following broad theoretical structure to examine both the time series data set of the employment rate of young adults and the impact of minimum wages in the panel data set of industries. Standard competitive partial equilibrium models of the labour market predict that a minimum wage, set and enforced at above market-clearing levels, raises real wage costs to firms. The labour demand curve becomes the binding constraint, and therefore higher real product wages reduce employment (Brown, Gilroy and Kohen 1982, pp 488–489). The size of the negative employment effect caused by a binding minimum wage increase is determined by the size of the elasticity of substitution at the appropriate level of aggregation. The standard theory of labour demand also predicts that the employment of any group of workers depends on the prices of various productive inputs (other forms of labour, intermediate goods and capital) and the output price. Given that the standard assumption of rationality applies homogeneity of degree zero of the demand function, labour demand can be expressed as a function of the real output price of labour inputs, of intermediate inputs and of capital. The leap from economic theory to econometric estimation of the impact of minimum wages is typically substantial. Specifications examining the impact of minimum wage changes are usually ad hoc reduced form equations, including a range of demand and (sometimes) supply shifters, which are explicitly or implicitly treated as exogenous, as well as the minimum wage relative to average economy-wide or industry-wide wages, which is treated as exogenous. Based on the discussions above, a specification more consistent with standard production theory would include the minimum wage deflated by the output price and the average wage deflated by the output price separately (Card and Krueger 1995, p 185). Such a specification presumes the average real wage is exogenous. Furthermore, economic theory suggests that, under most circumstances, the specification should include the real price of intermediate inputs and the real price of capital separately (Card and Krueger 1995, pp 184–185). The response of employment to the average real wage and the real minimum wage is predicted to be zero or negative by standard competitive theory, while other coefficients may have positive or negative signs depending on whether they are complements (negative) or substitutes (positive) in the production process. Unfortunately, economic theory can provide little or no guidance regarding the functional form of the employment equation, or about the lag structure, which

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describes the speed of adjustment of employment to equilibrium following an exogenous shock, such as a rise in nominal minimum wages. The common structure in both the time series data set and the panel data set of industries is that employment is presumed to be a function of the real (output price deflated) minimum wage, the real wage, other real input prices and a range of other control variables. In the time series study, the specification controls for the business cycle and different vulnerabilities to the minimum wage by locating the group at risk of losing their jobs (young adults) relative to the position of the rest of the labour force. In the industry panel, the dependent variable is industry employment, and, in addition to the inclusion of the same set of relative prices, a wider range of business cycle and sectoral control variables are necessary. The industry panel data are annual and cover a longer time period (1980–97) than the quarterly time series data (1985.4–97.1). This means that assumptions about the lag structure differ. Given specification uncertainty, this study chooses to rely on residual-based tests and sensitivity to changes in specification as important criteria for assessing the robustness of any conclusions.

4 The Maloney studies As indicated in the introduction (section 1), the only significant empirical studies of the minimum wage in New Zealand are those of Maloney (1995; 1997). They are serious studies and are worthy of close attention. As befits pioneering work with a short time series, Maloney generally provides cautionary remarks regarding his conclusions. Maloney (1995) estimates employment and unemployment regressions for relative employment rates of teenage, young adult and unqualified young adult workers over the 1985.4–93.4 period. His employment regressions contain quarterly dummies, a time trend (not statistically significant) and controls for other variables, including older adult unemployment (significant), the age-specific unemployment benefit (this is the ratio of the unemployment benefit relevant to the younger age group to that for older adults, and is not significant), and agespecific educational enrolment (significant). Maloney finds that the real adult minimum wage significantly increases employment of teenagers and reduces employment of young adults. The latter elasticity is -0.35. Thus young adults lose jobs because they are over-represented amongst low-paid workers. Maloney’s unemployment regressions contain quarterly dummies, a time trend (statistically significant in some cases) and controls for the age-specific unemployment benefit (this is significant and positive for teenagers, but significant and negative for young adults) and age-specific educational enrolment (not significant). He concludes that the real adult minimum wage significantly decreases unemployment of teenagers and increases unemployment of young adults.

Simon Chapple

Different specifications are reported by Maloney (1997) using a longer data set (1985.4–96.2). The dependent variable is the employment–population ratio of two groups: teenagers and young adults. The lag structure is different, with the real minimum entered as a second-order polynomial and the coefficient presented as the cumulative effect of a three-quarter (rather than five-quarter) lag. Unlike the earlier work, he finds auto-correlation, which is suggestive of mis-specification. Given the specification of the real minimum as a second-order polynomial, the elasticity of employment with respect to the minimum varies according to the level of the minimum wage. Maloney reports an elasticity of -0.38. Maloney (1997, p 195) also discusses the robustness of his findings. Perhaps for space reasons, neither specifications nor results of the alternative formulations are formally presented (it would be interesting to know, for example, which alternatives had and which did not have auto-correlation). He reports variation of between -0.10 and -0.40 in alternative specifications of the elasticity of youth employment with respect to the minimum wage, noting that, at the lower end of the range, the effect was no longer statistically significant. He remarks that ‘if one chooses any single specification it is possible to produce results that show no impact of the minimum wage on employment’. Equally, one can conclude that by choosing any single specification it is possible to produce results that show a negative impact of the minimum wage! Overall, Maloney (1997, p 195) concludes that ‘the results reported above are far from conclusive, [but] they suggest that increases in the minimum wage reduce the employment of the age group most likely to be directly affected by the legislation’. Earlier unpublished work undertaken by the Department of Labour suggests that the significance of Maloney’s (1995) results depends on the first five observations through 1985 and 1986. Dropping these observations caused a loss of purchase of the minimum wage on employment. However, the time series is short, and it could be legitimately argued that, particularly given the big jump in minimum wages through 1985, this valuable sample variation should not be thrown away. I have considerable sympathy with this argument. However, it does mean that conclusions must be drawn with less certainty than otherwise. In both studies, Maloney deflates the minimum wage by using average hourly earnings. Maloney (1997, p 194) points out that similar results are obtained when the Consumer Price Index (CPI) is used as a deflator. Given that the equation is implicitly estimating a demand curve, an output (producer price) index rather than the CPI (which may be more appropriate for labour supply behaviour) seems more appropriate. This is particularly the case since the CPI and the producer price index exhibit quite different behaviour over the period under consideration.

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5 The impact of minimum wages on employment of young adults This section considers determinants of the employment–population ratio of 20–24 year olds – the same dependent variable as used by Maloney (1997) for New Zealand and Card and Krueger (1995, pp 194–203) for the United States. The quarterly labour market data come from the Household Labour Force Survey (HLFS ) and run from 1985.4 to 1997.1. This study shares with Maloney’s the problem of using a very short time series to establish robust empirical results. Given that robust time series results have proven difficult to find in the United States, with time series of three or more times the length of the New Zealand series, I am cautious about drawing strong conclusions from any New Zealand results. The limited degrees of freedom available when undertaking short time series studies restricts the use of very general dynamic models. In addition, the dependent variable measures employment outcomes for a small subset of the employed population. Given that the data come from a sample, the noise–signal ratio in the data is likely to be quite high. Again, this suggests we should be cautious about drawing definitive conclusions from the results. The following general log–log equation is estimated:4 lne20- 24 = α + β lnet +

4

4

k= 0

k= 0

∑ γ k ln ( MIN / PPO )t − k + ∑ δ k ln (AHE / PPO )t − k +

4

∑ ϕ k ln ( PPI/ PPO )t − k + θ t + u t

k =0

t is a linear time trend, e20 -24 is the employment–population ratio for 20 to 24 year olds and e is the employment–population ratio for the rest of the population. The latter controls for the behaviour of the employment–population ratio for the rest of the population and hence for the business cycle (see Card and Krueger 1995, p 200).5 Thus it is deviations of the employment–population ratio for those aged 20–24 years from the ratio for the rest of the economy that are this study’s focus of attention. The nominal minimum wage deflated by the producer price output index (MIN/ PPO ) and the average hourly earnings deflated by the producer price output index (AHE/PPO ) are included separately in the regression as advocated by Starting from a general specification is consistent with the UK rather than the US time series econometric tradition. US time series tend to start from a restrictive specification and work towards a more general formulation. Semi-log and levels equations were also estimated. Results in terms of elasticities evaluated at sample means were very similar. The residual-based tests did not generally perform as well. 5 Other less direct business cycle controls could potentially be used. However, the superior nature of the employment–population ratio for the rest of the economy and limited degrees of freedom meant that other business cycle controls were not tried. 4

Simon Chapple

Card and Krueger (1995, p 185), since this avoids the potential mis-specification inherent in equations which deflate the minimum wage by average hourly earnings. In addition, the price of inputs relative to outputs is included (PPI /PPO ) because, unless strong assumptions are made regarding production function separability, its exclusion may again lead to mis-specification (the PPO is the output price series and the PPI is the input price series for all market groups). A specification including all available relative prices is in accordance with standard production theory and is less ad hoc than many other specifications commonly employed in the time series literature. The real price of capital is omitted from the regression, since there is no ready measure available. If the real price of capital is orthogonal to other regressors, its omission will be irrelevant. However, given that in theory it should be included, I am alert for signs of mis-specification in the estimated equations. Finally, a four-quarter lag is used. This lag structure is typically used in the quarterly time series literature. Testing a longer lag structure would consume too many degrees of freedom. Three seasonal dummies are also included. There are 46 observations.6 What criteria are available, apart from prior views regarding sign, significance and size of the relevant coefficient, to distinguish between estimated equations? First, the more alternative specifications suggest the same general conclusion, the more trustworthy the preferred specification. Second, consistency with economic theory, residual-based tests and tests of mis-specification are all useful candidates for ranking different specifications. Lastly, specifications which include significant ‘black box’ time trends can be regarded with less favour than those that do not. The results are shown in Table 2. Despite low correlations between the explanatory variables, 7 the instability of the elasticity estimates when real wage terms are included and the increased standard errors are suggestive of multicollinearity. Multi-collinearity is a particular problem in short data sets and can only be solved by more or better information. If multi-collinearity is a problem, it is not readily solvable by dropping coefficients with low t (or in combination F) statistics. While this approach may give greater efficiency, it is at the expense of introducing bias into the estimated coefficients. Of the four equations reported, the best performing is the first and full equation. The reasoning is as follows. First, in theory it is closer to the ideal, in that it includes relative prices of more production inputs. Second, the DurbinWatson (DW) statistic cannot reject the null of no auto-correlation, while it does As an additional sensitivity test, a dummy for the introduction of a youth minimum wage was included. As with Maloney (1997), it was not statistically significant. 7 Pair-wise correlation coefficients between the nominal (and real) logged prices were very low: 0.16 between ln MIN/PPO and ln AHE /PPO, -0.03 between ln MIN/PPO and ln PPI/PPO, and -0.45 between ln AHE/PPO and ln PPI/PPO. 6

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TABLE 2: Time series results for the employment–population ratio of 20–24 year olds (log–log specification) Sln MIN/PPO

-0.177 (-0.90)

-0.342 (-3.70) ***

-0.167 (-1.02)

-0.269 (-4.40) ***

ln employment rate

1.504 (7.902) ***

1.200 (10.63) ***

1.222 (10.55) ***

1.221 (18.25) ***

ln ( MIN/PPO)

-0.155 (-1.02)

-0.131 (-1.46)

-0.012 (-0.10)

-0.083 (-1.14)

ln ( MIN/PPO )-1

-0.215 (-1.58)

-0.235 (-2.49) **

-0.236 (-2.49) **

-0.223 (-3.06)

ln ( MIN/PPO )-2

0.117 (2.40) **

ln ( MIN/PPO )-3

0.067 (1.14)

-0.023 (-0.50)

ln ( MIN/PPO )-4

0.042 (1.09)

0.083 (1.87) *

0.037 (1.10)

-0.006 (-0.12)

-0.016 (-0.48) 0.017 (0.64)

0.009 (0.17)

0.005 (0.12)

0.004 (0.12)

ln ( AHE/PPO)

-0.320 (-0.59)

_

-0.420 (-1.13)

_

ln ( AHE/PPO) -1

-0.202 (-0.26)

_

0.175 (0.34)

_

ln ( AHE/PPO) -2

-0.132 (-0.26)

_

-0.153 (-0.40)

_

ln ( AHE/PPO) -3

0.512 (1.00)

_

0.159 (0.46)

_

ln ( AHE/PPO) -4

-0.064 (-0.18)

_

-0.009 (-0.03)

_

ln ( PPI/PPO )

1.117 (1.18)

0.005 (0.01)

_

_

ln ( PPI/PPO )-1

0.519 (0.49)

0.255 (0.30)

_

_

ln ( PPI/PPO )-2

0.335 (0.44)

0.008 (0.01)

_

_

ln ( PPI/PPO )-3

-0.884 (-0.95)

-0.660 (-0.95)

_

_

ln ( PPI/PPO )-4 Time

0.058 (0.80) -0.001 (-0.96)

0.137 (0.25) -0.002 (-6.84) ***

_ -0.001 (-1.03)

_ -0.002 (-9.12) ***

D1

0.006 (0.57)

0.017 (2.60) **

0.015 (2.12) **

0.018 (3.08) ***

D2

0.005 (0.38)

0.012 (1.88) *

0.013 (1.67)

0.014 (2.41) **

D3

-0.000 (-0.01)

0.003 (0.50)

0.002 (0.32)

0.003 (0.58)

0.975

0.965

0.969

0.9542

DW

1.88 (p=0.124)

1.48 (p=0.017)

1.64 (p=0.045)

1.43 (p=0.016)

RESET

1.79 (p=0.178)

3.65 (p=0.025)

1.61 (p=0.211) 1.08 (Prob=0.374)

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R2

Observations

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Note: * = 10%, ** = 5%, *** = 1% significance

in all the other specifications.8 This means that the first equation has more efficient estimates of the coefficients and reliable t statistics. In addition, auto-correlation is usually a good informal test of mis-specification. Since omitting variables increases the probability of auto-correlation, the full specification is preferable. Third, the full specification has a small and statistically insignificant time trend, especially in comparison with equations two and four. Since the time trend is an ad hoc inclusion, an absence of a time trend is again informal evidence of a lack of mis-specification. Fourth, if multi-collinearity is a problem, dropping regressors Exact ranges for the Durbin-Watson statistic were calculated through the S hazam programme.

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Simon Chapple

is likely to introduce bias into estimated coefficients. Finally, the Ramsey RESET test for omitted variables indicates the null of no omitted variables cannot be rejected in equation one.9 Now to interpretation. The long-run elasticity of the employment rate with respect to the real minimum wage is Σγ. The sum of the minimum wage coefficients is obtained, and a t test on its significance is calculated. The estimated long-run elasticities range between -0.18 and -0.34. The largest is slightly below the level estimated by Maloney (1995; 1997). While t tests in equations two and four indicate a minimum wage elasticity significantly different from zero, the test is unreliable in the presence of auto-correlation. In the preferred specification, the elasticity is much lower than that found by Maloney, and the hypothesis that the long-run impact of minimum wages on youth employment is zero cannot be rejected. The range and the instability in the estimated elasticity is very similar to the results summarised by Maloney (1997), despite the longer time series and different functional forms used. How do we choose between these elasticities to determine which is the most likely estimate? One way would be to take a simple average. However, this approach ignores information that some estimates are more robust than others. Another method would be to use prior experience of size and significance of the elasticity to choose, subjectively, the most likely response and make a mechanical adjustment for auto-correlation. As already outlined above, the preferred approach is to use both economic theory and statistical tests to distinguish between specifications. On this basis, the first equation performs best. It is consistent with theory because it includes the widest variety of relative prices, the null of no auto-correlation cannot be rejected, the hypothesis of no omitted variables cannot be rejected, it copes better with problems of multi-collinearity, and the model does not include a significant black box time trend. The preferred equation indicates a small, negative but statistically insignificant response of youth employment to the minimum wage. As a final check, I ran a Chow test for structural change on the preferred equation, breaking the sample into two before and after the introduction of the An attempt was made to examine the sensitivity of the minimum wage elasticity in the more restricted but auto-correlated equations to a mechanical adjustment (using the PraisWinsten method) for first-order auto-correlation. The transformation tended to raise the estimated elasticities, but, judging by the Durbin-Watson statistics for the transformed equations, it introduced more serious negative auto-correlation problems into the residuals than the positive auto-correlation found under ordinary least squares (OLS) estimation. This suggests the existence of a more complex lag structure than allowed for under the simple AR(1) adjustment. In addition, in terms of further interpretation of these results even in the absence of continued evidence of auto-correlation, Monte Carlo experiments on the small sample properties of such estimators suggest that significance tests on transformed data lack reliability. 9

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in the second quarter of 1991. The hypothesis of no structural change postcould be rejected at conventional (5 percent) levels of confidence (F actual = 5.16, F critical = 3.87).10 What are the implications of this structural change? As a check, the preferred equation, as estimated up until the introduction of the ECA, was used to forecast forward employment of young adults. Particularly during the 1991–93 period, the preferred equation over-predicted youth employment. There are several possible explanations for this. One likely explanation is that the relative returns on education were rising, causing more young people to chose training rather than employment. Another possible explanation is that minimum wages were more likely to be binding post-ECA , thus reducing youth employment. However, the latter does not seem plausible. For this to be the case, I would expect average wages for the 20–24 year age group to have fallen considerably relative to other workers, as many more of them are swept up by the minimum wage. Household Economic Survey (HES) data indicate that average hourly earnings for the 20–24 year age group fell from 79.4 percent of total hourly earnings in March 1992 to 78.0 percent in March 1995. The ratio of the minimum wage to youth average hourly earnings did not vary greatly also, falling slightly from 56.8 percent in March 1992 to 55.8 percent in March 1995.11 Much of the falls in average youth wages through the period are actually nominal falls. Since nominal wages for jobs fall very rarely (see Chapple 1996), the observation of falling average nominal wages for youth workers is consistent with the hypothesis that otherwise higherpaid youth workers are exiting into training of various sorts, changing the composition of the employed youth cohort towards the lower paid. Regardless of the preferred equation, we should be cautious about drawing definitive conclusions from these results regarding the impact of minimum wages on youth employment, because of the short sample, instability of the coefficient for long-run elasticity across functional forms and noisy data. Furthermore, the dependent variable measures bodies employed rather than hours worked by young adults. Theoretically speaking, an ‘hours worked’ variable would have been preferable. For this reason, HLFS hours worked data for the 20-24 year age group and rest of the population were also checked as employment variables. Unfortunately there exist two problems with hours worked data which lead to a preference for employment over hours. First, hours worked are for the principal job only. Second, much employment and hours worked data is reported in the HLFS by proxy. Discussions with Statistics New ECA

ECA

The first five observations were also dropped to examine sensitivity of the minimum wage elasticity, following the lead of earlier unpublished work by the Department of Labour. The elasticities rose but remained insignificant in the full specification and the specification without real input prices. The DW statistics improved markedly in the two equations without real wages. 11 I wish to thank my colleague Sylvia Dixon for extracting and compiling the HES data. 10

Simon Chapple

Zealand suggest that proxy reporting is at a higher rate for the 20–24 year age group than for other groups. It is probable that proxy responses are much more likely to be inaccurate in reporting hours than in reporting employment status. Nevertheless the same sets of regressions were run on the hours worked data. In all equations there were problems of auto-correlation. The sum of the minimum wage coefficients was positive (an unbiased although not efficient estimate of the true coefficient in the presence of auto-correlation), rather than negative as in the employment equations. T tests, which are biased upwards when auto-correlation is present, suggested the summed minimum wage coefficient was not significantly different from zero.12 In addition, ideally it would have been valuable to test the homogeneity restrictions implicit in the models examined. This would have involved entering logged minimum wages and the various price deflators separately in the regression with the pre-existing lag structure. Unfortunately the available degrees of freedom were too few to derive any meaningful results from this. If the nominal minimum is significant and takes on a negative sign and the homogeneity restriction is accepted, I could place more confidence in the results. 13 Finally, the order of integration of the time series variables used here may be an issue. The dependent variable is by definition likely to be a stationary series as it is bounded between zero and one, while real wages, and perhaps the real minimum wage, are likely to be non-stationary. Therefore it may be illegitimate to include them in the same regression. Again, the time series is so short that unit root tests cannot obtain much purchase on the data. 14 The above analysis has broadly replicated Maloney’s results in terms of generating a similar minimum wage elasticity, albeit using slightly different specifications. However, the results are not robust in terms of significant problems of auto-correlation and omitted variables (particularly the real product wage) in the equations with the larger negative elasticities. In the equation where the diagnostics are more satisfactory, the significance of the minimum wage coefficient disappears and the size is much reduced.

6 Panel data: Descriptive material on the industries This section cuts up the data in a different way by using a panel data set of industries to investigate the potential negative employment effects of minimum wages. Research interest focuses on the lower end of the tail of the wage For reasons of space, complete results are not presented here. These are available from the author on request. 13 However, unrestricted equations entering all lags of nominal prices were estimated. The estimated elasticity on nominal minimum wages varied wildly from large and positive in the most general equation, to small and negative in the most restricted equation. 14 As a general rule of thumb, unit root tests are not worth undertaking unless there are several hundred quarterly observations. The small property samples of the unit root tests are poorly understood. 12

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distribution. Unfortunately direct employment information on this tail is unavailable, so I had to use indirect information. The panel data set is annual and runs from 1980 to 1997 for 29 industries. Employment and wage data are taken from the Supplementary Tables to the Labour and Employment Gazette for 1980–88 and from the Statistics New Zealand Quarterly Employment Survey (QES) for 1989–97. There is a potential break in the data between 1988 and 1989, when the QES was taken over by TABLE 3: Industries by employment, ranked by average ordinary time hourly earnings February 1980 ordinary time hourly earnings ($) Retail trade Personal and household services Restaurants and hotels Textiles, clothing and leather Sanitary services Manufacturing NEC Wood and wood products Other food, beverages and tobacco Electrical machinery and equipment Construction Machinery, except electrical Non-metallic mineral products Financial institutions Forestry and logging Metals products and engineering Other community services Wholesale trade Health services Transport equipment Real estate and business services Chemical, petroleum, rubber and plastics Electricity, gas and water Recreational and cultural services Transport and storage (excluding seasonal) Educational services Paper and paper products, printing and publishing Mining and quarrying Insurance Seasonal food processing

3.85 3.90 3.95 4.01 4.14 4.16 4.28 4.38 4.43 4.64 4.64 4.67 4.74 4.77 4.84 4.84 4.85 4.86 4.96 5.02 5.05 5.12 5.14 5.16 5.25 5.27 5.37 5.44 6.16

Employment 74,443 35,452 29,607 43,746 8,078 4,217 20,342 17,502 12,981 54,809 17,378 9,169 24,098 8,118 31,087 7,930 60,708 62,994 23,230 29,524 23,145 15,338 11,537 55,150 74,841 29,983 3,626 10,471 54,414

Simon Chapple

Statistics New Zealand and a number of definitions of the data set changed. These changes are dealt with in more detail in the Appendix. Public administration is excluded from the panel. Post-1989 data on the communications, and research and scientific institutions were not consistently available due to Statistics New Zealand’s confidentiality policies. This left 29 industries in the panel. The industries are a mixture of one, two and three digit categories by the old New Zealand Standard Industrial Classification (NZSIC) taxonomy (see Appendix). To sketch a picture of the industries, I focus on the industrial structure at the beginning of the period (1980). Wage and employment data is provided in Table 3. Much as expected, the low-wage industries are retail trade, personal and household services, restaurants and hotels, and textiles, clothing and leather. The highest-wage industry, seasonal food processing, seems to pay a compensating differential for the seasonal nature of work in the freezing works. In addition to the variation in average wages paid, the industries differ substantially by employment size. Some low-wage industries are amongst the larger sectors by employment, in particular, retail trade, and textiles, clothing and leather. Industries also differ substantially in terms of the ratio of part-time to fulltime workers, which suggests that production technologies differ between industries. Both low-wage and high-employment sectors are well represented in industries with a high proportion of part-time staff. Industries also differ substantially by gender mix, with low-paid sectors, unsurprisingly, being disproportionately female.

7 A simple panel specification Card and Krueger (1995, pp 217–218) use the following specification for examining the employment effects of minimum wage increases using panel data sets at a state level in the United States: log E it = α + β log (MIN t/P it) + γ Xi + δ T + u it E is employment, MIN is the nominal adult minimum wage, P is an industry price deflator, X is a vector of dummy variables for the cross-sectional units, T are dummies for each time period and u is an error term. The i is an industry subscript while the t represents time. Card and Krueger (1995, pp 219–220) show that in the above fixed effects model – where the minimum wage does not vary across the i cross-sectional units – the coefficient on the real minimum wage β is determined solely by variation in the inverse of the price deflator used on nominal minimum wages. The specification ensures that any variation in the nominal minimum wage is completely soaked up by the year dummies. However, employment could respond positively to the price deflator for reasons that have little to do with the minimum wage. For example, a positive demand shock in the product market could raise prices and increase labour demand, lowering real product wages and raising employment.

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Given that the substantial variation in the nominal minimum wage over this period is valuable information which this study wishes to take into account in estimating β, what solutions are available to the problem raised by Card and Krueger? One way of getting around this specification is to replace the time dummies with the aggregate economic drivers behind employment, allowing deviations from this to be influenced by the time path of minimum wages. In terms of this study, the obvious variables are the annual change in the inflation rate, which will pick up transitory aggregate demand effects, lagged log gross domestic product (GDP) to pick up the impact of economic growth,15 and the log of the real exchange rate times a tradable dummy to pick up sectoral differences in employment growth as a consequence of real exchange rate changes (service sectors are defined as non-tradable; non-service sectors as tradable).16 Real exchange rate variations were substantial over the period in question (1980– 97) and, being a small open economy, the real exchange rate is likely to be an important variable in the New Zealand context. This approach saves degrees of freedom and allows time series changes in the nominal minimum wage to obtain some bite. In addition, as in the time series specification, this specification includes the real minimum wage deflated by the producer price (output) index, real wages and the real price of intermediate inputs in close accordance with standard production theory. Unlike the time series model of employment of young adults, this model uses annual rather than quarterly data, so I assume it is contemporaneous rather than incorporating a lag structure. Thus I estimate variants of: logE it = α0 + α1 log ( MINt / PPOit ) + a2log (AHEit /PPOit) + a3log( PPI it/PPOit) + a2X + u it

This specification is designed to be as consistent as possible with the time series specification estimated in section 5. It includes the full suite of relative prices.17 X is a vector of control variables.18 I include two sets of intercept industry dummies separately for two QES data series. In terms of other control variables, as indicated, I include lagged real GDP, the change in the inflation rate (to pick up Another possibility would be to use sectoral GDP data. Since my interest was in both the scale and substitution effects of minimum wages, the specification employs aggregate GDP data. In addition, sectoral GDP figures matching the QES sectors were not available. 16 In terms of taxonomy, two services sectors could be considered tradable: wholesaling, which includes producer boards, and transport. The results reported below were not sensitive to shifting these two sectors into tradables. 17 Like the time series study, self-employment is omitted from employment as measured by the QES. This is unlikely to affect the accuracy of the specification, because these workers are not expected to be negatively influenced by minimum wages. In addition, the omission of smaller firms from the QES sample is unlikely to be a major issue, since the vast majority of employment will be captured by the firms in the sample. 18 As indicated, the control variables are GDP, the change in inflation, the real exchange rate/tradable dummy, and industry and data set dummies. 15

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disequilibrium effects), and the real exchange rate multiplied by a dummy variable for tradable industries. These control variables pick up business cycle influences (as does the employment–population ratio for the rest of the economy in the time series study) and sectoral shifts. All cross-sectional specifications were estimated with OLS using White-robust standard errors to account for any cross-sectional heteroskedasticity. International evidence summarised in Brown, Gilroy and Kohen (1982) suggests a small, negative and significant elasticity of employment with respect to the minimum wage. More recent and controversial international evidence questions the existence of even a small negative effect (Card and Krueger 1995). The sign on real input prices will depend on whether inputs are complements or substitutes with respect to employment. The change in the inflation rate is expected to have a positive effect on employment and the interacted real exchange rate to have a negative effect (as the real exchange rate appreciates, this has a detrimental impact on employment in the tradable goods sector).19 The initial results, given in Table 4, show that in the most simple specification, where the only relative price variable entered is the real minimum wage, the elasticity is at its largest and most significant. Adding the real wage to the estimated equation lowers the elasticity by about a third and reduces its significance. The coefficient remains statistically significant at a 5 percent level. The implied elasticity suggests that a 10 percent rise in the real minimum wage would cost about 5,300 of the 850,000-odd QES jobs. RESET tests on equations including both minimum wages and all relative prices show no evidence of mis-specification. The specification constrains the estimated effects of a rise in the nominal minimum wage and the price deflator to be the same size and have the opposite TABLE 4: Panel results, 1980–97 Independent variable log (MINt /PPOit) log (AHEit/PPOit) log (PPIit/PPOit) log GDPt-1 ∆∆ CPI t ln RERt × TRAD Industry dummies RESET

F statistic R2

-0.062 (-2.26) ** -0.386 (-4.96) *** 0.204 (1.14) 0.321 (3.99) *** 0.001 (1.36) 0.025 (0.31) Yes 0.70 (Prob=0.55) 1677.16 0.9895

-0.063 (-2.31) ** -0.322 (-4.01) *** _ 0.314 (3.85) *** 0.002 (1.48) -0.008 (-0.09) Yes 2.15 (Prob=0.09) 1800.13 0.9895

-0.099 (-3.83) *** _ _ 0.370 (4.30) *** 0.002 (2.66) ** -0.097 (-1.16) Yes 0.28 (Prob=0.84) 2094.35 0.9889

Note: * = 10%, ** = 5%, *** = 1% significance

The real exchange rate index was taken from Cooper (1988) and is the Reserve Bank of New Zealand’s official trade-weighted measure based on relative consumer prices.

19

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sign. If the coefficient on the real minimum wage was accurately estimating the impact of minimum wages on employment, the homogeneity restriction made on the real minimum should hold. In addition, policymakers would be more confident in asserting that a rise in real minimum wages has a negative employment effect if the coefficient on the policy instrument, the nominal minimum wage, is itself statistically significant. I investigated whether relaxing the restriction made any material difference to the results by entering the two variables separately – nominal minimum wages and the PPO – in the econometric equation. The results are shown in Table 5. Only when relative prices are not controlled for is the coefficient on the nominal minimum wage negative and statistically significant.20 In the two other cases the coefficient is much smaller and not statistically significant. TABLE 5: Testing whether the nominal minimum matters, 1980–97 Independent variable log (MIN t) log (PPO it) log (AHEit/PPOit) log (PPIit/PPOit) log GDPt-1 ∆∆ CPI t ln RERt × TRAD Industry dummies F test on restriction RESET

F statistic R2

-0.037 (-1.32) -0.036 (-1.32) -0.085 (-1.31) -0.092 (-1.40) -0.464 (-5.39) *** -0.419 (-5.27) *** 0.163 (0.94) _ 0.638 (4.33) *** 0.650 (4.34) *** 0.001 (1.02) 0.001 (1.12) -0.043 (-0.46) -0.008 (-0.09) Yes Yes 5.29 (Prob = 0.02) 5.51 (Prob =0.019) 0.27 (Prob = 0.85) 0.68 (Prob = 0.56) 1915.66 2066.50 0.9897 0.9897

-0.096 (-3.68) *** 0.069 (1.05) _ _ 0.443 (2.79) ** 0.003 (2.72) ** -0.116 (-1.15) Yes 0.22 (Prob = 0.64) 0.27 (Prob = 0.85) 2145.15 0.9889

Note: * = 10%, ** = 5%, *** = 1% significance

As already indicated, the QES panel data set comprises two separate data sources, one collected by the Department of Labour (1980–88) and the other by Statistics New Zealand (1989–97). The next step was to split the sample into each of the two sets and re-estimate the equations. The time series is naturally shorter so results need to be treated with some caution. Results using the first data set are shown in Table 6. They indicate that over the sub-period where increases in the nominal and real minimum wage were substantial, the real minimum wage had a positive, Totally unrestricted equations were also run, entering the minimum wage average hourly earnings and producer prices for inputs and outputs separately. The coefficient on the nominal minimum wage was negative, small (-0.037) and statistically insignificant at a 10 percent level. 20

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economically small and statistically insignificant impact on employment across all specifications. RESET tests indicate little evidence of mis-specification in any of the equations. Lagged GDP has a lower elasticity and much less significance than in the full sample, while the inflation surprise variable and the real exchange rate take on expected signs and are statistically significant. TABLE 6: Panel results, 1980–88 Independent variable log (MINt /PPOit) log (AHEit/PPOit) log (PPIit/PPOit) log GDPt-1 ∆∆ CPI t ln RER t × TRAD Industry dummies RESET

F statistic R2

0.021 (0.61) -0.128 (-1.50) -0.071 (-0.22) 0.182 (1.50) 0.002 (2.31) ** -0624 (-4.11) *** Yes 0.38 (Prob =0.76) 2168.84 0.9886

0.023 (0.76) -0.146 (-1.32) _ 0.173 (1.58) 0.002 (1.98) * -0.609 (-3.39) *** Yes 0.42 (Prob = 0.74) 2186.24 0.9886

0.009 (0.32) _ _ 0.233 (2.02) ** 0.003 (2.95) *** -0.670 (-4.07) *** Yes 1.36 (Prob = 0.25) 2373.47 0.9884

Note: * = 10%, ** = 5%, *** = 1% significance

The results for the second sub-sample collected by Statistics New Zealand are shown in Table 7. The results indicate a fairly wild variation in the sign and significance of the real minimum wage variable. When real wages are dropped from the list of regressors, the minimum wage takes on a very large and significant negative coefficient. When the real wage is included, sign, size and significance of the real minimum wage are reversed. At the same time, only the equation excluding all relative prices shows evidence of a lack of omitted variables. The results for the second sub-sample show a dramatic change in the coefficient on the real minimum wage, higher standard errors and unbelievably large coefficients on real wages. It is possible that collinearity problems may be particularly acute, and limited variation in the real minimum wage may be making it difficult to identify a stable coefficient. As with the time series study, I investigated the hypothesis of structural change initiated by the introduction of the ECA in 1991. As before, the null of no change can be rejected (the value of the F test was 2.78, while the 5 percent critical value was in the vicinity of 1.5). To check the implications of this structural change, the equation, as estimated up until the introduction of the ECA, was used to forecast forward to 1997. Graphs of actual and predicted industry employment revealed no obvious systematic tendency to over- or under-predict industry employment post- ECA.

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TABLE 7: Panel results, 1989–97 Independent variable log (MINt /PPOit) log (AHEit/PPOit) log (PPIit/PPOit) log GDPt-1 ∆∆ CPI t ln RER t × TRAD Industry dummies RESET

F statistic R2

-0.472 (-3.68) *** _ _ 0.170 (1.10) -0.005 (-1.86) * 0.268 (2.55) ** Yes 1.92 (Prob = 0.13) 1798.50 0.9910

0.663 (2.68) ** -1.102 (-4.93) *** _ 0.903 (4.58) *** -0.001 (-0.44) 0.153 (1.49) Yes 5.07 (Prob = 0.00) 1965.96 0.9924

0.344 (1.50) -1.154 (-5.16) *** 0.591 (3.01) *** 0.874 (4.52) *** -0.002 (-0.93) 0.186 (1.85) * Yes 3.89 (Prob = 0.01) 1671.22 0.9927

Note: * = 10%, ** = 5%, *** = 1% or less significance

As an additional sensitivity test, to allow for more sluggish adjustment to relative prices, the same series of regressions were run including current and lagged relative price variables by one year. This approach made little substantive difference to the conclusions outlined above.21

8 Allowing elasticities to vary across sectors The above specifications assume that all responses to the exogenous variables are constant across industries in the sample or the sub-samples. Given obvious differences in technologies between industries, this constant elasticities specification seems implausible. The next step was to estimate 29 separate equations using OLS , with a dummy to allow for the change in the QES in 1989. This approach allows each industry to have a different employment response to the minimum wage (and to other control variables) and aims to get a heterogeneity of outcomes between industries. In low-wage industries the real minimum should bite more strongly than in high-wage industries. Of course, the problem with this approach is the very limited number of degrees of freedom that remain for each equation – only 10. However, my focus will be on sign and pattern of the results, rather than their statistical significance (which would depend on residual-based tests which have low power on such small samples). The results in Table 8 show that 14 industries have negative responses to the real minimum wage, and 15 have positive responses – roughly what I would assume if they were distributed randomly. Thus there is no overall tendency for minimum wages to be negatively associated with employment.

21

These results are available from the author on request.

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In addition, since the study is trying to get at the lower end of the tail of the wage distribution, if minimum wages had an adverse impact on employment, I would expect the impact to be stronger in low-wage industries, because the industries would have more wages pushed up by a higher minimum. Figure 2, which cross plots the estimated elasticities and the 1980 wage levels for each of the 29 industries, shows no strong evidence of a systematic relationship between the size of the response and the initial average wage level in the industry. Indeed, the weak correlation runs in the wrong direction for the hypothesis. Note that this TABLE 8: Time series employment–minimum wage elasticities by industry, 1980–97 1980 ordinary time hourly earnings Retail trade Personal and household services Restaurants and hotels Textiles, clothing and leather Sanitary services Manufacturing NEC Wood and wood products Other food, beverages and tobacco Electrical machinery and equipment Construction Machinery, except electrical Non-metallic mineral products Financial institutions Forestry and logging Metals products and engineering Other community services Wholesale trade Health services Transport equipment Real estate and business services Chemicals, petroleum, rubber and plastics Electricity, gas and water Recreational and cultural services Transport and storage (excluding seasonal) Educational services Paper and paper products, printing and publishing Mining and quarrying Insurance Seasonal food processing

3.85 3.90 3.95 4.01 4.14 4.16 4.28 4.38 4.43 4.64 4.64 4.67 4.74 4.77 4.84 4.84 4.85 4.86 4.96 5.02 5.05 5.12 5.14 5.16 5.25 5.27 5.37 5.44 6.16

Estimated minimum wage elasticity 0.040 0.210 0.104 -0.008 -0.209 0.122 0.399 -0.003 0.012 0.267 -0.193 -0.296 0.135 -0.320 -0.119 -0.193 -0.049 0.186 -0.337 0.148 0.053 -0.130 -0.039 0.097 -0.225 0.056 -0.155 0.157 0.032

(1.09) (1.95) (2.07) (-0.08) (-2.03) (0.73) (1.00) (-0.06) (0.94) (1.18) (-1.40) (-2.45) (0.96) (-0.80) (-1.31) (-0.85) (-0.75) (2.21) (-1.65) (1.56) (0.62) (-0.75) (-0.19) (0.98) (-1.47) (1.28) (-0.88) (2.26) (0.37)

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FIGURE 2: Cross plot of elasticities and 1980 wages 0.4 Elasticity = 0.28–0.06 wage

Estimated elasticity

0.3

2

R = 0.032

0.2 0.1 0 -0.1

3.5

4

4.5

5

5.5

6

6.5

-0.2 -0.3 -0.4 Wage rate in 1980 ($)

hypothesis maintains that the numbers swept up by the minimum wage in the industry are negatively related to the average industry wage. Unfortunately there is no ready way of directly testing this hypothesis by examining other moments of the industry wage distribution.

9 Conclusion Using time series and panel data sets and a specification which is as consistent as possible between the two data sets, this study has shown that it is possible to calculate a significant negative impact of real minimum wages on employment. However, this study concludes that a tentative working hypothesis is that increases in the real minimum wage have a minimal negative impact on employment rates. In terms of the time series analysis, this study has been able to broadly replicate Maloney’s (1995; 1997) findings of a significant and comparatively large elasticity of employment of young workers in response to changes in the minimum wage. Nevertheless, the study also shows that the equations with significant elasticities suffer from auto-correlation and omitted variables problems that render them suspect. In particular, including the real minimum wage and the real product wage separately is more consistent with theoretical models and results in an equation with better statistical properties. In this most theoretically consistent and statistically robust equation, the elasticity is half that estimated by Maloney and not significantly different from zero. Furthermore, the preferred time series equation does not appear to be stable over the entire period. Thus the employment effects of minimum wage changes are far from being well defined.

Simon Chapple

Moreover, in the context of the industry panel data set, the study has shown that the significant (and much smaller) negative coefficients are typically not robust to sample splitting in several directions, relaxing the homogeneity postulate or adding other relative price variables. In addition, there is no apparent tendency for lower-wage industries to throw more workers out of work when minimum wages rise. Thus, overall consideration of the employment impact of minimum wage rates using panel and time series data over both annual and quarterly frequencies suggests that conclusions regarding significant negative employment effects from real minimum wage increases are strikingly non-robust. Caution and judgment are needed if the results in this paper are to be used to guide policy decisions. Much of the difficulty of arriving at definitive conclusions about the impact of minimum wage rates arises because of problems in observing employment outcomes for those in the lower tail of the wage distribution. It is worthwhile briefly exploring some of the possible reasons for this finding. It could be that the minimum wage had no apparent negative impact on employment because it was below the market clearing level or below the alternative set of minimum wages set by the award system up until the introduction of the ECA in 1991. Alternatively it could have no effect because employers’ compliance with minimum wage regulations is low. It may be that the negative employment effects were concentrated in sectors outside the coverage of the data (such as the agricultural sector) or in very small firms not covered by the QES . Or the elasticity of labour demand for low-wage workers may be close to zero due to limited substitution possibilities. Employers may make off-setting cuts in non-wage parts of the remuneration package for workers whose money wages are forced upward by minimum wage regulations. There could be monopsony power in particular labour markets. Alternatively the effect of a rise in real minimum wages may shock firms into becoming more efficient, with the resulting improvements in labour productivity fully off-setting the higher real wages for workers swept up by the minimum. Overall, the focus of the overseas debate has shifted from suggesting that changes in real minimum wages have a small negative impact on employment to debating about whether or not they have no impact on employment. On the basis of both the overseas evidence and the research undertaken here on New Zealand data, a sensible working hypothesis is that increases in the New Zealand real minimum wage within the realms of historical experience have a minimal negative impact on employment overall or amongst the industries and groups examined. However, given the short time series, fairly limited variation in the key explanatory variable and some suggestion of structural change post-1991, this conclusion can be no more than tentative.

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10 Appendix: Data sources 10.1 The Quarterly Employment Survey The data used in this study were collected in the Quarterly Employment Survey (QES ). For the period 1980–88, the survey was administered by the Department of Labour. For the period 1989–97, the QES was administered by Statistics New Zealand. The Department of Labour’s survey was a postal survey obtaining responses for the pay week ending on or immediately before the 20th of the middle month of the quarter (in the case of the data used above February). The February survey is full coverage and the other three quarters are sample surveys. The Department of Labour survey covered 79 percent of total employment. It excluded a number of industries which a priori may be considered to be poorly paid. Industries excluded from the survey were agriculture and agricultural contracting, hunting and trapping, fishing, seagoing work, domestic services in households, armed forces, owning and leasing of real estate, waterfront, and police. Within the industries included in the survey, the only firms excluded were those with only one person engaged. For the few units that did not make returns in time, estimates were made on the basis of previous survey returns. Statistics New Zealand’s QES begins in February 1987. It samples a much greater proportion of total employment than the earlier survey (about 92 percent), largely due to improved coverage of forestry and logging, and mining and quarrying. The survey excludes firms with fewer than two full-time equivalent employees. It excludes the same set of industries as the earlier survey by the Department of Labour. TABLE 9: Industry groups and

NZSIC

categories

Industry Forestry and logging Mining and quarrying Seasonal food processing Other food, beverages and tobacco Textiles, clothing and leather Wood and wood products Paper and paper products, printing and publishing Chemicals, petroleum, rubber and plastics Non-metallic mineral products Metal products and engineering Machinery, except electrical Electrical machinery and equipment

NZSIC

12 2 3111–3114 3115–3140 32 33 34 35 36 37, 381 382 383

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TABLE 9: continued Industry

NZSIC

Transport equipment Manufacturing NEC Electricity, gas and water Construction Wholesale trade Retail trade Restaurants and hotels Transport and storage (excluding seasonal) Financial institutions Insurance Real estate and business services Sanitary services Educational services Health services Other community services Recreational and cultural services Personal and household services

384 385–390 4 5 61 62 63 71 81 82 83 92 931 933 934–939 94 95

10.2 The real exchange rate The various series used in this study were obtained from the Reserve Bank of New Zealand. For 1980–95 the data came from Cooper (1988) and updates and revisions from various Reserve Bank Bulletins. The Reserve Bank stopped publishing the series in 1995. However, they still calculate the series and provided me with data for the 1994–97 period, which was linked with the previous published series. 10.3 The inflation data The change in the inflation rate was constructed from Statistics New Zealand’s official CPI series on a March year basis. 10.4

GDP

data

For the period 1979–90 data were taken from the 1996 New Zealand Official Yearbook (p 352). For the period 1990–96 production GDP figures were taken from Statistics New Zealand’s Hot off the Press GDP release, July 1997. The two series were linked in 1990.

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References Brown, Charles (1988) ‘Minimum Wage Laws: Are They Over-rated?’, Journal of Economic Perspectives, 2(3): 133–147. Brown, Charles; Gilroy, Curtis; and Kohen, Andrew (1982) ‘The Effect of the Minimum Wage on Employment and Unemployment’, Journal of Economic Literature, 20: 487–528. Card, David and Krueger, Alan (1995) Myth and Measurement: The New Economics of the Minimum Wage, Princeton, Princeton University Press. Chapple, Simon (1996) ‘Money Wage Rigidity in New Zealand’, Labour Market Bulletin, 2: 23–50. Coleman, William (1992) ‘Concord and Discord Amongst New Zealand Economists: The Results of an Opinion Survey’, New Zealand Economic Papers, 26(1): 47–82. Cooper, Shelley (1988) ‘Estimating New Zealand’s Real Effective Exchange Rate’, Reserve Bank Bulletin, 51(3): 177–183. Cumming, Jackie (1988) ‘A Theoretical and Empirical Analysis of Minimum Wage Legislation and its Impact on Low Pay: A New Zealand Perspective’, Master of Arts in Economics Thesis, University of Auckland, February. Department of Labour (various) Labour and Employment Gazette, Wellington, Department of Labour. Dixon, Sylvia (1995) ‘The Inter-industry Wage Structure: 1971–1994’, Labour Market Bulletin, 1: 41–71. Dixon, Sylvia (1996) ‘The Distribution of Earnings in New Zealand 1984–94’, Labour Market Bulletin, 1: 45–100. Dolado, Juan; Kramarz, Francis; Machin, Stephen; Manning, Alan; Margolis, David; and Teulings, Coen (1996) ‘The Economic Impact of Minimum Wages in Europe’, Economic Policy, 23 (October): 317–357. Easton, Brian (1996) ‘Income Distribution’. In Silverstone, Brian; Bollard, Alan; and Lattimore, Ralph (eds) A Study of Economic Reform: The Case of New Zealand, Amsterdam, North-Holland. Fernie, Sue and Metcalf, David (1996) ‘Low Pay and Minimum Wages: The British Evidence’, Special Report, Centre for Economic Performance, London School of Economics, September. Harbridge, Raymond (no date) ‘Labour Market Regulation and Employment: Trends in New Zealand’, mimeo, 59 pp. Harbridge, Raymond and McCaw, Stuart (1993) ‘The First Wage Round Under the Labour Relations Act 1987: Changing Relative Power’, New Zealand Journal of Industrial Relations, 14(2): 275–287. Kennan, John (1995) ‘The Elusive Effects of Minimum Wages’, Journal of Economic Literature, 33 (December): 1949–1965. Maloney, Tim (1995) ‘Does the Adult Minimum Wage Affect Employment and Unemployment in New Zealand?’, New Zealand Economic Papers, 29(1): 1–19. Maloney, Tim (1997) ‘The “New Economics” of the Minimum Wage? Evidence from New Zealand’, Agenda, 4(2): 185–196. Neumark, David and Wascher, William (1994) ‘Minimum Wage Effects and Low-Wage Labor Markets: A Disequilibrium Approach’, National Bureau of Economic Research Working Paper No. 4617. Schmitt, John and Bernstein, Jared (1997) ‘Three Tests of the Employment Impact of the October 1996 Increase in the US Minimum Wage’, mimeo, Economic Policy Institute, Washington, December 5. Statistics New Zealand (1996) New Zealand Official Yearbook 1996, Wellington, Statistics New Zealand. Statistics New Zealand (1997) Gross Domestic Product – March 1997 Quarter, Hot off the Press series, Wellington, Statistics New Zealand.