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Minimum wage effects on educational enrollments in New Zealand
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Economics of Education Review 26 (2007) 574–587 www.elsevier.com/locate/econedurev
Minimum wage effects on educational enrollments in New Zealand Gail A. Pachecoa,, Amy A. Cruickshankb a
Auckland University of Technology, Private Bag 92006, Auckland 1020, New Zealand b University of Auckland, Private Bag 92019, Auckland 1020, New Zealand Received 17 October 2005; accepted 29 May 2006
Abstract This paper empirically examines the impact of minimum wages on educational enrollments in New Zealand. A significant reform to the youth minimum wage since 2000 has resulted in some age groups undergoing a 91% rise in their real minimum wage over the last 10 years. Three panel least squares multivariate models are estimated from a national sample of nine age cohorts each year over 19 years. This allows analysis of the impact of increases in minimum wage over time and of the introduction of the minimum wage for teenagers in 1994. Our findings indicate that in New Zealand, changes to minimum wages appear to have an insignificant impact on the enrollment levels of 16–24 year olds and the subgroup of 20–24 year olds. In both cases, the standard errors are large making these results unclear. For the subgroup of 16–19 year olds, minimum wage rises have a statistically significant negative effect on enrollment levels. However, the introduction of the minimum wage appears to have had a significantly positive impact on teenagers’ enrollment levels, a possible indication of the ineffective level the minimum wage was set at, in terms of reservation wages of youth in New Zealand. r 2006 Elsevier Ltd. All rights reserved. Jel Classification: J18; J23; J24 Keywords: Minimum wages; Educational economics
1. Introduction The majority of economic research on minimum wages has been concerned with understanding how a minimum wage affects employment outcomes. However, in recent years there has been renewed Corresponding author. Tel.: +64 9 9219999; fax: +64 9 9219629. E-mail address: [email protected] (G.A. Pacheco).
0272-7757/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.econedurev.2006.05.001
interest in the effects of minimum wages upon individuals’ educational decisions, with several papers being published on this subject in the United States (US, Ehrenberg & Marcus, 1980, 1982; Neumark & Wascher, 1995a, b), Canada (Landon, 1997) and recently in New Zealand (NZ, Hyslop & Stillman, 2004). The objective of this paper is to analyse the effects of minimum wages on educational enrollments of 16–24 year olds in NZ over the period 1986–2004.
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NZ presents as a very useful country for minimum wage analysis, as the relative minimum wage1 (i.e. the bite of this legislation) for adults and youth is high, relative to the US (where most minimum wage research emanates from). Also, substantial variation in minimum wages over the last 20 years makes this country and its minimum wage regime more conducive to research on its educational impacts. There are two other motivations for this analysis. Firstly, the effect of minimum wages on educational enrollments is a potentially important factor that has rarely been taken into account in the debates surrounding minimum wages in NZ. The issue’s lack of visibility has become even more topical in NZ given recent calls to raise the youth minimum wage further to 90% of the adult minimum (Regulatory Impact Statement (2003) Minimum Wage Review). Whether or not a higher minimum wage encourages early school leaving is an important question since dropping out of school early has been associated with large private and social costs. These include, but are not limited to, high rates of unemployment among affected groups, increased inequality, and even higher crime rates (Landon, 1997). Additionally, increased unemployment may not fully show the potential effects of an individual leaving school/enrollment early to look for minimum wage work. The higher minimum wage may induce an increase in churning in the labour market. This is the phenomenon of repeated movements in and out of the workforce, which is especially prevalent at the bottom of the labour market. Secondly, Hyslop and Stillman (2004) is the only study to have analysed the effect of minimum wages on study decisions in NZ. One major drawback of their study is that the enrollment measure they adopt tends to misclassify students as non-students. They use a measure of enrollment from the Household Labour Force Survey (HLFS), which counts as not enrolled, all individuals who are out of secondary school, and working more than 2 h/ week.2 This measure has the potential to signifi1 The relative minimum wage refers to the level of the minimum wage relative to a measure of average earnings of individuals, i.e. an indicator of the potential number of workers affected, especially within the lower half of the income distribution, by changes to the minimum wage legislation. 2 The structure of the HLFS is such that a person’s employment status is prioritised over their enrollment status, i.e. if an individual is employed for more than 2 h/week, they are not asked whether or not they are also enrolled.
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cantly underestimate true enrollment rates, and renders as extremely difficult any attempt to compare enrollment rates across age groups that have different labour force participation rates. In a US study, Evans and Turner (1995) found that results can be critically sensitive to the measure of enrollment which is used. In particular, they find that upon switching from a narrower measure of enrollment that is dependent on labour force status, to a broader measure of enrollment that is constructed independently of employment status, the effect of minimum wages on school enrollments becomes negligible. While Neumark and Wascher (2003) question these conclusions, the issue the study raises about misclassifications resulting from enrollment measures that are dependent on labour force status still remain valid. In light of the apparent limitations of the enrollment measure used by Hyslop and Stillman (2004), this study adopts a broader measure of enrollment that is constructed independently of labour force participation, using data from the Ministry of Education. Also, this study examines a much longer time period and a wider range of age groups. This paper is organised as follows: Section 2 reviews the background of minimum wage legislation in NZ. The theoretical links between the minimum wage and educational enrollments will be briefly outlined in Section 3. The data construction and key variables will be discussed in Section 4. The empirical methodology and results are presented in Sections 5 and 6, respectively. Finally, Section 7 shall outline possible policy implications in addition to making some concluding remarks.
2. Minimum wage policy background Although NZ has had a legal minimum wage since 1894, the period under discussion here is the country’s recent history of minimum wage legislation. The 1983 Minimum Wage Act introduced a statutory minimum wage applicable to workers 20 years old or over. There are very few exemptions from paying the minimum wage in NZ. It does not apply to those who hold under rate permits3 and 3 An under rate permit lets a person work for less than the minimum wage. It is granted by Labour Inspectors to a person with a recognised disability that significantly slows down their work and who is incapable of earning the minimum wage (Employment Relations Service, 2004).
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Table 1 Nominal hourly (Gross $/h) minimum wage changes since 1984 Date of change
Pre February 1985 February 1985 September 1985 February 1987 February 1988 May 1989 September 1990 March 1994 March 1995 March 1996 March 1997 March 2000 March 2001 March 2002 March 2003 March 2004
Age groups 16–17 years
18–19 years
20 years+
No minimum applies
No minimum applies
2.500 2.800 4.250 5.250 5.625 5.875 6.125 6.125 6.250 6.375 7.000 7.550 7.700 8.000 8.500 9.000
3.68 3.75 3.83 4.20 4.55 5.40 6.40 6.80 7.20
(1.9) (2.1) (9.7) (8.3) (18.7) (18.5) (6.3) (5.9)
3.68 3.75 3.83 4.20 4.55 7.70 8.00 8.50 9.00
(1.9) (2.1) (9.7) (8.3) (69.2) (3.9) (6.3) (5.6)
(12.0) (51.8) (23.5) (7.1) (4.4) (4.3) (—) (2.0) (2.0) (9.8) (7.9) (2.0) (3.9) (6.3) (5.6)
Percentage change in nominal minimum wage for each age group is reported in parenthesis. Information supplied by the Labour Market Policy Group (Department of Labour).
until June 2003 did not apply to persons undergoing training recognised under the Industry Training Act4. This low rate of exclusion from paying the minimum wage is an important difference in NZ, compared to the situation in the US for example, where most minimum wage research requires coverage adjusted minimum wage ratios to be calculated. Also, the high coverage rate means that the potential individuals have of choosing the alternative to go work in the uncovered sector is negated. In 1994 a youth minimum wage was introduced for 16–19 year olds that was set at 60% of the adult minimum wage. In 1999 a Labour-Alliance coalition government came into office. The Alliance party, in particular, campaigned for improving labour market conditions of young workers and believed that increasing youth minimum wages was an important step toward achieving this objective. Minimum wage reforms were introduced in March 2001 and were comprised of two components: a lowering of the age of eligibility for the adult minimum from 20 to 18 years and an increase in the 4 This included any workers undertaking 60 credits or more from the National Qualifications Framework. These workers are now eligible for the training minimum wage, which is equivalent to the youth minimum wage (Employment Relations Service, 2004).
youth minimum in two annual steps (enacted in March 2001 and March 2002, raising it from 60% to 80% of the adult minimum). Consequently, the statutory minimum wages covering 16–17 year olds and 18–19 year olds increased by 49.5% and 86.8%, respectively, over the period 2000–2003. The adult minimum wage also increased, but to a much lesser extent, 12.6% over these 3 years. A summary of all statutory minimum wage changes for all age groups since 1984 are shown in Table 1. 3. Theoretical framework The theoretical relationship between minimum wages and educational enrollments can be understood using the framework of the human capital model. Analysis under this model assumes that an individual’s schooling decisions are made by weighing the net present value of education’s benefits and costs against one another. In purely economic terms, benefits of education are usually in the form of improved future earnings, while costs are in terms of earnings foregone while in school. According to this framework, the effects of minimum wages on educational enrollments are theoretically ambiguous. On the one hand, simple labour market models predict that introducing a binding minimum wage will reduce employment levels and moreover, will
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increase the minimum productivity necessary to gain employment (Brown, 1999). The minimum wage would therefore force some individuals, who would otherwise have sought employment at wages below the minimum wage, to acquire more education (Ravn and Sorensen, 1997). For this effect to take place, two assumptions must hold. Firstly, there must be a close connection between wages paid and worker productivity. This implies that if the productivity of a worker falls short of the level of productivity implied by the minimum wage, then they cannot find employment. Secondly, workers believe they can increase their productivity to a level sufficient to gain employment through additional education. These two effects imply that raising minimum wages could cause an increase in education levels (Ravn and Sorensen, 1997). However, to the extent that the minimum wage is binding, we would expect a higher minimum wage to compress the lower end of the wage distribution, thus improving the earnings of low-wage workers (Hyslop and Stillman, 2004) and hence, increasing the short run opportunity cost of education for these workers. An implication that has been drawn from this result is that individuals may choose to invest less in education because by improving labour market conditions for low-skilled workers, the expected long-run returns to further education are reduced (Chaplin, Turner & Pape, 20035). Consequently, the possibility of a negative relationship between minimum wages and educational enrollments is established. In addition to these ‘‘price effects’’ (the opportunity cost of time spent in school and returns to education), the minimum wage may also have ‘‘income effect’’, according to Chaplin et al., (2003). If a higher minimum wage raises the lifetime income of the household, and education is a normal good, then we would expect educational enrollments to increase. However, if a higher minimum wage reduces the probability of gaining employment, it could lower household income and, consequently, the level of human capital investment. Therefore, the direction of income effects is critically dependent on how minimum wages affect household income. 5
Chaplin et al (2003) estimated the effects of a higher minimum wage on enrollment in the US and found evidence of a higher minimum wage reducing enrollment in states where students can drop out prior to 18 years of age.
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Given that in this simple framework it is theoretically ambiguous whether minimum wages will result in an increase or a decrease in educational enrollments, previous research has, as in this case, appealed to empirical evidence. 4. Data and key variables The data set used for this study consists of a synthetic panel of annual national-level data from 1986 to 2004 for nine age cohorts each year. The age cohorts are classified as individual years between 16 and 24 years. The data used includes information on enrollment rates and education spending from the Ministry of Education, labour market conditions data from the HLFS6 and minimum wage levels. The sample begins in 1986 with the first sweep of the HLFS and terminates in 2004 because enrollment data was not available for 2005 at the time of this study. 4.1. Enrollment measure One of the main contributions of this study is the use of a broader measure of enrollment, which is independent of labour force status. There has been considerable debate in the literature concerning what the most appropriate enrollment measure to be used is. Neumark and Wascher (1995a, b) use CPS data in the US that counts students as enrolled only if their major activity was ‘‘going to school’’. If individuals reported their major activity as working instead, they were not asked whether they were also enrolled. This enrollment measure thus has the potential to underestimate true enrollment rates. Evans and Turner (1995) use a broader measure of enrollment constructed independently of employment status from the October enrollment supplement of the CPS. They then find that the effects of minimum wages on school enrollments become negligible. In response to this research, Neumark and Wascher (2003) accept that it may be more appropriate to use a broader measure of educational enrollment, but they refute the evidence presented by Evans and Turner (1995). They maintain that Evans & Turner’s results rely on the mis-measurement of the minimum wage variable as they used the 6
The HLFS is a quarterly data source that consists of a sample of between 16,000 and 32,000 households since December 1985. It is an ongoing data set that includes a wide array of information on the working age population in NZ.
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October supplement of the CPS to measure school enrollment, and continued to use the May minimum wage variable. Neumark and Wascher (2003) show that when the October measure of the minimum wage is used rather than the May measure, the negative effects that they found in their 1995 studies persist, regardless of whether a narrow or broad measure of enrollment is used. In the only NZ study to analyse the link between increases in the minimum wage and educational enrollments, Hyslop and Stillman (2004) use a very narrow measure of enrollment7. Their study uses HLFS data where one of the data constraints is that it is impossible to tell whether an individual out of secondary school and working is studying as well. Individuals are not asked about studying if they are employed for at least 2 h of work per week. Consequently, this paper uses a broader measure of enrollment. Specifically, data on the apparent percentage of the relevant population cohort participating in education is taken from an annual survey of educational institutions conducted by the Ministry of Education. The survey currently covers a broad range of education providers including primary, composite, secondary, special and home schooling, in addition to public and private tertiary educational institutions. These enrollment rates are only ‘apparent’ because students who enroll concurrently in more than one type of institution/service will be counted more than once (Education Statistics of New Zealand reports, 1986–2003). This factor may cause estimated enrollment ratios to have a slight upwardbias, however, the bias should be reasonably consistent across the entire sample8. Also, given that this data comes from an annual survey, it is possible that it underestimates enrollment rates, because enrollment is counted as of July 31st in our data. Students not enrolled on that date are not counted9. Also, students who are enrolled in non-formal courses of study will not be captured by this enrollment measure (Hedges, 2001).
variable the ratio of the minimum wage to average hourly earnings, average wage statistics in NZ are not consistent over the period of this study10. Hence, minimum wage studies in NZ typically deflate by the producer price index (PPI—Inputs) (Chapple, 1997).11 Thus, the real minimum wage variable is defined as the statutory minimum wage as of 31 June each year deflated by the PPI (Inputs). The real statutory minimum wage covering teenagers (16–17 and 18–19 year olds) and adults (20+ years) is plotted in Fig. 1. The most notable features of Fig. 1 are the introduction of a youth minimum wage covering 16–19 year olds in 1994, and the major minimum wage reforms that were introduced in March 2001 and 2002 affecting 16–19 year olds. As shown in the graph, 18–19 year olds experienced the largest increase in their real minimum wage when this reform took place in March 2001 and they became part of the adult group, experiencing adult levels of the minimum wage. Finally, there is little need to adjust this real minimum wage variable for the coverage rate in this country as only a relatively small number of workers are exempt from minimum wages (Pacheco and Maloney, 1999). 4.3. Other variables Education spending. Several US and Canadian studies have included education spending and/or education structure measures as explanatory variables in their estimating equation. It is argued that increases in educational spending or improved educational structure (such as reduced student– teacher ratios), makes education relatively more attractive and thus may alter the individual’s relative preferences for work and school and consequently increase school enrollments (Landon, 1997). 10
4.2. Minimum wage Although many US studies in this field of research generally use as their minimum wage 7 Their measure is even narrower than the early narrow measures used by Neumark and Wascher (1995a, b). 8 This upward bias can also be thought of in terms of an increasing intensity in enrollment. 9 The academic year begins in February each year in NZ.
The average hourly wage statistics from the Quarterly Employment Survey are not comparable for the period before and after 1989 because pre-1989 this data was handled by the Department of Labour, and post-1989 it was handled by Statistics NZ. 11 PPI (Inputs) is used by much NZ minimum wage literature as the appropriate deflator as we are implicitly estimating the impact of a higher minimum wage on labour demand (through the value of PPI (Inputs)), which consequently affects the employment propensity of young individuals vulnerable to minimum wage rises, and thus the opportunity cost of these individuals’ enrollment decision.
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real min wage (adult: 20 + years) real min wage (youth:16-17 years)
Jun. 2003
Mar. 2004
Sep. 2002
Dec. 2001
Jun. 2000
Mar. 2001
Sep. 1999
Dec. 1998
Jun. 1997
Mar. 1998
Sep. 1996
Dec. 1995
Jun. 1994
Mar. 1995
Sep. 1993
Dec. 1992
Jun. 1991
Mar. 1992
Sep. 1990
Dec. 1989
Jun. 1988
Mar. 1989
Sep. 1987
Mar. 1986
real min wage (youth:18-19 years)
Dec. 1986
Real minimum wage ($)
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Fig. 1. The real minimum wage for adults since 1986 and youth since 1994. Note: nominal adult minimum wages over the period 1986:1–2004:2 and nominal youth minimum wages over the period 1994:1–2004:2 are deflated by the PPI (Inputs), with a base year of 1997:4. Source: minimum wage levels supplied by the Labour Market Policy Group, PPI (Inputs) from Statistics NZ.
However, since 1991 in NZ, education spending has been primarily demand-driven. This implies that education expenditure is likely to be endogenous, i.e. increased enrollment rates will lead to increased education spending, rather than causation running in the other direction. Thus, the log of the lagged value of education spending as a percentage of GDP is included in the regressions. Control variables. It is important that the estimated model in this research includes controls for the cyclical fluctuations in the economy. This is important as it is often suggested that the government could possibly choose the timing of minimum wage increases in response to changes in the economy, i.e. the government may find it easier to raise the minimum wage when employment is expanding (Card and Krueger, 1995). In NZ, the 1983 Minimum Wage Act stipulates that minimum wages must be reviewed by 31 December every year, and by convention any changes are typically made the following March (Regulatory Impact Statement (2003) Minimum Wage Review). On average, minimum wages are not increased by a fixed amount every year, and it is worth noting that large increases did not occur until recent years when the economy was buoyant. The possible controls used to capture the effect of the aggregate business cycle, or more particularly, labour demand, on education-work decisions, are either the unemployment rate of prime-aged males (taken to be 25–54 year old males12 in this study) or 12
The reason a broad age group (25–54 year olds) for the unemployment variable is chosen rather than a narrower one is
real GDP per capita13. The idea is that a higher unemployment rate (or lower levels of real GDP per capita) is likely to signal both a lower likelihood of finding a job and an increased likelihood of having lost work, and thus is expected to be associated with increased enrollment rates. Some simple descriptive statistics of all variables used in the following models are presented in Appendix A. 5. Estimation methods To model the enrollment decision empirically, the log of the proportion of individuals (Y) of age cohort i (i ¼ 16,y,24) enrolled in an educational institution at time t14 (t ¼ 1986,y,2004) will be described by three synthetic panel data models (S1, S2 and S3). Given the relatively short data series available, there is limited time-series data for each age cohort and thus insufficient power to test hypotheses separately for each age group. To the extent that it is reasonable to impose a homogeneity condition on the coefficient on the minimum wage variable across age groups (16–24 year olds, and subgroups (footnote continued) because it reflects cyclical variations in labour market conditions better than narrower age bands, which may reflect a lot of ‘noise’. 13 Both measures are used in the upcoming regression analysis in Section 5 as a check of robustness of results. 14 Hence, the numerator of this enrollment rate variable is the number of individuals enrolled for that age cohort and the denominator is the population (for that age cohort), using data from Statistics NZ.
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of 16–19 and 20–24 year olds), a synthetic panel data model, by providing additional power, allows the detection of a relationship that may not have been evident from individual time-series regressions. 5.1. S1 The first enrollment regression model S1 is given by
Y it ¼ ai þ bi E t1 þ dN it þ gZit þ ji t þ it . S2 includes age-specific time trends (ji t). Enrollment rates of different age groups have exhibited different trending behaviour over the sample period. Consequently, as in Hyslop and Stillman (2004), the linear time trend variable t is used to capture these age-specific trends in enrollment rates. Other variables are as defined in S1.
Y it ¼ ai þ bi E t1 þ dN it þ gZit þ it .
5.3. S3
This is a linear model specification, which is consistent with the majority of research in the minimum wage arena. In this model, Yit is the log of the enrollment rate as defined previously, ai is the age-specific fixed effect, and Et1 is the log of the lagged value of education spending as a percentage of GDP. Nit is the next independent variable in S1 and is one for individuals if a minimum wage is applicable and zero if not. Hence, it captures the introduction of the teen minimum in 1994. Nit is then interacted with Xit (Zit ¼ NitXit) which is the log of the real minimum wage. The real minimum wage is the nominal minimum wage deflated by the PPI (Inputs), as defined in the previous section. This variable is expressed in logarithmic form so that the coefficient on it has a clear interpretation: the elasticity of enrollment with respect to the minimum wage. This variable is also later deflated by the consumer price index (CPI) as a test for sensitivity of results to choice of deflator. Lastly, the use of the Levin Lin and Chu (LLC) panel unit root test did not reject the hypothesis of no unit root, for each of these independent variables. In this model specification, (as in the upcoming specifications of S2 and S3), it is likely that there are unobserved effects, (such as cross-sectional heterogeneity in skills or preferences), that are constant over time, and correlated with regressors. Consequently, least squares coefficient estimates may be biased and inconsistent, as a result of an omitted variable. Therefore, age-specific fixed effects are included to control for these time-invariant characteristics specific to age cohorts and to produce more consistent coefficient estimates.
The effects of labour market conditions on enrollment decisions are controlled for in model S3. As stated earlier, either the unemployment rate of prime-aged males or real GDP per capita is used as a control for the state of the economy. Initially the former measure is used15, and subsequently it will be shown that results are robust to the use of the latter cyclical control. It is assumed that the coefficient on the labour demand variable is heterogeneous across age cohorts. There is strong theoretical justification for this. There is evidence that during recessions, older, more established workers suffer less adverse employment effects than younger, more inexperienced workers. Given the inter-related nature of education-work decisions, this is likely to mean that the aggregate business cycle has different effects on enrollment decisions across age cohorts. Enrollment regression model S3 is specified by Y it ¼ ai þ bi E t1 þ dN it þ gZit þ ji t þ yi ut þ it . The control for labour demand, ut, is defined as in the previous section. The other variables are defined as in S2. 6. Results 6.1. Estimates Table 2 presents panel least squares (PLS) regression results from the three alternative synthetic panel data models for 16–24 year olds for the time period 1986–2004. Estimated panel corrected standard errors are given in parentheses; these 15
5.2. S2 The next enrollment regression model S2 is specified by
Controls using unemployment rate are commonly used in NZ minimum wage research to reflect cyclical changes in economic growth (see Pacheco and Maloney (1999) and Maloney (1995) who both use the unemployment rate of older adults to capture any systematic movement in the dependent variable over the NZ business cycle).
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Table 2 PLS estimates of the effects of minimum wages on educational enrollments of 16–24 year olds, 1986–2004 Independent variables
S1
S2
S3
Real minimum wage Impact of introduction of minimum wage for teenagers aged 16–19
0.0909 (0.0823) 0.0182 (0.0432)
0.0685 (0.0618) 0.1480*** (0.0278)
0.0887 (0.0618) 0.1116*** (0.0292)
Education spending as a percentage of GDP: age-specific 16 years 17 years 18 years 19 years 20 years 21 years 22 years 23 years 24 years
0.2979 (0.2424) 0.6604** (0.3055) 0.7415** (0.3769) 0.4350 (0.3540) 0.1385 (0.3605) 0.1899 (0.6266) 0.4857 (0.4521) 0.4826 (0.5572) 0.2877 (0.6166)
0.7326*** (0.1631) 1.4259*** (0.2090) 1.2876*** (0.2820) 0.7932*** (0.2379) 0.3030 (0.2455) 0.5631 (0.3732) 0.2629 (0.2723) 0.4375 (0.3762) 0.3572 (0.4486)
0.3641**(0.1756) 0.4276 (0.3590) 0.8925**(0.3707) 0.7021** (0.3129) 0.2002 (0.3428) 0.4958 (0.5237) 0.1448 (0.3809) 0.1836 (0.5205) 0.3226 (0.6319)
Unemployment rate of prime-aged males: age-specific 16 years 17 years 18 years 19 years 20 years 21 years 22 years 23 years 24 years R2 Observations
— — — — 0.9538 171
— — — — 0.9785 171
0.1100*** (0.0399) 0.2526*** (0.0802) 0.1184 (0.0878) 0.0123 (0.0742) 0.0339 (0.0808) 0.0214 (0.1236) 0.0432 (0.0898) 0.0906 (0.1229) 0.0141 (0.1492) 0.9796 171
Note: * 10%, ** 5%, *** 1% significance level; Panel corrected standard errors are given in parentheses. Coefficient estimates on the linear time trend variables and the cross-sectional fixed effects dummies are reported in Appendix B.
ensure that the estimator is robust to cross-sectional (contemporaneous) correlation as well as different error variances in each cross-section. For simplicity, coefficient estimates on the linear time trend variables and the cross-section fixed effects are given in Appendix B. Table 2 shows that education spending has a positive impact on enrollment rates. This impact tends to be larger in magnitude and more significant for teenagers (16–19 year olds). However, it is important to keep in mind when reading these coefficients, that although the lagged value of education spending is used in the model specifications S1–S3, it is possible this instrument has not fully addressed the endogeneity issue associated with the use of an education related variable as a control in these regressions16. The primary focus of this study is on the minimum wage variable in the enrollment equation. PLS estimates of S1 indicate that higher minimum 16
Many thanks to the Associate Editor of this journal for making this observation.
wages have a negative and insignificant effect on enrollment rates of 16–24 year olds. Specifically, the elasticity of enrollment with respect to the minimum wage is 0.0909. This implies that a 10% increase in the minimum wage is likely to be associated with a 0.909% point fall in the number of persons enrolled in education. When age specific time trends are included in the second specification (S2), PLS estimates still indicate an insignificant and negative relationship between minimum wages and educational enrollments. The magnitude of the elasticity of enrollment with respect to the minimum wage is now 0.0685, and still statistically insignificant. In the last specification (S3), labour demand is controlled for via the inclusion of age specific responses to the unemployment rate of prime aged males. The parameter estimates in Table 2 suggest that the influence of labour demand on educational enrollments is significant and positive for younger age groups (16 and 17 year olds). This result is consistent with what would be expected as it shows that younger age groups increase their consumption of education, as the unemployment rate of prime
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aged males increases (i.e. as there is a general reduction in work opportunities available). PLS estimates of S3 again show a statistically insignificant negative relationship between minimum wages and educational enrollment rates. While, these results focus on the effects on changes in the log of the real minimum wage (Xit), estimates in Table 2 also show the impact of the introduction of the minimum wage. These estimates essentially illustrate the effect of switching on the minimum for teens in April 1994 and are captured by qY it =qN it ¼ d þ gX¯ it where X¯ it is the value of the log of the real minimum wage when introduced for teens (i.e. 1.3282). The results in Table 2 indicate that the impact on enrollment rates for 16–24 year olds when the teen minimum was introduced was positive. Results across the three specifications point to this one off effect increasing enrollment rates. In the preferred specification, S3, the increase is equivalent to enrollment rates rising by 1.48% points and this impact is significant at the 1% level. 6.2. Estimates for age sub groups Tables 3A and B presents PLS regression results for 16–19 and 20–24 year olds for the time period 1986–2004. The results contained within Tables 3A and B are interesting as they appear to clearly show the 16–19 year olds as the age subgroup which experiences a significant negative decline in enrollment rates when their applicable real minimum wage levels are raised. These effects are statistically more significant, as well as larger in magnitude in comparison to the estimates provided in Table 2. For example, in the preferred specification S3, where age specific time trends and responses to business cycle fluctuations are controlled for, the minimum wage coefficient implies that a 10% increase in the real minimum wage decreases enrollment rates by 1.535% points17. Table 3A also shows that the impact of the introduction of the minimum wage on teenagers aged 16–19, in terms of their enrollment decision, was positive. Under all three specifications, there is a positive and significant impact on enrollment rates ranging from 0.998% to 1.526% points. Again, it is important to understand that this estimated effect is 17 The estimates for the subgroups of 16–17 and 18–19 year olds were not statistically different and very similar to the results for the aggregate group of 16–19 year olds.
the impact of the change in minimum wage legislation in April 1994. Table 3B presents the comparable specifications for 20–24 year olds with the obvious exception of the variable Nit, since individuals aged 20 and over have had a minimum apply throughout the time period under study. We find, that the coefficient on the minimum wage variable for 20–24 year olds is positive and large in magnitude, but noticeably insignificant. Hence, indicating that we cannot reject the hypothesis that raising the minimum wage has no impact on the enrollment decision of 20–24 year olds in NZ. These results are consistent with early US studies by Matilla (1978) and Cunningham (1981) (for white youth) which found a significant negative relationship between minimum wages and educational enrollments for 16–19 year olds, but no significant relationship for 20–24 year olds. Latter studies have tended to focus solely on teenage age cohorts. The results of this study add weight to this view that the impact of minimum wage rises has a significant impact on the enrollment decision of 16–19 year olds. One of the key motivations of this paper was the importance of investigating empirical findings using other data sets. As mentioned earlier, this issue is potentially important in the NZ case, given that the enrollment measure in the HLFS (used by Hyslop and Stillman, 2004) potentially underestimates true enrollment rates and makes it difficult to compare enrollment rates across age groups with different labour force participation rates. Yet, this study, which uses the broader measure of enrollment from the Ministry of Education data, as well as covering a longer time span and wider range of age cohorts, still finds broadly consistent results. 6.3. Sensitivity tests To examine the sensitivity of our results, firstly S3 is re-estimated using real GDP per capita rather than the unemployment rate of prime-aged males. The minimum wage estimates found are quantitatively close to those reported in Tables 2, 3A and B. For example, the estimates of the elasticity of enrollment with respect to the minimum wage for the model S3 becomes 0.0641 and insignificant for 16–24 year olds and 0.1656 and significant at the 1% level for 16–19 year olds. This is in comparison to 0.0887 (insignificant) and 0.1535 (significant at the 1% level) in the original models.
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Table 3A PLS estimates of the effects of minimum wages on educational enrollments of 16–19 year olds, 1986–2004 Independent variable
S1
S2
S3
Real minimum wage Impact of introduction of minimum wage for teenagers aged 16–19
0.0561 (0.0479) 0.1526*** (0.0199)
0.1174* (0.0615) 0.1425*** (0.0279)
0.1535*** (0.0591) 0.0998*** (0.0288)
Education spending as a percentage of GDP: age-specific 16 years 17 years 18 years 19 years
0.5021*** 1.3357*** 1.3988*** 1.0428***
0.6981*** 1.4250*** 1.2471*** 0.7526***
0.3179* (0.1732) 0.3006 (0.3542) 0.8655** (0.3626) 0.6751** (0.3230)
Unemployment rate of prime-aged males: age-specific 16 years 17 years 18 years 19 years R2 Observations
— — — — 0.9568 76
(0.1688) (0.1817) (0.2402) (0.2210)
(0.1634) (0.2122) (0.2760) (0.2446)
— — — — 0.9635 76
0.1083*** (0.0394) 0.2844*** (0.0790) 0.1072 (0.0859) 0.0012 (0.0766) 0.9710 76
Note: *10%, **5%, ***1% significance level; Panel corrected standard errors are given in parentheses. Coefficient estimates on the linear time trend variables and the cross-sectional fixed effects dummies are reported in Appendix C.
Table 3B PLS estimates of the effects of minimum wages on educational enrollments of 20–24 year olds, 1986–2004 Independent variables
S1
S2
S3
Real minimum wage
0.8292 (0.6358)
0.3391 (0.2276)
0.2924 (0.2465)
Education spending as a percentage of GDP: age-specific 20 years 0.1126 (0.0795) 21 years 0.0719 (0.1344) 22 years 0.0046 (0.0965) 23 years 0.0102 (0.1150) 24 years 0.0147 (0.1416)
0.0806 (0.0559) 0.1250 (0.0919) 0.0123 (0.0756) 0.0359 (0.0969) 0.0109 (0.1126)
0.0007 (0.0818) 0.0750 (0.1178) 0.0801 (0.0979) 0.1517 (0.1240) 0.2235* (0.1333)
Unemployment rate of prime-aged males: age-specific 20 years — 21 years — 22 years — 23 years — 24 years — 0.9114 R2 Observations 95
— — — — — 0.9408 95
0.0860 (0.0563) 0.0521 (0.0654) 0.1027** (0.0474) 0.2061*** (0.0631) 0.2568*** (0.0633) 0.9482 95
Note: *10%, **5%, ***1% significance level; panel corrected standard errors are given in parentheses. Coefficient estimates on the linear time trend variables and the cross-sectional fixed effects dummies are reported in Appendix C.
Next, the cyclical control is changed back to the unemployment rate of prime-aged males and the use of an alternative deflator (CPI) for the minimum wage variable is employed. Appendix C shows the results of this sensitivity test. Again, minimum wage estimates are qualitatively and quantitatively similar to those reported in the original models. For example, the minimum wage coefficient is now 0.0816 and insignificant for 16–24 year olds,
whereas it was 0.0887 and insignificant in the original S3. 7. Conclusion 7.1. Findings and policy implications The results of this paper add to the growing body of empirical evidence on the contribution of higher
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minimum wages to reduced educational enrollment rates. Evidence presented in this paper, based on a panel of nine age cohorts each year over a 19 year period (1986–2004), indicates that higher minimum wages tends to result in reduced enrollment levels. The empirical estimates imply that an increase in the minimum wage has a small negative but insignificant impact on the number of persons enrolled in education in the aggregate group of 16–24 year olds. Results from disaggregating age cohorts into 16–19 year olds and 20–24 year olds suggests that there is a significant and negative relationship between minimum wages and educational enrollments for the former age subgroup, and an insignificant impact on educational enrollments for the latter age subgroup. This is what we would expect, and is consistent with the findings of other studies (Hyslop & Stillman, 2004). To put these results into context, the magnitude of the significant declines in enrollment rates for teenagers, under S3, would have amounted to 2506 students nationally in 2004, out of a total enrollment of 238,940 students. Results also appear to point towards an interesting contrast between the impact of the minimum wage becoming applicable versus the impact of changes in the minimum wage. Overall, estimates indicate that the introduction of the teen minimum for 16–19 year olds in 1994 actually increased enrollment rates significantly. This increase was in comparison to the negative and significant impact on enrollment rates found for teenagers when the level of the real minimum wage increased subsequently. A possible hypothesis for these contrasting results is that different sub-groups of teenagers are impacted differently. For example, it is plausible to expect that teens with the least skills and hence lowest wages and enrollment rates may have been affected most by the introduction of the minimum wage and consequently switched from employment (which was no longer available) to school enrollment. Later, as the minimum wage was increased more, higher skilled individuals then became affected. These teens were more likely to have been in school before the minimum wage increase and to not have considered employment until the minimum wage was increased so that it surpassed their reservation wage. Therefore, increasing the minimum wage may change the distribution of skill levels of youth in school vs. those in the job market, pulling the more skilled youth out of school and into the job market and having the opposite impact on those with less skills. To check this hypothesis,
there is a need for future research on differential impacts on youth by skill levels, if and when such data become available. The results on the minimum wage coefficients are broadly consistent with other recent research in this area, and add credence to calls for the impact of minimum wages on educational enrollments to be taken into account in debates surrounding minimum wages (especially the youth minimum in NZ). Proponents of minimum wages in NZ see minimum wage legislation, among other things, as an instrument for promoting social justice, addressing poverty, rewarding additional work effort, protecting vulnerable employees and ensuring that low-wage workers share in the gains from economic growth (Coutts, 2004). While this paper does not address the efficacy of minimum wages in achieving these broad objectives, it does highlight the unplanned negative consequences of minimum wages on enrollment rates in NZ. Hence, the impact of the minimum wage on educational outcomes should be weighed against other perceived benefits of the legislation, and the potential trade-off examined when considering minimum wage rate increases. Additionally, policies to mitigate the negative effects of minimum wages on educational enrollments could also be considered. The negative side effects could be offset by other policies to encourage greater investment in education such as increasing the accessibility to post-secondary school education by reducing the financial barriers to investment in further education. Furthermore, evidence from US studies18 suggests that raising the minimum school leaving age is an effective policy instrument for ensuring that minimum wages do not negatively affect the school enrollments of young people.
Appendix A Descriptive statistics are shown in Table A1.
Appendix B PLS Estimates of the Effects of Minimum Wages on Educational Enrollments of 16–24 year olds, 1986–2004 are continued in Table B1. 18
See Chaplin et al (2003).
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Table A1 Descriptive statistics Variable
Mean
Standard deviation
Enrollment rates (% population) 1986–2004 16–17 years 18–19 years 20–24 years
77.63 45.82 27.49
12.94 7.54 10.47
1986–1990 16–17 years 18–19 years 20–24 years
66.06 34.38 20.32
13.84 2.51 6.15
1991–1995 16–17 years 18–19 years 20–24 years
83.00 49.00 26.68
10.21 4.87 10.17
1996–2000 16–17 years 18–19 years 20–24 years
84.16 50.75 28.76
9.49 4.60 10.05
2001–2004 16–17 years 18–19 years 20–24 years
79.04 48.76 36.02
9.68 6.26 9.78
Real minimum wage (deflated by PPI—Inputs; NZ$) 16–17 years 18–19 years 20+ years
4.62 5.20 6.77
0.88 1.62 0.43
Real minimum wage (deflated by CPI; NZ$) 16–17 years 18–19 years 20+ years
4.78 5.4 7.11
0.96 1.77 0.39
1.69 6222.56 5.42
0.52 801.29 0.43
Unemployment rate of prime aged males Real GDP per capita ($NZ) Education spending (% of GDP)
Note: Enrollment rates and Education spending are sourced from the Ministry of Education, Nominal minimum wage levels supplied by the Labour Market Policy Group, and the remaining variables are sourced from Statistics NZ.
Table B1 Table 2 continued Independent variable
S1
S2
S3
Linear time trend: age-specific 16 years 17 years 18 years 19 years 20 years 21 years 22 years 23 years 24 years
— — — — — — — — —
0.0062 (0.0039) 0.0022 (0.0037) 0.0037 (0.0055) 0.0076 (0.0051) 0.0299*** (0.0036) 0.0322*** (0.0054) 0.0399*** (0.0040) 0.0390*** (0.0055) 0.0379*** (0.0065)
0.0028 (0.0039) 0.0071 (0.0045) 0.0074 (0.0057) 0.0116** (0.0053) 0.0300*** (0.0037) 0.0323*** (0.0056) 0.0402*** (0.0041) 0.0394*** (0.0056) 0.0381*** (0.0068)
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Table B1 (continued ) Independent variable
S1
S2
S3
Cross-sectional fixed effects 16 years 17 years 18 years 19 years 20 years 21 years 22 years 23 years 24 years Constant
0.3143 0.4621 0.9440 0.4993 0.5200 0.1669 0.7644 0.4922 0.0186 0.8589***
0.6665 0.7995 0.9564 0.2768 0.1867 0.4301 0.6030 0.6569 0.3497 2.0496***
0.9288 0.4077 0.6548 0.4403 0.0949 0.5764 0.1731 0.0194 0.0476 1.7533***
Note: *10%, **5%, ***1% significance level; panel corrected standard errors are given in parentheses.
Table C1 Table 3A continued Independent variable
S1
S2
S3
Linear time trend: age-specific 16 years 17 years 18 years 19 years
— — — —
0.0043 (0.0039) 0.0013 (0.0037) 0.0062 (0.0055) 0.0102* (0.0052)
0.00008 (0.0037) 0.0095** (0.0044) 0.0112** (0.0055) 0.0154*** (0.0052)
Cross-sectional fixed effects 16 years 17 years 18 years 19 years Constant
1.3444 0.3161 0.7569 0.2715 2.4044***
1.0167 0.4955 0.6004 0.0793 2.3516***
0.8532 0.4603 0.7640 0.5495 1.6138***
Note: *10%, **5%, ***1% significance level; panel corrected standard errors are given in parentheses.
Table C2 Table 3B continued Independent variable
S1
S2
S3
Linear time trend: age-specific 20 years 21 years 22 years 23 years 24 years
— — — — —
0.0261*** 0.0287*** 0.0355*** 0.0342*** 0.0328***
Cross-sectional fixed effects 20 years 21 years 22 years 23 years 24 years Constant
0.0182 0.0853 0.2360 0.0047 0.3079 2.6548 (3.2696)
0.2044 0.2176 0.1782 0.0699 0.2348 0.1552 (1.2089)
(0.0053) (0.0064) (0.0059) (0.0074) (0.0082)
Note: *10%, **5%, ***1% significance level; panel corrected standard errors are given in parentheses.
0.0293*** 0.0307*** 0.0327*** 0.0282*** 0.0251***
(0.0056) (0.0071) (0.0065) (0.0080) (0.0086)
0.7386 0.2108 0.1246 0.3056 0.7683 0.6475 (1.1380)
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Table C3 PLS estimates of the effects of minimum wages on educational enrollments using CPI as the deflator for S3 Independent variables
16–24 year olds
16–19 year olds
20–24 year olds
Real minimum wage R2 Observations
0.0816 (0.0567) 0.9796 171
0.1433*** (0.0542) 0.9712 76
0.4124 (0.2589) 0.9432 95
Note: *10%, **5%, ***1% significance level; panel corrected standard errors are given in parentheses.
Appendix C Tables 3A and B Continued and PLS Estimates of the effects of minimum wages on educational enrollments using CPI as the deflator for S3 are shown in Tables C1–C3. References Brown, C. (1999). Minimum wages, employment, and the distribution of income. In O. Ashenfelter, & D. Card (Eds.), Handbook of labor economics (3b) (pp. 2101–2158). Amsterdam: North-Holland. Coutts, J. (2004). How do we solve the conundrum of minimum wages? Policy Backgrounder, Business Round Table, pp. 1–5. Card, D., & Krueger, A. (1995). Myth and measurement: The new economics of the minimum wage. Princeton: Princeton University Press. Chaplin, D., Turner, M., & Pape, A. (2003). Minimum wages and school enrollment of teenagers: A look at the 1990s. Economics of Education Review, 22(1), 11–21. Chapple, S. (1997). Do minimum wages have an adverse impact on employment? Evidence from New Zealand. Journal of Labour Market Research, Department of Labour(2), 25–50. Cunningham, J. (1981). The impact of minimum wages on youth employment, hours of work, and school attendance: Crosssectional evidence from the 1960 and 1970 censuses. In S. Rottenberg (Ed.), The economics of legal minimum wages (pp. 88–123). Washington, DC: American Enterprise Institute. Education Statistics of New Zealand reports, (1986–2003). Data Management and Analysis Division, Ministry of Education, Government of New Zealand. Ehrenberg, R., & Marcus, A. (1980). Minimum wage legislation and the educational outcomes of youths. Research in Labor Economics(3), 61–93.
Ehrenberg, R., & Marcus, A. (1982). Minimum wages and teenagers’ enrollment-employment outcomes. Journal of Human Resources, 17(1), 39–58. Employment Relations Service. (2004). New Zealand: Department of Labour. Evans, W., & Turner, M. (1995). Minimum wage effects on employment and school enrollment: Comment, working paper. College Park: University of Maryland. Hedges, M. (2001). Participation Effects of the New Zealand Student Loans Scheme. Auckland: The University of Auckland. Hyslop, D., Stillman, S. (2004). Youth minimum wage reform and the labor market. IZA Discussion paper 1091. Landon, S. (1997). High school enrollment, minimum wages and education spending. Canadian Public Policy, 23(2), 141–163. Matilla, J. (1978). Youth labor markets, enrollments, and minimum wages. In Proceedings of the thirty first annual meetings (Chicago, Aug. 29– 31, 1978). Madison, WI: Industrial Relations Research Association, pp. 134–140. Maloney, T. (1995). Does the adult minimum wage affect employment and unemployment in New Zealand. NZ Economic Papers, 29(1), 1–19. Neumark, D., & Wascher, W. (1995a). Minimum wage effects on employment and school enrollment. Journal of Business & Economic Statistics, 13(2), 199–210. Neumark, D., & Wascher, W. (1995b). Minimum-wage effects on school and work transitions of teenagers. The American Economic Review, 85(2), 244–249. Neumark, D., & Wascher, W. (2003). Minimum wages and skill acquisition: another look at schooling effects. Economics of Education Review(22), 1–10. Pacheco, G., & Maloney, T. (1999). Does the minimum wage reduce the employment prospects of unqualified New Zealand women. Journal of Labour Market Research, Department of Labour, 4, 51–69. Ravn, M., & Sorensen, J. (1997). Minimum wages: Curse or blessing. Research in Labor Economics(16), 343–368. Regulatory Impact Statement. (2003). Minimum Government of New Zealand: Wage Review.
Economics of Education Review 26 (2007) 574–587 www.elsevier.com/locate/econedurev
Minimum wage effects on educational enrollments in New Zealand Gail A. Pachecoa,, Amy A. Cruickshankb a
Auckland University of Technology, Private Bag 92006, Auckland 1020, New Zealand b University of Auckland, Private Bag 92019, Auckland 1020, New Zealand Received 17 October 2005; accepted 29 May 2006
Abstract This paper empirically examines the impact of minimum wages on educational enrollments in New Zealand. A significant reform to the youth minimum wage since 2000 has resulted in some age groups undergoing a 91% rise in their real minimum wage over the last 10 years. Three panel least squares multivariate models are estimated from a national sample of nine age cohorts each year over 19 years. This allows analysis of the impact of increases in minimum wage over time and of the introduction of the minimum wage for teenagers in 1994. Our findings indicate that in New Zealand, changes to minimum wages appear to have an insignificant impact on the enrollment levels of 16–24 year olds and the subgroup of 20–24 year olds. In both cases, the standard errors are large making these results unclear. For the subgroup of 16–19 year olds, minimum wage rises have a statistically significant negative effect on enrollment levels. However, the introduction of the minimum wage appears to have had a significantly positive impact on teenagers’ enrollment levels, a possible indication of the ineffective level the minimum wage was set at, in terms of reservation wages of youth in New Zealand. r 2006 Elsevier Ltd. All rights reserved. Jel Classification: J18; J23; J24 Keywords: Minimum wages; Educational economics
1. Introduction The majority of economic research on minimum wages has been concerned with understanding how a minimum wage affects employment outcomes. However, in recent years there has been renewed Corresponding author. Tel.: +64 9 9219999; fax: +64 9 9219629. E-mail address: [email protected] (G.A. Pacheco).
0272-7757/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.econedurev.2006.05.001
interest in the effects of minimum wages upon individuals’ educational decisions, with several papers being published on this subject in the United States (US, Ehrenberg & Marcus, 1980, 1982; Neumark & Wascher, 1995a, b), Canada (Landon, 1997) and recently in New Zealand (NZ, Hyslop & Stillman, 2004). The objective of this paper is to analyse the effects of minimum wages on educational enrollments of 16–24 year olds in NZ over the period 1986–2004.
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NZ presents as a very useful country for minimum wage analysis, as the relative minimum wage1 (i.e. the bite of this legislation) for adults and youth is high, relative to the US (where most minimum wage research emanates from). Also, substantial variation in minimum wages over the last 20 years makes this country and its minimum wage regime more conducive to research on its educational impacts. There are two other motivations for this analysis. Firstly, the effect of minimum wages on educational enrollments is a potentially important factor that has rarely been taken into account in the debates surrounding minimum wages in NZ. The issue’s lack of visibility has become even more topical in NZ given recent calls to raise the youth minimum wage further to 90% of the adult minimum (Regulatory Impact Statement (2003) Minimum Wage Review). Whether or not a higher minimum wage encourages early school leaving is an important question since dropping out of school early has been associated with large private and social costs. These include, but are not limited to, high rates of unemployment among affected groups, increased inequality, and even higher crime rates (Landon, 1997). Additionally, increased unemployment may not fully show the potential effects of an individual leaving school/enrollment early to look for minimum wage work. The higher minimum wage may induce an increase in churning in the labour market. This is the phenomenon of repeated movements in and out of the workforce, which is especially prevalent at the bottom of the labour market. Secondly, Hyslop and Stillman (2004) is the only study to have analysed the effect of minimum wages on study decisions in NZ. One major drawback of their study is that the enrollment measure they adopt tends to misclassify students as non-students. They use a measure of enrollment from the Household Labour Force Survey (HLFS), which counts as not enrolled, all individuals who are out of secondary school, and working more than 2 h/ week.2 This measure has the potential to signifi1 The relative minimum wage refers to the level of the minimum wage relative to a measure of average earnings of individuals, i.e. an indicator of the potential number of workers affected, especially within the lower half of the income distribution, by changes to the minimum wage legislation. 2 The structure of the HLFS is such that a person’s employment status is prioritised over their enrollment status, i.e. if an individual is employed for more than 2 h/week, they are not asked whether or not they are also enrolled.
575
cantly underestimate true enrollment rates, and renders as extremely difficult any attempt to compare enrollment rates across age groups that have different labour force participation rates. In a US study, Evans and Turner (1995) found that results can be critically sensitive to the measure of enrollment which is used. In particular, they find that upon switching from a narrower measure of enrollment that is dependent on labour force status, to a broader measure of enrollment that is constructed independently of employment status, the effect of minimum wages on school enrollments becomes negligible. While Neumark and Wascher (2003) question these conclusions, the issue the study raises about misclassifications resulting from enrollment measures that are dependent on labour force status still remain valid. In light of the apparent limitations of the enrollment measure used by Hyslop and Stillman (2004), this study adopts a broader measure of enrollment that is constructed independently of labour force participation, using data from the Ministry of Education. Also, this study examines a much longer time period and a wider range of age groups. This paper is organised as follows: Section 2 reviews the background of minimum wage legislation in NZ. The theoretical links between the minimum wage and educational enrollments will be briefly outlined in Section 3. The data construction and key variables will be discussed in Section 4. The empirical methodology and results are presented in Sections 5 and 6, respectively. Finally, Section 7 shall outline possible policy implications in addition to making some concluding remarks.
2. Minimum wage policy background Although NZ has had a legal minimum wage since 1894, the period under discussion here is the country’s recent history of minimum wage legislation. The 1983 Minimum Wage Act introduced a statutory minimum wage applicable to workers 20 years old or over. There are very few exemptions from paying the minimum wage in NZ. It does not apply to those who hold under rate permits3 and 3 An under rate permit lets a person work for less than the minimum wage. It is granted by Labour Inspectors to a person with a recognised disability that significantly slows down their work and who is incapable of earning the minimum wage (Employment Relations Service, 2004).
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Table 1 Nominal hourly (Gross $/h) minimum wage changes since 1984 Date of change
Pre February 1985 February 1985 September 1985 February 1987 February 1988 May 1989 September 1990 March 1994 March 1995 March 1996 March 1997 March 2000 March 2001 March 2002 March 2003 March 2004
Age groups 16–17 years
18–19 years
20 years+
No minimum applies
No minimum applies
2.500 2.800 4.250 5.250 5.625 5.875 6.125 6.125 6.250 6.375 7.000 7.550 7.700 8.000 8.500 9.000
3.68 3.75 3.83 4.20 4.55 5.40 6.40 6.80 7.20
(1.9) (2.1) (9.7) (8.3) (18.7) (18.5) (6.3) (5.9)
3.68 3.75 3.83 4.20 4.55 7.70 8.00 8.50 9.00
(1.9) (2.1) (9.7) (8.3) (69.2) (3.9) (6.3) (5.6)
(12.0) (51.8) (23.5) (7.1) (4.4) (4.3) (—) (2.0) (2.0) (9.8) (7.9) (2.0) (3.9) (6.3) (5.6)
Percentage change in nominal minimum wage for each age group is reported in parenthesis. Information supplied by the Labour Market Policy Group (Department of Labour).
until June 2003 did not apply to persons undergoing training recognised under the Industry Training Act4. This low rate of exclusion from paying the minimum wage is an important difference in NZ, compared to the situation in the US for example, where most minimum wage research requires coverage adjusted minimum wage ratios to be calculated. Also, the high coverage rate means that the potential individuals have of choosing the alternative to go work in the uncovered sector is negated. In 1994 a youth minimum wage was introduced for 16–19 year olds that was set at 60% of the adult minimum wage. In 1999 a Labour-Alliance coalition government came into office. The Alliance party, in particular, campaigned for improving labour market conditions of young workers and believed that increasing youth minimum wages was an important step toward achieving this objective. Minimum wage reforms were introduced in March 2001 and were comprised of two components: a lowering of the age of eligibility for the adult minimum from 20 to 18 years and an increase in the 4 This included any workers undertaking 60 credits or more from the National Qualifications Framework. These workers are now eligible for the training minimum wage, which is equivalent to the youth minimum wage (Employment Relations Service, 2004).
youth minimum in two annual steps (enacted in March 2001 and March 2002, raising it from 60% to 80% of the adult minimum). Consequently, the statutory minimum wages covering 16–17 year olds and 18–19 year olds increased by 49.5% and 86.8%, respectively, over the period 2000–2003. The adult minimum wage also increased, but to a much lesser extent, 12.6% over these 3 years. A summary of all statutory minimum wage changes for all age groups since 1984 are shown in Table 1. 3. Theoretical framework The theoretical relationship between minimum wages and educational enrollments can be understood using the framework of the human capital model. Analysis under this model assumes that an individual’s schooling decisions are made by weighing the net present value of education’s benefits and costs against one another. In purely economic terms, benefits of education are usually in the form of improved future earnings, while costs are in terms of earnings foregone while in school. According to this framework, the effects of minimum wages on educational enrollments are theoretically ambiguous. On the one hand, simple labour market models predict that introducing a binding minimum wage will reduce employment levels and moreover, will
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increase the minimum productivity necessary to gain employment (Brown, 1999). The minimum wage would therefore force some individuals, who would otherwise have sought employment at wages below the minimum wage, to acquire more education (Ravn and Sorensen, 1997). For this effect to take place, two assumptions must hold. Firstly, there must be a close connection between wages paid and worker productivity. This implies that if the productivity of a worker falls short of the level of productivity implied by the minimum wage, then they cannot find employment. Secondly, workers believe they can increase their productivity to a level sufficient to gain employment through additional education. These two effects imply that raising minimum wages could cause an increase in education levels (Ravn and Sorensen, 1997). However, to the extent that the minimum wage is binding, we would expect a higher minimum wage to compress the lower end of the wage distribution, thus improving the earnings of low-wage workers (Hyslop and Stillman, 2004) and hence, increasing the short run opportunity cost of education for these workers. An implication that has been drawn from this result is that individuals may choose to invest less in education because by improving labour market conditions for low-skilled workers, the expected long-run returns to further education are reduced (Chaplin, Turner & Pape, 20035). Consequently, the possibility of a negative relationship between minimum wages and educational enrollments is established. In addition to these ‘‘price effects’’ (the opportunity cost of time spent in school and returns to education), the minimum wage may also have ‘‘income effect’’, according to Chaplin et al., (2003). If a higher minimum wage raises the lifetime income of the household, and education is a normal good, then we would expect educational enrollments to increase. However, if a higher minimum wage reduces the probability of gaining employment, it could lower household income and, consequently, the level of human capital investment. Therefore, the direction of income effects is critically dependent on how minimum wages affect household income. 5
Chaplin et al (2003) estimated the effects of a higher minimum wage on enrollment in the US and found evidence of a higher minimum wage reducing enrollment in states where students can drop out prior to 18 years of age.
577
Given that in this simple framework it is theoretically ambiguous whether minimum wages will result in an increase or a decrease in educational enrollments, previous research has, as in this case, appealed to empirical evidence. 4. Data and key variables The data set used for this study consists of a synthetic panel of annual national-level data from 1986 to 2004 for nine age cohorts each year. The age cohorts are classified as individual years between 16 and 24 years. The data used includes information on enrollment rates and education spending from the Ministry of Education, labour market conditions data from the HLFS6 and minimum wage levels. The sample begins in 1986 with the first sweep of the HLFS and terminates in 2004 because enrollment data was not available for 2005 at the time of this study. 4.1. Enrollment measure One of the main contributions of this study is the use of a broader measure of enrollment, which is independent of labour force status. There has been considerable debate in the literature concerning what the most appropriate enrollment measure to be used is. Neumark and Wascher (1995a, b) use CPS data in the US that counts students as enrolled only if their major activity was ‘‘going to school’’. If individuals reported their major activity as working instead, they were not asked whether they were also enrolled. This enrollment measure thus has the potential to underestimate true enrollment rates. Evans and Turner (1995) use a broader measure of enrollment constructed independently of employment status from the October enrollment supplement of the CPS. They then find that the effects of minimum wages on school enrollments become negligible. In response to this research, Neumark and Wascher (2003) accept that it may be more appropriate to use a broader measure of educational enrollment, but they refute the evidence presented by Evans and Turner (1995). They maintain that Evans & Turner’s results rely on the mis-measurement of the minimum wage variable as they used the 6
The HLFS is a quarterly data source that consists of a sample of between 16,000 and 32,000 households since December 1985. It is an ongoing data set that includes a wide array of information on the working age population in NZ.
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October supplement of the CPS to measure school enrollment, and continued to use the May minimum wage variable. Neumark and Wascher (2003) show that when the October measure of the minimum wage is used rather than the May measure, the negative effects that they found in their 1995 studies persist, regardless of whether a narrow or broad measure of enrollment is used. In the only NZ study to analyse the link between increases in the minimum wage and educational enrollments, Hyslop and Stillman (2004) use a very narrow measure of enrollment7. Their study uses HLFS data where one of the data constraints is that it is impossible to tell whether an individual out of secondary school and working is studying as well. Individuals are not asked about studying if they are employed for at least 2 h of work per week. Consequently, this paper uses a broader measure of enrollment. Specifically, data on the apparent percentage of the relevant population cohort participating in education is taken from an annual survey of educational institutions conducted by the Ministry of Education. The survey currently covers a broad range of education providers including primary, composite, secondary, special and home schooling, in addition to public and private tertiary educational institutions. These enrollment rates are only ‘apparent’ because students who enroll concurrently in more than one type of institution/service will be counted more than once (Education Statistics of New Zealand reports, 1986–2003). This factor may cause estimated enrollment ratios to have a slight upwardbias, however, the bias should be reasonably consistent across the entire sample8. Also, given that this data comes from an annual survey, it is possible that it underestimates enrollment rates, because enrollment is counted as of July 31st in our data. Students not enrolled on that date are not counted9. Also, students who are enrolled in non-formal courses of study will not be captured by this enrollment measure (Hedges, 2001).
variable the ratio of the minimum wage to average hourly earnings, average wage statistics in NZ are not consistent over the period of this study10. Hence, minimum wage studies in NZ typically deflate by the producer price index (PPI—Inputs) (Chapple, 1997).11 Thus, the real minimum wage variable is defined as the statutory minimum wage as of 31 June each year deflated by the PPI (Inputs). The real statutory minimum wage covering teenagers (16–17 and 18–19 year olds) and adults (20+ years) is plotted in Fig. 1. The most notable features of Fig. 1 are the introduction of a youth minimum wage covering 16–19 year olds in 1994, and the major minimum wage reforms that were introduced in March 2001 and 2002 affecting 16–19 year olds. As shown in the graph, 18–19 year olds experienced the largest increase in their real minimum wage when this reform took place in March 2001 and they became part of the adult group, experiencing adult levels of the minimum wage. Finally, there is little need to adjust this real minimum wage variable for the coverage rate in this country as only a relatively small number of workers are exempt from minimum wages (Pacheco and Maloney, 1999). 4.3. Other variables Education spending. Several US and Canadian studies have included education spending and/or education structure measures as explanatory variables in their estimating equation. It is argued that increases in educational spending or improved educational structure (such as reduced student– teacher ratios), makes education relatively more attractive and thus may alter the individual’s relative preferences for work and school and consequently increase school enrollments (Landon, 1997). 10
4.2. Minimum wage Although many US studies in this field of research generally use as their minimum wage 7 Their measure is even narrower than the early narrow measures used by Neumark and Wascher (1995a, b). 8 This upward bias can also be thought of in terms of an increasing intensity in enrollment. 9 The academic year begins in February each year in NZ.
The average hourly wage statistics from the Quarterly Employment Survey are not comparable for the period before and after 1989 because pre-1989 this data was handled by the Department of Labour, and post-1989 it was handled by Statistics NZ. 11 PPI (Inputs) is used by much NZ minimum wage literature as the appropriate deflator as we are implicitly estimating the impact of a higher minimum wage on labour demand (through the value of PPI (Inputs)), which consequently affects the employment propensity of young individuals vulnerable to minimum wage rises, and thus the opportunity cost of these individuals’ enrollment decision.
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real min wage (adult: 20 + years) real min wage (youth:16-17 years)
Jun. 2003
Mar. 2004
Sep. 2002
Dec. 2001
Jun. 2000
Mar. 2001
Sep. 1999
Dec. 1998
Jun. 1997
Mar. 1998
Sep. 1996
Dec. 1995
Jun. 1994
Mar. 1995
Sep. 1993
Dec. 1992
Jun. 1991
Mar. 1992
Sep. 1990
Dec. 1989
Jun. 1988
Mar. 1989
Sep. 1987
Mar. 1986
real min wage (youth:18-19 years)
Dec. 1986
Real minimum wage ($)
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Fig. 1. The real minimum wage for adults since 1986 and youth since 1994. Note: nominal adult minimum wages over the period 1986:1–2004:2 and nominal youth minimum wages over the period 1994:1–2004:2 are deflated by the PPI (Inputs), with a base year of 1997:4. Source: minimum wage levels supplied by the Labour Market Policy Group, PPI (Inputs) from Statistics NZ.
However, since 1991 in NZ, education spending has been primarily demand-driven. This implies that education expenditure is likely to be endogenous, i.e. increased enrollment rates will lead to increased education spending, rather than causation running in the other direction. Thus, the log of the lagged value of education spending as a percentage of GDP is included in the regressions. Control variables. It is important that the estimated model in this research includes controls for the cyclical fluctuations in the economy. This is important as it is often suggested that the government could possibly choose the timing of minimum wage increases in response to changes in the economy, i.e. the government may find it easier to raise the minimum wage when employment is expanding (Card and Krueger, 1995). In NZ, the 1983 Minimum Wage Act stipulates that minimum wages must be reviewed by 31 December every year, and by convention any changes are typically made the following March (Regulatory Impact Statement (2003) Minimum Wage Review). On average, minimum wages are not increased by a fixed amount every year, and it is worth noting that large increases did not occur until recent years when the economy was buoyant. The possible controls used to capture the effect of the aggregate business cycle, or more particularly, labour demand, on education-work decisions, are either the unemployment rate of prime-aged males (taken to be 25–54 year old males12 in this study) or 12
The reason a broad age group (25–54 year olds) for the unemployment variable is chosen rather than a narrower one is
real GDP per capita13. The idea is that a higher unemployment rate (or lower levels of real GDP per capita) is likely to signal both a lower likelihood of finding a job and an increased likelihood of having lost work, and thus is expected to be associated with increased enrollment rates. Some simple descriptive statistics of all variables used in the following models are presented in Appendix A. 5. Estimation methods To model the enrollment decision empirically, the log of the proportion of individuals (Y) of age cohort i (i ¼ 16,y,24) enrolled in an educational institution at time t14 (t ¼ 1986,y,2004) will be described by three synthetic panel data models (S1, S2 and S3). Given the relatively short data series available, there is limited time-series data for each age cohort and thus insufficient power to test hypotheses separately for each age group. To the extent that it is reasonable to impose a homogeneity condition on the coefficient on the minimum wage variable across age groups (16–24 year olds, and subgroups (footnote continued) because it reflects cyclical variations in labour market conditions better than narrower age bands, which may reflect a lot of ‘noise’. 13 Both measures are used in the upcoming regression analysis in Section 5 as a check of robustness of results. 14 Hence, the numerator of this enrollment rate variable is the number of individuals enrolled for that age cohort and the denominator is the population (for that age cohort), using data from Statistics NZ.
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of 16–19 and 20–24 year olds), a synthetic panel data model, by providing additional power, allows the detection of a relationship that may not have been evident from individual time-series regressions. 5.1. S1 The first enrollment regression model S1 is given by
Y it ¼ ai þ bi E t1 þ dN it þ gZit þ ji t þ it . S2 includes age-specific time trends (ji t). Enrollment rates of different age groups have exhibited different trending behaviour over the sample period. Consequently, as in Hyslop and Stillman (2004), the linear time trend variable t is used to capture these age-specific trends in enrollment rates. Other variables are as defined in S1.
Y it ¼ ai þ bi E t1 þ dN it þ gZit þ it .
5.3. S3
This is a linear model specification, which is consistent with the majority of research in the minimum wage arena. In this model, Yit is the log of the enrollment rate as defined previously, ai is the age-specific fixed effect, and Et1 is the log of the lagged value of education spending as a percentage of GDP. Nit is the next independent variable in S1 and is one for individuals if a minimum wage is applicable and zero if not. Hence, it captures the introduction of the teen minimum in 1994. Nit is then interacted with Xit (Zit ¼ NitXit) which is the log of the real minimum wage. The real minimum wage is the nominal minimum wage deflated by the PPI (Inputs), as defined in the previous section. This variable is expressed in logarithmic form so that the coefficient on it has a clear interpretation: the elasticity of enrollment with respect to the minimum wage. This variable is also later deflated by the consumer price index (CPI) as a test for sensitivity of results to choice of deflator. Lastly, the use of the Levin Lin and Chu (LLC) panel unit root test did not reject the hypothesis of no unit root, for each of these independent variables. In this model specification, (as in the upcoming specifications of S2 and S3), it is likely that there are unobserved effects, (such as cross-sectional heterogeneity in skills or preferences), that are constant over time, and correlated with regressors. Consequently, least squares coefficient estimates may be biased and inconsistent, as a result of an omitted variable. Therefore, age-specific fixed effects are included to control for these time-invariant characteristics specific to age cohorts and to produce more consistent coefficient estimates.
The effects of labour market conditions on enrollment decisions are controlled for in model S3. As stated earlier, either the unemployment rate of prime-aged males or real GDP per capita is used as a control for the state of the economy. Initially the former measure is used15, and subsequently it will be shown that results are robust to the use of the latter cyclical control. It is assumed that the coefficient on the labour demand variable is heterogeneous across age cohorts. There is strong theoretical justification for this. There is evidence that during recessions, older, more established workers suffer less adverse employment effects than younger, more inexperienced workers. Given the inter-related nature of education-work decisions, this is likely to mean that the aggregate business cycle has different effects on enrollment decisions across age cohorts. Enrollment regression model S3 is specified by Y it ¼ ai þ bi E t1 þ dN it þ gZit þ ji t þ yi ut þ it . The control for labour demand, ut, is defined as in the previous section. The other variables are defined as in S2. 6. Results 6.1. Estimates Table 2 presents panel least squares (PLS) regression results from the three alternative synthetic panel data models for 16–24 year olds for the time period 1986–2004. Estimated panel corrected standard errors are given in parentheses; these 15
5.2. S2 The next enrollment regression model S2 is specified by
Controls using unemployment rate are commonly used in NZ minimum wage research to reflect cyclical changes in economic growth (see Pacheco and Maloney (1999) and Maloney (1995) who both use the unemployment rate of older adults to capture any systematic movement in the dependent variable over the NZ business cycle).
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Table 2 PLS estimates of the effects of minimum wages on educational enrollments of 16–24 year olds, 1986–2004 Independent variables
S1
S2
S3
Real minimum wage Impact of introduction of minimum wage for teenagers aged 16–19
0.0909 (0.0823) 0.0182 (0.0432)
0.0685 (0.0618) 0.1480*** (0.0278)
0.0887 (0.0618) 0.1116*** (0.0292)
Education spending as a percentage of GDP: age-specific 16 years 17 years 18 years 19 years 20 years 21 years 22 years 23 years 24 years
0.2979 (0.2424) 0.6604** (0.3055) 0.7415** (0.3769) 0.4350 (0.3540) 0.1385 (0.3605) 0.1899 (0.6266) 0.4857 (0.4521) 0.4826 (0.5572) 0.2877 (0.6166)
0.7326*** (0.1631) 1.4259*** (0.2090) 1.2876*** (0.2820) 0.7932*** (0.2379) 0.3030 (0.2455) 0.5631 (0.3732) 0.2629 (0.2723) 0.4375 (0.3762) 0.3572 (0.4486)
0.3641**(0.1756) 0.4276 (0.3590) 0.8925**(0.3707) 0.7021** (0.3129) 0.2002 (0.3428) 0.4958 (0.5237) 0.1448 (0.3809) 0.1836 (0.5205) 0.3226 (0.6319)
Unemployment rate of prime-aged males: age-specific 16 years 17 years 18 years 19 years 20 years 21 years 22 years 23 years 24 years R2 Observations
— — — — 0.9538 171
— — — — 0.9785 171
0.1100*** (0.0399) 0.2526*** (0.0802) 0.1184 (0.0878) 0.0123 (0.0742) 0.0339 (0.0808) 0.0214 (0.1236) 0.0432 (0.0898) 0.0906 (0.1229) 0.0141 (0.1492) 0.9796 171
Note: * 10%, ** 5%, *** 1% significance level; Panel corrected standard errors are given in parentheses. Coefficient estimates on the linear time trend variables and the cross-sectional fixed effects dummies are reported in Appendix B.
ensure that the estimator is robust to cross-sectional (contemporaneous) correlation as well as different error variances in each cross-section. For simplicity, coefficient estimates on the linear time trend variables and the cross-section fixed effects are given in Appendix B. Table 2 shows that education spending has a positive impact on enrollment rates. This impact tends to be larger in magnitude and more significant for teenagers (16–19 year olds). However, it is important to keep in mind when reading these coefficients, that although the lagged value of education spending is used in the model specifications S1–S3, it is possible this instrument has not fully addressed the endogeneity issue associated with the use of an education related variable as a control in these regressions16. The primary focus of this study is on the minimum wage variable in the enrollment equation. PLS estimates of S1 indicate that higher minimum 16
Many thanks to the Associate Editor of this journal for making this observation.
wages have a negative and insignificant effect on enrollment rates of 16–24 year olds. Specifically, the elasticity of enrollment with respect to the minimum wage is 0.0909. This implies that a 10% increase in the minimum wage is likely to be associated with a 0.909% point fall in the number of persons enrolled in education. When age specific time trends are included in the second specification (S2), PLS estimates still indicate an insignificant and negative relationship between minimum wages and educational enrollments. The magnitude of the elasticity of enrollment with respect to the minimum wage is now 0.0685, and still statistically insignificant. In the last specification (S3), labour demand is controlled for via the inclusion of age specific responses to the unemployment rate of prime aged males. The parameter estimates in Table 2 suggest that the influence of labour demand on educational enrollments is significant and positive for younger age groups (16 and 17 year olds). This result is consistent with what would be expected as it shows that younger age groups increase their consumption of education, as the unemployment rate of prime
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aged males increases (i.e. as there is a general reduction in work opportunities available). PLS estimates of S3 again show a statistically insignificant negative relationship between minimum wages and educational enrollment rates. While, these results focus on the effects on changes in the log of the real minimum wage (Xit), estimates in Table 2 also show the impact of the introduction of the minimum wage. These estimates essentially illustrate the effect of switching on the minimum for teens in April 1994 and are captured by qY it =qN it ¼ d þ gX¯ it where X¯ it is the value of the log of the real minimum wage when introduced for teens (i.e. 1.3282). The results in Table 2 indicate that the impact on enrollment rates for 16–24 year olds when the teen minimum was introduced was positive. Results across the three specifications point to this one off effect increasing enrollment rates. In the preferred specification, S3, the increase is equivalent to enrollment rates rising by 1.48% points and this impact is significant at the 1% level. 6.2. Estimates for age sub groups Tables 3A and B presents PLS regression results for 16–19 and 20–24 year olds for the time period 1986–2004. The results contained within Tables 3A and B are interesting as they appear to clearly show the 16–19 year olds as the age subgroup which experiences a significant negative decline in enrollment rates when their applicable real minimum wage levels are raised. These effects are statistically more significant, as well as larger in magnitude in comparison to the estimates provided in Table 2. For example, in the preferred specification S3, where age specific time trends and responses to business cycle fluctuations are controlled for, the minimum wage coefficient implies that a 10% increase in the real minimum wage decreases enrollment rates by 1.535% points17. Table 3A also shows that the impact of the introduction of the minimum wage on teenagers aged 16–19, in terms of their enrollment decision, was positive. Under all three specifications, there is a positive and significant impact on enrollment rates ranging from 0.998% to 1.526% points. Again, it is important to understand that this estimated effect is 17 The estimates for the subgroups of 16–17 and 18–19 year olds were not statistically different and very similar to the results for the aggregate group of 16–19 year olds.
the impact of the change in minimum wage legislation in April 1994. Table 3B presents the comparable specifications for 20–24 year olds with the obvious exception of the variable Nit, since individuals aged 20 and over have had a minimum apply throughout the time period under study. We find, that the coefficient on the minimum wage variable for 20–24 year olds is positive and large in magnitude, but noticeably insignificant. Hence, indicating that we cannot reject the hypothesis that raising the minimum wage has no impact on the enrollment decision of 20–24 year olds in NZ. These results are consistent with early US studies by Matilla (1978) and Cunningham (1981) (for white youth) which found a significant negative relationship between minimum wages and educational enrollments for 16–19 year olds, but no significant relationship for 20–24 year olds. Latter studies have tended to focus solely on teenage age cohorts. The results of this study add weight to this view that the impact of minimum wage rises has a significant impact on the enrollment decision of 16–19 year olds. One of the key motivations of this paper was the importance of investigating empirical findings using other data sets. As mentioned earlier, this issue is potentially important in the NZ case, given that the enrollment measure in the HLFS (used by Hyslop and Stillman, 2004) potentially underestimates true enrollment rates and makes it difficult to compare enrollment rates across age groups with different labour force participation rates. Yet, this study, which uses the broader measure of enrollment from the Ministry of Education data, as well as covering a longer time span and wider range of age cohorts, still finds broadly consistent results. 6.3. Sensitivity tests To examine the sensitivity of our results, firstly S3 is re-estimated using real GDP per capita rather than the unemployment rate of prime-aged males. The minimum wage estimates found are quantitatively close to those reported in Tables 2, 3A and B. For example, the estimates of the elasticity of enrollment with respect to the minimum wage for the model S3 becomes 0.0641 and insignificant for 16–24 year olds and 0.1656 and significant at the 1% level for 16–19 year olds. This is in comparison to 0.0887 (insignificant) and 0.1535 (significant at the 1% level) in the original models.
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Table 3A PLS estimates of the effects of minimum wages on educational enrollments of 16–19 year olds, 1986–2004 Independent variable
S1
S2
S3
Real minimum wage Impact of introduction of minimum wage for teenagers aged 16–19
0.0561 (0.0479) 0.1526*** (0.0199)
0.1174* (0.0615) 0.1425*** (0.0279)
0.1535*** (0.0591) 0.0998*** (0.0288)
Education spending as a percentage of GDP: age-specific 16 years 17 years 18 years 19 years
0.5021*** 1.3357*** 1.3988*** 1.0428***
0.6981*** 1.4250*** 1.2471*** 0.7526***
0.3179* (0.1732) 0.3006 (0.3542) 0.8655** (0.3626) 0.6751** (0.3230)
Unemployment rate of prime-aged males: age-specific 16 years 17 years 18 years 19 years R2 Observations
— — — — 0.9568 76
(0.1688) (0.1817) (0.2402) (0.2210)
(0.1634) (0.2122) (0.2760) (0.2446)
— — — — 0.9635 76
0.1083*** (0.0394) 0.2844*** (0.0790) 0.1072 (0.0859) 0.0012 (0.0766) 0.9710 76
Note: *10%, **5%, ***1% significance level; Panel corrected standard errors are given in parentheses. Coefficient estimates on the linear time trend variables and the cross-sectional fixed effects dummies are reported in Appendix C.
Table 3B PLS estimates of the effects of minimum wages on educational enrollments of 20–24 year olds, 1986–2004 Independent variables
S1
S2
S3
Real minimum wage
0.8292 (0.6358)
0.3391 (0.2276)
0.2924 (0.2465)
Education spending as a percentage of GDP: age-specific 20 years 0.1126 (0.0795) 21 years 0.0719 (0.1344) 22 years 0.0046 (0.0965) 23 years 0.0102 (0.1150) 24 years 0.0147 (0.1416)
0.0806 (0.0559) 0.1250 (0.0919) 0.0123 (0.0756) 0.0359 (0.0969) 0.0109 (0.1126)
0.0007 (0.0818) 0.0750 (0.1178) 0.0801 (0.0979) 0.1517 (0.1240) 0.2235* (0.1333)
Unemployment rate of prime-aged males: age-specific 20 years — 21 years — 22 years — 23 years — 24 years — 0.9114 R2 Observations 95
— — — — — 0.9408 95
0.0860 (0.0563) 0.0521 (0.0654) 0.1027** (0.0474) 0.2061*** (0.0631) 0.2568*** (0.0633) 0.9482 95
Note: *10%, **5%, ***1% significance level; panel corrected standard errors are given in parentheses. Coefficient estimates on the linear time trend variables and the cross-sectional fixed effects dummies are reported in Appendix C.
Next, the cyclical control is changed back to the unemployment rate of prime-aged males and the use of an alternative deflator (CPI) for the minimum wage variable is employed. Appendix C shows the results of this sensitivity test. Again, minimum wage estimates are qualitatively and quantitatively similar to those reported in the original models. For example, the minimum wage coefficient is now 0.0816 and insignificant for 16–24 year olds,
whereas it was 0.0887 and insignificant in the original S3. 7. Conclusion 7.1. Findings and policy implications The results of this paper add to the growing body of empirical evidence on the contribution of higher
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minimum wages to reduced educational enrollment rates. Evidence presented in this paper, based on a panel of nine age cohorts each year over a 19 year period (1986–2004), indicates that higher minimum wages tends to result in reduced enrollment levels. The empirical estimates imply that an increase in the minimum wage has a small negative but insignificant impact on the number of persons enrolled in education in the aggregate group of 16–24 year olds. Results from disaggregating age cohorts into 16–19 year olds and 20–24 year olds suggests that there is a significant and negative relationship between minimum wages and educational enrollments for the former age subgroup, and an insignificant impact on educational enrollments for the latter age subgroup. This is what we would expect, and is consistent with the findings of other studies (Hyslop & Stillman, 2004). To put these results into context, the magnitude of the significant declines in enrollment rates for teenagers, under S3, would have amounted to 2506 students nationally in 2004, out of a total enrollment of 238,940 students. Results also appear to point towards an interesting contrast between the impact of the minimum wage becoming applicable versus the impact of changes in the minimum wage. Overall, estimates indicate that the introduction of the teen minimum for 16–19 year olds in 1994 actually increased enrollment rates significantly. This increase was in comparison to the negative and significant impact on enrollment rates found for teenagers when the level of the real minimum wage increased subsequently. A possible hypothesis for these contrasting results is that different sub-groups of teenagers are impacted differently. For example, it is plausible to expect that teens with the least skills and hence lowest wages and enrollment rates may have been affected most by the introduction of the minimum wage and consequently switched from employment (which was no longer available) to school enrollment. Later, as the minimum wage was increased more, higher skilled individuals then became affected. These teens were more likely to have been in school before the minimum wage increase and to not have considered employment until the minimum wage was increased so that it surpassed their reservation wage. Therefore, increasing the minimum wage may change the distribution of skill levels of youth in school vs. those in the job market, pulling the more skilled youth out of school and into the job market and having the opposite impact on those with less skills. To check this hypothesis,
there is a need for future research on differential impacts on youth by skill levels, if and when such data become available. The results on the minimum wage coefficients are broadly consistent with other recent research in this area, and add credence to calls for the impact of minimum wages on educational enrollments to be taken into account in debates surrounding minimum wages (especially the youth minimum in NZ). Proponents of minimum wages in NZ see minimum wage legislation, among other things, as an instrument for promoting social justice, addressing poverty, rewarding additional work effort, protecting vulnerable employees and ensuring that low-wage workers share in the gains from economic growth (Coutts, 2004). While this paper does not address the efficacy of minimum wages in achieving these broad objectives, it does highlight the unplanned negative consequences of minimum wages on enrollment rates in NZ. Hence, the impact of the minimum wage on educational outcomes should be weighed against other perceived benefits of the legislation, and the potential trade-off examined when considering minimum wage rate increases. Additionally, policies to mitigate the negative effects of minimum wages on educational enrollments could also be considered. The negative side effects could be offset by other policies to encourage greater investment in education such as increasing the accessibility to post-secondary school education by reducing the financial barriers to investment in further education. Furthermore, evidence from US studies18 suggests that raising the minimum school leaving age is an effective policy instrument for ensuring that minimum wages do not negatively affect the school enrollments of young people.
Appendix A Descriptive statistics are shown in Table A1.
Appendix B PLS Estimates of the Effects of Minimum Wages on Educational Enrollments of 16–24 year olds, 1986–2004 are continued in Table B1. 18
See Chaplin et al (2003).
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Table A1 Descriptive statistics Variable
Mean
Standard deviation
Enrollment rates (% population) 1986–2004 16–17 years 18–19 years 20–24 years
77.63 45.82 27.49
12.94 7.54 10.47
1986–1990 16–17 years 18–19 years 20–24 years
66.06 34.38 20.32
13.84 2.51 6.15
1991–1995 16–17 years 18–19 years 20–24 years
83.00 49.00 26.68
10.21 4.87 10.17
1996–2000 16–17 years 18–19 years 20–24 years
84.16 50.75 28.76
9.49 4.60 10.05
2001–2004 16–17 years 18–19 years 20–24 years
79.04 48.76 36.02
9.68 6.26 9.78
Real minimum wage (deflated by PPI—Inputs; NZ$) 16–17 years 18–19 years 20+ years
4.62 5.20 6.77
0.88 1.62 0.43
Real minimum wage (deflated by CPI; NZ$) 16–17 years 18–19 years 20+ years
4.78 5.4 7.11
0.96 1.77 0.39
1.69 6222.56 5.42
0.52 801.29 0.43
Unemployment rate of prime aged males Real GDP per capita ($NZ) Education spending (% of GDP)
Note: Enrollment rates and Education spending are sourced from the Ministry of Education, Nominal minimum wage levels supplied by the Labour Market Policy Group, and the remaining variables are sourced from Statistics NZ.
Table B1 Table 2 continued Independent variable
S1
S2
S3
Linear time trend: age-specific 16 years 17 years 18 years 19 years 20 years 21 years 22 years 23 years 24 years
— — — — — — — — —
0.0062 (0.0039) 0.0022 (0.0037) 0.0037 (0.0055) 0.0076 (0.0051) 0.0299*** (0.0036) 0.0322*** (0.0054) 0.0399*** (0.0040) 0.0390*** (0.0055) 0.0379*** (0.0065)
0.0028 (0.0039) 0.0071 (0.0045) 0.0074 (0.0057) 0.0116** (0.0053) 0.0300*** (0.0037) 0.0323*** (0.0056) 0.0402*** (0.0041) 0.0394*** (0.0056) 0.0381*** (0.0068)
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Table B1 (continued ) Independent variable
S1
S2
S3
Cross-sectional fixed effects 16 years 17 years 18 years 19 years 20 years 21 years 22 years 23 years 24 years Constant
0.3143 0.4621 0.9440 0.4993 0.5200 0.1669 0.7644 0.4922 0.0186 0.8589***
0.6665 0.7995 0.9564 0.2768 0.1867 0.4301 0.6030 0.6569 0.3497 2.0496***
0.9288 0.4077 0.6548 0.4403 0.0949 0.5764 0.1731 0.0194 0.0476 1.7533***
Note: *10%, **5%, ***1% significance level; panel corrected standard errors are given in parentheses.
Table C1 Table 3A continued Independent variable
S1
S2
S3
Linear time trend: age-specific 16 years 17 years 18 years 19 years
— — — —
0.0043 (0.0039) 0.0013 (0.0037) 0.0062 (0.0055) 0.0102* (0.0052)
0.00008 (0.0037) 0.0095** (0.0044) 0.0112** (0.0055) 0.0154*** (0.0052)
Cross-sectional fixed effects 16 years 17 years 18 years 19 years Constant
1.3444 0.3161 0.7569 0.2715 2.4044***
1.0167 0.4955 0.6004 0.0793 2.3516***
0.8532 0.4603 0.7640 0.5495 1.6138***
Note: *10%, **5%, ***1% significance level; panel corrected standard errors are given in parentheses.
Table C2 Table 3B continued Independent variable
S1
S2
S3
Linear time trend: age-specific 20 years 21 years 22 years 23 years 24 years
— — — — —
0.0261*** 0.0287*** 0.0355*** 0.0342*** 0.0328***
Cross-sectional fixed effects 20 years 21 years 22 years 23 years 24 years Constant
0.0182 0.0853 0.2360 0.0047 0.3079 2.6548 (3.2696)
0.2044 0.2176 0.1782 0.0699 0.2348 0.1552 (1.2089)
(0.0053) (0.0064) (0.0059) (0.0074) (0.0082)
Note: *10%, **5%, ***1% significance level; panel corrected standard errors are given in parentheses.
0.0293*** 0.0307*** 0.0327*** 0.0282*** 0.0251***
(0.0056) (0.0071) (0.0065) (0.0080) (0.0086)
0.7386 0.2108 0.1246 0.3056 0.7683 0.6475 (1.1380)
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Table C3 PLS estimates of the effects of minimum wages on educational enrollments using CPI as the deflator for S3 Independent variables
16–24 year olds
16–19 year olds
20–24 year olds
Real minimum wage R2 Observations
0.0816 (0.0567) 0.9796 171
0.1433*** (0.0542) 0.9712 76
0.4124 (0.2589) 0.9432 95
Note: *10%, **5%, ***1% significance level; panel corrected standard errors are given in parentheses.
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