Reassessing the impact of minimum wages on wage dispersion and employment: evidence from an institutionalized wage bargaining system.

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1 Reassessing the impact of minimum wages on wage dispersion and employment: evidence from an institutionalized wage bargaining system. Sem Vandekerckhove, Maarten Goos, and Guy Van Gyes PEAC Conference Brussels, 5-7 March 24 Abstract Research on minimum wages is struggling with the measurement of wage inequality and disemployment effects. Earlier studies (e.g. Lee, 999) found that higher minimum wages decrease lower-tail wage inequality as one would expect but also that higher minimum wages increase higher percentile wages and therefore even overall wage inequality. More recent work by Autor, Manning, and Smith (2), however, finds that higher minimum wages unambiguously decrease lower-tail as well as overall wage inequality because they do not lead to strong positive spillover effects to higher wage percentiles. In line with Autor, Manning, and Smith (AMS) for the United States, we find similar evidence for Belgium a country with considerably more exogenous variation in minimum wages that higher minimum wages unambiguously reduce not only lower-tail but also overall wage dispersion. To show this, we use administrative data for the period in Belgium and sector and time variation in minimum wages. Following AMS we re-estimate Lee s 999 model while correcting for biases in the estimation procedure to find that higher minimum wages indeed reduce lower-tail wage and therefore overall inequality because of a direct effect as well as a positive spillover effect that is highest around the minimum wage. The panel structure of the data also allows to show that these spillover effects are not driven by disemployment effects from higher minimum wages. Introduction Research on wage inequality can be divided into two groups: one which stresses the importance of supply and demand factors, the other taking into account This research originates from a project on wage bargaining, supported by the Ministry of Labour. We are also grateful for the support of the HR service provider Acerta for access to the database of negotiated pay levels. KU Leuven HIVA, CES, and HIVA respectively, correspondence goes to sem.vandekerckhove@kuleuven.be

2 institutional variables such as the minimum wage. The debate may be framed around measurement issues: while the former makes use of less tactile indicators such as demands and changing technology, the latter approach involves modelling issues that will need to be addressed. Early analyses in the first group focused on the changes in skill supply and experience levels (Katz & Murphy, 992; Murphy & Welch, 992; Juhn, Murphy, & Pierce, 993) in order to explain the rapid rise in wage inequality in the US in the 98s. This tradition evolved into theories on skill biased technical change that, in contrast, emphasized the changing demand for skills, caused by the decline of high-rent industries (Bound & Johnson, 992) or by routinization and computerization (Autor, Levy, & Murnane, 23). The effects of skill biased technical change impact both jobs and wages in a polarizing manner. With regard to jobs, we find decreasing employment for the middling groups in terms of skills, as routine non-manual labour gets substituted by technology. With regard to wages, technological advances are complementing productivity of highly skilled employees and therefore increase their wages (Autor, Katz, & Kearney, 28; Goos & Manning, 27). Other explanations include off-shoring and globalization (Murphy & Welch, 992; Freeman, Katz, Freeman, & Katz, 995). As empirical evidence pointed in the direction of institutional factors, a second strand of research studied the effects of minimum wages, a clear cut variable with unambiguously exogenous variation. In this respect, DiNardo, Fortin, and Lemieux (996) use a semiparametric approach to decompose the growth in wage inequality in the United States. Comparing the observerd wage distribution of 988 with a counterfactual distribution in which workers maintain the same set of characteristics as in 979, they found that the fall in the minimum wage may explain up to 25% of the increase in wage inequality for men, and up to 3% for women. Importantly, the minimum wage increases need not only level up the wages of workers with previous earnings below the minimum wage level (the mechanical effect), but there may also be spillover effects to higher percentiles, for instance in order to maintain relative wages difference to a certain extent (Brown, 999). One problem in linking the trends in minimum wages and wage inequality is the difficulty of separating the effect of the minimum wage from a time trend. For this reason, Lee (999) used cross state variation in the minimum wage level, generated by expressing the minimum wage relative to the state median wage. Assuming that the latent wage distributions of states are similar in shape, different observed wage distributions may be caused by this relative minimum wage. The results are fairly strong: nearly all of the changes in wage inequality in the United States during the 98s may have been due to changes in the minimum wage. Criticizing this institutionist view, Autor, Katz, and Kearney (28) questioned the odd result of minimum wage effects (spillovers) above the median. Lee did not provide an explanation for this, finding no effects of the minimum wage on the return to education. This was corrected by Teulings (23), using a different specification of the model including non linear effects. In addition, Autor, Manning, and Smith (2) correct for the division bias in 2

3 the specification of Lee by using a 2SLS approach, which results in much more plausible coefficients that are only significant for the lower part of the wage distribution. Disemployment effects may further disturb the measurement. From elementary economic theory, labour demand curves are downward sloping, so that a wage increase automatically implies job losses. Moreover, the decrease in wage inequality would be artificial in case the remaining workforce concentrates around the denser part of a censored, but otherwise unchanged wage distribution. An early expression of this intuitive stance is by Stigler (946), who stated that the direct unemployment is substantial and certain. However, there are two problems with this view. The first, and least problematic, is that it does not tell much about inequality in the upper tail of the wage distribution where minimum wage effects are not anticipated. The second is that it runs counter to what is found in reality. Moderate increases in the minimum wage do not seem to influence employment in any particular direction. There could even be a positive effect, as was found by Card and Krueger (994), comparing employment in fast-food restaurants New Jersey and Pennsylvania when the minimum wage in New Jersey was raised. The absence of a negative effect of minimum wage increases on employment has repeatedly been confirmed (Dolado et al., 996; Dube, Lester, & Reich, 2), leaving only some doubt concerning the employment of young employees (Brown, 999). This indicates that the labour market is not perfectly competitive and labour supply may increase with higher minimum wages, effectively restoring the equilibrium (Katz & Murphy, 992). Other possibilities involve increased efficiency and motivation (Hirsch, Kaufman, & Zelenska, 2), long run endogeneity of technology (Chennells & Reenen, 997), or general equilibrium effects (Stockhammer, Onaran, & Ederer, 29). This contribution moves the focus to Belgium, a case in point. The wage bargaining system in Belgium is fully institutionalized and has an extensive reach, having both a national minimum wage and sector minimum pay scales, set by negotiations between employers and employees organizations. Moreover, these agreements in general cover all employees through the extension of the agreements by the state, and, although undocumented, it is assumed that the negotiated pay takes up the largest part of the employee s wage, as individual wage negotiations are rare amongst the vast majority of workers. Two particular characteristic further shape wage setting in Belgium. The first is that it is the primary example of a medium sized economy with automatic wage indexation for nearly all employees. This system is not state imposed but, again, results from negotiations in joint committees, broadly defined by an industry. The state does decide on the index to be used in case of indexation. The second particularity is the law of 996 on the safeguarding of competitiveness and employment. This law puts forward a wage norm, to be negotiated every two years, which defines an upper limit to wage increases beyond indexation. It is based on projections of the wage evolution in France, Germany, and the Netherlands. The norm itself is indicative, although it can be set by the government, yet in practice it has 3

4 effectively acted as a centralized control of the wage growth. The advantage of this setting is threefold. First, it provides considerable cross sectional variation in the minimum wages (at the sector level). Second, there is near certainty that these minimum wage levels are binding because of the extension mechanism. Third, as wage growth has upper limits as well as lower limits, the possibility for wage inequalities to arise is restricted. This prevents the issue of having to disentangle time and minimum wage effects. Earnings inequality is low and has been stable in the past decades, yet minimum wages have been steadily increasing, both nominally through indexation, and in real terms in the period before the Great Recession of The high coverage and strong coordination of wage bargaining does, however, bring back the issue of spillover effects above the median. In fact, it is likely that wage agreements take into account not only the minimum wage, but the full wage scale. This would rule out negative effects on wage inequality. In that case, when all wages are rising, something has got to give. Therefore we will need to investigate the possibility of disemployment effects. In sum, in order to find minimum wage effects, we can make use of relevant cross-sectional variation. As minimum wages are in general not set at the region, but by industry, this will be the level of the analysis. The research questions then are threefold: () do we observe a relation between the minimum wage and wage inequality; (2) does this relation pierce through to the upper tail of the wage distribution; and (3) does the deeply rooted institutionalization imply that employment effects are inevitable? Our findings indicate that minimum wages are set according to the desired room for wage dispersion within sectors. As this violates the identifying assumption in Lee s model, we need to rule out the sector effect. When also instrumenting the relative minimum wages, it is found that an increase in the minimum wage compresses the wage distribution in the lower tail. The analysis does not reveal strong effects above the median. If any, it would be some minor wage compression up to the 8th percentile. Finally, applying a latent wage structure to the data to map spillovers, and comparing the changing with the stable workforce, no indication of disemployment effects was found. The structure of this paper is as follows: first we discuss the data in section 2, next we have a closer look on the descriptive statistics of the main variables in section 3. In section 4, we elaborate on the model and present the results. Section 5 concludes. 2 Data The main data come from the state social security administration (RSZ-ONSS). The wages refer to the gross wage employers have to register, so that social security benefits can be derived from it. The wage variable includes regular premiums, such as an individual permanent wage raise and task compensation (e.g. for shift work, heavy work, night work, extra hours etc.), and part of the holiday allowance for some employees. In order to equalize the concept for 4

5 all statutory groups, we divide the observed wage by.8 for (white collar) employees for which the holiday allowance was included in the wage variable, as this allowance is set at about 8% of the base wage. The sample amounts to one third of the employees working in the private sector between 996 and 26, which is large enough to allow a sector approach in nearly all major industries. Besides wages and labour volume, there is little additional worker information available in the social security data. There is an indicator for company size, as well as gender and age variables. Importantly, we keep only employees aged 2 to 65. The legal retirement age is 65 and below 2 years old, subminimum wages and special statutes exist which cannot easily be traced. At the same time, this step simplifies the analysis by excluding those employees for which different employment effects have been found, as mentioned above. Despite coming from an administrative source, the data had to be thoroughly cleaned. First of all, the wage negotiations take place in joint committees which only loosely collide with industrial classifications such as NACE. Most importantly, there are separate joint committees for white and blue collar workers in most industries. Note that we will further use sectors and joint committees interchangeably as the main unit of analysis. In this respect, it was unpractical not to have a reliable indicator for these units between 996 to 22. To solve this issue, an algorithm was used to reconstruct the joint committee based on probabilities using available data and other indicators (i.e. the NACE-code, white- or blue collar employment, and an administrative key variable for employer s collective funds). This was successful in smoothing the employment evolution of sectors, which serves as a visual proof of the effectiveness (see appendix, figure 8 and 9). Second, the administrative data have many special categories for workers for which exceptions in labour regulation exist (apprentices, protected occupations such as miners and messengers, etc.). We excluded these categories from the data if possible. Third, the yearly wage should be related to the yearly labour volume in order to obtain a full time equivalent wage. Even though the wage data is accurate, the labour volume is not. To alleviate the possible bias, we truncated the labour volume at 5% of a full time job, and kept only employees working at least 5% of the year (being 5% all year or % during half of the year). Also, we only kept the job in which the employee worked the longest period of time. The choices made for the data cleaning result in a loss of half of the original sample, so that the final sample is about /6th of the total population working in the private sector, which is still a very large number. The minimum wage data come from three sources. National minimum wages were provided by the National Labour Council and are publicly available (upon request). Sectoral minimum wages were collected by us from 6 sector agreements for 34 of the largest joint committees, accounting for 65% of the total workforce, using the sector wage scale database of the HR service provider Acerta, which gives legal advice on payroll administration. The available information only stretches back to 2 for most joint committees. Data for 996 to 999 were completed applying the index of negotiated pay raises (ICL) provided by the Ministry of Labour and publicly available upon request. Our approach 5

6 Table : Descriptives statistics for the sample of joint committees (996, 26) Employment Median wage Min. wage p9-p Sector Source: Wages: RSZ-ONSS. Agreements: Acerta, Ministry of Labour. 6

7 however was different from the ICL in following the evolution of the lowest wage scale in a joint committee, looking only at the base wage and following current pay levels. The ICL follows the average pay scale increases, based on just one base level. Contrasting the indicators for the years in which both are available we find a moderate correlation of r =.49. Upon closer inspection, it seems the difference is mainly caused by one-off increases of the minimum wage scale only. Since we do not want to rule out such pay increases by design, we prefer keeping with the minimum wage when available, and accept the more smoothed ICL (applied to the earliest known pay level) for the years in which actualized minimum wages are not available. Finally, minimum wages change at different moments of the year, so there is a need to translate this into yearly figures. For the national and the sectoral minimum wage, we computed the yearly aggregates by taking a weighted average of the minimum wages that were in place during the year. This is the objective minimum for a worker who was employed in the sector all year. For workers that do not have a labour volume of % (full time, all year), there may be some misalignment, since we do not know in which months they were employed. Table shows descriptive statistics for the 34 joint committees in the sample. The labels for the joint committee are listed in the appendix, table 3. Wages are expressed in logs of the yearly wage. The total count ranges from employees in 996 to 44 8 in 26. Median log wages range from 9.69 in joint committee (textile cleaning, blue collar) to.8 in joint committee 2 (petrol industry, white collar) in 26. The smallest wage inequality (difference between ln W p9 and ln W p ) is.6 in joint committee (textile cleaning, blue collar), the largest is.6, again in joint committee 2 (petrol industry, white collar). Interestingly, the highest minimum wage is found in joint committee 2 (petrol industry, white collar), followed by 24 (construction, blue collar) and 26 (carpenters, blue collar), and the lowest in joint committee 2 (independent stores, white collar), followed by 37 (brokers, mixed) and 3 (large stores, mixed). In conclusion, the high paying white collar joint committees have more wage dispersion, but differ in minimum wages, while the blue collar joint committees have low wage dispersion and average to high minimum wages, and the mixed joint committees have low minimum wages and differ in wage dispesion. 3 Descriptive analysis Figure shows the evolution of minimum wages and wage inequality between 996 and 26 for the selected sectors split up by three groups of joint committees based on the median pay level in this period. The distribution of the wages in each group is shown in figure in the appendix. In the upper left graph we see the level and the evolution of (real) minimum wages in prices of 26. The solid line shows the national minimum wage, which has a slightly negative slope since the consumer price index grew faster than the health index used for indexing the minimum wage. Remarkably, the minimum pay level in 7

8 Figure : Trends in the (real) minimum wage and wage inequality (996-26), by average median pay level of joint committees (996-26). 9.9 NMW and SMW.2 p9-p p5-p p9-p All/NMW Low pay Mid pay High pay the mid pay sectors is higher and grew faster than the minimum pay level in the low pay sectors. This may collide with the higher share of company level negotiations in the high pay sectors, which effectively replace sector level agreements. The upper right graph shows the evolution of overall wage inequality (9- differential). Higher score imply more wage inequality. The mid paying joint committees have a lower inequality than the low paying joint committees, which makes sense from their position in the middle of the overall wage distribution. High paying joint committees still have a far higher wage inequality. There is no trend within groups, but overall, inequality is slightly rising. The bottom graphs show the lower tail wage inequality (on the left) and the upper tail wage inequality (on the right hand side). We notice some volatility in the lower tail but no trend, yet a clear rise in inequality in the upper tail over time for the total sample. In sum, the differences between the pay groups in levels are substantial, with the low pay joint committees showing more upper tail inequality, and the mid pay joint committees showing more lower tail and overall inequality, however always far below the levels of the high paying joint committees. Next, in figure 2, the same indicators are indexed, with groups of joint 8

9 Figure 2: Trends in the (real) minimum wage and wage inequality (996-26), by growth (996-26). NMW and SMW p9-p p5-p p9-p All Low growth Mid growth High growth committees defining the tertiles on the slope for each indicator. In the upper left graph, we notice a divergence in minimum wages between the three groups, with a real increase of over % for the high growth group, and nearly no real growth for the low growth joint committees. For the inequality figures, the overall trend is unclear, but looking at the tails, we find one group with a marked decline of lower tail wage inequality and more stable figures for two thirds of the sectors, while in the upper tail, two thirds notice a market rise, and one third is not changing over this period. In light of the analyses in the next paragraph, both figures suggest a correlation between minimum wages and wage inequality. In terms of minimum wage levels, joint committees with high minimum wages may have lower wage inequality and vice versa. In terms of minimum wage growth, the same group that has the highest growth in the sector minimum wage may witness a decrease in wage inequality, while the dynamics in the upper tail may or may not respond to such movements. The marked stability overall, however, will ease the distinction of minimum wage effects from time effects. 9

10 4 Analysis 4. Effects of the relative minimum wage on wage inequality In an inventive contribution, Lee (999) developed a methodology to find minimum wage effects from cross-sectional data. In his case, the variation comes from differences over states in the median wage. As a result, the national minimum wage implies different relative minimum wages in each state. Where the median wage is low, the relative minimum wage is high, and this would go together with a strong effect on the wage distribution. On the other hand, when the median wage is high, the relative minimum wage is low and there is little impact to be expected. The relation between the relative minimum wage and wage inequality across states is therefore a test of the effect of minimum wages in general. The identifying assumption of the Lee model requires that there is no correlation between the inverse of the latent wage distribution function and the median. The latent wage will then be expressed as () and Cov(w q µ st, w min,t w p5,jt t) will be zero. In this case, a correlation with the observed wage will be a true minimum wage effect. w q = µ st + σ st F t (q) () We replicate the model for Belgium, with some important changes. First, the variation comes from sectors instead of states (or regions). Second, following from this, we use sector specific minimum wages, as outlined above. Third, we do not include a quadratic effect, as this troubled the effect of the linear specification without adding to the explanatory power. The model specification is thus (2). Here w p denotes the wage for a given percentile p and w smw,st is the minimum wage for sector s at time t. α is a vector with year dummies. w p,st w 5,st =α t + β (w smw,st w 5,st ) + ε st (2) Figure 3 shows six variations built of Lee s model. The first model is the original estimation of the change in wage inequality for a change in the relative minimum wage, evaluated at the average. The effect is around.5 at the th percentile and -.5 at the 9th percentile. Towards the extremes, it becomes and - or below. The sign has a different interpretation on both sides of the median. Below the median, a positive sign means that an increase in the relative minimum wage cause a decrease in wage inequality. Above the median, a positive sign implies an increase in wage inequality. Remarkably, Lee s model suggests that an increase in the relative minimum wage compresses the upper tail of the wage distribution. The effects in model, although unmistakenly present, raise doubt on what is actually measured. We addressed two such issues. First, there may be a violation of the identifying assumption. As mentioned by Lee, this should be less

11 of a concern for states than for other aggregations (e.g. by sector or occupation). Nevertheless, in a European context, it is not easy to maintain that the differences between countries are smaller than the differences between industries within countries. Autor, Manning, and Smith (2) argue that state (sector) fixed effects should be included (model 2), as well as state-year trends (model 3). This implies a change of the α term to α st,s t. As the figures show, the remaining within sector effects are more in line with the puzzling findings of Lee: decreasing wage inequality in the lower tail and increasing wage inequality in the upper tail. A second measurement problem is readily seen in (2). The median wage appears on both sides of the equaition, giving rise to a division bias. That is, if by sampling error (e.g. through the proces of retrieving the joint committee, see appendix 6.), the median wage is somewhat off, this will cause an artificial correlation. We prove the problem of an endogeneous relative minimum wage in appendix 6.2. In order to solve this problem, we need a different formulation of the relative minimum wage rmw. This is made explicit in equation (3). w p,st w 5,st =α st,s t + β ( rmw) + ε st (3) We will apply three modifications of rmw. Model 4 and 5 follow a two step least square approach, with the actual sector minimum wages as an instrument in model 4 and as a regressor in the first step predicting rmw. Model 6 uses a lagged median to construct rmw. Alternatively, as suggested by Autor, Manning, and Smith, a trimmed mean could also have been used, although in practice this corresponds too closesly to the median. The 2SLS approaches yield very similar results that clearly show strong effects for very low wages and around the th percentile until the 25th percentile, gradually fading towards the median. To the right of the median, however, some evidence of minor wage compression is noted. This effect stops around the 9th percentile. Model 6, finally, has smaller effects in the lower tail and no effects in the upper tail. As a further test of the robustness of these results, we estimate the effect at the individual level in a quantile regression. The equation is similar to the model 5 built on (3), since a quantile IV regression as in model 4 would be computationally too intensive. There are three adjustments to the model: sector-year trends are not entered (α is as in model 2) as they needlessly increase the number of variables, the left had side of the equation for each employee i becomes w i,st w 5,st, and we included a quadratic term, estimating β 2. Note that there is a difference in the estimate for the p-th percentile of the overall wage distribution in the quantile regression, in contrast to the estimated effect of the relative minimum wage at the p-th percentile of each sector s wage distribution. The regression is evaluated at the th, 2th, 8th and 9th percentile. In table 2, we provide two estimates: the standard specification from model 2 and the two stage variation as in model 5 (without year-trend effects). The results from the one stage quantile regression are counter-intuitive: the effect of a rise in

12 Figure 3: The effect of sector minimum wages on wage inequality, by percentile..5 Model - p.5 Model 2 - p pct samnq samnqsq i.year pct samnq samnqsq i.year i.parcom.5 Model 3 - p.5 Model 4 - p pct samnq samnqsq i.year i.parcom##c.year pct i.year i.parcom##c.year (samnq samnqsq = logicla logiclasq).5 Model 5 - p.5 Model 6 - p pct samnq samnqsq i.year i.parcom##c.year pct samnq samnqsq i.year i.parcom##c.year Note: the 95% confidence interval is given by the gray bands. The upper limit was set to.5, the lower limit to - to maintain the scale of the graphs. 2

13 Table 2: The effect of the relative minimum wage on wage inequality: quantile regression. One stage Two stage β β 2 β β 2 p.98 ***.2 ***.82 ***.3 *** p2.339 ***.469 ***.373 ***.526 *** p8.366 ***.67 *** -.43 *.52 *** p9.453 ***.35 ***.98 **.4 n.s. Note: the two stage approach first estimates the median wage by sector from sector fixed effects, sector year-trends, and the sector minimum wage the relative sector minimum wage has weak effects in the lower tail, and strong effects in the upper tail. The two stage approach was done using estimates of the median wage for each sector in the definition of the relative sector minimum wage. This increases the estimates for β to figures close to the ones from figure 3. In the upper tail, we observe some minor wage compression around the 8th percentile, and limited wage expansion around the 9th percentile. This mimics the stretched U-shape found above the median in models 4 and 5 analysed at the sector level, and the uplifting in model Spillover effects and disemployment We have seen that the minimum wages correlate negatively with wage inequality, at least in the lower tail, and may cause minor wage compression in the upper tail, although the latter effect is unsure. Yet wage compression may well be one compensating effect of higher minimum wages. A second channel of adjustment could be disemployment. If we assume that disemployment (by ways of firing or not hiring) is not affecting all workers over the full wage distribution, but rather those feeling the closest pressure from the minimum wage, the observed effect on wage inequality would actually be artificial. It is readily seen that if one takes a slice of, say, ten percent at the lower tail of a normal distribution, the inequality from the now moved tenth percentile relative to the moved median will always be smaller. To test disemployment effects, we will estimate spillovers in the full sample and compare this with estimated spillovers in a sample of workers who were already present in the sample one year before. As a stable workforce cannot give rise to spillovers from disemployment, this comparison may falsify the disemployment hypothesis. The estimation of the spillovers follows the logic outlined in Autor, Manning, and Smith. We return to () and estimate this equation between the 65th and the 9th percentile, allowing the mean µ st,s t and the standard deviation σ st,s t to vary by sector and time. We assume that in this region, the correspondence 3

14 between the observed and the latent wage distribution is closest, and using the parameters µ and σ of this distribution, we define a latent distribution for all percentiles by sector. Figure 4: Deviation from the estimated latent wage distribution (full sample, sector-year). W-W* MW-W* Next, we produce two graphs to compare the stable and the full sample. In figures 4 and 5, the difference between the observed wage and the latent wage by sector-year is plotted on the y-axis, and the difference between the sector minimum wage and the latent wage on the x-axis. If the minimum wage is higher than the latent minimum wage, a positive deviation is expected along a 45 slope, as the minimum wage is binding. If the minimum wage is much lower than the latent wage, we expect the latent and observed wage to coincide. This means that spillovers are most likely around the point where the minimum wage is equal to the latent wage. As a consequence, we should observe a triangular cloud fitting between the 45 line and the y-axis. This is indeed the case in both graphs, giving no indication of an absence of spillover effects in the stable workforce. Finally, in graphs 6 and 7, the average deviation of the y-axis is plotted by percentile. As the latent wage distribution was derived from the distribution between the 65th and the 9th percentile, this line is flattest in this area. Towards the lower percentile, it rises strongly, indicating more spillovers. In fact, in the stable workforce, the spillovers appear to be largest. 4

15 Figure 5: Deviation from the estimated latent wage distribution(stable workforce, sector-year). W-W* MW-W* Figure 6: Average deviation from the latent wage distribution (full sample).5 E(W-W*) Percentile 5 8

16 Figure 7: Average deviation from the latent wage distribution (stable workforce).2.5 E(W-W*) Percentile 6

17 5 Conclusion In this article we discussed the effects of minimum wages on wage inequality using data from Belgium, which features a strongly institutionalized wage setting system. The margins for wage growth are set by sector minimum wages and wages indexation on the one hand, and by the wage norm defining the upper limit of growth on the other hand. As a result, wage inequality is at a low and stable overall level. In this respect, we applied Lee s model for testing minimum wage effects using cross sectional variation in wage levels, wage inequality and the minimum wage. Without controls, we found a very high degree of wage compression on both sides of the wage distribution. Controlling for sector effects, this effect largely vanished. We therefore hypothesize that institutional arrangements between sectors have a very strong effect on wage inequality, and minimum wages are set in accordance with an optimal degree of wage dispersion. Within sectors, however, the effect of minimum wage rises is much in line with earlier research, finding that a % increase in the minimum wages compresses wage inequality below the median with around.5%. Still, spillover effects in the high wage region showed up, pointing to an expansion of wage inequality. Using three different approaches (instrumented variable regression, a manual two step approach, and a lagged variable approach), we found consistent estimates in the lower half of the wage distribution, and possibly some minor wage compression in the upper half. Quantile regression estimates confirmed these results. Finally, we addressed the issue of unemployment, expanding on a method of Autor, Manning, and Smith. This is commonly coined in the debate as a trade off factor for anti-poverty or anti-inequality policy measures. Other than that, it is also important in measuring true minimum wage effects, avoiding composition biases when part of the workforce is dismissed or not replaced, thereby shifting lower wage percentiles up on the former distribution and decreasing wage inequality. We calculated spillover effects through the estimation of a latent wage distribution and mapped these over percentiles and relative to the minimum wage. Comparing the graphs for the total and the stable workforce, no evidence was found for lower spillovers in the latter group. In sum, minimum wages appear to have a compressing effect on the wage distribution, at least in the lower half. Disemployment does not appear to be the main driving factor behind decreases in wage inequality. The main question remaining, then, is what compensates for the increases in minimum wages. Furthermore, the between-sector variation in the effect of minimum wages on wage inequality points to institutional mechanisms that may reverse causality: are minimum wages set in function of the wage dispersion that prevails in the sector, and what factors decide on this wage dispersion, if not the minimum wage? 7

18 References Autor, D. H., Katz, L. F., & Kearney, M. S. (28). Trends in US wage inequality: revising the revisionists. The Review of Economics and Statistics, 9 (2), Autor, D. H., Levy, F., & Murnane, R. J. (23, November). The skill content of recent technological change: an empirical exploration. The Quarterly Journal of Economics, 8 (4), Autor, D. H., Manning, A., & Smith, C. L. (2). The contribution of the minimum wage to US wage inequality over three decades: a reassessment. National Bureau of Economic Research. Bound, J. & Johnson, G. (992, June). Changes in the structure of wages in the 98 s: an evaluation of alternative explanations. The American Economic Review, 82 (3), Brown, C. (999). Minimum wages, employment, and the distribution of income. In O. C. Ashenfelter & D. Card (Eds.), Handbook of labor economics (3B, 2 263). Amsterdam: Elsevier. Card, D. & Krueger, A. B. (994, September). Minimum wages and employment: a case study of the Fast-Food industry in new jersey and pennsylvania. The American Economic Review, 84 (4), Chennells, L. & Reenen, J. V. (997, November). Technical change and earnings in british establishments. Economica, 64 (256), DiNardo, J., Fortin, N. M., & Lemieux, T. (996, September). Labour market institutions and the distribution of wages, : a semiparametric approach. Econometrica, 64 (5), 44. Dolado, J., Kramarz, F., Machin, S., Manning, A., Margolis, D., Teulings, C.,... Keen, M. (996, October). The economic impact of minimum wages in europe. Economic Policy, (23), Dube, A., Lester, T. W., & Reich, M. (2, November). Minimum wage effects across state borders: estimates using contiguous counties. Institute for Research on Labor and Employment. Freeman, R. B., Katz, L. F., Freeman, R. B., & Katz, L. F. (995, January). Introduction and summary. In Differences and changes in wage structure (pp. 22). Chicago: The University of Chicago Press. Goos, M. & Manning, A. (27). Lousy and lovely jobs: the rising polarization of work in britain. The Review of Economics and Statistics, 89 (), Hirsch, B. T., Kaufman, B. E., & Zelenska, T. (2). Minimum wage channels of adjustment. IZA Discussion Paper. Juhn, C., Murphy, K. M., & Pierce, B. (993, June). Wage inequality and the rise in returns to skill. The Journal of Political Economy, (3), Katz, L. F. & Murphy, K. M. (992). Changes in relative wages, : supply and demand factors. The Quarterly Journal of Economics, 7 (), Lee, D. S. (999, August). Wage inequality in the united states during the 98s: rising dispersion or falling minimum wage? The Quarterly Journal of Economics, 4 (3),

19 Murphy, K. M. & Welch, F. (992, February). The structure of wages. The Quarterly Journal of Economics, 7 (), Stigler, G. J. (946, June). The economics of minimum wage legislation. The American Economic Review, 36 (3), Stockhammer, E., Onaran, Ö., & Ederer, S. (29, January). Functional income distribution and aggregate demand in the euro area. Cambridge Journal of Economics, 33 (), Teulings, C. N. (23, October). The contribution of minimum wages to increasing wage inequality. The Economic Journal, 3 (49), Appendix 6. Assigning joint committees From 996 to 22, the joint committee was not a mandatory field to be completed in the social security administration form. As a result, there is a artificial growth in the employment of the joint committees year after year because of habits, and in 23 and 24 because of the obligation. In order to straighten the evolution, we have determined the most likely joint committee under which the worker resorts, based on groups defined by worker status (white/blue collar), the NACE classification of economic activities (5 digit), and a social security number (SSN) used for funding unemployment benefits, that is linked to the joint committee. Missing values were replaced with the joint committee that was linked to at least 9% of the employees in a combination of these groups. The assignment respects the following order:. NACE, worker status, SSN, in the same year 2. NACE, worker status, SSN, in all years 3. NACE worker status, in the same year 4. SSN, worker status, in the same year 5. SSN, worker status, in the same year In this way, a solution was found for all but.3% of all combinations according to item from the list above. Visual inspection of graphs 8 and 9 shows that the employment evolution is indeed straightened. More importantly, we can connect sector minimum wages to individual employees using the estimated joint committee. 6.2 Endogeneity proof This is a proof for the endogeneity bias in the standard model of Lee (999), based on Baltagi (28) and Hayashi (2). Notation-wise, the deviation from the mean is indicated with. 9

20 = x E(x) The structural forms for inequality (N Q) and the relative minumum wage (rmw) are given below. Any quantile is denoted by px, the median being p5. We only discuss the possibility of only rmw being endogenous. NQ = α + βrmw + u (4) = px p5 (5) p5 = px NQ (6) rmw = mw p5 (7) = mw px + NQ (8) From the above, we can derive the reduced form equations. NQ = α + βmw + βnq βpx + u (9) α + βmw βpx + u = β () rmw = mw + α + βrmw + u px () mw + α + u px = β (2) We can now control for a correlation with the error term of RMW in (4). It is non-zero if there is any error and equal to (8) if all other covariances in (4) are zero, that is: the minimum wage and the value of the Xth percentile are exogenous. ( mw + α + u px Cov(RMW, u) = Cov β ) = Cov ( mw β, u + Cov ), u ( α β, u (3) ) +... (4) = E( rmw u) (5) E( mw u) + E( α u) +... = β (6) u = E( u) β (7) = σ2 β (8) We then find the possible bias of the OLS estimate, using the within transformation of NQ in (22). 2

21 NQ = β rmw + u (9) = β rmw + u (2) β OLS = E rmw NQ E( rmw) 2 (2) = E rmwβ rmw E( rmw) 2 = β + σ2 u/ β Var(rmw) + E rmwu E( rmw) 2 (22) (23) The bias will therefore be upward when β <, and its impact depends on the ratio of the model fit and the variance in rmw. 2

22 Table 3: Joint committees in the sample JC Label Type 9 clothing blue collar textile cleaning blue collar 2 car maintenance blue collar 5 glas manufacturing blue collar 6 chemical manufacturing blue collar 8 food manufacturing blue collar 9 food distribution blue collar 2 textile manufacturing blue collar 2 cleaning blue collar 24 construction blue collar 26 carpenters blue collar 3 publishing blue collar 36 paper industry blue collar 4 transport and logistics blue collar 45 gardening blue collar 49 metal related industries blue collar 2 independant stores white collar 22 food trade white collar 27 chemical industry white collar 29 metal industry white collar 2 petrol industry white collar 24 textile industry white collar 25 clothing white collar 28 various service industries white collar 22 food industry white collar 32 accomodation mixed 36 insurances mixed 37 brokers mixed 38 savings bank mixed 3 banking mixed 3 large stores mixed 32 large stores mixed 33 farmacies mixed 32 trade and distribution of drugs mixed 22

23 Figure 8: Employment evolution by joint committe (original and imputed) Est. Orig. PC PC PC PC PC PC PC PC PC PC PC PC PC PC PC PC

24 Figure 9: Employment evolution by joint committe (original and imputed) [continued] Est. Orig. PC PC PC PC PC PC PC PC PC PC 32 4 PC 36 5 PC PC PC PC PC PC PC

25 Figure : Kernel density estimates of the log wage distribution (26), by average median pay level of joint committees (996-26). 3 Kernel density of the log wage Total sample Mid pay Low pay High pay 25

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