The Effect of Legislated Minimum Wage Increases on Employment and Hours: A Dynamic Analysis
2010; Wiley; Volume: 24; Issue: 1 Linguagem: Inglês
10.1111/j.1467-9914.2010.00468.x
ISSN1467-9914
Autores Tópico(s)Economic Policies and Impacts
ResumoLABOURVolume 24, Issue 1 p. 1-25 Free Access The Effect of Legislated Minimum Wage Increases on Employment and Hours: A Dynamic Analysis Dale L. Belman, Dale L. Belman SLIR, MSU, East Lansing, MI 48104, USASearch for more papers by this authorPaul Wolfson, Corresponding Author Paul Wolfson SLIR, MSU, East Lansing, MI 48104, USADale L. Belman — Paul Wolfson (author for correspondence), SLIR, MSU, East Lansing, MI 48104, USA. E-mail: drdale@msu.edu. Search for more papers by this author Dale L. Belman, Dale L. Belman SLIR, MSU, East Lansing, MI 48104, USASearch for more papers by this authorPaul Wolfson, Corresponding Author Paul Wolfson SLIR, MSU, East Lansing, MI 48104, USADale L. Belman — Paul Wolfson (author for correspondence), SLIR, MSU, East Lansing, MI 48104, USA. E-mail: drdale@msu.edu. Search for more papers by this author First published: 09 February 2010 https://doi.org/10.1111/j.1467-9914.2010.00468.xCitations: 3 The authors thank Jared Bernstein, David Neumark, Steven Woodbury, and an anonymous referee for comments on earlier versions of this research. Any errors and omissions are the responsibility of the authors. All estimates discussed in this paper are available on request. AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract We present a dynamic policy simulation analysing what would have happened to wages, employment, and total hours had the federal minimum wage increased in September 1998, a year after the last actual increase in our data. Prior work suggests that employment responses take 6 years to play out. Using a time-series model for 23 low-wage industries, we find a positive response of average wages over 54 months following an increase in the minimum wage, but neither employment nor hours can be distinguished from random noise. Ignoring confidence intervals, the adjustment of hours is complete after 1 year, the adjustment of employment after no more than two and one half years. 1. Introduction Dynamic policy simulations of the minimum wage and its effect on employment and wages are exceedingly rare. This absence is especially unfortunate in light of findings that the minimum wage reaches its full effect only over several years (Baker et al., 1999; Neumark et al., 2004). It is, however, understandable, given the data most often used in minimum wage studies are short panels of either business establishments or states (prominent examples include Card, 1992; Card and Krueger, 1994, 2000; Neumark and Wascher, 1992, 2000). Dynamic policy simulation requires, first of all, reliable estimates of dynamic behavior and response. These, in turn, require sufficiently long panels or time series in combination with appropriate statistical techniques. Minimum wage studies once relied heavily on time-series data and methods. Four-fifths of the studies cited in Brown et al. (1982) literature review used time-series methods. This changed with the New Minimum Wage Research, the efflorescence that followed in the wake of the four federal minimum wage increases of the 1990s. To our knowledge, only four of the more than 200 studies comprising this literature use time-series methods (Bazen and Marimoutou, 2002; Wolfson and Belman, 2001; Zavodny, 2000; Wolfson and Belman, 2004).1 The current analysis addresses this deficiency, beginning with a simultaneous equations, time-series analysis of monthly data for earnings, employment, and hours in 23 two-digit and three-digit Standard Industrial Classification (SIC) industries. The main criterion for selecting these industries is that they had low average wages, the presumption being that they would be more than typically responsive to variation in the federal minimum wage. The data cover the period January 1972 through February 2003. The time-series models use more realistic lag specifications and simpler computational methods than prior work. Following some hypothesis testing to get a sense of the estimates, we compare what would have happened had the minimum wage increased by 10 per cent in September 1998 with the actual history of no change. We do this through a stochastic simulation on all the industries over the period September 1998–February 2003, which we then aggregate to a single simulation for each of wages, jobs, and hours. Several results stand out. First, the only clear effect of the minimum wage over time is on the nominal wage, and its effect grows (proportionately) over time. The effect of the minimum wage on both employment and total hours worked is small and cannot be measured with precision. Second, we do not find that employers adjust to minimum wage increases by reducing hours rather than employment. Third, most adjustment in total hours occurred in the first year, and most adjustment in employment was complete within 3 years following minimum wage increases. This contrasts with the 6 years that Baker et al. (1999) found in their analysis of Canadian provincial data. The current analysis also advances minimum wage research by considering the effect of the minimum wage on total hours worked. Although minimum wage research has focused on employment effects, employers can adjust their labor demand by altering hours, the number of employees, or both. Depending on the direction and magnitude of the hours response, the change in hours of work may reinforce or attenuate employment effects. Unless all employees work the same, constant, number of hours, the employment response will be an inaccurate estimate of the response in total hours worked (Hamermesh, 1993). Several recent articles have estimated the effect of the minimum wage on working hours but, parallel to the recent estimates of employment effects, the estimated responses vary from strongly negative (Couch and Wittenberg, 2001; Orazem and Mattila, 2002) to none at all (Connolly and Gregory, 2002; Zavodny, 2000), and include results that are not easily susceptible to brief summation (Neumark et al., 2004). Incorporating hours equations into our model allows us to measure the dynamics of hours in response to changes in the minimum wage, and understand how employers use the employment and hours margins in their adjustments. The estimates themselves indicate that increases in the minimum wage result in a rapid increase in industry average wages for two-thirds of our industries, but have the anticipated negative effect on employment and hours for between a fifth and a quarter of industries. The wage response outweighs the employment response, leading to an increase in total weekly earnings. 2. Data The empirical foundation for this work is data on industry employment, average hours, and average production earnings collected as part of the Current Employment Statistics (CES) project of the Bureau of Labor Statistics. Covering the pay period that contains the 12th of the month, this project surveys roughly 400,000 business establishments that employ nearly a third of all payroll workers employed across the almost 500 three-digit SIC industries. The universe of the CES, derived from state unemployment insurance (UI) records, includes 99 per cent of private sector non-farm employment.2 The start dates of CES series vary considerably by industry and variable. This research uses a data set that begins in 1972, a year in which earnings, employment, and hours series were initiated for many different industries. The data end in February 2003, when the data were switched from the SIC to the North American Industry Classification System (NAICS) system. Our prior research, which used samples beginning in 1947, 1958, 1964, 1972, and 1982, indicates that negative employment elasticities with respect to the minimum wage are most frequent among industries in the 1972 sample. The data are available at http://stats.bls.gov/. Using two- and three-digit SIC industries, we define low-wage industries as those for which the mean ratio of the minimum wage to the CES industry average wage is greater than one-half for the period under consideration (Table 1).3 An exception is SIC 58, Eating and Drinking Places, which has a large proportion of employees who are subject to a reduced minimum wage; the minimum wage for tipped workers is half that for others so long as their regular and tip income exceeds the statutory minimum. Based on this lower minimum, the ratio for SIC 58 varies between 0.36 and 0.40.4 Table 1. Average value of the relative minimum wage by industry SIC Description RMW SIC Description RMW 22x a Textile Mill Products 0.54 525 Hardware Stores 0.60 221 Broadwoven Fabric Mills, Cotton 0.50 531 Department Stores 0.57 224 Narrow Fabric Mills 0.55 533 Variety Stores 0.71 225 Knitting Mills 0.56 546 Retail Bakeries 0.62 227 Carpets & Rugs 0.51 553 Auto & Home Supply Stores 0.53 228 Yarn & Thread Mills 0.55 554 Gasoline Service Stations 0.65 23x a Apparel & Other Textile Products 0.63 56 Apparel & Accessory Stores 0.63 231 Men's & Boys' Suits & Coats 0.54 58 Eating & Drinking Places 0.78 232 Men's & Boys' Furnishings 0.65 59x* Miscellaneous Retail Establishments 0.57 233 Women's & Misses' Outerwear 0.62 591 Drug Stores and Proprietary Stores 0.57 234 Women's & Children's Undergarments 0.65 594 Miscellaneous Shopping Goods Stores 0.60 236 Girl's & Children's Outerwear 0.66 596 Non-store Retailers 0.51 244 Wooden Containers 0.58 599 Retail Stores, nec. 0.55 31x a Leather & Leather Products, excluding Tanning & Finishing (311) 0.60 701 Hotels & Motels 0.61 313/4 Footwear, Except Rubber 0.60 723 Beauty Shops 0.58 316 Luggage 0.57 734 Services to Buildings 0.57 317 Handbags & Personal Leather Goods 0.62 752 Automobiles Parking 0.62 385 Ophthalmic Goods 0.50 754 Automotive Services, Except Repair 0.64 387 Watches, Clocks, Watch cases, & Parts 0.51 805 Nursing & Personal Care Facilities 0.60 394 Toys & Sporting Goods 0.52 835 Child Day Care Services 0.65 a Composite industry that aggregates several related three-digit industries. Boldface indicates industries included in the sample. Do these data comprise enough low-wage workers for detection of a minimum wage effect, if present? Because they are national aggregates, it is not possible to incorporate relevant information about the level or variation of state minimum wages. Beginning in 1973, micro-data on wages became available in the May Current Population Survey (CPS). Table 2 uses this information to present the proportion of employees in the low-wage industries earning less than the new minimum wage in the year prior to the increase. Table 2. Fraction of employees earning below the new minimum wage SIC industry 1974 1975 1976 1978 1979 1980 1981 1990 1991 1996 1997 Per cent change in minimum wage 5.3 10 4.5 15.2 9.4 6.9 8.1 13.4 11.8 11.8 8.4 Low-wage industries: by SIC 533 Variety Stores 50.1 14.6 21.6 59.8 54.5 16.2 16.2 27 9.1 24.6 44.1 723 Beauty Shops 32.9 15.3 22.2 42.6 38.9 19.3 22.0 19.7 13.9 19.1 32.6 554 Gas Service Stn 42.1 19.1 23.0 46.6 49.9 21.3 21.0 15.7 10.2 12.3 27.2 835 Child Day Care na na na na na na na 20.6 8.7 17.3 25.2 560 Apparel & Shoe Stores 39.2 43.8 32.8 33.5 40.0 39.9 37.5 17.9 23.7 15.2 24.2 23x Apparel Mfg 36.5 28.0 25.0 31.8 44.6 42.0 50.0 12.0 23.2 16.0 23.0 734 Budding Services 13.6 10.0 12.0 31.5 32.4 14.4 14.4 11.9 6.7 10.1 21.7 546 Retail Bakeries s s s 61.0 s 11.6 12.4 20.0 7.5 14.3 20.1 701 Hotels & Motels 48.7 32.6 29.3 45.9 49.1 35.4 45.6 14.2 8.5 11.7 19.6 525 Hardware Stores 16.2 11.6 13.1 28.2 19.4 11.7 12.4 11.7 4.0 9.1 19.1 59x Misc. Retail Stores 46.2 42.5 40.5 41.1 40.0 51.6 47.3 31.0 40.0 11.9 19.2 244 Wooden Containers 29.0 19.0 15.0 21.4 29.1 19.0 26.0 na na na na 553 Auto/Home Supply 9.7 7.0 9.0 21.0 25.2 1.0 1.0 4.7 1.5 4.5 12.1 805 Nursing Facilities 43.9 34.5 37.0 48.4 50.3 33.0 37.9 9.3 4.2 5.9 11.9 531 Department Store 28.4 35.6 36.2 33.3 33.5 37.5 39.3 9.7 4.2 7.9 10.5 754 Auto Services x. repair 9.4 7.0 10.0 15.0 14.1 11.0 9.9 5.2 3.7 2.3 9.9 31x Leather x. Tanning 47.2 33.1 45.8 46.2 45.9 56.2 58.5 11.5 21.1 6.3 8.9 394 Toy's and Sporting Goods na na na na na na na 5.9 8.3 9.6 8.2 22x Textile Mill Products 16.1 13.0 12.0 5.5 11.5 19.0 20.0 4.3 4.6 3.5 6.5 Eating and drink places 580 Eating/Drinking Placesa 11.7 0.8 5.0 0.4 2.0 3.4 3.9 0.7 6.8 7.3 9.6 580 Eating/Drinking 53.6 68 73.4 3.9 61.7 74.2 78.4 35.2 24.5 33.7 48.6 a Calculated at the legal minimum for tipped employees. na — not a distinct category in CPS industry classification in that year; s — very small sample (under 30). Two patterns are apparent. First, a substantial fraction of employees in these industries earned less than the 'new' minimum wage in the year before the increase. In 1997, for example, this fraction ranged from 6.5 per cent (SIC 22x, Textile Mill Products) to 44.1 per cent (SIC 533, Variety Stores): half lie between 9 per cent and 25 per cent, and most (90 per cent) between 7 per cent and 48 per cent. In contrast, only 8.5 per cent of the labor force as a whole was bound by the minimum wage in that year.5 Overall, half of the numbers for the low-wage industries lie between 3 per cent and 52 per cent. Second, the fraction of employees below the new minimum declines over time, but this trend reverses prior to the 1996 and 1997 increases. In the 1970s and early 1980s, between 28.4 per cent and 39.3 per cent of department store employees earned below the new minimum. This fraction had fallen to 9.7 per cent by the 1990 increase and 4.2 per cent by the 1991 increase. The proportion of employees affected by the new minimum then increased to 7.9 per cent in 1996 and 10.5 per cent in 1997. This pattern is consistent with the declining real value of the minimum wage during the 1980s and the cumulative effect of the four increases in the 1990s. Returning to the CES, employment of production workers in the 23 industries in our sample ranged from 10.8 million (February 1972) to 21.1 million (December 2000). Total weekly hours ranged from 353 million (February 1975) to 611 million (July 2000). 3. Analytic framework We estimate the effect of the minimum wage with a Box-Jenkins forecasting model of employment and wages. Specification of the model involves testing for the presence of unit roots and cointegrating relationships; modeling of seasonal and higher frequency serial correlation; avoiding spurious correlation through appropriate controls for macroeconomic conditions; and distinguishing between the effects of legislated increases in the minimum wage and the effects of declines in the real minimum wage that occur through inflation and nominal wage increases. Because our prior work, Wolfson and Belman (2004), develops many of these issues, we limit the current discussion to the framework and improvements on prior work. The model consists of three equations for each industry, one each for the average nominal production wage, employment, and total production hours. We include a wage equation both to provide a full portrait of the economic consequences of the minimum wage and, more importantly to this research, for its use as 'wage filter' for the employment and hours equations. The general specifications of the three equations are (all variables in logs and Δ indicating the first difference) ([1]) ([2]) ([3]) The variables are: C constant term (may include monthly dummies) CPI consumer price index EC error-correction term (only for industries with cointegration between PW and TH) MW nominal federal minimum wage PW employment of production workers in the industry PW HI employment of production workers in the composite high-wage industry RMW the relative minimum wage: the value of the federal minimum wage relative to industry average wage, ΔRMW+ absolute value of the change in the relative minimum wage when that is positive and zero when it is negative ΔRMW- absolute value of the change in the relative minimum wage when that is negative and zero when it is positive TH total hours worked by production employees in an industry TH HI total hours worked by production employees in the composite high-wage industry UN national civilian unemployment rate industry average wage average wage for the composite high-wage industry. The dependent variables for the first two equations are the CES series on the average wage and employment of production workers. The dependent variable for the third equation, total hours, is the product of the CPS measure of the average hours of production employees and production employment. By construction, it captures both adjustments to the hours of the employed and the effect of hiring and lay-offs on the volume of hours worked in an industry. As such, it provides a more complete measure of employment changes than the typical employment equation. The division of the industry equations into nominal (wage) and real (employment and total hours) is carried into the specification of explanatory variables: average wages are a function of nominal variables, employment and hours are a function of real variables. This division, which is characteristic of macroeconomic time series, excludes nominal variables, such as the inflation rate, from the real equation and real variables, such as the unemployment rate, from the nominal equation.6 This model may be estimated under different assumptions about the inter-equation error structure: without intra- or inter-industry correlation in contemporaneous errors, in a seemingly unrelated equations (SUR) framework with contemporaneous correlation between wage, employment, and hours equations within industries but no inter-industry correlation, or in a SUR framework with contemporaneous correlation of the error terms between all equations in the model. We focus on the last, as it is the most efficient and prior work finds only modest differences in the point estimates among these specifications.7 3.1 Unit roots and cointegration Use of time-series data suggests both a Box-Jenkins framework, which stresses the importance of serial and seasonal correlation in the dependent variables, and testing for the presence of unit roots and cointegration. Using Leybourne and McCabe's (1994) test for unit roots, we reject the null of stationarity at the 0.01 level for the logarithms of employment, the average (nominal) wage, and the relative minimum wage.8 Consequently, all variables are differenced. Engle-Granger tests uniformly reject cointegration between the nominal minimum wage and the industry average wage, the relative minimum wage and employment, and the relative minimum wage and total hours in all industries, at a test size of 0.1 or better. However, the two employment measures, number of jobs and total hours, show evidence of cointegration in seven industries. The equations for employment and total hours in these industries include an error-correction term from an Engle-Granger cointegrating equation. 3.2 Short-term serial correlation and seasonal correlation Box-Jenkins modeling is a non-structural approach to analysing an industry's internal dynamics and response to external factors. Its advantage is its focus on issues of immediate import, in this case measurement of the minimum wage effect on employment and wages, without requiring construction of a comprehensive structural model. Equations [1], [2], and [3] include two sets of lagged dependent variables: the first, , allows for short-term serial correlation in the dependent variable; the second, , for seasonality at a lag of 12 months. The lag structure varies for each series across both industry and dependent variable. 3.3 Macroeconomic controls Spurious correlations between either the minimum wage and employment or the minimum wage and the industry average wage may arise when events occur contemporaneously with a minimum wage increase. Prior research on employment suggests that improved wage and employment forecasts result from accounting for macroeconomic conditions, specifically the unemployment and inflation rates (Brown et al., 1982). We further control for broad economic trends by including a wage and employment series for a composite high-wage industry. These series are constructed from several of the highest-wage industries with complete information for the period under consideration: e.g. petroleum refining, carpentry, and railroad equipment (see Table 3). The employment and total hours series are the monthly averages of the corresponding variables for the series used; the wage series is an employment weighted average. The effects of inflation, unemployment, and high-wage industry are estimated with 12 lagged values for each variable. Although arbitrary, most research on the minimum wage suggests that a year lag is sufficient to capture dynamic effects (Wolfson and Belman, 2004). Table 3. High-wage industries average value of the relative minimum wage by industry SIC Description RMW 122 Bituminous Coal and Lignite Mining 0.25 162 Heavy Construction, Except Highway 0.29 175 Carpentry and Floor Work 0.29 211 Cigarettes 0.26 281 Industrial Inorganic Chemicals 0.29 291 Petroleum Refining 0.24 321 Flat Glass 0.28 335 Nonferrous Rolling and Drawing 0.33 372 Aircraft and Parts 0.28 374 Railroad equipment 0.30 481 Telephone Communications 0.30 491 Electric Services 0.27 731 Advertising 0.31 3.4 Specification of the minimum wage measures The specification of the minimum wage term differs in the wage and employment equations. Except for the constant term and the monthly dummies, all variables in the wage equations refer to nominal quantities. There is only one minimum wage term, the contemporaneous minimum wage. This differs from the specification of the employment effect, and reflects both the view that the direct effect of legislated wage increases on the industry average wage is immediate and empirical evidence from formulations with lags.9 Further change in the industry average wage would then be an indirect response to a minimum wage increase, most likely due to the time-series dynamics of wages and employment. Except for the constant term and the monthly dummies, all variables in the employment equation refer to real quantities. Minimum wage effects on employment are driven by both contemporaneous and lagged values of the relative minimum wage (RMW), defined as the ratio of the legislated minimum wage to the average industry wage.10 The equation contains 24 RMW terms, 12 for increases in the relative minimum wage, 12 for decreases. Changes are split to avoid imposing the restriction that employment responds symmetrically to increases and decreases. With this, it is possible to distinguish between the effects of legislated increases (the only time when the relative minimum wage increases) and those of other changes in the relative minimum wage. As a compromise between the assumption of instantaneous adjustment, common to earlier time-series models, and a lag structure that requires estimation of many terms, the specification here has 12 lags of the relative minimum wage, permitting the direct employment response to be spread over a year.11 3.5 Generalized least squares estimation With the exception of the error-correction term in the seven industries where the two employment measures are cointegrated, the system defined by equations [1], [2], and [3] does not explicitly allow for relationships between equations or industries. Although ordinary least squares (OLS) would provide consistent estimates of the parameters, the presence of contemporaneous events that are not explicitly modeled across equations and across industry suggest that joint estimation would be more efficient. Further, tests of cross equation hypotheses require joint estimation. As prior work suggests that the point estimates obtained with SUR are similar to those obtained from OLS, we estimate the system allowing for contemporaneous correlation of error terms across all equations to take advantage of the efficiency gains (Wolfson and Belman, 2004).12 4. Results Before turning to the results that are of central interest, it would be nice to examine how well the equations perform. First, do the equations fit the data at least adequately? The mean R2 for the 69 equations is 0.66, with systematic differences by type of equation. The wage equations have the lowest R2, with a mean value of 0.51 [standard deviation (SD) = 0.21] and low and high values of 0.12 to 0.82. The employment and volume of hours equations perform similarly to each other: mean R2 are 0.75 and 0.74, respectively (SDs = 0.18 and 0.20), with identical maxima at 0.98 and minima of 0.41 and 0.32, respectively. Within industry, R2s are highly correlated. For example, SIC 59x, Miscellaneous Retail Establishments excluding Fuel Dealers, has the highest R2 in all three categories, whereas SIC 387, Watches, Clocks, Watch cases, & Parts, brings up the rear in all three. These two industries are unusual only for being the most extreme. The correlation between the R2 of the volume and employment equations is 0.91, whereas that for the volume and wage equations is 0.76, and for the employment and wage equations is 0.81. The answer to the question of adequacy is, 'Yes the equations fit the data well'. Second, are the estimates of the controls consistent with expectations? The employment response to the unemployment rate (business cycle) is positive in only one industry, and this coefficient is among the four that are not statistically significant at conventional levels. The mean estimated elasticity of employment with respect to the unemployment rate is −0.11 (SD = 0.09), with values ranging from −0.44 to 0.02. The estimated elasticity of change in total hours with respect to change in the unemployment rate is uniformly negative, ranging from −0.53 to −0.02, with a mean of −0.17 (SD = 0.09).13 Turning to the wage response to inflation, the mean estimated elasticity is 0.16 (SD = 0.49), with values ranging between −1.06 and 0.82. Twelve of the 23 estimates are significant at conventional levels (α = 0.05), of which only two are negative.14,15 A threshold issue, one that precedes testing our industry-specific estimates of minimum wage effects, is whether it is possible to reject zero constraints on estimates of the effect of the minimum wage on wages, employment, and hours elasticities for the full model; that is, whether some variables do not belong in any of the equations.16 All hypotheses about combinations of minimum wage terms, such as that all minimum wage terms are zero, or the minimum wage terms in the employment equations are zero, are strongly rejected in likelihood ratio tests (p-value less than 0.001).17 4.1 Industry-specific wage and employment elasticities Because of the large number of estimates, a minimum wage elasticity for the wage, and two each for employment and total hours (for real minimum wage increases and decreases) in each of 23 industries, a graphical presentation is more efficient than tables. Figure 1 depicts estimates of the response of the wage, employment, and hours and their p-values; Figure 2 depicts paired wage and employment and wage and hours elasticities. Estimated elasticities and p-values for Figure 1 results are provided in Table 4. Figure 1Open in figure viewerPowerPoint Minimum wage sum of lags probability values (t-statistics) Figure 2Open in figure viewerPowerPoint Applying the wage filter to the results Table 4. Minimum wage sum of lags probability values Employment elasticities Volume elasticity Wage elasticity Employment elasticity (RMW+) Employment elasticity (RMW-) Volume elasticity (RMW+) Volume elasticity (RMW-) SIC ε P-value ε P-value ε P-value ε P-value ε P-value 220 0.011 0.491 0.223 0.010 0.024 0.929 0.360 0.176 −1.055 0.137 230 0.106 0.000 0.338 0.004 0.070 0.821 0.553 0.031 0.937 0.095 244 0.048 0.082 −0.101 0.530 −0.241 0.525 −0.081 0.746 −1.358 0.014 310 0.106 0.000 −0.019 0.936 0.502 0.462 −0.054 0.867 1.442 0.092 385 −0.020 0.524 0.106 0.570 −0.750 0.068 0.245 0.367 −1.154 0.049 387 −0.071 0.177 0.150 0.699 −0.419 0.516 0.659 0.182 −0.506 0.535 394 0.013 0.627 0.326 0.136 0.454 0.492 0.361 0.235 −0.892 0.322 525 0.046 0.023 −0.043 0.580 0.481 0.005 0.066 0.616 0.798 0.004 531 0.175 0.000 −0.228 0.014 −0.023 0.883 −0.394 0.036 −0.460 0.141 533 0.428 0.000 0.376 0.056 −0.675 0.005 0.438 0.083 −0.674 0.027 546 0.050 0.039 −0.053 0.613 −0.406 0.108 −0.184 0.286 −0.257 0.526 553 0.144 0.000 −0.133 0.162 0.221 0.139 −0.211 0.137 0.682 0.001 554 0.138 0.026 −0.045 0.313 −0.349 0.047 0.141 0.071 −0.323 0.283 56 0.074 0.000 −0.032 0.760 −0.593
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