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Discuss the view that minimum wage legislation leads to unemployment. Has this been the recent experience of the UK?

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Discuss the view that minimum wage legislation leads to unemployment. Has this been the recent experience of the UK? Minimum wage legislation is often contemplated by governments as a way of combating poor wages and the poverty that typically accompanies these wages. This is a direct form of government intervention and its use causes a great deal of debate, particularly from Classical (or free market) economists. Free Market economists argue that the minimum wage causes unemployment. This paper will discuss the argument that minimum wage causes unemployment and go on to analyse whether this has been the case in the UK over recent years. When a government introduces a minimum wage it sets a wage level to which every worker is entitled. Any employer which pays less than this minimum wage does so only by breaking the law. A minimum wage only has an effect if placed above the market equilibrium as can be seen in Fig.1. When the minimum wage is implemented the supply of workers increases as more are willing to work for the higher wage and we move up the labour supply curve. At the same time employers are less able to afford as many workers at the higher wage level and reduce the amount of workers that they employ. ...read more.


can hopefully show the effect of the NMW in numerical terms. Obviously unemployment is affected by other variables but if we expect these variables to be relatively stable then it is the introduction of the NMW that will have the greatest effect. All that can be seen is whether there is a strong positive effect on unemployment after the NMW is introduced.2 Using Eviews, the coefficient obtained for the minimum wage dummy variable was 0.495058. This tells us that the impact of the minimum wage has been to increase unemployment. The R squared value of 0.92607 suggests that the model is representative but this may also be due to heteroskedasticity being present in the model. On the basis of this model the NMW has increased unemployment. Whilst the results of my simple regression were positive showing a connection between the NMW and unemployment, it is unsafe to draw a firm conclusion from the results. This is because it includes too few variables. Even the work of Benjamin Nardella3 which included a total of nine variables cannot statistically prove the effect of a minimum wage. Most models will confirm a connection between minimum wages and unemployment, but Machin4 argues 'the effect of a minimum wage always depends on the assumptions that the modeller makes prior to the experiment.' ...read more.


Appendix 1.1 Annual unemployment figures for Fig.2 (quarterly figures included in chart and regression for greater accuracy) 1992 9.8 1993 10.5 1994 9.8 1995 8.8 1996 8.3 1997 7.2 1998 6.2 1999 6.1 2000 5.6 2001 4.9 2002 5.2 2003 5.0 1.2 Results of statistical regression: Dependent Variable: UNEMPLOYMENT Method: Least Squares Date: 03/30/04 Time: 15:56 Sample(adjusted): 1992:1 2003:3 Included observations: 47 after adjusting endpoints Variable Coefficient Std. Error t-Statistic Prob. C 18.26817 0.749137 24.38561 0 MW 0.495058 0.314841 1.572408 0.123 GDP -5.43E-05 4.08E-06 -13.30164 0 R-squared 0.92607 Mean dependent var 7.140426 Adjusted R-squared 0.92271 S.D. dependent var 1.943625 S.E. of regression 0.540349 Akaike info criterion 1.668499 Sum squared resid 12.84699 Schwarz criterion 1.786593 Log likelihood -36.20972 F-statistic 275.5801 Durbin-Watson stat 0.141813 Prob(F-statistic) 0 1.3 Results of Benjamin Nardella's regression: Dependent Variable: UNEMP Method: Least Squares Date: 11/04/03 Time: 23:43 Sample(adjusted): 1959:01 2002:09 Included observations: 525 after adjusting endpoints Variable Coefficient Std. Error t-Statistic Prob. MINIMUMWAGE 1.113130 0.180466 6.168085 0.0000 MONEYSUPPLY 0.001590 0.000205 7.741704 0.0000 POPULATION 0.000194 1.33E-05 14.62016 0.0000 REALGDP -0.005619 0.000180 -31.22630 0.0000 LABORFORCE 0.000416 1.76E-05 23.66911 0.0000 INFLATION -0.173792 0.016102 -10.79290 0.0000 GOVTSUP 0.000626 0.000459 1.362775 0.1735 CPI -0.158707 0.009237 -17.18170 0.0000 C -37.48853 2.088863 -17.94686 0.0000 R-squared 0.829552 Mean dependent var 5.917714 Adjusted R-squared 0.826909 S.D. dependent var 1.478307 S.E. of regression 0.615038 Akaike info criterion 1.882727 Sum squared resid 195.1879 Schwarz criterion 1.955814 Log likelihood -485.2159 F-statistic 313.9142 Durbin-Watson stat 0.334728 Prob(F-statistic) 0. ...read more.

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