1. Briefly discuss what types of biases might still exist in your fixed effects regression model. You can mention any omitted factor that you might think is relevant for the question at hand; and has not been captured by the province and year fixed effects. [3 marks]

xi: reg unemployment_rate min_wage i.province i.year i.province _Iprovince_1-10 (_Iprovince_1 for prove==Alberta omitted) i. year _Iyear_2001-2013 (naturally coded; _Iyear_2001 omitted) note: _Iprovince_6 omitted because of collinearity Source SS df MS = Model Residual 1163.38742 57.5526028 21 95 55.3994007 .605816872 Number of obs F(21, 95) Prob > F R-squared Adj R-squared Root MSE 117 91.45 0.0000 0.9529 0.9424 .77834 = Total 1220.94002 116 10.525345 = unemploymenre Coef. Std. Err. t P>t| [95% Conf. Intervall -. 1173657 1.999383 .2624748 -0.66 6.20 0.82 0.511 0.000 0.414 0.000 0.000 -.4706823 1.359073 -.3721146 4.093254 9.114923 .2359509 2.639693 .8970641 5.311732 10.33044 4.702493 9.722679 15.32 31.76 min_wage _Iprovince_2 _Iprovince_3 _Iprovince_4 _Iprovince_5 _Iprovince_6 _Iprovince_7 _Iprovince_8 _Iprovince_9 _Iprovince_10 _Iyear_2002 _Iyear_2003 _Iyear_2004 _Iyear_2005 _Iyear_2006 _Iyear_2007 _Iyear_2008 _Iyear_2009 _Iyear_2010 _Iyear_2011 _Iyear_2012 _Iyear_2013 -cons . 1779 707 .3225335 .3196519 .3068825 .3061357 (omitted) .3429317 .3068136 .3306701 .3154823 .366916 .3669278 .3669376 .3669278 .3699454 .375013 .3933554 .4315669 .4651747 .4791071 .5125738 .5210683 2.572242 6.481671 3.437627 .2139919 .2007825 -.0131977 -. 3249607 -.7756915 -1.191055 -1.726658 -1.506499 .2387257 .1330119 -.3634935 -.4972979 -.6337875 6.261714 1.891436 5.872569 2.781164 -.4123197 -.527638 -.7416414 -1.053424 7.50 21.13 10.40 0.68 0.55 -0.04 -0.89 -2.11 -3.22 -4.60 -3.83 0.55 0.29 -0.76 -0.97 -1.22 4.48 0.000 0.000 0.000 0.499 0.586 0.971 0.378 0.037 0.002 0.000 0.000 0.581 0.776 0.450 0.334 0.227 0.000 -1.504135 -1.925489 -2.471153 -2.287408 -.618043 -.7904766 -1.314641 -1.514886 -1.668239 3.486387 3.253047 7.090773 4.09409 .8403035 9292029 .715246 .4035026 -.0472478 -.4566206 -.9821636 -.7255895 1.095494 1.0565 .5876544 .5202898 4006639 9.03704 1.397973 xi: reg unemployment_rate min_wage i.province i.year i.province _Iprovince_1-10 (_Iprovince_1 for prove==Alberta omitted) i. year _Iyear_2001-2013 (naturally coded; _Iyear_2001 omitted) note: _Iprovince_6 omitted because of collinearity Source SS df MS = Model Residual 1163.38742 57.5526028 21 95 55.3994007 .605816872 Number of obs F(21, 95) Prob > F R-squared Adj R-squared Root MSE 117 91.45 0.0000 0.9529 0.9424 .77834 = Total 1220.94002 116 10.525345 = unemploymenre Coef. Std. Err. t P>t| [95% Conf. Intervall -. 1173657 1.999383 .2624748 -0.66 6.20 0.82 0.511 0.000 0.414 0.000 0.000 -.4706823 1.359073 -.3721146 4.093254 9.114923 .2359509 2.639693 .8970641 5.311732 10.33044 4.702493 9.722679 15.32 31.76 min_wage _Iprovince_2 _Iprovince_3 _Iprovince_4 _Iprovince_5 _Iprovince_6 _Iprovince_7 _Iprovince_8 _Iprovince_9 _Iprovince_10 _Iyear_2002 _Iyear_2003 _Iyear_2004 _Iyear_2005 _Iyear_2006 _Iyear_2007 _Iyear_2008 _Iyear_2009 _Iyear_2010 _Iyear_2011 _Iyear_2012 _Iyear_2013 -cons . 1779 707 .3225335 .3196519 .3068825 .3061357 (omitted) .3429317 .3068136 .3306701 .3154823 .366916 .3669278 .3669376 .3669278 .3699454 .375013 .3933554 .4315669 .4651747 .4791071 .5125738 .5210683 2.572242 6.481671 3.437627 .2139919 .2007825 -.0131977 -. 3249607 -.7756915 -1.191055 -1.726658 -1.506499 .2387257 .1330119 -.3634935 -.4972979 -.6337875 6.261714 1.891436 5.872569 2.781164 -.4123197 -.527638 -.7416414 -1.053424 7.50 21.13 10.40 0.68 0.55 -0.04 -0.89 -2.11 -3.22 -4.60 -3.83 0.55 0.29 -0.76 -0.97 -1.22 4.48 0.000 0.000 0.000 0.499 0.586 0.971 0.378 0.037 0.002 0.000 0.000 0.581 0.776 0.450 0.334 0.227 0.000 -1.504135 -1.925489 -2.471153 -2.287408 -.618043 -.7904766 -1.314641 -1.514886 -1.668239 3.486387 3.253047 7.090773 4.09409 .8403035 9292029 .715246 .4035026 -.0472478 -.4566206 -.9821636 -.7255895 1.095494 1.0565 .5876544 .5202898 4006639 9.03704 1.397973