I am facing a bit of a problem and I can't seem to figure out what the issue might be. I am currently running regressions on average disposable income and employment rates between countries with a minimum wage, youth minimum wage and without. I have been running the following regression compares the agegroup below 25 and between 25-40 to see if there was a difference in these two outcome variable (as well as running fixed effect for country and year).
Cross posted with Reddit: https://www.reddit.com/r/stata/comme...tiple_outcome/
Whilst my results for the employment rates seem to be fine, when I run the same regression for average incomes, the results are completely off sink. I originally thought there might be an outlier problem but have done a few test, including generating a standard error variable, all observation are within (or just outside 3 SE of the mean). It seem in particular my inwagedummy variable is nearly perfect correlated (but again, when running a scatter plot graph everything seems fine). I have attached the regression table outputs to this post as well as some code below. If anyone has any ideas of what I might be doing wrong or not be doing it would be greatly appreciated.
Note: Since I am really interested in looking at the avg_income between age_groups, I have repeated this regression with a variable representing the percentage difference from average disposable income of the age group 25-40, whilst the results are not significant, when I use this percentage variable, my issues of collinearity disappear.
Thank you.
Cross posted with Reddit: https://www.reddit.com/r/stata/comme...tiple_outcome/
Code:
reg avg_inc_2 inwagedummy youthwagedummy mw_y ymw_y i.*country_n#i.*year i.*age_groups if (age_groups==1|age_groups==2), cluster(country_n) reg m_employmentratio inwagedummy youthwagedummy mw_y ymw_y i.*country_n#i.*year i.*age_groups if (age_groups==1|age_groups==2), cluster(country_n)
Note: Since I am really interested in looking at the avg_income between age_groups, I have repeated this regression with a variable representing the percentage difference from average disposable income of the age group 25-40, whilst the results are not significant, when I use this percentage variable, my issues of collinearity disappear.
Thank you.
Code:
Linear regression Number of obs = 942 F(2, 28) = . Prob > F = . R-squared = 0.8651 Root MSE = 11.7 (Std. err. adjusted for 29 clusters in country_n) --------------------------------------------------------------------------------------- | Robust m_employmentratio | Coefficient std. err. t P>|t| [95% conf. interval] ----------------------+---------------------------------------------------------------- inwagedummy | -24.56775 2.732159 -8.99 0.000 -30.16432 -18.97117 youthwagedummy | 22.57252 3.581028 6.30 0.000 15.23712 29.90792 mw_y | -14.00416 5.464318 -2.56 0.016 -25.19731 -2.811015 ymw_y | -5.223345 7.480217 -0.70 0.491 -20.54587 10.09919
Code:
Linear regression Number of obs = 942 F(5, 28) = . Prob > F = . R-squared = 0.9960 Root MSE = 898.81 (Std. err. adjusted for 29 clusters in country_n) --------------------------------------------------------------------------------------- | Robust avg_inc_2 | Coefficient std. err. t P>|t| [95% conf. interval] ----------------------+---------------------------------------------------------------- inwagedummy | -32052.84 425.568 -75.32 0.000 -32924.58 -31181.1 youthwagedummy | 1552.72 223.6783 6.94 0.000 1094.536 2010.904 mw_y | 770.16 851.1361 0.90 0.373 -973.3132 2513.633 ymw_y | 82.27966 934.0052 0.09 0.930 -1830.943 1995.503 |
Code:
* Example generated by -dataex-. For more info, type help dataex clear input double country_n float(age_groups year) byte(inwagedummy youthwagedummy) float(mw_y ymw_y) double(m_employmentratio avg_inc_2) 1 1 1 1 1 0 1 61.905171585 18383.638671875 1 2 1 1 1 0 0 75.74068471 17202.3203125 1 1 2 1 1 0 1 61.392848445 19168.96484375 1 2 2 1 1 0 0 75.91077349666666 18253.72509765625 1 1 3 1 1 0 1 61.208336079999995 19954.291015625 1 2 3 1 1 0 0 76.02173251666666 19305.1298828125 1 1 4 1 1 0 1 61.894535415 20739.6171875 1 2 4 1 1 0 0 76.63595531666667 20356.53466796875 2 1 1 0 0 0 0 53.079152300000004 21157.3125 2 2 1 0 0 0 0 83.91216592 19720.9375 2 1 2 0 0 0 0 51.85248355 21655.984375 2 2 2 0 0 0 0 83.66375077999999 20229.453125 2 1 3 0 0 0 0 51.713445715 22154.65625 2 2 3 0 0 0 0 84.41059247333334 20737.96875 2 1 4 0 0 0 0 50.86173013 22653.328125 2 2 4 0 0 0 0 84.56957792333334 21246.484375 3 1 1 1 1 0 1 28.895548146499998 18363.091796875 3 2 1 1 1 0 0 81.70424055333334 18829.4375 3 1 2 1 1 0 1 29.2099718905 18795.86865234375 3 2 2 1 1 0 0 80.37262525999999 19314.953125 3 1 3 1 1 0 1 28.732249623999998 19228.6455078125 3 2 3 1 1 0 0 80.26854347666666 19800.46875 3 1 4 1 1 0 1 26.7154846145 19661.42236328125 3 2 4 1 1 0 0 79.71293698666666 20285.984375 4 1 1 1 0 1 0 56.314160165000004 20919.876953125 4 2 1 1 0 0 0 80.44234257333333 21245.01171875 4 1 2 1 0 1 0 56.36382499 21663.576171875 4 2 2 1 0 0 0 80.21062512333333 22499.021484375 4 1 3 1 0 1 0 57.42928177 20234.41796875 4 2 3 1 0 0 0 80.13730665666667 21533.2734375 4 1 4 1 0 1 0 58.028286165 19462.677734375 4 2 4 1 0 0 0 80.83274671666668 20466.453125 5 1 1 1 0 1 0 35.327390609 5241.46337890625 5 2 1 1 0 0 0 79.09015721333334 5013.427734375 5 1 2 1 0 1 0 33.3712065415 5358.9814453125 5 2 2 1 0 0 0 79.23279219999999 5135.900634765625 5 1 3 1 0 1 0 31.5679606975 5476.49951171875 5 2 3 1 0 0 0 79.9887269 5258.37353515625 5 1 4 1 0 1 0 29.887344158999998 5664.02099609375 5 2 4 1 0 0 0 79.29896395666667 5440.704833984375 6 1 1 0 0 0 0 65.42606062499999 21517.95703125 6 2 1 0 0 0 0 84.11411576333333 23682.38671875 6 1 2 0 0 0 0 61.762080735000005 22030.3078125 6 2 2 0 0 0 0 83.97361708 24371.433984375002 6 1 3 0 0 0 0 63.570820595 22542.658593750002 6 2 3 0 0 0 0 83.47532549666666 25060.48125 6 1 4 0 0 0 0 59.55431093 23055.009375 6 2 4 0 0 0 0 82.35377544999999 25749.528515625003 7 1 1 1 0 1 0 35.283146985 3940.94287109375 7 2 1 1 0 0 0 74.69602227 4142.5947265625 end label values country_n country1 label def country1 1 "Australia", modify label def country1 2 "Austria", modify label def country1 3 "Belgium", modify label def country1 4 "Canada", modify label def country1 5 "Czech Republic", modify label def country1 6 "Denmark", modify label def country1 7 "Estonia", modify label values age_groups age_groups_lbl label def age_groups_lbl 1 "15-24", modify label def age_groups_lbl 2 "26-39", modify label values year year_n label def year_n 1 "2000", modify label def year_n 2 "2001", modify label def year_n 3 "2002", modify label def year_n 4 "2003", modify
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