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).

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)
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.

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) float(per_to_25_39 stdlfprate_m)
1 1  1 1 1 0 1       61.905171585   18383.638671875  106.8672  -.3402818
1 2  1 1 1 0 0        75.74068471     17202.3203125       100  -.3402818
1 3  1 1 1 0 0        78.66788015   18233.490234375 105.99436  -.3402818
1 4  1 1 1 0 0  54.09412395333334   16983.509765625  98.72802  -.3402818
1 1  2 1 1 0 1       61.392848445    19168.96484375   105.014 -.23995534
1 2  2 1 1 0 0  75.91077349666666 18253.72509765625       100 -.23995534
1 3  2 1 1 0 0        78.38746349       19160.34375 104.96676 -.23995534
1 4  2 1 1 0 0  54.60154921666666 17701.13232421875  96.97271 -.23995534
1 1  3 1 1 0 1 61.208336079999995   19954.291015625 103.36263 -.13962887
1 2  3 1 1 0 0  76.02173251666666  19305.1298828125       100 -.13962887
1 3  3 1 1 0 0        79.07494859   20087.197265625 104.05109 -.13962887
1 4  3 1 1 0 0  55.98275992333333  18418.7548828125  95.40861 -.13962887
1 1  4 1 1 0 1       61.894535415     20739.6171875 101.88187 -.03930241
1 2  4 1 1 0 0  76.63595531666667 20356.53466796875       100 -.03930241
1 3  4 1 1 0 0       79.375593615    21014.05078125    103.23 -.03930241
1 4  4 1 1 0 0        57.40405967 19136.37744140625  94.00607 -.03930241
1 1  5 1 1 0 1       62.136433595   21524.943359375 100.54655  .06102405
1 2  5 1 1 0 0  76.73670893666667   21407.939453125       100  .06102405
1 3  5 1 1 0 0       79.519111255   21940.904296875 102.48956  .06102405
1 4  5 1 1 0 0 58.573308759999996             19854  92.74129  .06102405
1 1  6 1 1 0 1 63.170592385000006    23661.93359375 100.66637   .2611571
1 2  6 1 1 0 0  77.87832071000001    23505.30078125       100   .2611571
1 3  6 1 1 0 0       80.937467405 24024.89697265625 102.21055   .2611571
1 4  6 1 1 0 0  60.31463328666666 22291.32275390625   94.8353   .2611571
1 1  7 1 1 0 1 63.493052445000004   25798.923828125 100.76657   .4612901
1 2  7 1 1 0 0  78.43671636666667   25602.662109375       100   .4612901
1 3  7 1 1 0 0        81.06239939  26108.8896484375 101.97725   .4612901
1 4  7 1 1 0 0        61.89366863  24728.6455078125  96.58623   .4612901
1 1  8 1 1 0 1       63.896869845     27935.9140625  100.8516   .6614231
1 2  8 1 1 0 0  79.28298021666667     27700.0234375       100   .6614231
1 3  8 1 1 0 0       81.826284695 28192.88232421875 101.77927   .6614231
1 4  8 1 1 0 0  63.00212025666667 27165.96826171875  98.07201   .6614231
1 1  9 1 1 0 1       64.198685575   30072.904296875 100.92464   .8615562
1 2  9 1 1 0 0        79.70918984   29797.384765625       100   .8615562
1 3  9 1 1 0 0  82.06593212499999         30276.875 101.60917   .8615562
1 4  9 1 1 0 0  63.93898066666666   29603.291015625  99.34863   .8615562
1 1 10 1 1 0 1       60.697728655  34159.3154296875 101.50795  1.2293557
1 2 10 1 1 0 0        78.14557024  33651.8623046875       100  1.2293557
1 3 10 1 1 0 0        81.02484167    34092.95703125 101.31076  1.2293557
1 4 10 1 1 0 0  65.10186506000001  33093.6494140625  98.34121  1.2293557
1 1 11 1 1 0 1       59.934069255     38245.7265625 101.97137   1.597155
1 2 11 1 1 0 0  78.33510748666667    37506.33984375       100   1.597155
1 3 11 1 1 0 0       81.161630255     37909.0390625 101.07368   1.597155
1 4 11 1 1 0 0        66.59804761     36584.0078125  97.54086   1.597155
1 1 12 1 1 0 1 59.755601799999994   42428.107421875  99.74764  2.0770395
1 2 12 1 1 0 0  79.18128143333333    42535.44921875       100  2.0770395
1 3 12 1 1 0 0        81.01241693    42178.56640625  99.16097  2.0770395
1 4 12 1 1 0 0  66.88963640666667   41460.669921875  97.47321  2.0770395
1 1 13 1 1 0 1        58.92321488    46610.48828125  97.99416   2.556924
1 2 13 1 1 0 0        78.83483037    47564.55859375       100   2.556924
end
label values country_n country1
label def country1 1 "Australia", 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 def age_groups_lbl 3 "40-49", modify
label def age_groups_lbl 4 "50-65", 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
label def year_n 5 "2004", modify
label def year_n 6 "2005", modify
label def year_n 7 "2006", modify
label def year_n 8 "2007", modify
label def year_n 9 "2008", modify
label def year_n 10 "2009", modify
label def year_n 11 "2010", modify
label def year_n 12 "2011", modify
label def year_n 13 "2012", modify