I have the following data (dataex below) and I just want to confirm if I am doing the right approach given what I want to find. Initially, I want to group all variables in the dataex by country, category (tech_intensity), and year. Therefore, I did the following command
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collapse Establishments Employment share_emp Wages OutputINDSTAT4 ValueAdded GrossFixed r_valworker r_output_worker lval_per_worker ln_1980 perc_wanted2, by(country1 tech_intensity year)
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by year country1 (tech_intensity), sort: assert tech_intensity == _n by year country1(tech_intensity): gen share_emp_high = share_emp[3] by year country1(tech_intensity): gen share_emp_medium = share_emp[2] by year country1(tech_intensity): gen share_emp_low = share_emp[1]
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* Example generated by -dataex-. To install: ssc install dataex clear input int(country1 year) float(tech_intensity share_emp perc_wanted2 ln_1980 lval_per_worker) long Employment double(Wages OutputINDSTAT4) 4 1973 1 .05658884 . 0 . 1287 . . 4 1973 1 0 . 0 . 0 . . 4 1973 1 .552038 . 0 . 12555 . . 4 1973 1 0 . 0 . 0 . . 4 1973 1 .18999253 . 0 . 4321 . . 4 1973 1 .03187794 . 0 . 725 . . 4 1973 1 . . 0 . . . . 4 1973 1 .08534494 . 0 . 1941 . . 4 1974 1 0 . 0 . 0 . . 4 1974 1 .5064898 . 0 . 14243 . . 4 1974 1 .04715337 . 0 . 1326 . . 4 1974 1 .02560364 . 0 . 720 . . 4 1974 1 0 . 0 . 0 . . 4 1974 1 .17229117 . 0 . 4845 . . 4 1974 1 .07268589 . 0 . 2044 . . 4 1974 1 . . 0 . . . . 4 1975 1 .04365534 . 0 . 1447 . . 4 1975 1 0 . 0 . 0 . . 4 1975 1 .5189766 . 0 . 17202 . . 4 1975 1 0 . 0 . 0 . . 4 1975 1 .1539552 . 0 . 5103 . . 4 1975 1 .07002353 . 0 . 2321 . . 4 1975 1 . . 0 . . . . 4 1975 1 .02636819 . 0 . 874 . . 4 1976 1 0 . 0 . 0 . . 4 1976 1 .56875193 . 0 . 20520 . . 4 1976 1 .02328224 . 0 . 840 . . 4 1976 1 .06422018 . 0 . 2317 . . 4 1976 1 .04060534 . 0 . 1465 . . 4 1976 1 . . 0 . . . . 4 1976 1 .12583497 . 0 . 4540 . . 4 1976 1 0 . 0 . 0 . . 4 1977 1 0 . 0 . 0 . . 4 1977 1 . . 0 . . . . 4 1977 1 .05816822 . 0 . 2240 . . 4 1977 1 .16383183 . 0 . 6309 . . 4 1977 1 .5333818 . 0 . 20540 . . 4 1977 1 0 . 0 . 0 . . 4 1977 1 .02389052 . 0 . 920 . . 4 1977 1 .04536602 . 0 . 1747 . . 4 1978 1 0 . 0 . 0 . . 4 1978 1 . . 0 . . . . 4 1978 1 0 . 0 . 0 . . 4 1978 1 .04207927 . 0 . 1772 . . 4 1978 1 .152288 . 0 . 6413 . . 4 1978 1 .02355679 . 0 . 992 . . 4 1978 1 .5435397 . 0 . 22889 . . 4 1978 1 .06133789 . 0 . 2583 . . 4 1979 1 .05224232 . 0 . 2218 . . 4 1979 1 .5590023 . 0 . 23733 . . 4 1979 1 . . 0 . . . . 4 1979 1 .021174863 . 0 . 899 . . 4 1979 1 .05949689 . 0 . 2526 . . 4 1979 1 .1414641 . 0 . 6006 . . 4 1979 1 0 . 0 . 0 . . 4 1979 1 0 . 0 . 0 . . 4 1980 1 0 . 0 . 0 . . 4 1980 1 . . 0 . . . . 4 1980 1 .1470306 . 0 . 5672 . . 4 1980 1 .06897893 . 0 . 2661 . . 4 1980 1 0 . 0 . 0 . . 4 1980 1 .05783239 . 0 . 2231 . . 4 1980 1 .52979755 . 0 . 20438 . . 4 1980 1 .019908236 . 0 . 768 . . 4 1981 1 . . 0 . . . . 4 1981 1 .01993137 . 0 . 697 . . 4 1981 1 .24252217 . 0 . 8481 . . 4 1981 1 0 . 0 . 0 . . 4 1981 1 .09917071 . 0 . 3468 . . 4 1981 1 .4308836 . 0 . 15068 . . 4 1981 1 .063768946 . 0 . 2230 . . 4 1981 1 0 . 0 . 0 . . 4 1982 1 .22308142 . 0 . 6866 . . 4 1982 1 .018974593 . 0 . 584 . . 4 1982 1 .05880824 . 0 . 1810 . . 4 1982 1 .4349535 . 0 . 13387 . . 4 1982 1 .10432777 . 0 . 3211 . . 4 1982 1 0 . 0 . 0 . . 4 1982 1 . . 0 . . . . 4 1982 1 0 . 0 . 0 . . 4 1983 1 .033794936 . 0 . 942 . . 4 1983 1 . . 0 . . . . 4 1983 1 .005632489 . 0 . 157 . . 4 1983 1 .07085456 . 0 . 1975 . . 4 1983 1 .12796871 . 0 . 3567 . . 4 1983 1 .1822487 . 0 . 5080 . . 4 1983 1 0 . 0 . 0 . . 4 1983 1 .3611609 . 0 . 10067 . . 4 1984 1 .005718865 . 0 . 157 . . 4 1984 1 .14854479 . 0 . 4078 . . 4 1984 1 .1889411 . 0 . 5187 . . 4 1984 1 .034568172 . 0 . 949 . . 4 1984 1 .04498598 . 0 . 1235 . . 4 1984 1 .3718355 . 0 . 10208 . . 4 1984 1 0 . 0 . 0 . . 4 1984 1 . . 0 . . . . 4 1985 1 .3619766 . 0 . 10468 . . 4 1985 1 .06995401 . 0 . 2023 . . 4 1985 1 0 . 0 . 0 . . 4 1985 1 . . 0 . . . . end
0 Response to Collapse variables by country, category, year
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