/
arclab1.log
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arclab1.log
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------------------------------------------------------------------------------------
name: <unnamed>
log: /Users/shawn/src/econ485/lab4/arclab1.log
log type: text
opened on: 11 Mar 2013, 20:26:34
. use "arclab";
. /*a. Create a dummy variable, arc that is equal to one if the arc_county variable
> indicates it is an ARC county. Otherwise, non-ARC counties should have a
> value of zero. This variable will be used to see if there is a difference
> in employment growth between the ARC and non-ARC counties.*/
>
> gen arc=1 if arc_county=="ARC";
(138 missing values generated)
. replace arc=0 if arc==.;
(138 real changes made)
. /*b. Create a set of dummy variables for each of the 13 states in the ARC region.
>
> Use tabulate with the prefix state to create these variables. Hint: the state
> variable has the unique names of the 13 states for each county in the dataset.*/
>
> tabulate state, gen(state);
state | Freq. Percent Cum.
------------+-----------------------------------
AL | 47 8.44 8.44
GA | 50 8.98 17.41
KY | 74 13.29 30.70
MD | 4 0.72 31.42
MS | 36 6.46 37.88
NC | 36 6.46 44.34
NY | 33 5.92 50.27
OH | 45 8.08 58.35
PA | 59 10.59 68.94
SC | 11 1.97 70.92
TN | 69 12.39 83.30
VA | 38 6.82 90.13
WV | 55 9.87 100.00
------------+-----------------------------------
Total | 557 100.00
. /*c. Create a new variable empgrowth_9006 that is the percent change in employment
> from 1990 to 2006. Use the label command to label this “Percent change in
> employment from 1990 to 2006”. Make sure you save the data as arcdata1.dta so you
> can use it for the next problem.*/
>
> gen empgrowth_9006 = (emp06 - emp90) / emp90;
(4 missing values generated)
. save arcdata_n.dta, replace;
file arcdata_n.dta saved
. /*d. Use the summarize command to look at the description of the variables in
> this dataset. */
> summarize;
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
fips | 558 33813.85 15810.15 1001 54109
state | 0
county | 0
manu90 | 554 6446.449 11222.06 13 118484
farm90 | 554 925.0144 653.6925 0 4762
-------------+--------------------------------------------------------
percoll90 | 554 11.39375 5.642082 3.689338 41.72341
emp90 | 557 35498.5 79429.55 795 819868
emp06 | 554 44022.81 94828.9 897 963372
arc_county | 0
totpop90 | 554 67051.16 125637.7 2124 1336449
-------------+--------------------------------------------------------
pop60 | 554 54965.85 114567.6 2443 1628587
popsqmi_60 | 554 112.2741 239.375 8 3735
rural | 554 .3122744 .4638399 0 1
permanu90 | 554 21.17014 10.98425 .7445443 53.52263
perfarm90 | 554 7.988959 7.926842 0 55.84906
-------------+--------------------------------------------------------
pci90 | 554 14090.18 2814.397 7825 25984
perse90 | 557 16.13464 4.663423 4.079602 38.20309
pci90_thou~s | 554 14.09018 2.814397 7.825 25.984
arc | 558 .7526882 .4318366 0 1
state1 | 557 .0843806 .2782076 0 1
-------------+--------------------------------------------------------
state2 | 557 .0897666 .286104 0 1
state3 | 557 .1328546 .3397226 0 1
state4 | 557 .0071813 .0845138 0 1
state5 | 557 .064632 .2460963 0 1
state6 | 557 .064632 .2460963 0 1
-------------+--------------------------------------------------------
state7 | 557 .059246 .2362967 0 1
state8 | 557 .0807899 .2727572 0 1
state9 | 557 .1059246 .3080177 0 1
state10 | 557 .0197487 .1392604 0 1
state11 | 557 .1238779 .3297384 0 1
-------------+--------------------------------------------------------
state12 | 557 .0682226 .2523542 0 1
state13 | 557 .0987433 .2985852 0 1
empgrow~9006 | 554 .3210966 .8151637 -.7296684 15.54692
. /*e. Use the regression command to estimate the following linear
> regression model, under the assumption that college educated people
> contribute to employment growth: */
> regress empgrowth_9006 percoll90;
Source | SS df MS Number of obs = 554
-------------+------------------------------ F( 1, 552) = 3.42
Model | 2.26518362 1 2.26518362 Prob > F = 0.0648
Residual | 365.198846 552 .661592112 R-squared = 0.0062
-------------+------------------------------ Adj R-squared = 0.0044
Total | 367.46403 553 .664491916 Root MSE = .81338
------------------------------------------------------------------------------
empgrow~9006 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
percoll90 | .0113436 .0061305 1.85 0.065 -.0006983 .0233855
_cons | .1918507 .07793 2.46 0.014 .0387751 .3449264
------------------------------------------------------------------------------
. /*f. What is the estimated slope coefficient for percoll90?
> What is the interpretation of this slope coefficient?
>
> INTERPRETATION
>
> */
>
> /*g. Use the regression command to estimate the following
> linear regression model, where we now test whether college
> educated people and having a higher percentage of self-employed
> individuals are important to employment growth in this region:*/
> regress empgrowth_9006 percoll90 perse90;
Source | SS df MS Number of obs = 554
-------------+------------------------------ F( 2, 551) = 24.61
Model | 30.1386418 2 15.0693209 Prob > F = 0.0000
Residual | 337.325388 551 .612205785 R-squared = 0.0820
-------------+------------------------------ Adj R-squared = 0.0787
Total | 367.46403 553 .664491916 Root MSE = .78244
------------------------------------------------------------------------------
empgrow~9006 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
percoll90 | .0180694 .0059809 3.02 0.003 .0063213 .0298175
perse90 | .0490121 .0072637 6.75 0.000 .0347442 .06328
_cons | -.6767699 .1489679 -4.54 0.000 -.9693844 -.3841554
------------------------------------------------------------------------------
. /*h. What is the estimated slope coefficient for percoll90 in
> the model from part g? What is the interpretation of this slope coefficient?
>
> INTERPRETATION
>
> */
>
> /*i. Using your results from parts e and g, does the regression model in part
> e suffer from omitted variable bias? Explain.
>
> INTERPRETATION
>
> */
>
>
> /*j. Use the regression command to estimate the following linear
> regression model:*/
>
> regress percoll90 perse90;
Source | SS df MS Number of obs = 554
-------------+------------------------------ F( 1, 552) = 15.77
Model | 488.962959 1 488.962959 Prob > F = 0.0001
Residual | 17114.7368 552 31.0049579 R-squared = 0.0278
-------------+------------------------------ Adj R-squared = 0.0260
Total | 17603.6997 553 31.8330917 Root MSE = 5.5682
------------------------------------------------------------------------------
percoll90 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
perse90 | -.2024086 .0509691 -3.97 0.000 -.3025257 -.1022916
_cons | 14.66448 .8569131 17.11 0.000 12.98127 16.34769
------------------------------------------------------------------------------
. /*k. Use the predict command to capture the residuals from the regression in
> part j in a new variable named res.*/
>
> predict residual, res;
(4 missing values generated)
. /*l. Use the covariance option of the correlate command to examine the
> covariance between res and empgrowth_9006.*/
> correlate res empgrowth_9006, covariance;
(obs=554)
| residual emp~9006
-------------+------------------
residual | 30.9489
empgrow~9006 | .559228 .664492
. save arcdata1.dta, replace;
file arcdata1.dta saved
. log close;
name: <unnamed>
log: /Users/shawn/src/econ485/lab4/arclab1.log
log type: text
closed on: 11 Mar 2013, 20:26:34
------------------------------------------------------------------------------------