INTRODUCTORY ECONOMETRICS – REPLICATING EXAMPLES

Chapter 15 Instrumental Variables & 2SLS – Examples

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       log:  ~Wooldridge\intro-econx\iexample15.smcl
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 opened on:  17 Jan 2019, 16:10:54
. **********************************************
. * Solomon Negash - Replicating Examples
. * Wooldridge (2016). Introductory Econometrics: A Modern Approach. 6th ed.  
. * STATA Program, version 15.1. 
. *
. * CHAPTER 15 Instrumental Variables Estimation and Two Stage Least Squares 
. * Computer Exercises (Examples)
. ******************** SETUP *********************
. *Example 15.1. Estimating the Return to Education for Married Women
. u mroz, clear
. reg lwage educ
      Source |       SS           df       MS      Number of obs   =       428
-------------+----------------------------------   F(1, 426)       =     56.93
       Model |  26.3264193         1  26.3264193   Prob > F        =    0.0000
    Residual |  197.001022       426  .462443713   R-squared       =    0.1179
-------------+----------------------------------   Adj R-squared   =    0.1158
       Total |  223.327441       427  .523015084   Root MSE        =    .68003
------------------------------------------------------------------------------
       lwage |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        educ |   .1086487   .0143998     7.55   0.000     .0803451    .1369523
       _cons |  -.1851968   .1852259    -1.00   0.318    -.5492673    .1788736
------------------------------------------------------------------------------
. reg educ fathedu
      Source |       SS           df       MS      Number of obs   =       753
-------------+----------------------------------   F(1, 751)       =    182.81
       Model |  765.465719         1  765.465719   Prob > F        =    0.0000
    Residual |  3144.57412       751  4.18718259   R-squared       =    0.1958
-------------+----------------------------------   Adj R-squared   =    0.1947
       Total |  3910.03984       752  5.19952106   Root MSE        =    2.0463
------------------------------------------------------------------------------
        educ |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    fatheduc |   .2824277   .0208884    13.52   0.000     .2414211    .3234343
       _cons |   9.799013   .1985373    49.36   0.000     9.409259    10.18877
------------------------------------------------------------------------------
. ivreg lwage (educ=fathedu) 
Instrumental variables (2SLS) regression
      Source |       SS           df       MS      Number of obs   =       428
-------------+----------------------------------   F(1, 426)       =      2.84
       Model |  20.8673606         1  20.8673606   Prob > F        =    0.0929
    Residual |   202.46008       426  .475258404   R-squared       =    0.0934
-------------+----------------------------------   Adj R-squared   =    0.0913
       Total |  223.327441       427  .523015084   Root MSE        =    .68939
------------------------------------------------------------------------------
       lwage |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        educ |   .0591735   .0351418     1.68   0.093    -.0098994    .1282463
       _cons |   .4411034   .4461018     0.99   0.323    -.4357312    1.317938
------------------------------------------------------------------------------
Instrumented:  educ
Instruments:   fatheduc
------------------------------------------------------------------------------

. *Example 15.2. Estimating the Return to Education for Men
. u wage2, clear
. reg educ sibs
      Source |       SS           df       MS      Number of obs   =       935
-------------+----------------------------------   F(1, 933)       =     56.67
       Model |  258.055048         1  258.055048   Prob > F        =    0.0000
    Residual |   4248.7642       933  4.55387374   R-squared       =    0.0573
-------------+----------------------------------   Adj R-squared   =    0.0562
       Total |  4506.81925       934  4.82528828   Root MSE        =     2.134
------------------------------------------------------------------------------
        educ |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        sibs |  -.2279164   .0302768    -7.53   0.000     -.287335   -.1684979
       _cons |   14.13879   .1131382   124.97   0.000     13.91676    14.36083
------------------------------------------------------------------------------
. ivreg lwage (educ=sibs) 
Instrumental variables (2SLS) regression
      Source |       SS           df       MS      Number of obs   =       935
-------------+----------------------------------   F(1, 933)       =     21.59
       Model | -1.51973315         1 -1.51973315   Prob > F        =    0.0000
    Residual |  167.176016       933  .179181154   R-squared       =         .
-------------+----------------------------------   Adj R-squared   =         .
       Total |  165.656283       934  .177362188   Root MSE        =     .4233
------------------------------------------------------------------------------
       lwage |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        educ |   .1224326   .0263506     4.65   0.000     .0707194    .1741459
       _cons |   5.130026   .3551712    14.44   0.000     4.432999    5.827053
------------------------------------------------------------------------------
Instrumented:  educ
Instruments:   sibs
------------------------------------------------------------------------------

. reg lwage edu, nohead
------------------------------------------------------------------------------
       lwage |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        educ |   .0598392   .0059631    10.03   0.000     .0481366    .0715418
       _cons |   5.973063   .0813737    73.40   0.000     5.813366    6.132759
------------------------------------------------------------------------------

. *Example 15.3. Estimating the Effect of Smoking on Birth Weight
. u bwght, clear
. reg packs cigprice
      Source |       SS           df       MS      Number of obs   =     1,388
-------------+----------------------------------   F(1, 1386)      =      0.13
       Model |  .011648626         1  .011648626   Prob > F        =    0.7179
    Residual |  123.684481     1,386  .089238442   R-squared       =    0.0001
-------------+----------------------------------   Adj R-squared   =   -0.0006
       Total |  123.696129     1,387  .089182501   Root MSE        =    .29873
------------------------------------------------------------------------------
       packs |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    cigprice |   .0002829    .000783     0.36   0.718    -.0012531    .0018188
       _cons |   .0674257   .1025384     0.66   0.511    -.1337215    .2685728
------------------------------------------------------------------------------
. ivreg lbwght (packs=cigprice)
Instrumental variables (2SLS) regression
      Source |       SS           df       MS      Number of obs   =     1,388
-------------+----------------------------------   F(1, 1386)      =      0.12
       Model | -1171.28207         1 -1171.28207   Prob > F        =    0.7312
    Residual |  1221.70241     1,386  .881459168   R-squared       =         .
-------------+----------------------------------   Adj R-squared   =         .
       Total |  50.4203336     1,387  .036352079   Root MSE        =    .93886
------------------------------------------------------------------------------
      lbwght |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       packs |   2.988676   8.698888     0.34   0.731    -14.07573    20.05309
       _cons |   4.448136   .9081552     4.90   0.000     2.666629    6.229644
------------------------------------------------------------------------------
Instrumented:  packs
Instruments:   cigprice
------------------------------------------------------------------------------

. *Example 15.4. Using College Proximity as an IV for Education
. u card, clear
. qui reg educ nearc4 exper expersq black smsa south smsa66 reg6* 
. display "Constant = " _[_cons] ", b1 = " _b[nearc4] ", b2 = " _b[exper]
Constant = 1, b1 = .31989894, b2 = -.41253338
. eststo OLS: qui reg lwage educ exper* black smsa south smsa66 reg6* 
. eststo IV: qui ivreg lwage (educ=nearc4) exper* black smsa south smsa66 reg6* 
. estout, cells(b(nostar fmt(3)) se(par fmt(3))) stats(r2 N, fmt(%9.3f %9.0g) labels( ///
 R-squared Observations)) varlabels(_cons constant) varwidth(20) ti("Table 15.1 ///
Dependent Variable: (lwage)")
Table 15.1 Dependent Variable: (lwage)
----------------------------------------------
                              OLS           IV
                             b/se         b/se
----------------------------------------------
educ                        0.075        0.132
                          (0.003)      (0.055)
exper                       0.085        0.108
                          (0.007)      (0.024)
expersq                    -0.002       -0.002
                          (0.000)      (0.000)
black                      -0.199       -0.147
                          (0.018)      (0.054)
smsa                        0.136        0.112
                          (0.020)      (0.032)
south                      -0.148       -0.145
                          (0.026)      (0.027)
smsa66                      0.026        0.019
                          (0.019)      (0.022)
reg661                      0.056        0.083
                          (0.051)      (0.059)
reg662                      0.153        0.184
                          (0.044)      (0.055)
reg663                      0.201        0.231
                          (0.043)      (0.054)
reg664                      0.112        0.133
                          (0.049)      (0.055)
reg665                      0.184        0.229
                          (0.049)      (0.067)
reg666                      0.197        0.246
                          (0.052)      (0.072)
reg667                      0.174        0.218
                          (0.052)      (0.068)
reg668                      0.000        0.000
                              (.)          (.)
reg669                      0.175        0.191
                          (0.046)      (0.051)
constant                    4.564        3.583
                          (0.079)      (0.951)
----------------------------------------------
R-squared                   0.300        0.238
Observations                 3010         3010
----------------------------------------------
. est clear

. *Example 15.5. Return to Education for Working Women
. u mroz, clear
. qui reg educ exper* fatheduc motheduc 
. test fatheduc motheduc
 ( 1)  fatheduc = 0
 ( 2)  motheduc = 0
       F(  2,   748) =  124.76
            Prob > F =    0.0000
. ivreg lwage (educ=fatheduc motheduc) exper*
Instrumental variables (2SLS) regression
      Source |       SS           df       MS      Number of obs   =       428
-------------+----------------------------------   F(3, 424)       =      8.14
       Model |  30.3074256         3  10.1024752   Prob > F        =    0.0000
    Residual |  193.020015       424  .455235885   R-squared       =    0.1357
-------------+----------------------------------   Adj R-squared   =    0.1296
       Total |  223.327441       427  .523015084   Root MSE        =    .67471
------------------------------------------------------------------------------
       lwage |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        educ |   .0613966   .0314367     1.95   0.051    -.0003945    .1231878
       exper |   .0441704   .0134325     3.29   0.001     .0177679    .0705729
     expersq |   -.000899   .0004017    -2.24   0.026    -.0016885   -.0001094
       _cons |   .0481003   .4003281     0.12   0.904    -.7387744     .834975
------------------------------------------------------------------------------
Instrumented:  educ
Instruments:   exper expersq fatheduc motheduc
------------------------------------------------------------------------------
. qui reg lwage educ exper*
. display "b1 = " _b[educ]
b1 = .10748964

. *Example 15.6. Using Two Test Scores as Indicators of Ability
. u wage2, clear
. ivreg lwage educ exper tenure married south urban black (IQ=KWW)
Instrumental variables (2SLS) regression
      Source |       SS           df       MS      Number of obs   =       935
-------------+----------------------------------   F(8, 926)       =     36.96
       Model |  31.4665121         8  3.93331401   Prob > F        =    0.0000
    Residual |  134.189771       926   .14491336   R-squared       =    0.1900
-------------+----------------------------------   Adj R-squared   =    0.1830
       Total |  165.656283       934  .177362188   Root MSE        =    .38067
------------------------------------------------------------------------------
       lwage |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          IQ |   .0130473   .0049341     2.64   0.008     .0033641    .0227305
        educ |   .0250321   .0166068     1.51   0.132    -.0075591    .0576234
       exper |     .01442   .0033208     4.34   0.000     .0079029    .0209371
      tenure |   .0104562   .0026012     4.02   0.000     .0053512    .0155612
     married |   .2006903   .0406775     4.93   0.000     .1208595    .2805211
       south |  -.0515532   .0311279    -1.66   0.098    -.1126426    .0095361
       urban |   .1767058   .0282117     6.26   0.000     .1213394    .2320722
       black |  -.0225612   .0739597    -0.31   0.760    -.1677093    .1225869
       _cons |   4.592453   .3257807    14.10   0.000     3.953099    5.231807
------------------------------------------------------------------------------
Instrumented:  IQ
Instruments:   educ exper tenure married south urban black KWW
------------------------------------------------------------------------------

. *Example 15.7. Return to Education for Working Women
. u mroz, clear
. qui reg educ exper* fatheduc motheduc if inlf==1
. predict v2, res
. ivreg lwage (educ=fatheduc motheduc) exper* v2
Instrumental variables (2SLS) regression
      Source |       SS           df       MS      Number of obs   =       428
-------------+----------------------------------   F(4, 423)       =     20.50
       Model |  36.2573098         4  9.06432745   Prob > F        =    0.0000
    Residual |  187.070131       423  .442246173   R-squared       =    0.1624
-------------+----------------------------------   Adj R-squared   =    0.1544
       Total |  223.327441       427  .523015084   Root MSE        =    .66502
------------------------------------------------------------------------------
       lwage |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        educ |   .0613966   .0309849     1.98   0.048      .000493    .1223003
       exper |   .0441704   .0132394     3.34   0.001     .0181471    .0701937
     expersq |   -.000899   .0003959    -2.27   0.024    -.0016772   -.0001208
          v2 |   .0581666   .0348073     1.67   0.095    -.0102502    .1265834
       _cons |   .0481003   .3945753     0.12   0.903    -.7274721    .8236727
------------------------------------------------------------------------------
Instrumented:  educ
Instruments:   exper expersq v2 fatheduc motheduc
------------------------------------------------------------------------------
. qui reg lwage educ exper*
. display "The OLS estimate is " _b[educ] " (" _se[educ] ")"
The OLS estimate is .10748964 (.01414648)

. *Example 15.8. Return to Education for Working Women
. u mroz, clear
. qui ivreg lwage (educ=fatheduc motheduc) exper* 
. predict u1, res
(325 missing values generated)
. reg u1 exper* fatheduc motheduc 
      Source |       SS           df       MS      Number of obs   =       428
-------------+----------------------------------   F(4, 423)       =      0.09
       Model |  .170503136         4  .042625784   Prob > F        =    0.9845
    Residual |   192.84951       423  .455909007   R-squared       =    0.0009
-------------+----------------------------------   Adj R-squared   =   -0.0086
       Total |  193.020013       427  .452037502   Root MSE        =    .67521
------------------------------------------------------------------------------
          u1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       exper |  -.0000183   .0133291    -0.00   0.999    -.0262179    .0261813
     expersq |   7.34e-07   .0003985     0.00   0.999    -.0007825     .000784
    fatheduc |   .0057823   .0111786     0.52   0.605    -.0161902    .0277547
    motheduc |  -.0066065   .0118864    -0.56   0.579    -.0299704    .0167573
       _cons |   .0109641   .1412571     0.08   0.938    -.2666892    .2886173
------------------------------------------------------------------------------
. display "N*Rsquared =" e(r2)*e(N)
N*Rsquared =.37807138
. qui ivreg lwage (educ=fatheduc motheduc huseduc) exper* 
. predict u1_h, res
(325 missing values generated)
. qui reg u1_h exper* fatheduc motheduc huseduc
. display "N*Rsquared =" e(r2)*e(N)
N*Rsquared =1.115043
. qui ivreg lwage (educ=fatheduc motheduc huseduc) exper* 
. display "The IV estimate using all three instruments is " _b[educ] " (" _se[educ] ")"
The IV estimate using all three instruments is .08039176 (.02177397)
. qui ivreg lwage (educ=fatheduc motheduc) exper* 
. display "The IV estimate using two instruments is " _b[educ] " (" _se[educ] ")"
The IV estimate using two instruments is .06139663 (.0314367)

. *Example 15.9. Effect of Education on Fertility
. u fertil1, clear
. ivreg kids (educ=meduc feduc) age agesq black-y84
Instrumental variables (2SLS) regression
      Source |       SS           df       MS      Number of obs   =     1,129
-------------+----------------------------------   F(17, 1111)     =      7.72
       Model |   395.36632        17  23.2568424   Prob > F        =    0.0000
    Residual |  2690.14298     1,111  2.42137082   R-squared       =    0.1281
-------------+----------------------------------   Adj R-squared   =    0.1148
       Total |   3085.5093     1,128  2.73538059   Root MSE        =    1.5561
------------------------------------------------------------------------------
        kids |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        educ |  -.1527395   .0392232    -3.89   0.000    -.2296993   -.0757796
         age |   .5235536   .1390348     3.77   0.000     .2507532     .796354
       agesq |   -.005716   .0015705    -3.64   0.000    -.0087976   -.0026345
       black |   1.072952   .1737155     6.18   0.000      .732105      1.4138
        east |   .2285554   .1338537     1.71   0.088    -.0340792    .4911901
    northcen |   .3744188    .122061     3.07   0.002     .1349228    .6139148
        west |   .2076398   .1676568     1.24   0.216    -.1213199    .5365995
        farm |  -.0770015   .1513718    -0.51   0.611    -.3740083    .2200053
    othrural |  -.1952451    .181551    -1.08   0.282    -.5514666    .1609764
        town |     .08181   .1246821     0.66   0.512     -.162829    .3264489
      smcity |   .2124996    .160425     1.32   0.186    -.1022706    .5272698
         y74 |   .2721292    .172944     1.57   0.116    -.0672045    .6114629
         y76 |  -.0945483   .1792324    -0.53   0.598    -.4462205    .2571239
         y78 |  -.0572543   .1825536    -0.31   0.754     -.415443    .3009343
         y80 |   -.053248   .1847175    -0.29   0.773    -.4156825    .3091865
         y82 |  -.4962149   .1765888    -2.81   0.005       -.8427   -.1497297
         y84 |  -.5213604   .1779205    -2.93   0.003    -.8704586   -.1722623
       _cons |  -7.241244   3.136642    -2.31   0.021    -13.39565   -1.086834
------------------------------------------------------------------------------
Instrumented:  educ
Instruments:   age agesq black east northcen west farm othrural town smcity
               y74 y76 y78 y80 y82 y84 meduc feduc
------------------------------------------------------------------------------
. qui reg kids educ age agesq black-y84
. display "The OLS estimate is " _b[educ] " (" _se[educ] ")"
The OLS estimate is -.12842683 (.0183486)

. //Endogeneity 
. reg educ meduc feduc
      Source |       SS           df       MS      Number of obs   =     1,129
-------------+----------------------------------   F(2, 1126)      =    207.06
       Model |  2114.27432         2  1057.13716   Prob > F        =    0.0000
    Residual |  5748.84171     1,126  5.10554326   R-squared       =    0.2689
-------------+----------------------------------   Adj R-squared   =    0.2676
       Total |  7863.11603     1,128  6.97084755   Root MSE        =    2.2595
------------------------------------------------------------------------------
        educ |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       meduc |   .1844065    .021749     8.48   0.000     .1417333    .2270796
       feduc |   .2208784    .024996     8.84   0.000     .1718344    .2699225
       _cons |   8.860898   .2034806    43.55   0.000     8.461654    9.260142
------------------------------------------------------------------------------
. predict v2, res
. ivreg kids (educ=meduc feduc) age agesq black-y84 v2
Instrumental variables (2SLS) regression
      Source |       SS           df       MS      Number of obs   =     1,129
-------------+----------------------------------   F(18, 1110)     =      9.21
       Model |  400.801638        18  22.2667576   Prob > F        =    0.0000
    Residual |  2684.70766     1,110  2.41865555   R-squared       =    0.1299
-------------+----------------------------------   Adj R-squared   =    0.1158
       Total |   3085.5093     1,128  2.73538059   Root MSE        =    1.5552
------------------------------------------------------------------------------
        kids |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        educ |  -.1507639   .0367462    -4.10   0.000    -.2228636   -.0786641
         age |   .5305436   .1384365     3.83   0.000     .2589168    .8021704
       agesq |   -.005796   .0015647    -3.70   0.000    -.0088661   -.0027259
       black |   1.061535   .1747383     6.07   0.000     .7186803     1.40439
        east |   .2208112   .1329111     1.66   0.097    -.0399742    .4815965
    northcen |   .3715649   .1215229     3.06   0.002     .1331244    .6100054
        west |   .2044791   .1672389     1.22   0.222    -.1236609     .532619
        farm |  -.0651969   .1483216    -0.44   0.660    -.3562192    .2258255
    othrural |  -.1777856   .1767678    -1.01   0.315    -.5246223    .1690511
        town |   .0798824   .1247224     0.64   0.522    -.1648358    .3246006
      smcity |   .2099867   .1603553     1.31   0.191     -.104647    .5246204
         y74 |   .2719416   .1728386     1.57   0.116    -.0671855    .6110688
         y76 |  -.0984073   .1790925    -0.55   0.583    -.4498053    .2529906
         y78 |  -.0637286   .1818614    -0.35   0.726    -.4205596    .2931023
         y80 |  -.0651716   .1830214    -0.36   0.722    -.4242785    .2939352
         y82 |  -.5143435   .1728653    -2.98   0.003    -.8535231   -.1751638
         y84 |   -.534601   .1752043    -3.05   0.002      -.87837    -.190832
          v2 |   .0291597   .0415585     0.70   0.483    -.0523823    .1107017
       _cons |  -7.407479   3.089573    -2.40   0.017    -13.46954   -1.345417
------------------------------------------------------------------------------
Instrumented:  educ
Instruments:   age agesq black east northcen west farm othrural town smcity
               y74 y76 y78 y80 y82 y84 v2 meduc feduc
------------------------------------------------------------------------------
. display "The OLS estimate is " _b[v2] " (" _b[v2]/_se[v2] ")"
The OLS estimate is .02915968 (.70165427)

. *Example 15.10. Job Training and Worker Productivity
. u jtrain, clear
. reg chrsemp cgrant if year==1988
      Source |       SS           df       MS      Number of obs   =       125
-------------+----------------------------------   F(1, 123)       =     79.37
       Model |  18117.5987         1  18117.5987   Prob > F        =    0.0000
    Residual |  28077.3319       123  228.270991   R-squared       =    0.3922
-------------+----------------------------------   Adj R-squared   =    0.3873
       Total |  46194.9306       124  372.539763   Root MSE        =    15.109
------------------------------------------------------------------------------
     chrsemp |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      cgrant |   27.87793   3.129216     8.91   0.000     21.68384    34.07202
       _cons |   .5093234   1.558337     0.33   0.744     -2.57531    3.593956
------------------------------------------------------------------------------
. ivreg clscrap (chrsemp = cgrant) if year==1988
Instrumental variables (2SLS) regression
      Source |       SS           df       MS      Number of obs   =        45
-------------+----------------------------------   F(1, 43)        =      3.20
       Model |  .274951237         1  .274951237   Prob > F        =    0.0808
    Residual |  17.0148885        43  .395695081   R-squared       =    0.0159
-------------+----------------------------------   Adj R-squared   =   -0.0070
       Total |  17.2898397        44  .392950903   Root MSE        =    .62904
------------------------------------------------------------------------------
     clscrap |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     chrsemp |  -.0141532   .0079147    -1.79   0.081    -.0301148    .0018084
       _cons |  -.0326684   .1269512    -0.26   0.798    -.2886898     .223353
------------------------------------------------------------------------------
Instrumented:  chrsemp
Instruments:   cgrant
------------------------------------------------------------------------------
. ivreg clscrap chrsemp if year==1988
Instrumental variables (2SLS) regression
      Source |       SS           df       MS      Number of obs   =        45
-------------+----------------------------------   F(1, 43)        =      2.84
       Model |  1.07071245         1  1.07071245   Prob > F        =    0.0993
    Residual |  16.2191273        43  .377189007   R-squared       =    0.0619
-------------+----------------------------------   Adj R-squared   =    0.0401
       Total |  17.2898397        44  .392950903   Root MSE        =    .61416
------------------------------------------------------------------------------
     clscrap |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     chrsemp |  -.0076007   .0045112    -1.68   0.099    -.0166984    .0014971
       _cons |  -.1035161    .103736    -1.00   0.324    -.3127197    .1056875
------------------------------------------------------------------------------
(no endogenous regressors)
------------------------------------------------------------------------------

. log close
      name:  SN
       log:  ~Wooldridge\intro-econx\iexample15.smcl
  log type:  smcl
 closed on:  17 Jan 2019, 16:10:56
-------------------------------------------------------------------------------------




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