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Diff in Diff OLS Estimation and Interpretation [message #19313] Thu, 28 May 2020 12:21 Go to next message
Goethe2014 is currently offline  Goethe2014
Messages: 10
Registered: May 2020
Member
Dear all,

Currently I am running a OLS Difference and Difference regression on Nigerian DHS data (2008 and 2018) and am trying to estimate the effect of terror (Boko Haram in this case) on childmarriage and teenage childbirth/pregnancy. In this regard I also want to estimate the effect on age at childmarriage and age at teenage childbirth/pregnancy for the women who did actually face these circumstances.

My questions are: How would you judge the effects on attacknumber and attacknumber*BH in both the cases 1) age at childmarriage and 2) age at teenage childbirth. From my understanding this would mean an additional attack in a non-BH area raises the age at childmarriage by +0.06years and the age at teenage childbirth by +0.07 years. If we now look at the interaction effects we see that these are pretty much the same amount but with the opposite sign (negative) (-0.06 years/-0.07 years). I would interpret it in this way, that the overall effect of an attack on a BH affected states is essentially zero as the two coefficients cancel out (beta1+beta3=0) so the age stays the same with an increase in one attack. In non-BH states however an additional attack increases the age (beta1>0). Would this be the correct interpretation?

Another question would be: Why do my models omit the state Kaduna? One state is already omitted (Sokoto) as the baseline surveystate. Nonetheless, all my estimations omit Kaduna as well. Note: Kaduna is part of the BH coded group(surveystates: Borno, Yobe, Adamawa, Kano, Gombe, Bauchi, Kauna then BH=1). So could this be due to some colinearity etc? I checked and all observations and values for Kaduna are correctly coded as for all other states which do not seem to present this "omitted" issue.

Thank you very much in advance for any help!
Greetings



Running my final model using the following commands I get these results:

svy: reg agefirstunionCM i.BH c.attacknumberCM c.attacknumberCM#i.BH i.muslim i.urban i.kanuri i.hhheadmale i.literacy i.wealthindex i.edulevel c.eduyears i.edulevelpartner c.eduyearspartner  i.largefamily i.polygamoushh i.surveystate
Survey: Linear regression

Number of strata   =       146              Number of obs     =          7,825
Number of PSUs     =     1,640              Population size   =  7,693,726,278
                                            Design df         =          1,494
                                            F(  53,   1442)   =          10.67
                                            Prob > F          =         0.0000
                                            R-squared         =         0.0935

-------------------------------------------------------------------------------------
                    |             Linearized
    agefirstunionCM |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
               1.BH |   .2678601   .1535981     1.74   0.081    -.0334307    .5691509
     attacknumberCM |   .0606883   .0080648     7.53   0.000     .0448688    .0765078
                    |
BH#c.attacknumberCM |
                 1  |  -.0597985    .008065    -7.41   0.000    -.0756184   -.0439785
                    |
           1.muslim |  -.1875304   .1036764    -1.81   0.071    -.3908971    .0158363
            1.urban |  -.0253483    .067249    -0.38   0.706    -.1572608    .1065642
           1.kanuri |   -.093941   .1311277    -0.72   0.474    -.3511549     .163273
       1.hhheadmale |  -.0833878   .0745814    -1.12   0.264    -.2296831    .0629076
         1.literacy |   .0773257   .0785068     0.98   0.325    -.0766695    .2313209
                    |
        wealthindex |
            poorer  |   .0855394   .0556279     1.54   0.124    -.0235776    .1946564
            middle  |   .1246874   .0750185     1.66   0.097    -.0224653    .2718401
            richer  |   .2579857   .0921339     2.80   0.005     .0772601    .4387113
           richest  |    .302223   .1318766     2.29   0.022     .0435401    .5609059
                    |
         1.edulevel |   .0766587   .1100335     0.70   0.486    -.1391778    .2924952
           eduyears |   .0187935   .0120535     1.56   0.119      -.00485     .042437
  1.edulevelpartner |  -.1190187   .1186531    -1.00   0.316    -.3517631    .1137257
    eduyearspartner |   .0193016   .0103064     1.87   0.061    -.0009149    .0395181
      1.largefamily |   .0050871   .0386496     0.13   0.895    -.0707262    .0809003
     1.polygamoushh |  -.1149114   .0461247    -2.49   0.013    -.2053874   -.0244355
                    |
        surveystate |
           zamfara  |  -.1567509   .1577283    -0.99   0.320    -.4661433    .1526415
           katsina  |   -.058353   .1397715    -0.42   0.676    -.3325222    .2158162
            jigawa  |   .6201046   .1272395     4.87   0.000     .3705176    .8696916
              yobe  |  -.0515254   .1592621    -0.32   0.746    -.3639265    .2608758
             borno  |  -.7478194    .205227    -3.64   0.000    -1.150383   -.3452558
           adamawa  |   .2552234   .1539995     1.66   0.098    -.0468549    .5573016
             gombe  |   .1744793   .1335148     1.31   0.191    -.0874171    .4363757
            bauchi  |  -.0511485   .1345675    -0.38   0.704    -.3151098    .2128128
              kano  |  -.0184404   .1325222    -0.14   0.889    -.2783897    .2415089
            kaduna  |          0  (omitted)
             kebbi  |   .5643663   .1536166     3.67   0.000     .2630391    .8656934
             niger  |     .32048   .1612237     1.99   0.047     .0042311    .6367289
             abuja  |  -.3547092   .2775083    -1.28   0.201    -.8990565    .1896382
          nasarawa  |   .4572007   .1733962     2.64   0.008     .1170748    .7973266
           plateau  |   -.065136   .2224453    -0.29   0.770    -.5014744    .3712023
            taraba  |   .5864252   .1233278     4.76   0.000     .3445111    .8283393
             benue  |   .5949883   .1740201     3.42   0.001     .2536386    .9363379
              kogi  |   .1937181   .1784986     1.09   0.278    -.1564164    .5438526
             kwara  |   .7585649   .2532547     3.00   0.003     .2617923    1.255338
               oyo  |   .9811194   .1860203     5.27   0.000     .6162307    1.346008
              osun  |   .8110708   .2348476     3.45   0.001     .3504048    1.271737
             ekiti  |   .9938482   .2102819     4.73   0.000      .581369    1.406327
              ondo  |   1.057972   .2461233     4.30   0.000     .5751884    1.540756
               edo  |   .5724927   .3749659     1.53   0.127    -.1630229    1.308008
           anambra  |   .5366899   .2776328     1.93   0.053    -.0079017    1.081281
             enugu  |   .0549801   .3721721     0.15   0.883    -.6750553    .7850155
            ebonyi  |   .6244667   .2307461     2.71   0.007      .171846    1.077087
       cross river  |   .1285754   .3151316     0.41   0.683     -.489572    .7467227
         akwa ibom  |   .6034849   .2663215     2.27   0.024     .0810811    1.125889
              abia  |   .3807716   .2884868     1.32   0.187    -.1851106    .9466537
               imo  |    .795596   .2736021     2.91   0.004     .2589111    1.332281
            rivers  |   .3901126   .3199313     1.22   0.223    -.2374497    1.017675
           bayelsa  |   .2268694   .2062523     1.10   0.272    -.1777054    .6314442
             delta  |   .4423073   .2656391     1.67   0.096     -.078758    .9633726
             lagos  |   .1382112   .4069651     0.34   0.734    -.6600724    .9364948
              ogun  |   .7265004   .2636357     2.76   0.006     .2093649    1.243636
                    |
              _cons |    14.4783   .1604158    90.25   0.000     14.16363    14.79296
-------------------------------------------------------------------------------------
Note: Strata with single sampling unit centered at overall mean.


svy: reg agefirstbirthpregTP c.attacknumberTP i.BH c.attacknumberTP#i.BH i.muslim i.urban i.kanuri i.hhheadmale i.literacy i.wealthindex i.edulevel c.eduyears i.edulevelpartner c.eduyearspartner i.largefamily i.polygamoushh i.surveystate
Survey: Linear regression

Number of strata   =       148              Number of obs     =          9,375
Number of PSUs     =     1,892              Population size   =  9,144,835,358
                                            Design df         =          1,744
                                            F(  53,   1692)   =          12.96
                                            Prob > F          =         0.0000
                                            R-squared         =         0.0901

-------------------------------------------------------------------------------------
                    |             Linearized
agefirstbirthpregTP |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
     attacknumberTP |    .071027    .006938    10.24   0.000     .0574193    .0846346
               1.BH |  -.1112492    .126494    -0.88   0.379    -.3593451    .1368466
                    |
BH#c.attacknumberTP |
                 1  |  -.0699279   .0069386   -10.08   0.000    -.0835368   -.0563191
                    |
           1.muslim |  -.0973278   .0873922    -1.11   0.266    -.2687322    .0740766
            1.urban |   .0955667   .0632548     1.51   0.131    -.0284964    .2196298
           1.kanuri |  -.1299689   .1442185    -0.90   0.368    -.4128283    .1528905
       1.hhheadmale |  -.0782056   .0865481    -0.90   0.366    -.2479547    .0915434
         1.literacy |    .151714   .0776939     1.95   0.051    -.0006691    .3040971
                    |
        wealthindex |
            poorer  |    .060747     .05595     1.09   0.278    -.0489892    .1704831
            middle  |  -.0071219   .0685636    -0.10   0.917    -.1415974    .1273536
            richer  |   .0532612   .0824339     0.65   0.518    -.1084185    .2149409
           richest  |    .010767    .113259     0.10   0.924    -.2113707    .2329047
                    |
         1.edulevel |   .1963236    .104823     1.87   0.061    -.0092684    .4019156
           eduyears |   .0174825   .0112269     1.56   0.120    -.0045371    .0395021
  1.edulevelpartner |  -.1930564   .1015509    -1.90   0.057    -.3922308     .006118
    eduyearspartner |   .0318046    .009385     3.39   0.001     .0133975    .0502117
      1.largefamily |   .0303519   .0405891     0.75   0.455    -.0492565    .1099602
     1.polygamoushh |  -.1620505   .0458664    -3.53   0.000    -.2520094   -.0720917
                    |
        surveystate |
           zamfara  |   .0858908   .1330291     0.65   0.519    -.1750224    .3468041
           katsina  |  -.5840868   .1207941    -4.84   0.000    -.8210034   -.3471702
            jigawa  |   .2658763   .1246078     2.13   0.033     .0214798    .5102728
              yobe  |  -.0821049   .1665512    -0.49   0.622     -.408766    .2445562
             borno  |  -.8917408    .213171    -4.18   0.000    -1.309838   -.4736431
           adamawa  |   .3680567    .139635     2.64   0.008     .0941872    .6419263
             gombe  |   .2724548   .1226303     2.22   0.026     .0319369    .5129728
            bauchi  |    .072955    .124723     0.58   0.559    -.1716674    .3175773
              kano  |   .2036385   .1214449     1.68   0.094    -.0345544    .4418315
            kaduna  |          0  (omitted)
             kebbi  |   .1575467   .1378783     1.14   0.253    -.1128773    .4279708
             niger  |  -.2972779   .1404352    -2.12   0.034    -.5727169   -.0218389
             abuja  |  -.6414893   .2574665    -2.49   0.013    -1.146465   -.1365138
          nasarawa  |   .0675104   .1564192     0.43   0.666    -.2392784    .3742992
           plateau  |  -.6286565   .2015918    -3.12   0.002    -1.024044   -.2332694
            taraba  |   .0477431   .1239716     0.39   0.700    -.1954055    .2908918
             benue  |   .0530447   .1622574     0.33   0.744    -.2651948    .3712841
              kogi  |  -.6367253   .1727088    -3.69   0.000    -.9754634   -.2979871
             kwara  |   .3226968   .1683973     1.92   0.055     -.007585    .6529785
               oyo  |   .5029193   .1618882     3.11   0.002     .1854039    .8204346
              osun  |   .3371306   .1697553     1.99   0.047     .0041854    .6700759
             ekiti  |    .242641   .2130414     1.14   0.255    -.1752024    .6604844
              ondo  |   .0348621   .2134846     0.16   0.870    -.3838506    .4535748
               edo  |   .3004096   .2602866     1.15   0.249     -.210097    .8109162
           anambra  |   .0860519   .2592354     0.33   0.740    -.4223929    .5944968
             enugu  |  -.2526511   .2600984    -0.97   0.332    -.7627886    .2574864
            ebonyi  |  -.0645027   .2070343    -0.31   0.755    -.4705643    .3415589
       cross river  |   .0781527   .2246077     0.35   0.728    -.3623761    .5186815
         akwa ibom  |  -.2286795   .2535716    -0.90   0.367    -.7260158    .2686568
              abia  |   .6430127   .2656633     2.42   0.016     .1219606    1.164065
               imo  |   .2211599   .2873089     0.77   0.442    -.3423463    .7846662
            rivers  |  -.3946292   .2624022    -1.50   0.133    -.9092852    .1200268
           bayelsa  |  -.4284522   .1931851    -2.22   0.027     -.807351   -.0495534
             delta  |  -.0722554   .2312345    -0.31   0.755    -.5257815    .3812707
             lagos  |    .409688   .2699203     1.52   0.129    -.1197135    .9390894
              ogun  |   .2558986   .1929527     1.33   0.185    -.1225443    .6343416
                    |
              _cons |   16.46443    .152316   108.09   0.000     16.16569    16.76317
-------------------------------------------------------------------------------------
Note: Strata with single sampling unit centered at overall mean.
Re: Diff in Diff OLS Estimation and Interpretation [message #19317 is a reply to message #19313] Fri, 29 May 2020 10:39 Go to previous messageGo to next message
Liz-DHS
Messages: 1516
Registered: February 2013
Senior Member
Dear User, a response from Dr. Tom Pullum:
Quote:

I would say much the same thing. The effect of an additional attack in a non-BH area is .061 (or .071). The effect of an additional attack in a BH area is effectively zero. However, in a BH area the coefficient of .268 (or -.111) cannot be ignored. For the first outcome, the effect of BH is equivalent to .268/.061 = 4.4 attacks in a non-BH area. However, and this is important, the coefficient of BH is not statistically significant. For the second outcome, it's harder to interpret--the effect of a BH area is equivalent to -.111/.071 = -1.6 attacks in a non-BH area. But that coefficient of BH also is not significant, so it's not a good idea to try to interpret it.

You have taken the first state in the entire list of states, Sokoto, as the reference state. Apparently the coefficient for Kaduna is so close to that of Sokoto that Stata has in effect grouped Kaduna with Sokoto, by giving it a coefficient of 0.

You might consider restricting to the northern zones of Nigeria, because I believe most of the states have no Boko Haram and no attacks. However, you know more about the context than I do!

Re: Diff in Diff OLS Estimation and Interpretation [message #19319 is a reply to message #19317] Fri, 29 May 2020 11:25 Go to previous message
Goethe2014 is currently offline  Goethe2014
Messages: 10
Registered: May 2020
Member
Dear Tom,
Thank you very much for the reassuring answer.

Regarding the omitted Kaduna state dummy. Is there anything I can do to change that? Kaduna is part of my BH treatment group (BH=1) while Sokoto is not (BH=0) so if Stata groups them this would work against the overall specification of my treatment groups somehow - I hope this makes sense.

With regard to the northeastern region you mean that instead of comparing the affected states to the non/less-affected states in Nigeria I run an usual OLS regression on the BH affected states only - so make use of the attacknumber as a typical continous independent variable similar to education years and only analyze the effects of an additional attack on my dependent variables in the conflict region?

Thank you very much in advance.
Greetings
Caspar
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