INTRODUCTORY ECONOMETRICS – REPLICATING EXAMPLES

Chapter 17. Limited Dependent Variable – Examples

-------------------------------------------------------------------------------------
      name:  SN
       log:  ~Wooldridge\intro-econx\iexample18.smcl
  log type:  smcl
 opened on:  18 Jan 2019, 20:45:49
. **********************************************
. * Solomon Negash - Replicating Examples
. * Wooldridge (2016). Introductory Econometrics: A Modern Approach. 6th ed.  
. * STATA Program, version 15.1. 

. * CHAPTER 17 Limited Dependent Variable Models and Sample Selection Corrections
. * Computer Exercises (Examples)
. ******************** SETUP *********************

. *Example 17.1. Married Women’s Labor Force Participation
. u mroz, clear
. eststo LPM_OLS: reg inlf nwifeinc educ exper* age kidslt6 kidsge6 
      Source |       SS           df       MS      Number of obs   =       753
-------------+----------------------------------   F(7, 745)       =     38.22
       Model |  48.8080578         7  6.97257969   Prob > F        =    0.0000
    Residual |  135.919698       745  .182442547   R-squared       =    0.2642
-------------+----------------------------------   Adj R-squared   =    0.2573
       Total |  184.727756       752  .245648611   Root MSE        =    .42713
------------------------------------------------------------------------------
        inlf |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    nwifeinc |  -.0034052   .0014485    -2.35   0.019    -.0062488   -.0005616
        educ |   .0379953    .007376     5.15   0.000      .023515    .0524756
       exper |   .0394924   .0056727     6.96   0.000     .0283561    .0506287
     expersq |  -.0005963   .0001848    -3.23   0.001    -.0009591   -.0002335
         age |  -.0160908   .0024847    -6.48   0.000    -.0209686    -.011213
     kidslt6 |  -.2618105   .0335058    -7.81   0.000    -.3275875   -.1960335
     kidsge6 |   .0130122    .013196     0.99   0.324    -.0128935    .0389179
       _cons |   .5855192    .154178     3.80   0.000     .2828442    .8881943
------------------------------------------------------------------------------
. eststo Logit_MLE: logit inlf nwifeinc educ exper* age kidslt6 kidsge6 
Iteration 0:   log likelihood =  -514.8732  
Iteration 1:   log likelihood = -402.38502  
Iteration 2:   log likelihood = -401.76569  
Iteration 3:   log likelihood = -401.76515  
Iteration 4:   log likelihood = -401.76515  
Logistic regression                             Number of obs     =        753
                                                LR chi2(7)        =     226.22
                                                Prob > chi2       =     0.0000
Log likelihood = -401.76515                     Pseudo R2         =     0.2197
------------------------------------------------------------------------------
        inlf |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    nwifeinc |  -.0213452   .0084214    -2.53   0.011    -.0378509   -.0048394
        educ |   .2211704   .0434396     5.09   0.000     .1360303    .3063105
       exper |   .2058695   .0320569     6.42   0.000     .1430391    .2686999
     expersq |  -.0031541   .0010161    -3.10   0.002    -.0051456   -.0011626
         age |  -.0880244    .014573    -6.04   0.000     -.116587   -.0594618
     kidslt6 |  -1.443354   .2035849    -7.09   0.000    -1.842373   -1.044335
     kidsge6 |   .0601122   .0747897     0.80   0.422     -.086473    .2066974
       _cons |   .4254524   .8603697     0.49   0.621    -1.260841    2.111746
------------------------------------------------------------------------------
. margins, dydx( educ)
Average marginal effects                        Number of obs     =        753
Model VCE    : OIM
Expression   : Pr(inlf), predict()
dy/dx w.r.t. : educ
------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        educ |   .0394965   .0072947     5.41   0.000     .0251992    .0537939
------------------------------------------------------------------------------

. eststo Probit_MLE: probit inlf nwifeinc educ exper* age kidslt6 kidsge6 
Iteration 0:   log likelihood =  -514.8732  
Iteration 1:   log likelihood = -402.06651  
Iteration 2:   log likelihood = -401.30273  
Iteration 3:   log likelihood = -401.30219  
Iteration 4:   log likelihood = -401.30219  
Probit regression                               Number of obs     =        753
                                                LR chi2(7)        =     227.14
                                                Prob > chi2       =     0.0000
Log likelihood = -401.30219                     Pseudo R2         =     0.2206
------------------------------------------------------------------------------
        inlf |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    nwifeinc |  -.0120237   .0048398    -2.48   0.013    -.0215096   -.0025378
        educ |   .1309047   .0252542     5.18   0.000     .0814074     .180402
       exper |   .1233476   .0187164     6.59   0.000     .0866641    .1600311
     expersq |  -.0018871      .0006    -3.15   0.002     -.003063   -.0007111
         age |  -.0528527   .0084772    -6.23   0.000    -.0694678   -.0362376
     kidslt6 |  -.8683285   .1185223    -7.33   0.000    -1.100628    -.636029
     kidsge6 |    .036005   .0434768     0.83   0.408     -.049208    .1212179
       _cons |   .2700768    .508593     0.53   0.595    -.7267473    1.266901
------------------------------------------------------------------------------

. margins, dydx( educ)
Average marginal effects                        Number of obs     =        753
Model VCE    : OIM
Expression   : Pr(inlf), predict()
dy/dx w.r.t. : educ
------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        educ |   .0393703   .0072216     5.45   0.000     .0252161    .0535244
------------------------------------------------------------------------------

. estout, cells(b(nostar fmt(4)) se(par fmt(4))) stats(N, fmt(%9.0g) labels(Observatiions ///
 varlabels(_cons constant) varwidth(10) ti("Table 17.1 LPM, Logit, and Probit ///
 Estimates of Labor Force Participation: (inlf)")
Table 17.1 LPM, Logit, and Probit Estimates of Labor Force Participation: (inlf)
-------------------------------------------------
                LPM_OLS    Logit_MLE   Probit_MLE
                   b/se         b/se         b/se
-------------------------------------------------
main                                             
nwifeinc        -0.0034      -0.0213      -0.0120
               (0.0014)     (0.0084)     (0.0048)
educ             0.0380       0.2212       0.1309
               (0.0074)     (0.0434)     (0.0253)
exper            0.0395       0.2059       0.1233
               (0.0057)     (0.0321)     (0.0187)
expersq         -0.0006      -0.0032      -0.0019
               (0.0002)     (0.0010)     (0.0006)
age             -0.0161      -0.0880      -0.0529
               (0.0025)     (0.0146)     (0.0085)
kidslt6         -0.2618      -1.4434      -0.8683
               (0.0335)     (0.2036)     (0.1185)
kidsge6          0.0130       0.0601       0.0360
               (0.0132)     (0.0748)     (0.0435)
constant         0.5855       0.4255       0.2701
               (0.1542)     (0.8604)     (0.5086)
-------------------------------------------------
Observat~s          753          753          753
-------------------------------------------------
. est clear

. eststo LPM: qui reg inlf nwifeinc educ exper* age kidslt6 kidsge6 
. qui logit inlf nwifeinc educ exper* age kidslt6 kidsge6 
. eststo Logit: qui margins, dydx( nwifeinc educ exper* age kidslt6 kidsge6) post
. qui probit inlf nwifeinc educ exper* age kidslt6 kidsge6 
. eststo Probit: qui margins, dydx( nwifeinc educ exper* age kidslt6 kidsge6) post
. estout, cells(b(nostar fmt(4)) se(par fmt(4))) drop(_cons expersq) varwidth(10) ///
 ti("Table 17.2 Average Partial Effects for the Labor Force Participation Models: (inlf)")
Table 17.2 Average Partial Effects for the Labor Force Participation Models: (inlf)
-------------------------------------------------
                    LPM        Logit       Probit
                   b/se         b/se         b/se
-------------------------------------------------
nwifeinc        -0.0034      -0.0038      -0.0036
               (0.0014)     (0.0015)     (0.0014)
educ             0.0380       0.0395       0.0394
               (0.0074)     (0.0073)     (0.0072)
exper            0.0395       0.0368       0.0371
               (0.0057)     (0.0052)     (0.0052)
age             -0.0161      -0.0157      -0.0159
               (0.0025)     (0.0024)     (0.0024)
kidslt6         -0.2618      -0.2578      -0.2612
               (0.0335)     (0.0319)     (0.0319)
kidsge6          0.0130       0.0107       0.0108
               (0.0132)     (0.0133)     (0.0131)
-------------------------------------------------
. est clear

. *Example 17.2.  Married Women’s Annual Labor Supply
. u mroz, clear
. eststo Linear_OLS: reg hours nwifeinc educ exper* age kidslt6 kidsge6 

      Source |       SS           df       MS      Number of obs   =       753
-------------+----------------------------------   F(7, 745)       =     38.50
       Model |   151647606         7  21663943.7   Prob > F        =    0.0000
    Residual |   419262118       745  562767.944   R-squared       =    0.2656
-------------+----------------------------------   Adj R-squared   =    0.2587
       Total |   570909724       752  759188.463   Root MSE        =    750.18
------------------------------------------------------------------------------
       hours |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    nwifeinc |  -3.446636      2.544    -1.35   0.176    -8.440898    1.547626
        educ |   28.76112   12.95459     2.22   0.027     3.329283    54.19297
       exper |   65.67251   9.962983     6.59   0.000     46.11365    85.23138
     expersq |  -.7004939   .3245501    -2.16   0.031    -1.337635   -.0633524
         age |  -30.51163   4.363868    -6.99   0.000    -39.07858   -21.94469
     kidslt6 |  -442.0899    58.8466    -7.51   0.000    -557.6148    -326.565
     kidsge6 |  -32.77923   23.17622    -1.41   0.158     -78.2777    12.71924
       _cons |   1330.482   270.7846     4.91   0.000     798.8906    1862.074
------------------------------------------------------------------------------
. eststo Linear: qui margins, dydx( nwifeinc educ exper* age kidslt6 kidsge6) post
. eststo Tobit_MLE: tobit hours nwifeinc educ exper* age kidslt6 kidsge6, ll(0)
Refining starting values:
Grid node 0:   log likelihood = -3961.1577
Fitting full model:
Iteration 0:   log likelihood = -3961.1577  
Iteration 1:   log likelihood = -3836.8928  
Iteration 2:   log likelihood = -3819.2637  
Iteration 3:   log likelihood = -3819.0948  
Iteration 4:   log likelihood = -3819.0946  
Tobit regression                                Number of obs     =        753
                                                   Uncensored     =        428
Limits: lower = 0                                  Left-censored  =        325
        upper = +inf                               Right-censored =          0
                                                LR chi2(7)        =     271.59
                                                Prob > chi2       =     0.0000
Log likelihood = -3819.0946                     Pseudo R2         =     0.0343
------------------------------------------------------------------------------
       hours |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    nwifeinc |  -8.814226   4.459089    -1.98   0.048    -17.56808   -.0603706
        educ |   80.64541   21.58318     3.74   0.000     38.27441    123.0164
       exper |    131.564   17.27935     7.61   0.000     97.64211     165.486
     expersq |  -1.864153   .5376606    -3.47   0.001    -2.919661   -.8086455
         age |  -54.40491   7.418483    -7.33   0.000     -68.9685   -39.84133
     kidslt6 |  -894.0202   111.8777    -7.99   0.000    -1113.653   -674.3875
     kidsge6 |  -16.21805    38.6413    -0.42   0.675    -92.07668    59.64057
       _cons |   965.3068   446.4351     2.16   0.031     88.88827    1841.725
-------------+----------------------------------------------------------------
 var(e.hours)|    1258927   93304.48                       1088458     1456093
------------------------------------------------------------------------------
. eststo Tobit: qui margins, dydx( nwifeinc educ exper* age kidslt6 kidsge6) post pred(ystar(0,.))
. estout Linear_OLS Tobit_MLE, cells(b(nostar fmt(4)) se(par fmt(4))) stats(N, fmt(%9.0g) ///
 labels(Observations)) varlabels(_cons constant) varwidth(10) ti("Table 17.3 OLS ///
 and Tobit Estimation of Annual Hours Worked: (hours)")
Table 17.3 OLS and Tobit Estimation of Annual Hours Worked: (hours)
------------------------------------
             Linear_OLS    Tobit_MLE
                   b/se         b/se
------------------------------------
main                                
nwifeinc        -3.4466      -8.8142
               (2.5440)     (4.4591)
educ            28.7611      80.6454
              (12.9546)    (21.5832)
exper           65.6725     131.5640
               (9.9630)    (17.2793)
expersq         -0.7005      -1.8642
               (0.3246)     (0.5377)
age            -30.5116     -54.4049
               (4.3639)     (7.4185)
kidslt6       -442.0899    -894.0202
              (58.8466)   (111.8777)
kidsge6        -32.7792     -16.2181
              (23.1762)    (38.6413)
constant      1330.4824     965.3068
             (270.7846)   (446.4351)
------------------------------------
Observat~s          753          753
------------------------------------
. estout Linear Tobit, cells(b(nostar fmt(4)) se(par fmt(4))) varwidth(10) ti("Table ///
 17.4 Average Partial Effects for the Hours Worked Models: (hours)")
Table 17.4 Average Partial Effects for the Hours Worked Models: (hours)
------------------------------------
                 Linear        Tobit
                   b/se         b/se
------------------------------------
nwifeinc        -3.4466      -5.1886
               (2.5440)     (2.6214)
educ            28.7611      47.4731
              (12.9546)    (12.6214)
exper           65.6725      77.4470
               (9.9630)     (9.9976)
expersq         -0.7005      -1.0974
               (0.3246)     (0.3156)
age            -30.5116     -32.0262
               (4.3639)     (4.2921)
kidslt6       -442.0899    -526.2776
              (58.8466)    (64.7062)
kidsge6        -32.7792      -9.5470
              (23.1762)    (22.7522)
------------------------------------
. est clear

. *Example 17.3.  Poisson Regression for Number of Arrests
. u crime1, clear
. eststo Linear: qui reg narr86 pcnv avgsen tottime ptime86 qemp86 inc86 black hispan born60 
. eststo Poisson: qui poisson narr86 pcnv avgsen tottime ptime86 qemp86 inc86 black hispan born60 
. estout, cells(b(nostar fmt(4)) se(par fmt(4))) stats(r2 rmse ll, fmt(%9.0g %9.0g %9.0g) ///
 labels(R-Squared rmse Log-Likelihood)) varlabels(_cons constant) varwidth(10) ///
 ti("Table 17.5 Determinants of Number of Arrests for Young Men: (narr86)")
Table 17.5 Determinants of Number of Arrests for Young Men: (narr86)
------------------------------------
                 Linear      Poisson
                   b/se         b/se
------------------------------------
main                                
pcnv            -0.1319      -0.4016
               (0.0404)     (0.0850)
avgsen          -0.0113      -0.0238
               (0.0122)     (0.0199)
tottime          0.0121       0.0245
               (0.0094)     (0.0148)
ptime86         -0.0409      -0.0986
               (0.0088)     (0.0207)
qemp86          -0.0513      -0.0380
               (0.0145)     (0.0290)
inc86           -0.0015      -0.0081
               (0.0003)     (0.0010)
black            0.3270       0.6608
               (0.0454)     (0.0738)
hispan           0.1938       0.4998
               (0.0397)     (0.0739)
born60          -0.0225      -0.0510
               (0.0333)     (0.0641)
constant         0.5766      -0.5996
               (0.0379)     (0.0673)
------------------------------------
R-Squared      .0724764             
rmse           .8287301             
Log-Like~d    -3349.678    -2248.761
------------------------------------
. est clear

. *Example 17.4.  Duration of Recidivism
. u recid, clear
. cnreg  ldurat workprg priors tserved felon alcohol drugs black married educ age, censor(cens)
Censored-normal regression                      Number of obs     =      1,445
                                                LR chi2(10)       =     166.74
                                                Prob > chi2       =     0.0000
Log likelihood =  -1597.059                     Pseudo R2         =     0.0496
------------------------------------------------------------------------------
      ldurat |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     workprg |  -.0625715   .1200369    -0.52   0.602    -.2980382    .1728951
      priors |  -.1372529   .0214587    -6.40   0.000    -.1793466   -.0951592
     tserved |  -.0193305   .0029779    -6.49   0.000    -.0251721    -.013489
       felon |   .4439947   .1450865     3.06   0.002     .1593903    .7285991
     alcohol |  -.6349092   .1442166    -4.40   0.000    -.9178072   -.3520113
       drugs |  -.2981602   .1327355    -2.25   0.025    -.5585367   -.0377837
       black |  -.5427179   .1174428    -4.62   0.000    -.7730958     -.31234
     married |   .3406837   .1398431     2.44   0.015      .066365    .6150024
        educ |   .0229196   .0253974     0.90   0.367    -.0269004    .0727395
         age |   .0039103   .0006062     6.45   0.000     .0027211    .0050994
       _cons |   4.099386    .347535    11.80   0.000     3.417655    4.781117
-------------+----------------------------------------------------------------
      /sigma |    1.81047   .0623022                      1.688257    1.932683
------------------------------------------------------------------------------
             0  left-censored observations
           552     uncensored observations
           893 right-censored observations

. *Example 17.5. Wage Offer Equation for Married Women
. u mroz, clear
. eststo OLS: reg lwage educ exper* 
      Source |       SS           df       MS      Number of obs   =       428
-------------+----------------------------------   F(3, 424)       =     26.29
       Model |  35.0222967         3  11.6740989   Prob > F        =    0.0000
    Residual |  188.305144       424  .444115906   R-squared       =    0.1568
-------------+----------------------------------   Adj R-squared   =    0.1509
       Total |  223.327441       427  .523015084   Root MSE        =    .66642
------------------------------------------------------------------------------
       lwage |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        educ |   .1074896   .0141465     7.60   0.000     .0796837    .1352956
       exper |   .0415665   .0131752     3.15   0.002     .0156697    .0674633
     expersq |  -.0008112   .0003932    -2.06   0.040    -.0015841   -.0000382
       _cons |  -.5220406   .1986321    -2.63   0.009    -.9124667   -.1316144
------------------------------------------------------------------------------

. eststo Heckit: heckman lwage educ exper*, twostep select(inlf = educ exper* nwifeinc age kidslt6 kidsge6) 
Heckman selection model -- two-step estimates   Number of obs     =        753
(regression model with sample selection)              Selected    =        428
                                                      Nonselected =        325

                                                Wald chi2(3)      =      51.53
                                                Prob > chi2       =     0.0000
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
lwage        |
        educ |   .1090655    .015523     7.03   0.000     .0786411      .13949
       exper |   .0438873   .0162611     2.70   0.007     .0120163    .0757584
     expersq |  -.0008591   .0004389    -1.96   0.050    -.0017194    1.15e-06
       _cons |  -.5781032   .3050062    -1.90   0.058    -1.175904     .019698
-------------+----------------------------------------------------------------
inlf         |
        educ |   .1309047   .0252542     5.18   0.000     .0814074     .180402
       exper |   .1233476   .0187164     6.59   0.000     .0866641    .1600311
     expersq |  -.0018871      .0006    -3.15   0.002     -.003063   -.0007111
    nwifeinc |  -.0120237   .0048398    -2.48   0.013    -.0215096   -.0025378
         age |  -.0528527   .0084772    -6.23   0.000    -.0694678   -.0362376
     kidslt6 |  -.8683285   .1185223    -7.33   0.000    -1.100628    -.636029
     kidsge6 |    .036005   .0434768     0.83   0.408     -.049208    .1212179
       _cons |   .2700768    .508593     0.53   0.595    -.7267473    1.266901
-------------+----------------------------------------------------------------
/mills       |
      lambda |   .0322619   .1336246     0.24   0.809    -.2296376    .2941613
-------------+----------------------------------------------------------------
         rho |    0.04861
       sigma |  .66362875
------------------------------------------------------------------------------

. estout, cells(b(nostar fmt(4)) se(par fmt(4)))  varlabels(_cons constant) varwidth(10) ///
 ti("Table 17.7 Wage Offer Equation for Married Women: (lwage)")
Table 17.7 Wage Offer Equation for Married Women: (lwage)
------------------------------------
                    OLS       Heckit
                   b/se         b/se
------------------------------------
main                                
educ             0.1075       0.1091
               (0.0141)     (0.0155)
exper            0.0416       0.0439
               (0.0132)     (0.0163)
expersq         -0.0008      -0.0009
               (0.0004)     (0.0004)
constant        -0.5220      -0.5781
               (0.1986)     (0.3050)
------------------------------------
inlf                                
educ                          0.1309
                            (0.0253)
exper                         0.1233
                            (0.0187)
expersq                      -0.0019
                            (0.0006)
nwifeinc                     -0.0120
                            (0.0048)
age                          -0.0529
                            (0.0085)
kidslt6                      -0.8683
                            (0.1185)
kidsge6                       0.0360
                            (0.0435)
constant                      0.2701
                            (0.5086)
------------------------------------
. est clear

. log close
      name:  SN
       log:  ~Wooldridge\intro-econx\iexample16.smcl
  log type:  smcl
 closed on:  18 Jan 2019, 20:45:51
-------------------------------------------------------------------------------------




0 replies

Leave a Reply

Want to join the discussion?
Feel free to contribute!

Leave a Reply

Your email address will not be published. Required fields are marked *

Recent
Comments

Archives

RSS Solomon Negash

  • IPIS Briefing May 2021 – Ethiopia-Tigray Conflict 2021-06-08
    Source: IPIS Briefing May 2021: “Ethiopia Tigray crisis – Warnings of genocide and famine” The IPIS briefing offers a selection of articles, news and updates on natural resources, armed conflict, Business & Human Rights and arms trade. Every month, an editorial and related publications shed a light on a specific topic in IPIS’ areas of research. […]
    Solomon
  • Ethiopia: Contemplating Elections and the Prospects for Peaceful Reform 2021-05-14
    Source: USIP  April 29, 2021 |  Amid ongoing violence across the country, the vote may offer opportunities to support political dialogue and decrease polarization. Ethiopia is approaching parliamentary elections on June 5. This will be the first vote since the process of reform launched in 2018 by Prime Minister Abiy Ahmed, and the stakes are […]
    Solomon
  • IPIS Briefing April 2021 – Ethiopia-Tigray Conflict 2021-05-14
    Source: IPIS Briefing April 2021: “In Tigray, Sexual Violence Has Become A Weapon Of War” The IPIS briefing offers a selection of articles, news and updates on natural resources, armed conflict, Business & Human Rights and arms trade. Every month, an editorial and related publications shed a light on a specific topic in IPIS’ areas […]
    Solomon
  • IPIS Briefing March 2021 – Ethiopia-Tigray Conflict 2021-04-10
    Source: IPIS Briefing March 2021 Ethiopian police arrest 359 for suspected murder and illicit arms trade | 29 March 2021 | Xinhua The Ethiopian Federal Police Commission disclosed the arrest of 359 people on suspicion of murder, illicit arms trade, money laundering and auto theft, the state-affiliated Fana Broadcasting Corporate reported Sunday. Scale of Tigray horror […]
    Solomon
  • FP – The U.N. Must End the Horrors of Ethiopia’s Tigray War 2021-03-08
    Foreign Policy | Recent human rights investigations confirm the atrocities that journalists reported in November. A strong multilateral push can force an Eritrean withdrawal and put the region on the path to peace. In November 2020, as war broke out in Ethiopia’s northern Tigray region, the scale of the suffering was already apparent to anyone […]
    Solomon
  • Ethiopia: Eritrean Forces Massacre Tigray Civilians – HRW 2021-03-07
    HRW | UN Should Urgently Investigate Atrocities by All Parties (Nairobi) – Eritrean armed forces massacred scores of civilians, including children as young as 13, in the historic town of Axum in Ethiopia’s Tigray region in November 2020, Human Rights Watch said today. The United Nations should urgently establish an independent inquiry into war crimes […]
    Solomon
  • 9 Things To Know About The Unfolding Crisis In Ethiopia’s Tigray Region 2021-03-07
    NPR | For months, a conflict in Ethiopia between the government in Addis Ababa and a defiant region has cost thousands of lives and displaced at least a million people. Despite the increasing brutality of the conflict in Tigray, until now, it has been largely overlooked by the outside world. But attention and concern is […]
    Solomon
  • Egypt’s Sisi ups pressure for Ethiopia dam deal on Sudan visit 2021-03-07
    MEMO | Egyptian President Abdel Fattah Al-Sisi called on Saturday for a binding deal by the summer on the operation of a giant Ethiopian hydropower dam, as he made his first visit to neighbouring Sudan since the 2019 overthrow of Omar Al-Bashir, Reuters reports. Egypt also signalled support for Sudan in a dispute with Ethiopia […]
    Solomon
  • Ethiopia: Persistent, credible reports of grave violations in Tigray underscore urgent need for human rights access – Bachelet  2021-03-07
    OHCHR | GENEVA (4 March 2021) – UN High Commissioner for Human Rights Michelle Bachelet on Thursday stressed the urgent need for an objective, independent assessment of the facts on the ground in the Tigray region of Ethiopia, given the persistent reports of serious human rights violations and abuses she continues to receive. “Deeply distressing […]
    Solomon