INTRODUCTORY ECONOMETRICS – REPLICATING EXAMPLES

Chapter 8 – Examples

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      name:  SN
       log:  ~Wooldridge\intro-econx\iexample8.smcl
  log type:  smcl
 opened on:   9 Jan 2019, 21:10:27
. **********************************************
. * Solomon Negash - Replicating Examples
. * Wooldridge (2016). Introductory Econometrics: A Modern Approach. 6th ed.  
. * STATA Program, version 15.1. 

. * Chapter 8  - Heteroskedasticity
. * Computer Exercises (Examples)
. ******************** SETUP *********************

. *Example 8.1. Log wage equation with Heteroskedasticity- Robust Standard Errors
. u wage1, clear
. g marrmale = (female==0 & married==1)
. g marrfem = (female==1 & married==1)
. g singfem = (female==1 & married==0)
. g singmen = (female==0 & married==0)

. eststo hetroskedastic: qui reg lwage marrmale marrfem singfem educ exper* tenur*
. eststo robust: qui reg lwage marrmale marrfem singfem educ exper* tenur*, robust 
. estout , cells(b(nostar fmt(3)) se(par fmt(3))) stats(r2 r2_a N, fmt(%9.3f %9.3f %9.0g) ///
 labels(R-squared Adj-R-squared)) varlabels(_cons Constant) varwidth(20)

----------------------------------------------
                     hetroskeda~c       robust
                             b/se         b/se
----------------------------------------------
marrmale                    0.213        0.213
                          (0.055)      (0.057)
marrfem                    -0.198       -0.198
                          (0.058)      (0.059)
singfem                    -0.110       -0.110
                          (0.056)      (0.057)
educ                        0.079        0.079
                          (0.007)      (0.007)
exper                       0.027        0.027
                          (0.005)      (0.005)
expersq                    -0.001       -0.001
                          (0.000)      (0.000)
tenure                      0.029        0.029
                          (0.007)      (0.007)
tenursq                    -0.001       -0.001
                          (0.000)      (0.000)
Constant                    0.321        0.321
                          (0.100)      (0.109)
----------------------------------------------
R-squared                   0.461        0.461
Adj-R-squared               0.453        0.453
N                             526          526
----------------------------------------------
. est clear

. *Example 8.2. Heteroskedasticity-Robust F Statistic
. u gpa3, clear
. eststo hetrosked: qui  reg cumgpa sat hsperc tothrs female black white if spring==1
. eststo robust: qui  reg cumgpa sat hsperc tothrs female black white if spring==1, robust
. estout , cells(b(nostar fmt(5)) se(par fmt(5))) stats(r2 r2_a N, fmt(%9.3f %9.3f %9.0g) ///
 labels(R-squared Adj-R-squared)) varlabels(_cons Constant) varwidth(20)

----------------------------------------------
                        hetrosked       robust
                             b/se         b/se
----------------------------------------------
sat                       0.00114      0.00114
                        (0.00018)    (0.00019)
hsperc                   -0.00857     -0.00857
                        (0.00124)    (0.00142)
tothrs                    0.00250      0.00250
                        (0.00073)    (0.00074)
female                    0.30343      0.30343
                        (0.05902)    (0.05914)
black                    -0.12828     -0.12828
                        (0.14737)    (0.11924)
white                    -0.05872     -0.05872
                        (0.14099)    (0.11139)
Constant                  1.47006      1.47006
                        (0.22980)    (0.22068)
----------------------------------------------
R-squared                   0.401        0.401
Adj-R-squared               0.391        0.391
N                             366          366
----------------------------------------------
. est clear

. *Example 8.3. Heteroskedasticity-Robust LM Statistic
. u crime1, clear
. g avgsensq = avgsen^2 
. eststo hetrosked: qui reg narr86 pcnv avgsen avgsensq ptime86 qemp86 inc86 black hispan
. eststo robust: qui reg narr86 pcnv avgsen avgsensq ptime86 qemp86 inc86 black hispan, r 
. estout , cells(b(nostar fmt(5)) se(par fmt(5))) stats(r2 r2_a N, fmt(%9.3f %9.3f %9.0g) ///
 labels(R-squared Adj-R-squared)) varlabels(_cons Constant) varwidth(20)

----------------------------------------------
                        hetrosked       robust
                             b/se         b/se
----------------------------------------------
pcnv                     -0.13560     -0.13560
                        (0.04037)    (0.03362)
avgsen                    0.01784      0.01784
                        (0.00970)    (0.01012)
avgsensq                 -0.00052     -0.00052
                        (0.00030)    (0.00021)
ptime86                  -0.03936     -0.03936
                        (0.00869)    (0.00622)
qemp86                   -0.05051     -0.05051
                        (0.01443)    (0.01420)
inc86                    -0.00148     -0.00148
                        (0.00034)    (0.00023)
black                     0.32460      0.32460
                        (0.04542)    (0.05851)
hispan                    0.19338      0.19338
                        (0.03970)    (0.04030)
Constant                  0.56701      0.56701
                        (0.03606)    (0.04028)
----------------------------------------------
R-squared                   0.073        0.073
Adj-R-squared               0.070        0.070
N                            2725         2725
----------------------------------------------
. est clear

. *LM Statistic  - not-robust (See Section 5-2)
. qui reg narr86 pcnv ptime86 qemp86 inc86 black hispan
. predict u, res
. reg u pcnv avgsen avgsensq ptime86 qemp86 inc86 black hispan

      Source |       SS           df       MS      Number of obs   =     2,725
-------------+----------------------------------   F(8, 2716)      =      0.43
       Model |  2.37155739         8  .296444674   Prob > F        =    0.9025
    Residual |  1863.99804     2,716  .686302664   R-squared       =    0.0013
-------------+----------------------------------   Adj R-squared   =   -0.0017
       Total |  1866.36959     2,724  .685157707   Root MSE        =    .82843

------------------------------------------------------------------------------
           u |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        pcnv |   -.003317   .0403699    -0.08   0.935    -.0824758    .0758418
      avgsen |   .0178411    .009696     1.84   0.066    -.0011713    .0368534
    avgsensq |  -.0005163    .000297    -1.74   0.082    -.0010987    .0000661
     ptime86 |  -.0015647   .0086935    -0.18   0.857    -.0186112    .0154819
      qemp86 |   .0004742   .0144345     0.03   0.974    -.0278295    .0287779
       inc86 |   .0000103   .0003405     0.03   0.976    -.0006574     .000678
       black |  -.0050861   .0454188    -0.11   0.911     -.094145    .0839729
      hispan |  -.0020709   .0397035    -0.05   0.958    -.0799229    .0757812
       _cons |  -.0033216   .0360573    -0.09   0.927    -.0740242    .0673809
------------------------------------------------------------------------------
. display as text "N*Rsq = " 2725*.0013
N*Rsq = 3.5425

. *LM Statistic  - robust
. foreach x of var avgsen avgsensq { 
  . qui reg `x'  pcnv ptime86 qemp86 inc86 black hispan 
  . predict r_`x', residual
  . gen ures`x'= u*r_`x'
  . }
. gen one=1
. reg one ures*, noc 
      Source |       SS           df       MS      Number of obs   =     2,725
-------------+----------------------------------   F(2, 2723)      =      2.00
       Model |  3.99708536         2  1.99854268   Prob > F        =    0.1355
    Residual |  2721.00291     2,723  .999266586   R-squared       =    0.0015
-------------+----------------------------------   Adj R-squared   =    0.0007
       Total |        2725     2,725           1   Root MSE        =    .99963

------------------------------------------------------------------------------
         one |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  uresavgsen |   .0277846   .0140598     1.98   0.048     .0002156    .0553537
uresavgsensq |  -.0010447   .0005479    -1.91   0.057     -.002119    .0000296
------------------------------------------------------------------------------

. display as text "N - SSR = " 2725 - 2721.0029 
N - SSR = 3.9971

. *Example 8.4. Heteroskedasticity in Housing Price Equations
. u hprice1, clear
. reg price lotsize sqrft bdrms 
      Source |       SS           df       MS      Number of obs   =        88
-------------+----------------------------------   F(3, 84)        =     57.46
       Model |  617130.701         3  205710.234   Prob > F        =    0.0000
    Residual |  300723.805        84   3580.0453   R-squared       =    0.6724
-------------+----------------------------------   Adj R-squared   =    0.6607
       Total |  917854.506        87  10550.0518   Root MSE        =    59.833

------------------------------------------------------------------------------
       price |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     lotsize |   .0020677   .0006421     3.22   0.002     .0007908    .0033446
       sqrft |   .1227782   .0132374     9.28   0.000     .0964541    .1491022
       bdrms |   13.85252   9.010145     1.54   0.128    -4.065141    31.77018
       _cons |  -21.77031   29.47504    -0.74   0.462    -80.38466    36.84405
------------------------------------------------------------------------------

. predict u, res
. g u2=u^2
. reg u2 lotsize sqrft bdrms
      Source |       SS           df       MS      Number of obs   =        88
-------------+----------------------------------   F(3, 84)        =      5.34
       Model |   701213780         3   233737927   Prob > F        =    0.0020
    Residual |  3.6775e+09        84  43780003.5   R-squared       =    0.1601
-------------+----------------------------------   Adj R-squared   =    0.1301
       Total |  4.3787e+09        87  50330276.7   Root MSE        =    6616.6

------------------------------------------------------------------------------
          u2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     lotsize |   .2015209   .0710091     2.84   0.006     .0603116    .3427302
       sqrft |   1.691037    1.46385     1.16   0.251    -1.219989    4.602063
       bdrms |    1041.76    996.381     1.05   0.299    -939.6526    3023.173
       _cons |  -5522.795   3259.478    -1.69   0.094    -12004.62    959.0348
------------------------------------------------------------------------------

. display as text "LM = N*Rsq =" 88 * .1601
LM = N*Rsq =14.0888
. display chi2tail(3, 88*.1601)
.00278674

. reg lprice llotsize lsqrft bdrms 
      Source |       SS           df       MS      Number of obs   =        88
-------------+----------------------------------   F(3, 84)        =     50.42
       Model |  5.15504028         3  1.71834676   Prob > F        =    0.0000
    Residual |  2.86256324        84  .034078134   R-squared       =    0.6430
-------------+----------------------------------   Adj R-squared   =    0.6302
       Total |  8.01760352        87  .092156362   Root MSE        =     .1846

------------------------------------------------------------------------------
      lprice |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    llotsize |   .1679667   .0382812     4.39   0.000     .0918404     .244093
      lsqrft |   .7002324   .0928652     7.54   0.000     .5155597    .8849051
       bdrms |   .0369584   .0275313     1.34   0.183    -.0177906    .0917074
       _cons |  -1.297042   .6512836    -1.99   0.050    -2.592191    -.001893
------------------------------------------------------------------------------
. predict lu, res
. g lu2=lu^2
. reg lu2 llotsize lsqrft bdrms
      Source |       SS           df       MS      Number of obs   =        88
-------------+----------------------------------   F(3, 84)        =      1.41
       Model |  .022620168         3  .007540056   Prob > F        =    0.2451
    Residual |  .448717194        84  .005341871   R-squared       =    0.0480
-------------+----------------------------------   Adj R-squared   =    0.0140
       Total |  .471337362        87  .005417671   Root MSE        =    .07309

------------------------------------------------------------------------------
         lu2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    llotsize |  -.0070156   .0151563    -0.46   0.645    -.0371556    .0231244
      lsqrft |  -.0627368   .0367673    -1.71   0.092    -.1358526    .0103791
       bdrms |   .0168407   .0109002     1.54   0.126    -.0048356     .038517
       _cons |    .509994    .257857     1.98   0.051    -.0027829    1.022771
------------------------------------------------------------------------------

. display as text "LM = N*Rsq =" 88 * .048
LM = N*Rsq =4.224
. display chi2tail(3, 88*.048)
.23826999

. *Example 8.5. Special Form of the White Test
. u hprice1, clear
. reg lprice llotsize lsqrft bdrms 
      Source |       SS           df       MS      Number of obs   =        88
-------------+----------------------------------   F(3, 84)        =     50.42
       Model |  5.15504028         3  1.71834676   Prob > F        =    0.0000
    Residual |  2.86256324        84  .034078134   R-squared       =    0.6430
-------------+----------------------------------   Adj R-squared   =    0.6302
       Total |  8.01760352        87  .092156362   Root MSE        =     .1846

------------------------------------------------------------------------------
      lprice |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    llotsize |   .1679667   .0382812     4.39   0.000     .0918404     .244093
      lsqrft |   .7002324   .0928652     7.54   0.000     .5155597    .8849051
       bdrms |   .0369584   .0275313     1.34   0.183    -.0177906    .0917074
       _cons |  -1.297042   .6512836    -1.99   0.050    -2.592191    -.001893
------------------------------------------------------------------------------

. predict u, res
. g u2=u^2
. predict y, xb
. gen y2 = y^2
. reg u2 y y2
      Source |       SS           df       MS      Number of obs   =        88
-------------+----------------------------------   F(2, 85)        =      1.73
       Model |  .018463986         2  .009231993   Prob > F        =    0.1830
    Residual |  .452873375        85  .005327922   R-squared       =    0.0392
-------------+----------------------------------   Adj R-squared   =    0.0166
       Total |  .471337362        87  .005417671   Root MSE        =    .07299

------------------------------------------------------------------------------
          u2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
           y |  -1.709221   1.163334    -1.47   0.145    -4.022241    .6037986
          y2 |   .1451354   .1009925     1.44   0.154    -.0556647    .3459354
       _cons |   5.046839   3.345002     1.51   0.135    -1.603921     11.6976
------------------------------------------------------------------------------

. display as text "LM = N*Rsq =" 88 * .0392
LM = N*Rsq =3.4496
. display chi2tail(2, 88*.0392)
.17820869

.*Example 8.6. Financial Wealth Equation
. u 401ksubs, clear
. keep if fsize==1 
(7,258 observations deleted)
. g age25sq=(age-25)^2
. eststo OLS1: qui reg nettfa inc, r
. eststo WLS1: qui reg nettfa inc [aw=1/inc]
. eststo OLS2: qui reg nettfa inc age25sq male e401k, r 
. eststo WLS2: qui reg nettfa inc age25sq male e401k [aw=1/inc]
. estout , cells(b(nostar fmt(5)) se(par fmt(5))) stats(r2 N, fmt(%9.3f %9.0g) labels(R-squared ///
 Adj-R-squared)) varlabels(_cons Constant) varwidth(20) ti(Table 8.1 Dependent Variable: nettfa)

Table 8.1 Dependent Variable: nettfa
------------------------------------------------------------------------
                             OLS1         WLS1         OLS2         WLS2
                             b/se         b/se         b/se         b/se
------------------------------------------------------------------------
inc                       0.82068      0.78705      0.77058      0.74038
                        (0.10359)    (0.06348)    (0.09957)    (0.06430)
age25sq                                             0.02513      0.01754
                                                  (0.00434)    (0.00193)
male                                                2.47793      1.84053
                                                  (2.05836)    (1.56359)
e401k                                               6.88622      5.18828
                                                  (2.28658)    (1.70343)
Constant                -10.57095     -9.58070    -20.98499    -16.70252
                        (2.53027)    (1.65328)    (3.49519)    (1.95799)
------------------------------------------------------------------------
R-squared                   0.083        0.071        0.128        0.112
Adj-R-squared                2017         2017         2017         2017
------------------------------------------------------------------------
. est clear

. *Example 8.7. Demand for Cigarettes
. u smoke, clear
. local x "lincome lcigpric educ age agesq restaurn" 
. eststo OLS: reg cigs `x'
      Source |       SS           df       MS      Number of obs   =       807
-------------+----------------------------------   F(6, 800)       =      7.42
       Model |  8003.02506         6  1333.83751   Prob > F        =    0.0000
    Residual |  143750.658       800  179.688322   R-squared       =    0.0527
-------------+----------------------------------   Adj R-squared   =    0.0456
       Total |  151753.683       806  188.280003   Root MSE        =    13.405

------------------------------------------------------------------------------
        cigs |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     lincome |   .8802682   .7277832     1.21   0.227     -.548322    2.308858
    lcigpric |  -.7508586   5.773343    -0.13   0.897    -12.08355    10.58183
        educ |  -.5014982   .1670772    -3.00   0.003    -.8294597   -.1735368
         age |   .7706936   .1601223     4.81   0.000      .456384    1.085003
       agesq |  -.0090228    .001743    -5.18   0.000    -.0124443   -.0056013
    restaurn |  -2.825085   1.111794    -2.54   0.011    -5.007462   -.6427078
       _cons |  -3.639841   24.07866    -0.15   0.880    -50.90466    43.62497
------------------------------------------------------------------------------
. predict u, res
. gen lu2=ln(u^2)
. qui reg lu2 `x'
. predict lu2h, xb
. g e_lu2h=exp(lu2h)
. eststo GLS: reg cigs `x' [aw=1/e_lu2h]
(sum of wgt is 19.97738570454038)

      Source |       SS           df       MS      Number of obs   =       807
-------------+----------------------------------   F(6, 800)       =     17.06
       Model |   10302.646         6  1717.10767   Prob > F        =    0.0000
    Residual |   80542.159       800  100.677699   R-squared       =    0.1134
-------------+----------------------------------   Adj R-squared   =    0.1068
       Total |   90844.805       806  112.710676   Root MSE        =    10.034

------------------------------------------------------------------------------
        cigs |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     lincome |    1.29524   .4370118     2.96   0.003     .4374148    2.153065
    lcigpric |  -2.940312   4.460144    -0.66   0.510    -11.69528    5.814656
        educ |  -.4634463   .1201587    -3.86   0.000    -.6993099   -.2275828
         age |   .4819479   .0968082     4.98   0.000     .2919197     .671976
       agesq |  -.0056272   .0009395    -5.99   0.000    -.0074713   -.0037831
    restaurn |  -3.461064    .795505    -4.35   0.000    -5.022588   -1.899541
       _cons |   5.635463   17.80314     0.32   0.752    -29.31092    40.58184
------------------------------------------------------------------------------

. estout , cells(b(nostar fmt(5)) se(par fmt(5))) stats(r2 r2_a N, fmt(%9.3f %9.3f %9.0g) 
> labels(R-squared Adj-R-squared)) varlabels(_cons Constant) varwidth(20)

----------------------------------------------
                              OLS          GLS
                             b/se         b/se
----------------------------------------------
lincome                   0.88027      1.29524
                        (0.72778)    (0.43701)
lcigpric                 -0.75086     -2.94031
                        (5.77334)    (4.46014)
educ                     -0.50150     -0.46345
                        (0.16708)    (0.12016)
age                       0.77069      0.48195
                        (0.16012)    (0.09681)
agesq                    -0.00902     -0.00563
                        (0.00174)    (0.00094)
restaurn                 -2.82508     -3.46106
                        (1.11179)    (0.79550)
Constant                 -3.63984      5.63546
                       (24.07866)   (17.80314)
----------------------------------------------
R-squared                   0.053        0.113
Adj-R-squared               0.046        0.107
N                             807          807
----------------------------------------------
. est clear

. *Example 8.8. Labor Force Participation of Married Women
. u mroz, clear
. local x "nwifeinc educ exper* age kidslt6 kidsge6"
. eststo heterosked: qui reg inlf `x'
. eststo Robust: qui reg inlf `x', r

. estout , cells(b(nostar fmt(5)) se(par fmt(5))) stats(r2 N, fmt(%9.3f %9.0g) labels(R-squared ///
Adj-R-squared)) varlabels(_cons Constant) varwidth(20) 

----------------------------------------------
                       heterosked       Robust
                             b/se         b/se
----------------------------------------------
nwifeinc                 -0.00341     -0.00341
                        (0.00145)    (0.00152)
educ                      0.03800      0.03800
                        (0.00738)    (0.00727)
exper                     0.03949      0.03949
                        (0.00567)    (0.00581)
expersq                  -0.00060     -0.00060
                        (0.00018)    (0.00019)
age                      -0.01609     -0.01609
                        (0.00248)    (0.00240)
kidslt6                  -0.26181     -0.26181
                        (0.03351)    (0.03178)
kidsge6                   0.01301      0.01301
                        (0.01320)    (0.01353)
Constant                  0.58552      0.58552
                        (0.15418)    (0.15226)
----------------------------------------------
R-squared                   0.264        0.264
Adj-R-squared                 753          753
----------------------------------------------
. est clear

. *Example 8.9. Determinants of Personal Computer Ownership
. u gpa1, clear
. g parcoll = ( fathcoll==1 | mothcoll==1)
. eststo heterosked: qui reg PC hsGPA ACT parcoll 
. eststo Robust: qui reg PC hsGPA ACT parcoll, r

. estout, cells(b(nostar fmt(5)) se(par fmt(5))) stats(r2 N, fmt(%9.3f %9.0g) labels(R-squared ///
 Adj-R-squared)) varlabels(_cons Constant) varwidth(20) 

----------------------------------------------
                       heterosked       Robust
                             b/se         b/se
----------------------------------------------
hsGPA                     0.06539      0.06539
                        (0.13726)    (0.14149)
ACT                       0.00056      0.00056
                        (0.01550)    (0.01607)
parcoll                   0.22105      0.22105
                        (0.09296)    (0.08804)
Constant                 -0.00043     -0.00043
                        (0.49054)    (0.49588)
----------------------------------------------
R-squared                   0.042        0.042
Adj-R-squared                 141          141
----------------------------------------------
. est clear

. qui reg PC hsGPA ACT parcoll 
. predict yhat, xb
. gen hhat = yhat*(1-yhat)
. reg PC hsGPA ACT parcoll [aw=1/hhat]
(sum of wgt is 628.1830743667747)

      Source |       SS           df       MS      Number of obs   =       141
-------------+----------------------------------   F(3, 137)       =      2.22
       Model |  1.54663033         3  .515543445   Prob > F        =    0.0882
    Residual |  31.7573194       137  .231805251   R-squared       =    0.0464
-------------+----------------------------------   Adj R-squared   =    0.0256
       Total |  33.3039497       140  .237885355   Root MSE        =    .48146

------------------------------------------------------------------------------
          PC |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       hsGPA |   .0327029   .1298817     0.25   0.802    -.2241292     .289535
         ACT |    .004272   .0154527     0.28   0.783    -.0262847    .0348286
     parcoll |   .2151862   .0862918     2.49   0.014       .04455    .3858224
       _cons |   .0262099   .4766498     0.05   0.956    -.9163323    .9687521
------------------------------------------------------------------------------

. log close
      name:  SN
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  • Al-Shabaab’s Attack in Ethiopia: One-off Incursion or Persistent Threat? 2022-09-15
    JamesTown.Org |  Terrorism Monitor Volume: 20 Issue: 17 Ethiopian forces in July contained and repulsed an attack conducted in the eastern part of the country by Somalia-based al-Shabaab. Fighters from the militant group entered from southwestern Somalia and targeted four border towns in Ethiopia’s Somali regional state known as Ogaden Region. The estimated 500 al-Shabaab […]
    Solomon
  • IPIS Briefing September 2021 – Ethiopia-Tigray Conflict 2021-10-30
    Source: IPIS Briefing September 2021 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. State-Sponsored Cover-Up of the War on Tigray | September 30, 2021 […]
    Solomon
  • IPIS Briefing August 2021 – Ethiopia-Tigray Conflict 2021-09-19
    Source: IPIS Briefing August 2021 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. U.S. Response To The Human Rights Crisis In Ethiopia’s […]
    Solomon
  • IPIS Briefing June/July 2021 – Ethiopia-Tigray Conflict 2021-09-19
    Source: IPIS Briefing June/July 2021 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.  Ethiopia accuses international community of ‘double standards’ in Tigray […]
    Solomon
  • 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