blau index

[julian mwanana]

Dear all,
Can anyone assist me on how to calculate nationality diversity on stata? I am working on data set which looks as follows: (Manager Nationality means total number of nationalities in top management and TMT size means total number of managers at top management)

		Manager	TMT size
Company	Year	Nationality
TBL	2007	3	19
TBL	2008	4	19
TBL	2009	4	21
TBL	2010	3	17
TBL	2011	4	18
TBL	2012	4	16
TBL	2013	3	16
TBL	2014	3	16
TBL	2015	5	17
TBL	2016	5	20
TBL	2017	5	15
TCC	2007	4	10
TCC	2008	4	8
TCC	2009	4	7
TCC	2010	4	8
TCC	2011	6	10
TCC	2012	6	8
TCC	2013	6	9
TCC	2014	7	9
TCC	2015	4	6
TCC	2016	4	6
TCC	2017	5	7
TTP	2009	2	5
TTP	2010	2	6
TTP	2011	2	6
TTP	2012	2	7
TTP	2013	2	6
TTP	2014	2	6
TTP	2015	2	6
TTP	2016	2	6
TTP	2017	2	6

[David Benson]

Hi Julian, welcome to Statalist!

So, I don't think you can calculate the Blau Index with the data you are showing. For example, for TBL, you indicate that there were 3 nationalities among the 19 members of their top management team (TMT). However, you are going to need the number (or percentage of managers) that are each nationality to calculate the Blau Index.

Also note, the Blau Index (or a variation on it) is known by a number of different names depending on the particular field (I would think of the Blau Index = 1 - the Herfindahl–Hirschman index (HHI)

Simpson index (SI) in ecology
= Herfindahl index (HI) in economics
= Herfindahl–Hirschman index (HHI) in economics
= Hunter–Gaston index ( HGI) in microbiology

Gibbs–Martin index (GMI) of sociology, psychology and management
= Blau index (BI)
= Gini–Simpson index (GSI)

Blau index = 1 - Simpson index

For examples of calculating it in Stata, so Statalist entries here, here, here, and here
There are also several modules for calculating this in Stata (ssc install divcat, ssc install ineq)

But, using your data, here's what it might look like:

* Example generated by -dataex-. To install: ssc install dataex
clear
input str3 firm int year byte(nat_count total nat1 nat2 nat3 nat4 nat5 nat6)
"TBL" 2007 3 19 16 2 1 . . .
"TBL" 2008 4 19 16 2 1 1 . .
"TBL" 2009 4 21 17 3 1 1 . .
"TBL" 2010 3 17 14 2 1 . . .
"TCC" 2007 4 10  6 2 1 . 1 .
"TCC" 2008 4  8  4 2 1 . 1 .
"TCC" 2009 4  7  3 2 1 1 . .
"TCC" 2010 4  8  2 2 2 2 . .
"TCC" 2011 6 10  1 2 2 2 2 1

. list, sepby(firm) noobs

  +--------------------------------------------------------------------------+
  | firm   year   nat_co~t   total   nat1   nat2   nat3   nat4   nat5   nat6 |
  |--------------------------------------------------------------------------|
  |  TBL   2007          3      19     16      2      1      .      .      . |
  |  TBL   2008          4      19     16      2      1      1      .      . |
  |  TBL   2009          4      21     17      3      1      1      .      . |
  |  TBL   2010          3      17     14      2      1      .      .      . |
  |--------------------------------------------------------------------------|
  |  TCC   2007          4      10      6      2      1      .      1      . |
  |  TCC   2008          4       8      4      2      1      .      1      . |
  |  TCC   2009          4       7      3      2      1      1      .      . |
  |  TCC   2010          4       8      2      2      2      2      .      . |
  |  TCC   2011          6      10      1      2      2      2      2      1 |
  +--------------------------------------------------------------------------+

* Creating the proportions of each nationality squared
forvalues i = 1/6 {
gen p`i' = (nat`i' / total )^2
}

egen sum_squares = rowtotal(p1-p6)  // sum of squared percentages, (HHI)
gen blau = 1 - sum_squares  // creating the blau index

. list firm year p1- p6 sum_squares blau, sepby(firm) noobs

  +-----------------------------------------------------------------------------------------------+
  | firm   year         p1         p2         p3         p4        p5    p6   sum_sq~s       blau |
  |-----------------------------------------------------------------------------------------------|
  |  TBL   2007   .7091413   .0110803   .0027701          .         .     .   .7229916   .2770084 |
  |  TBL   2008   .7091413   .0110803   .0027701   .0027701         .     .   .7257617   .2742383 |
  |  TBL   2009   .6553288   .0204082   .0022676   .0022676         .     .   .6802722   .3197278 |
  |  TBL   2010   .6782007   .0138408   .0034602          .         .     .   .6955018   .3044982 |
  |-----------------------------------------------------------------------------------------------|
  |  TCC   2007        .36        .04        .01          .       .01     .        .42        .58 |
  |  TCC   2008        .25      .0625    .015625          .   .015625     .     .34375     .65625 |
  |  TCC   2009   .1836735   .0816327   .0204082   .0204082         .     .   .3061224   .6938776 |
  |  TCC   2010      .0625      .0625      .0625      .0625         .     .        .25        .75 |
  |  TCC   2011        .01        .04        .04        .04       .04   .01        .18        .82 |
  +-----------------------------------------------------------------------------------------------+

[julian mwanana]

Thanks very Much Prof. David Benson for your help.

[julian mwanana]

Greetings all,

Once again thanks very much Professor David Benson for your help. I have installed: ssc install dataex, but using the codes given ( input str3 firm int year byte(nat_count total nat1 nat2 nat3 nat4 nat5 nat6) ) i cannot arrived at the same results as yours i.e i cannot generate nat1 nat 2 nat 3 etc. Please assist me further

[Nick Cox]

There are evidently some small errors in the example in #2 as in two observations the count is inconsistent with the individual frequencies.

Here I used entropyetc (SSC). with the individual frequencies.

* Example generated by -dataex-. To install: ssc install dataex
clear
input str3 firm int year byte(nat_count total nat1 nat2 nat3 nat4 nat5 nat6)
"TBL" 2007 3 19 16 2 1 . . .
"TBL" 2008 4 19 16 2 1 1 . .
"TBL" 2009 4 21 17 3 1 1 . .
"TBL" 2010 3 17 14 2 1 . . .
"TCC" 2007 4 10  6 2 1 . 1 .
"TCC" 2008 4  8  4 2 1 . 1 .
"TCC" 2009 4  7  3 2 1 1 . .
"TCC" 2010 4  8  2 2 2 2 . .
"TCC" 2011 6 10  1 2 2 2 2 1
end 

rename nat_count count 
reshape long nat, i(firm year) j(which) 
drop if nat == . 

entropyetc which [fw=nat], by(firm year) gen(3=Blau) 

----------------------------------------------------------------------
    Group |  Shannon H      exp(H)     Simpson   1/Simpson     dissim.
----------+-----------------------------------------------------------
 TBL 2007 |      0.537       1.710       0.723       1.383       0.675
 TBL 2008 |      0.708       2.031       0.655       1.527       0.633
 TBL 2009 |      0.752       2.121       0.620       1.613       0.606
 TBL 2010 |      0.578       1.783       0.696       1.438       0.657
 TCC 2007 |      1.089       2.971       0.420       2.381       0.467
 TCC 2008 |      1.213       3.364       0.344       2.909       0.417
 TCC 2009 |      1.277       3.586       0.306       3.267       0.381
 TCC 2010 |      1.386       4.000       0.250       4.000       0.333
 TCC 2011 |      1.748       5.743       0.180       5.556       0.133
----------------------------------------------------------------------



replace Blau = 1 - Blau 

tabdisp firm year, c(Blau) format(%4.3f) 

---------------------------------------------
          |               year               
     firm |  2007   2008   2009   2010   2011
----------+----------------------------------
      TBL | 0.277  0.345  0.380  0.304       
      TCC | 0.580  0.656  0.694  0.750  0.820
---------------------------------------------

[julian mwanana]

Greetings all,

Once again thanks very much Professor David Benson for your help. I have installed: ssc install dataex, but using the codes given ( input str3 firm int year byte(nat_count total nat1 nat2 nat3 nat4 nat5 nat6) ) i cannot arrived at the same results as yours i.e i cannot generate nat1 nat 2 nat 3 etc. Please assist me further on this step

[Nick Cox]

#4 and #6 are identical but more crucially I don't understand either. If you can input the toy data of David Benson then there is no need to generate nat1 and so forth as they are already variables in the data.

[julian mwanana]

Thanks very much Professor Nick Cox. The problem I am facing is that i can not get this clear in my full data-set especially the procedure just after installing the 'ssc install dataex'. Here is my full data-set I am working with. please assist me accordingly
nationality = number of nationality counts on TMT size
TMT size = total number of top managers

Company	Year	Nationality	TMT Size
SCOM	2008	3.00	8.00
SCOM	2009	3.00	13.00
SCOM	2010	3.00	11.00
SCOM	2011	3.00	12.00
SCOM	2012	3.00	11.00
SCOM	2013	3.00	11.00
SCOM	2014	3.00	13.00
SCOM	2015	3.00	13.00
SCOM	2016	3.00	12.00
SCOM	2017	3.00	12.00
KEN	2008	6.00	7.00
KEN	2009	6.00	7.00
KEN	2010	5.00	6.00
KEN	2011	5.00	7.00
KEN	2012	5.00	7.00
KEN	2013	4.00	6.00
KEN	2014	4.00	6.00
KEN	2015	4.00	6.00
KEN	2016	2.00	4.00
KEN	2017	3.00	5.00
KPLC	2008	1.00	11.00
KPLC	2009	1.00	11.00
KPLC	2010	1.00	10.00
KPLC	2011	1.00	11.00
KPLC	2012	1.00	10.00
KPLC	2013	1.00	11.00
KPLC	2014	1.00	10.00
KPLC	2015	1.00	10.00
KPLC	2016	2.00	12.00
KPLC	2017	1.00	10.00
TOTL	2008	3.00	8.00
TOTL	2009	3.00	8.00
TOTL	2010	2.00	8.00
TOTL	2011	2.00	8.00
TOTL	2012	2.00	10.00
TOTL	2013	4.00	11.00
TOTL	2014	4.00	7.00
TOTL	2015	5.00	10.00
TOTL	2016	5.00	10.00
TOTL	2017	3.00	10.00
KQ	2008	3.00	11.00
KQ	2009	2.00	12.00
KQ	2010	2.00	11.00
KQ	2011	2.00	12.00
KQ	2012	2.00	12.00
KQ	2013	2.00	12.00
KQ	2014	2.00	13.00
KQ	2015	2.00	13.00
KQ	2016	2.00	13.00
KQ	2017	2.00	13.00
EABL	2008	4.00	13.00
EABL	2009	4.00	13.00
EABL	2010	5.00	12.00
EABL	2011	6.00	13.00
EABL	2012	7.00	15.00
EABL	2013	7.00	14.00
EABL	2014	6.00	14.00
EABL	2015	6.00	11.00
EABL	2016	6.00	11.00
EABL	2017	6.00	12.00
KCB	2008	1.00	11.00
KCB	2009	1.00	10.00
KCB	2010	1.00	12.00
KCB	2011	1.00	11.00
KCB	2012	1.00	11.00
KCB	2013	2.00	12.00
KCB	2014	1.00	11.00
KCB	2015	2.00	12.00
KCB	2016	2.00	18.00
KCB	2017	2.00	19.00
EQTY	2008	2.00	13.00
EQTY	2009	2.00	13.00
EQTY	2010	3.00	11.00
EQTY	2011	4.00	13.00
EQTY	2012	3.00	13.00
EQTY	2013	4.00	13.00
EQTY	2014	2.00	12.00
EQTY	2015	3.00	10.00
EQTY	2016	3.00	9.00
EQTY	2017	3.00	8.00
COOP	2008	1.00	18.00
COOP	2009	1.00	19.00
COOP	2010	1.00	20.00
COOP	2011	1.00	19.00
COOP	2012	1.00	19.00
COOP	2013	1.00	23.00
COOP	2014	1.00	22.00
COOP	2015	1.00	20.00
COOP	2016	1.00	20.00
COOP	2017	1.00	18.00
BAMB	2008	3.00	13.00
BAMB	2009	4.00	13.00
BAMB	2010	4.00	10.00
BAMB	2011	4.00	12.00
BAMB	2012	5.00	13.00
BAMB	2013	6.00	13.00
BAMB	2014	5.00	12.00
BAMB	2015	4.00	10.00
BAMB	2016	5.00	10.00
BAMB	2017	5.00	10.00
BBK	2008	2.00	10.00
BBK	2009	2.00	10.00
BBK	2010	1.00	9.00
BBK	2011	2.00	10.00
BBK	2012	2.00	10.00
BBK	2013	3.00	8.00
BBK	2014	4.00	11.00
BBK	2015	3.00	9.00
BBK	2016	3.00	9.00
BBK	2017	3.00	9.00
SCBK	2008	4.00	14.00
SCBK	2009	4.00	13.00
SCBK	2010	3.00	10.00
SCBK	2011	4.00	12.00
SCBK	2012	4.00	10.00
SCBK	2013	4.00	10.00
SCBK	2014	4.00	10.00
SCBK	2015	4.00	9.00
SCBK	2016	6.00	13.00
SCBK	2017	5.00	12.00
DTK	2008	4.00	9.00
DTK	2009	6.00	13.00
DTK	2010	5.00	12.00
DTK	2011	5.00	10.00
DTK	2012	5.00	11.00
DTK	2013	3.00	10.00
DTK	2014	4.00	11.00
DTK	2015	4.00	11.00
DTK	2016	4.00	13.00
DTK	2017	4.00	12.00
IM	2008
IM	2009
IM	2010	4.00	10.00
IM	2011	4.00	9.00
IM	2012	4.00	9.00
IM	2013	4.00	9.00
IM	2014	3.00	6.00
IM	2015	2.00	9.00
IM	2016	3.00	8.00
IM	2017	2.00	7.00
BRIT	2008	3.00	6.00
BRIT	2009	3.00	6.00
BRIT	2010	3.00	7.00
BRIT	2011	3.00	9.00
BRIT	2012	3.00	9.00
BRIT	2013	4.00	11.00
BRIT	2014	2.00	9.00
BRIT	2015	2.00	9.00
BRIT	2016	2.00	8.00
BRIT	2017	3.00	9.00
JUB	2008	5.00	9.00
JUB	2009	5.00	9.00
JUB	2010	5.00	9.00
JUB	2011	5.00	9.00
JUB	2012	5.00	9.00
JUB	2013	5.00	8.00
JUB	2014	5.00	10.00
JUB	2015	5.00	10.00
JUB	2016	5.00	10.00
JUB	2017	5.00	10.00
CFC	2008	4.00	10.00
CFC	2009	3.00	7.00
CFC	2010	3.00	10.00
CFC	2011	3.00	10.00
CFC	2012	4.00	10.00
CFC	2013	4.00	11.00
CFC	2014	4.00	12.00
CFC	2015	4.00	11.00
CFC	2016	3.00	10.00
CFC	2017	3.00	10.00
BATK	2008	2.00	12.00
BATK	2009	3.00	11.00
BATK	2010	3.00	10.00
BATK	2011	3.00	10.00
BATK	2012	3.00	10.00
BATK	2013	2.00	8.00
BATK	2014	3.00	8.00
BATK	2015	3.00	10.00
BATK	2016	3.00	10.00
BATK	2017	3.00	10.00
UNGA	2008	2.00	8.00
UNGA	2009	2.00	8.00
UNGA	2010	2.00	8.00
UNGA	2011	2.00	8.00
UNGA	2012	2.00	6.00
UNGA	2013	2.00	8.00
UNGA	2014	2.00	8.00
UNGA	2015	2.00	9.00
UNGA	2016	2.00	9.00
UNGA	2017	2.00	9.00
NICB	2008	2.00	9.00
NICB	2009	2.00	9.00
NICB	2010	2.00	9.00
NICB	2011	2.00	11.00
NICB	2012	2.00	10.00
NICB	2013	2.00	11.00
NICB	2014	2.00	12.00
NICB	2015	2.00	12.00
NICB	2016	2.00	11.00
NICB	2017	2.00	11.00
CIC	2008	1.00	12.00
CIC	2009	1.00	12.00
CIC	2010	1.00	14.00
CIC	2011	1.00	14.00
CIC	2012	1.00	12.00
CIC	2013	1.00	13.00
CIC	2014	1.00	13.00
CIC	2015	1.00	12.00
CIC	2016	1.00	12.00
CIC	2017	1.00	12.00
KNRE	2008	1.00	9.00
KNRE	2009	1.00	11.00
KNRE	2010	1.00	11.00
KNRE	2011	1.00	11.00
KNRE	2012	1.00	12.00
KNRE	2013	1.00	11.00
KNRE	2014	1.00	11.00
KNRE	2015	1.00	13.00
KNRE	2016	1.00	12.00
KNRE	2017	1.00	13.00
WPP	2008	3.00	7.00
WPP	2009	3.00	7.00
WPP	2010	4.00	8.00
WPP	2011	3.00	7.00
WPP	2012	3.00	7.00
WPP	2013	3.00	7.00
WPP	2014	2.00	6.00
WPP	2015	2.00	7.00
WPP	2016	2.00	7.00
WPP	2017	2.00	8.00
ICDC	2008	2.00	9.00
ICDC	2009	2.00	9.00
ICDC	2010	2.00	9.00
ICDC	2011	2.00	9.00
ICDC	2012	2.00	9.00
ICDC	2013	2.00	9.00
ICDC	2014	2.00	9.00
ICDC	2015	2.00	9.00
ICDC	2016	3.00	9.00
ICDC	2017	3.00	11.00
NMG	2008	7.00	14.00
NMG	2009	8.00	14.00
NMG	2010	8.00	14.00
NMG	2011	8.00	16.00
NMG	2012	8.00	16.00
NMG	2013	8.00	15.00
NMG	2014	8.00	16.00
NMG	2015	8.00	16.00
NMG	2016	8.00	14.00
NMG	2017	7.00	17.00
CG	2008	2.00	9.00
CG	2009	3.00	8.00
CG	2010	3.00	8.00
CG	2011	3.00	7.00
CG	2012	3.00	7.00
CG	2013	3.00	7.00
CG	2014	3.00	7.00
CG	2015	3.00	7.00
CG	2016	3.00	7.00
CG	2017	2.00	7.00
ARM	2008	3.00	7.00
ARM	2009	3.00	10.00
ARM	2010	4.00	7.00
ARM	2011	3.00	9.00
ARM	2012	3.00	9.00
ARM	2013	3.00	9.00
ARM	2014	5.00	9.00
ARM	2015	5.00	8.00
ARM	2016	6.00	9.00
ARM	2017	6.00	9.00
NBK	2008	1.00	13.00
NBK	2009	1.00	12.00
NBK	2010	1.00	12.00
NBK	2011	1.00	12.00
NBK	2012	1.00	12.00
NBK	2013	1.00	10.00
NBK	2014	1.00	9.00
NBK	2015	1.00	9.00
NBK	2016	1.00	11.00
NBK	2017	1.00	11.00
BERG	2008	2.00	7.00
BERG	2009	2.00	5.00
BERG	2010	2.00	5.00
BERG	2011	2.00	5.00
BERG	2012	2.00	5.00
BERG	2013	2.00	5.00
BERG	2014	2.00	6.00
BERG	2015	2.00	10.00
BERG	2016	2.00	8.00
BERG	2017	2.00	5.00
TPSE	2008	5.00	13.00
TPSE	2009	6.00	14.00
TPSE	2010	5.00	14.00
TPSE	2011	4.00	14.00
TPSE	2012	4.00	12.00
TPSE	2013	4.00	13.00
TPSE	2014	4.00	13.00
TPSE	2015	3.00	13.00
TPSE	2016	3.00	11.00
TPSE	2017	4.00	12.00
TCL	2008
TCL	2009	1.00	9.00
TCL	2010	1.00	8.00
TCL	2011	1.00	8.00
TCL	2012	1.00	8.00
TCL	2013	1.00	8.00
TCL	2014	1.00	8.00
TCL	2015	1.00	10.00
TCL	2016	1.00	11.00
TCL	2017	2.00	8.00
PAFR	2008	3.00	7.00
PAFR	2009	3.00	9.00
PAFR	2010	3.00	9.00
PAFR	2011	3.00	9.00
PAFR	2012	3.00	9.00
PAFR	2013	3.00	9.00
PAFR	2014	3.00	9.00
PAFR	2015	3.00	9.00
PAFR	2016	3.00	9.00
PAFR	2017	3.00	8.00
REA	2008	2.00	6.00
REA	2009	2.00	5.00
REA	2010	2.00	5.00
REA	2011	2.00	5.00
REA	2012	2.00	5.00
REA	2013	2.00	5.00
REA	2014	2.00	5.00
REA	2015	2.00	5.00
REA	2016	2.00	5.00
REA	2017	2.00	5.00
SASN	2008	2.00	9.00
SASN	2009	2.00	9.00
SASN	2010	2.00	9.00
SASN	2011	2.00	9.00
SASN	2012	2.00	10.00
SASN	2013	2.00	8.00
SASN	2014	2.00	10.00
SASN	2015	1.00	8.00
SASN	2016	1.00	9.00
SASN	2017	1.00	10.00
HFCK	2008	3.00	11.00
HFCK	2009	3.00	11.00
HFCK	2010	2.00	10.00
HFCK	2011	2.00	7.00
HFCK	2012	2.00	8.00
HFCK	2013	2.00	8.00
HFCK	2014	2.00	8.00
HFCK	2015	2.00	10.00
HFCK	2016	1.00	9.00
HFCK	2017	1.00	9.00
KUKZ	2008	2.00	6.00
KUKZ	2009	2.00	7.00
KUKZ	2010	2.00	6.00
KUKZ	2011	2.00	6.00
KUKZ	2012	2.00	9.00
KUKZ	2013	2.00	8.00
KUKZ	2014	2.00	8.00
KUKZ	2015	2.00	8.00
KUKZ	2016	2.00	8.00
KUKZ	2017	2.00	8.00
FIRE	2008	1.00	7.00
FIRE	2009	1.00	7.00
FIRE	2010	2.00	7.00
FIRE	2011	2.00	7.00
FIRE	2012	3.00	7.00
FIRE	2013	2.00	7.00
FIRE	2014	2.00	6.00
FIRE	2015	2.00	6.00
FIRE	2016	2.00	6.00
FIRE	2017	3.00	8.00
FTGH	2008
FTGH	2009
FTGH	2010
FTGH	2011
FTGH	2012
FTGH	2013
FTGH	2014
FTGH	2015
FTGH	2016
FTGH	2017
CABL	2008	3.00	7.00
CABL	2009	3.00	7.00
CABL	2010	3.00	7.00
CABL	2011	3.00	7.00
CABL	2012	2.00	7.00
CABL	2013	2.00	7.00
CABL	2014	2.00	8.00
CABL	2015	2.00	8.00
CABL	2016	2.00	8.00
CABL	2017	2.00	6.00
LKL	2008
LKL	2009
LKL	2010
LKL	2011
LKL	2012	2.00	7.00
LKL	2013	2.00	7.00
LKL	2014	2.00	7.00
LKL	2015	1.00	9.00
LKL	2016	1.00	9.00
LKL	2017	1.00	9.00
KAPC	2008	2.00	6.00
KAPC	2009	2.00	6.00
KAPC	2010	2.00	6.00
KAPC	2011	2.00	6.00
KAPC	2012	2.00	6.00
KAPC	2013	2.00	6.00
KAPC	2014	2.00	6.00
KAPC	2015	2.00	8.00
KAPC	2016	2.00	8.00
KAPC	2017	2.00	8.00
MSC	2008	1.00	15.00
MSC	2009	1.00	15.00
MSC	2010	1.00	15.00
MSC	2011	1.00	15.00
MSC	2012	1.00	15.00
MSC	2013	1.00	15.00
MSC	2014	1.00	15.00
MSC	2015	1.00	15.00
MSC	2016	1.00	15.00
MSC	2017	1.00	15.00
BOC	2008	3.00	11.00
BOC	2009	3.00	11.00
BOC	2010	3.00	11.00
BOC	2011	3.00	9.00
BOC	2012	4.00	12.00
BOC	2013	4.00	10.00
BOC	2014	4.00	10.00
BOC	2015	2.00	9.00
BOC	2016	3.00	10.00
BOC	2017	3.00	8.00
CARB	2008	2.00	4.00
CARB	2009	2.00	4.00
CARB	2010	2.00	4.00
CARB	2011	2.00	4.00
CARB	2012	2.00	4.00
CARB	2013	2.00	4.00
CARB	2014	2.00	5.00
CARB	2015	2.00	5.00
CARB	2016	2.00	5.00
CARB	2017	2.00	6.00
NSE	2008
NSE	2009	2.00	11.00
NSE	2010	2.00	11.00
NSE	2011	1.00	9.00
NSE	2012	1.00	9.00
NSE	2013	1.00	9.00
NSE	2014	1.00	8.00
NSE	2015	1.00	11.00
NSE	2016	1.00	11.00
NSE	2017	1.00	11.00
OCH	2008	1.00	10.00
OCH	2009	1.00	10.00
OCH	2010	1.00	10.00
OCH	2011	1.00	8.00
OCH	2012	1.00	9.00
OCH	2013	1.00	7.00
OCH	2014	1.00	7.00
OCH	2015	1.00	6.00
OCH	2016	1.00	6.00
OCH	2017	1.00	5.00
HAFR	2008
HAFR	2009
HAFR	2010
HAFR	2011
HAFR	2012	1.00	10.00
HAFR	2013	1.00	10.00
HAFR	2014	1.00	11.00
HAFR	2015	1.00	7.00
HAFR	2016	1.00	7.00
HAFR	2017	2.00	7.00
EVRD	2008	4.00	8.00
EVRD	2009	4.00	8.00
EVRD	2010	4.00	8.00
EVRD	2011	3.00	7.00
EVRD	2012	2.00	7.00
EVRD	2013	3.00	8.00
EVRD	2014	3.00	7.00
EVRD	2015	3.00	8.00
EVRD	2016	3.00	6.00
EVRD	2017	3.00	6.00
EK	2008	2.00	5.00
EK	2009	2.00	5.00
EK	2010	2.00	4.00
EK	2011	2.00	4.00
EK	2012	2.00	5.00
EK	2013	2.00	5.00
EK	2014	2.00	5.00
EK	2015	2.00	5.00
EK	2016	2.00	5.00
EK	2017	2.00	5.00
NMB	2008
NMB	2009
NMB	2010
NMB	2011	3.00	8.00
NMB	2012	3.00	8.00
NMB	2013	3.00	9.00
NMB	2014	4.00	12.00
NMB	2015	3.00	9.00
NMB	2016	2.00	10.00
NMB	2017	2.00	9.00
TBL	2008	4.00	19.00
TBL	2009	4.00	21.00
TBL	2010	3.00	17.00
TBL	2011	4.00	18.00
TBL	2012	4.00	16.00
TBL	2013	3.00	16.00
TBL	2014	3.00	16.00
TBL	2015	5.00	17.00
TBL	2016	5.00	20.00
TBL	2017	5.00	15.00
CRDB	2008
CRDB	2009
CRDB	2010
CRDB	2011	2.00	12.00
CRDB	2012	2.00	12.00
CRDB	2013	2.00	10.00
CRDB	2014	2.00	11.00
CRDB	2015	2.00	12.00
CRDB	2016	2.00	12.00
CRDB	2017	2.00	13.00
TCC	2008	4.00	8.00
TCC	2009	4.00	7.00
TCC	2010	4.00	8.00
TCC	2011	6.00	10.00
TCC	2012	6.00	8.00
TCC	2013	6.00	9.00
TCC	2014	7.00	9.00
TCC	2015	4.00	6.00
TCC	2016	4.00	6.00
TCC	2017	5.00	7.00
TPCC	2008	5.00	6.00
TPCC	2009	6.00	8.00
TPCC	2010	6.00	8.00
TPCC	2011	5.00	6.00
TPCC	2012	4.00	6.00
TPCC	2013	5.00	6.00
TPCC	2014	5.00	6.00
TPCC	2015	4.00	6.00
TPCC	2016	4.00	6.00
TPCC	2017	4.00	6.00
TCCL	2008	3.00	6.00
TCCL	2009	3.00	6.00
TCCL	2010	3.00	8.00
TCCL	2011	4.00	8.00
TCCL	2012	4.00	8.00
TCCL	2013	2.00	6.00
TCCL	2014	2.00	7.00
TCCL	2015	2.00	9.00
TCCL	2016	2.00	5.00
TCCL	2017	2.00	10.00
SWISS	2008	4.00	6.00
SWISS	2009	4.00	6.00
SWISS	2010	4.00	6.00
SWISS	2011	4.00	5.00
SWISS	2012	4.00	5.00
SWISS	2013	4.00	6.00
SWISS	2014	4.00	5.00
SWISS	2015	4.00	5.00
SWISS	2016	4.00	6.00
SWISS	2017	4.00	6.00
DCB	2008
DCB	2009
DCB	2010
DCB	2011
DCB	2012	1.00	12.00
DCB	2013	1.00	11.00
DCB	2014	1.00	12.00
DCB	2015	1.00	12.00
DCB	2016	1.00	13.00
DCB	2017	1.00	12.00
TATEPA	2008	2.00	5.00
TATEPA	2009	2.00	5.00
TATEPA	2010	2.00	6.00
TATEPA	2011	2.00	6.00
TATEPA	2012	2.00	7.00
TATEPA	2013	2.00	6.00
TATEPA	2014	2.00	6.00
TATEPA	2015	2.00	6.00
TATEPA	2016	2.00	6.00
TATEPA	2017	2.00	6.00
MCB	2008
MCB	2009
MCB	2010
MCB	2011
MCB	2012
MCB	2013
MCB	2014	1.00	8.00
MCB	2015	1.00	10.00
MCB	2016	1.00	8.00
MCB	2017	1.00	10.00
TOL	2008	1.00	5.00
TOL	2009	1.00	9.00
TOL	2010	1.00	7.00
TOL	2011	1.00	7.00
TOL	2012	1.00	7.00
TOL	2013	1.00	7.00
TOL	2014	1.00	8.00
TOL	2015	1.00	7.00
TOL	2016	1.00	7.00
TOL	2017	1.00	7.00

[Nick Cox]

As I understand your data in #8 -- which raises a quite different question from that in #4 or #6 -- you know the number of nationalities and the number of managers. That isn't (usually) enough information for the Blau index.

For example for SCOM in 2008 you have 3 nationalities and 8 managers. Here are the possibilities I can think of quickly. Without the detail on counts, the Blau index always is indeterminate between possible partitions for this kind of observation. .

* Example generated by -dataex-. To install: ssc install dataex
clear
input byte nat1 float(nat2 nat3)
1 1 6
1 2 5
1 3 4
2 3 3
2 2 4
end

gen blau = 1 - (nat1/8)^2 - (nat2/8)^2 - (nat3/8)^2

 list

     +-----------------------------+
     | nat1   nat2   nat3     blau |
     |-----------------------------|
  1. |    1      1      6   .40625 |
  2. |    1      2      5   .53125 |
  3. |    1      3      4   .59375 |
  4. |    2      3      3   .65625 |
  5. |    2      2      4     .625 |
     +-----------------------------+

The exceptions I can think are where there are as many people as nationalities, so each nationality is represented just once. Then the Blau index is determinate.

[julian mwanana]

Thanks very much Professor Nick Cox. The problem I am facing is that i can not get this clear in my full data-set especially the procedure just after installing the 'ssc install dataex'. Here is my full data-set I am working with. please assist me accordingly
nationality = number of nationality counts on TMT size
TMT size = total number of top managers

[Nick Cox]

Sorry, no more from me. The FAQ Advice explains several different reasons why spreadsheet attachments are deprecated here. https://www.statalist.org/forums/help#stata

In any case I have already explained in detail that those two variables alone will not suffice, and I can't think of a different or better way to explain it. I think you might be better served by seeking advice at your workplace.

[David Benson]

Julian,

1) If you need help using Stata's dataex command to share data here, I've created a Youtube tutorial here

2) However, as Nick mentioned, you won't be able to calculate a Blau Index with just the data that you have. You could try using your count of nationalities and scaling it to board size (i.e. nationality / TMT_size), but you won't be able to calculate the Blau Index without more data.

Sorry I couldn't be more helpful.