*! version 0.5.1 07oct2021

[D] egen: resp -- RESP for egen.

Syntax

Generate a new variable with AI, AOER, RIC, RPA, RSI or RESP values using Stata's egen command.

[by varlist:]
egen
[type]
newvar
=
resp(varname)
[if] [in],
dim(1 2)
[mode(string)
duplicates]

There is a wrapper for the above function.

[by varlist:]
resp
newvar
varname
[if] [in],
dim(1 2)
[mode(string)
duplicates]

Option

dim(1 2) defines the variables for aggregation (see below);

mode(string) string is one of [ai|aoer|ric|rpa|rsi|resp];	default is ai;

duplicates tests for duplicates; default is no testing;

Description

resp is an extended function for egen. It calculates the activity
index or one of its forks for a variable. The activity index is a fraction of
shares, where the first share (the dividend) is the share of varname in the sum of all observations
with the same value for 1 and the second share (the divisor) is the sum of all observations
with the same value for 2 in the sum of all observations.

Symbolically:

ai(x) = x/sum(x|1) : sum(x|2)/sum(x)
with x uniquely identified by the tuple (1 2).

The indices AEOR and RIC are used to messure collaborations in networks and are constructed slightly different:

aeor(x) = x/sum(x|1) : sum(x|2)/(sum(x) - sum(x|1))
with x uniquely identified by the tuple (1 2).

ric(x) = x/sum(x|1) : (sum(x|2) - x)/(sum(x) - sum(x|1))
with x uniquely identified by the tuple (1 2).

Options (extended)

dim(1 2) defines the two dimensions for which the
Activity Index is calculated. Therefore, three different sum variables
are calculated temporarily, by 1, by 2 and overall.
Afterwards, every observation of varname is divided
by the first sum sharing the same value in 1,
divided by the quotient of the second sum sharing the
same value in 2 as varname and the overall sum.
(This holds for the AI and its scalings.
Analogue usage of the two dimensions for AOER and RIC.)

mode(string) controlls the kind of index, which is
retrieved. Possible options for string are
[ai|aoer|ric|rpa|rsi|resp]. Thereby ai stands for the Activity Index (see
above); aoer stands for the Asymmetric Observed to Expected Ratio (ibid.);
ric stand for the Relative Intensity of Collaboration (ibid.);
rpa is equal to the natural logarithm of the AI or ln(ai(x)) and stands for
the Relative Patent Activity (see also ln); rsi stands for
the Relative Specialization Index defined as (ai - 1)/(ai + 1) ; and
resp is equal to the hyperbolic tangent of the RPA scaled by 100 and stands for
the Index of Relative Specialization defined as
100 * (ai^2 - 1)/(ai^2 + 1) . ai is the default.

duplicates tests, if any tuple of (1 2) is
unique in every group defined by [by varlist]. Is this condition
not fullfilled, the program aborts. Because of the aggregation along 1,
2 and all observations, the uniqueness of the tuples is not required.
Nevertheless it is often assumed and sensible. The default is to not test
on duplicates.

Remarks

In the second half of the 20th century economists tried to measure comparative
advantages. It was observed, that simple relations could not reveal all
informations about partitions of ressources. For example, neither the relation
of exported cars from Belgium to all exports of Belgium, nor the relation of
exported cars from Belgium to all exported cars worldwide gives us a degree
of competitiveness of Belgiums car industry. The first relation is an internal
one. The second one does not consider, that Belgium is not capable of producing
numbers of cars like larger countries. The
Activity Index combines the two relations in the way, that the country
internal relation of exported cars to all exports of a country is compared
to the external relation of exported cars to all exports worldwide. For more
informations about this subject, see the references
(especially Grupp 1994, Grupp 1998
and Narin et al. 1987).

The Activity Index (AI) lacks some essential properties, so some common
transformations have been established. For statistic calculations the
Relative Patent Activity (RPA) Index has proven to be usefull. It is the
natural logarithm of the AI and is motivated by the fact that the values of the AI often
follow a log-normal distribution. Both, AI and RPA are not easy to
comprehend for a reader as a result of missing bounds on the indexes,
so in presentations or papers two other transformations
are often used instead: the Relative Specialization Index (RSI) and the
Index of Relative Specialization (RESP) also known as
the Scientific Specialization Index (SSI). The latter is the hyperbolic
tangent of the RPA scaled by 100, the former the hyperbolic tangent of
half of the RPA without scaling at the end.

While RPA, RSI and RESP are scalings of AI -- all four indexes
can be converted in each other -- AOER and RIC are folks of the AI,
but measuring not the importance of a field at a country in comparison to the average
field share, but the importance of the collaboration of two authors/sources for
the second author of this pair in a network in comparison to the importance of
the network for the second author without the first one. For more details on this
subject, see Fuchs et al. 2021.

Stored results

resp stores the following in r():

Macros

r(cmdline) exact command passed to egen

Author

Joel E. H. Fuchs a.k.a Fantastic Cpt. Fox

Organizational Sociology, University of Wuppertal.

jfuchs(at)uni-wuppertal.de

References

Abramo, G., D'Angelo, C. A., & Di Costa, F. 2014.
"A new bibliometric approach to assess the scientific specialization of regions."
Research Evaluation, 23(2), 183-194.

Chair of Organizational Sociology, 2019.
Research and teaching profiles of public universities in Germany. 2019. URL:
fachprofile.uni-wuppertal.de.

Fuchs, J.E., Sivertsen, G., Rousseau, R., 2021.
"Measuring the relative intensity of collaboration within a network"
Scientometrics, Springer Science and Business Media . (DOI: 10.1007/s11192-021-04110-x)

Grupp, H. 1994. "The Measurement of Technical Performance of Innovations
by Technometrics and Its Impact on Established Technology Indicators."
Research Policy 23, Pp. 175-193.

Grupp, H. 1998. "Measurement with Patent and Bibliometric Indicators."
Pp. 141-188 in Foundations of the Economics of Innovation. Theory, Measurement,
Practice, edited by H. Grupp. Cheltenham: Edward Elgar.

Heinze, T., Tunger, D., Fuchs, J.E., Jappe, A., Eberhardt, P. 2019.
"Research and teaching profiles of public universities in Germany.
A mapping of selected fields." Wuppertal: BUW. (DOI: 10.25926/9242-ws58).

Narin, F., Carpenter, M.P. and Woolf, P. 1987.
"Technological Assessments Based on Patents and Patent Citations."
Pp. 107-119 in Problems of Measuring Technological Change,
edited by H. Grupp. Köln: TÜV Rheinland.

Piro, F.N., Aksnes, D.W, Christensen, K.K., Finnbjörnsson, Þ.,
Fröberg, J., Gunnarsdottir, O., Karlsson, S., Klausen, P.H., Kronman, U., Leino,
Y., Magnusson, M.L., Miettinen, M., Nuutinen, A., Poropudas, O.,
Schneider, J.W. and Sivertsen, G. 2011.
"Comparing Research at Nordic Universities Using Bibliometric Indicators."
Policy Brief 4/2011. Oslo: NordForsk.

Piro, F.N., Aldberg, H., Finnbjörnsson, P., Gunnarsdottir, O.,
Karlsson, S., Larsen, K.S., Leino, Y., Nuutinen, A., Schneider, J.W.,
and Sivertsen, G. 2014.
"Comparing Research at Nordic Universities Using Bibliometric Indicators
– Second Report, Covering the Years 2000-2012." Policy Paper 2/2014.
Oslo: NordForsk.

Piro, F.N., Aldberg, H., Aksnes, D.W., Staffan, K.,
Leino, Y., Nuutinen, A., Overballe-Petersen, M.V.,
Sigurdsson, S.O. and Sivertsen, G. 2017.
"Comparing Research at Nordic Higher Education Institutions
Using Bibliometric Indicators Covering the Years 1999-2014."
Policy Paper 4/2017. Oslo: NIFU.