THE TWO HYPOTHESIS OF SOCIAL CAPITAL:
THE CASE OF ACCOUNTING SCIENCES IN BRAZIL
Silvio Salej Higgins
UFMG (Universidade Federal de Minas Gerais [Federal University of Minas Gerais]) - Department of Sociology
João Estevão Barbosa
UNIFAL (Universidade Federal de Alfenas [Federal University of Alfenas]) - Institute of Applied Social Sciences
Antônio Carlos Ribeiro
UFMG – Center of Social Science Quantitative Research
Jacqueline Veneroso Alves da Cunha
UFMG - Department of Accounting Sciences
Grupo Interdisciplinar de Pesquisa em Análise de Redes Sociais
CONTENT
1. Social capital : two hypotheses
2. The case study: the academic co-authorship networks of accounting
science programs in Brazil during the period from 2002-2010.
3. Data collection
4 and 5. Study hypothesis
6. Results and conclusions
1. Social capital: two hypotheses in an instrumental sense
With regards to social capital, and from the minimalist point of view, there
are two well established hypotheses in social sciences :
(A) The density hypothesis (Coleman, 1992)
The individual’s advantages result
from closure in networks.
(B) The structural hole hypothesis (Burt, 2005)
The individual’s advantages result
from structural holes in networks.
R Pearson
O ALGORITMO C
Seja zij número de vezes que i convida j. Burt começa por
mensurar a proporção de relações de i investidas no contato
q.
piq  z iq  z qi /  z ij  z ji 
j i
Ele calcula depois a « pressão » o « constraint » de j sobre i,
dito em forma simples, a soma de seus contatos diretos e
2
indiretos.


cij   pij   piq pqj 
q i , j


A « pressão » global que pesa sobre i é então a soma das
« pressões » vindas de cada um de seus contatos:
Ci   cij
j
CIA=(1/6+1/6*1/3+1/6*1/2+1/6*1/2)²=0.1512
LOCAL CLUSTERING COEFFICIENT
C= número tríades fechadas/ número total de tríades na vizinhança
C=1
C=0,33
C=0
2. The case study: the academic co-authorship network of
accounting science programs in Brazil during the period from
2002-2010
1. Progression of the number of postgraduate programs in Brazil
2. Publication of articles by faculty members of
postgraduate programs in accounting sciences in Brazil
4. Output in co-authorship by university in the period 2002-2010 programs in Brazil
Publication
output
3. Progression of the number of authors per article from 2002 to 2010
University
3. Data collection
Data sources
Stage 1
Survey of the faculty body.
Stage 2
Compilation of data extracted from the
faculty members' CVs contained in the
CNPq Lattes database.
Stage 3
Profile of programs and faculty body.
Stage 4
Stage 5
Reconstruction of cooperation networks
between faculty members of the programs.
Analysis of the dynamics of the
construction of scientific knowledge in the
field of accounting sciences.
Procedures
Analysis of the number and affiliations of
faculty members connected with the
studied programs.
Organization of the positional elements of
individuals whose characteristics were
analyzed.
Analysis of characteristics collected in the
previous stage to define academic and
scientific profiles of both the programs
and the faculty members who were the
objects of the analysis.
Analysis of network morphology .
Interpretation
of
the
information
collected in the previous stages.
Stage
Description
Final number of
articles
1st
Collection of all articles, including repeated articles.
3442
2nd
Elimination of duplicate articles.
2890
3rd
Elimination of articles according to the criteria
program inception date and entry of faculty
members.
2132
4th
Elimination of articles that were not authored by
two or more faculty members.
455
4. Structural hole hypothesis – Negative correlation


From an instrumental point of view, social capital is an essential
explanatory factor. More productive individuals exploit their position –
specifically, their structural autonomy – within the relational universe
(Burt, 2000).
This morphological property of the network constitutes an advantage that
permits greater ownership of the resources in circulation.
Thus, we might expect that the most productive authors in the field of
Brazilian accounting sciences would have an inverse relationship to their
degree of structural autonomy within the network; the more productive
authors will have lower C (constraint factors) within the network. Without
doubt, this means that the author at the edge of a structural hole may
access more and better information/partners that will encourage greater
scientific productivity.
5. Density hypothesis – Positive correlation


From an instrumental point of view, social capital is an essential
explanatory factor. More productive individuals exploit their dense
relational universe (Coleman, 1992).
This morphological property of the network constitutes an advantage that
permits greater ownership of the resources in circulation.
Thus, we might expect that the most productive authors in the field of
Brazilian accounting sciences would have an direct relationship to their
local clustering coefficient; the more productive authors will have a greater
local clustering coefficient..
6. Results: structural autonomy and local clustering coefficient
The structural hole hypothesis was tested with algorithm C . The constraint C that
the network exerts on i is the sum of all c from each of their contacts:
equals 1 when i has a single contact and approaches 0 when i's contacts are
numerous and not very interconnected.
Graph N°5 Evidence in favor of the structural hole hypothesis:
Inverse function – Constraint x Output: qualis score
A constant (-0.009) and Beta coefficient (0.026) at a 99% significance
level was observed.
The determination coefficient, R2 (0.434), was considered a modest
result.
Scatterplot of new log- linear variable output quails and
constraint
CORRELATIONS ALL THREE VARIABLES
Correlations
Coeffclust
Coeffclust
Pearson Correlation
constr
-,373**
prod
,242**
,000
,001
176
176
176
-,373**
1
-,563**
1
Sig. (2-tailed)
N
constr
prod
Pearson Correlation
Sig. (2-tailed)
,000
N
176
176
176
,242**
-,563**
1
Sig. (2-tailed)
,001
,000
N
176
176
Pearson Correlation
**. Correlation is significant at the 0.01 level (2-tailed).
,000
176
PARTIAL CORRELATION -I-
Correlations
Control Variables
Coeffclust
constr
prod
Correlation
Significance (2tailed)
df
Correlation
Significance (2tailed)
df
constr
1,000
.
0
-,525
,000 .
173
prod
-,525
,000
173
1,000
0
PARTIAL CORRELATION -II-
Correlations
Control Variables
constr
prod
Coeffclust
Correlation
Significance (2tailed)
df
Correlation
Significance (2tailed)
df
.
prod
Coeffclust
1,000
,042
,580
0
,042
,580 .
173
173
1,000
0
Thanks so much
Any questions?