A Proposal for a Standard With Innovation Management System

Oscar Cristi, José Ernesto Amorós and Juan Pablo Couyoumdjian
This is a clustering estimation and it causes higher efficiency on the parameter estimates
(Wooldridge, 2002).
Results are shown in table 3-column 1, and indicate that the coefficients that accompany the moving
average value of NEC and its squared value are both statistically significant. The lattersuggests that
increases in NEC cause increases on HDI, until it reaches certain threshold after which the
relationship between these two variables becomes inverse. Thus the relationship between those
variables can be described by a curve that has the form of an inverted U.
Estimates for our model of HDI, trend as a function of the moving average value of SE were
obtainedby using an Instrumental Variable approach, andthe error terms were allowed to be
correlated within countries (clustering estimation). The model fulfills the rank conditions and the
instruments for the moving average value of SE are the moving average values of EF, GEF and RQ.
These estimates are shown in table 3-column 2 and indicate that as SE is higher, the increase in the
country’s HDIis also higher. When we include SE together with its squared value as regressors, none
of the coefficients that accompany these variables are statistically significant, thus we omit this result
from the table.
Results for the equation with NIHDI as a function of the SEI average value, indicate that the efficiency
of the estimates is higher when they are obtained from a fixed effect model in which error terms are
allowed to be correlated within countries (clustering estimation). Results for this estimation are shown
in table 3-column 3 where it can be seen that as the moving average of SEI is higher, the increases in
a country’s NIHDI are also higher. When we included the former variable together with its squared
value as regressors, none of the coefficients that accompany these variables are statistically
significant. Thus we do not report those estimates.
Table 3: Model estimates for HDI and NIHDI trends as a function of different measures of informal
economy
Variable 5 years HDI trend
(random effect model
with clustering
estimation)
Constant .004*
(.002)
Moving average of
NEC
Squared moving
average of NEC
.005 ***
(.0021)
-.001**
(.000)
3 years HDI trend
(instrumental
variables approach
with clustering
estimation)
.007 ***
(.0015)
Moving average of SE .035 ***
(.010 )
3 years NIHDI trend
(fixed effect model
with clustering
estimation)
-0.016**
(0.006)
Moving average of SEI 0.0023**
(0.0009)
Number of countries 24 34 23
Number of
observations
91 126 96
R-sq overall 0.224 0.014
F Test 11.16*** 6.7**
Wald chi2 10.42***
*,**,***: significant at 10%, 5% and 1% respectively
5. Conclusions
In this paper we have examined the relationship between economic development and the size of the
informal economy with entrepreneurship as the bridge connecting these areas. While the different
data-sets we have used in our empirical work provide similar results, each has its advantages and
limitations. An additional endeavor would consider using specific labor force surveys as measures of
informality.
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