4.5 Results
4.5.3 Explaining ICT Infrastructure Growth During 2002-2012
Finally, we consider the results with ∆ lnIT as dependent variable, the average growth rate of ICT infrastructure during 2002-2012. As shown in table 4.4, the Lasso selects 8 variables to explain the growth rate of IT, whereas 6 variables are statistically signicant coecient estimates (at 5 percent level of signicance). These arelnIT1(the log IT variable averaged over the previous period 2008-2012), again Elec_m_log (access to electricity as percent of population), for the rst time the index of nancial freedom, the score of freedom from corruption, the standard deviation of the capital stock (KS estimator only) and (also for the rst time) the index for property rights.
Hence, Lasso found next to some familiar variable, also some new variables. The explanatory power of both, the OLS and the robust KS regression is again substantial. Describing the average growth rate of IT during 2002-2012, variables from the category concerning the extend of regulation dominate the selection.
Table 4.4: Regression Results for the Three-Stage Procedure (dependent variable is ∆ lnIT)
OLS KS GAM
c 2.656
(0.000) 2.616 (0.000)
c 1.000
(0.000)
log_IT1 -0.700
(0.000) -0.705 (0.000)
s(log_IT1) 1.000
(0.000)
Elec_m_log 0.183
(0.000) 0.169 (0.000)
s(Elec_m_log) 2.911
(0.000)
EMP_m 0.000
(0.548) 0.000
(0.073) s(EMP_m) 1.927
(0.099)
EMP_sd 2.005
(0.272) 2.254 (0.086)
s(EMP_sd) 1.000
(0.046)
nanc_freedom_m 0.003
(0.046) 0.002 (0.023)
s(nanc_freedom_m) 1.000
(0.006) free_corrupt_m_log 0.145
(0.004) 0.159
(0.000) s(free_corrupt_m_log) 1.000 (0.001)
K_sd 1.139
(0.052) 1.078 (0.025)
s(K_sd) 1.451
(0.011) prop_rights_m_log -0.169
(0.030) -0.151 (0.003)
s(prop_rights_m_log) 1.887 (0.000)
R2 0.961 0.963 R2 0.966
n 113 113 n 113
Note: Reported are the regression coecients or the equivalent degrees of freedom (edf) in the case of the spline variables indicated bys(·). Stated in parentheses arep-values of thet-statistics or theF-statistics for the signicance of the respective splines. In the case of OLS regressions the adjustedR2 is reported.
Renaud and Victoria-Feser (2010) explain theR2measure used in the case of the KS regressions.
The negative regression coecient of log_IT1 indicates a catch-up eect. Countries with a lower level of ICT infrastructure have higher growth rates in ICT infrastructure and therefore catch-up to countries with highly developed ICT infrastructure. A similar reason might be for the presence of the capital stock's standard deviation. Due to the catch-up in investments, the
standard deviation of the capital stock in less developed countries is higher than in well-developed countries.
The variable of access to electricity (Elec_m_log) also signicantly explains the average growth rate of IT. Again, the fundamental importance of electricity as an infrastructural prerequisite of ICT is pointed out.
The index of nancial freedom appears for the rst time. This index is calculated and provided by the Heritage Foundation and a measure of banking eciency, independence from government control and interference in the nancial sector. The basic idea of the score is that the nancial environment ideally has a minimum of governmental interference, a minimum of regulation of nancial institutions and an independent central bank supervision. The index of nancial freedom scores an economy's nancial freedom by analyzing: the extent of government regulation of nancial services, the degree of state intervention in banks and other nancial rms through direct and indirect ownership, the extent of nancial and capital market development, government inuence on the allocation of credit and openness to foreign competition.66 The index ranges from 0 (repressive, private nancial institutions are prohibited) to 100 (negligible government interference). The link between nancial freedom and ICT infrastructure is actually similar as with the variable of investment freedom. The more extensive the government interference in banking and nancing environment, the less credits are lent and deposits are accepted. Thus, less investment take place in general. Among these unrealized investments are those in ICT infrastructure, but also investments in those products that require ICT infrastructure.
The variable of property rights also appears for the rst time. It is also calculated and provided by the Heritage Foundation and indicates the ability of individuals to accumulate private property, secured by clear laws that are fully enforced by the state. It measures the degree to which a country's laws protect private property rights and the degree to which its government enforces those laws. It also assesses the likelihood that private property will be expropriated and analyzes the independence of the judiciary, the existence of corruption within the judiciary, and the ability of individuals and businesses to enforce contracts.67 The score ranges from 0 (no private property) to 100 (private property is guaranteed by the government). Intuitively one might think that a higher score in a country's property rights has a positive eect for the growth rate of ICT infrastructure in a country. However, the regression coecient of variable prop_rights_-m_log is negative (see table 4.4). We could interpret the negative coecient as an indication of a catch-up eect of countries with a low score of property rights and higher growth rates of ICT infrastructure to the group of well developed countries. But this assertion cannot be answered with certainty here. In the literature, Crenshaw and Robinson (2006) nd property rights signicantly positive, predicting global internet diusion in the period of 1995-2000. In their analysis on computer imports per worker during 1970-1990, Caselli and Coleman (2001) nd considerable evidence that computer adoption is enhanced by good property-rights protection.
66 Source of this information: http://www.heritage.org/index/nancial-freedom.
67 Source of the cite: http://www.heritage.org/index/property-rights.
Figure 4.4: GAM Results for the Three-Stage Procedure (dependent variable is ∆ lnIT)
1 2 3 4 5
−1.00.52.0
log_IT1
s(log_IT1,1)
1 2 3 4
−1.00.52.0
Elec_m_log
s(Elec_m_log,2.91)
0 100 300 500
−1.00.52.0
EMP_m
s(EMP_m,1.93)
0.01 0.03 0.05
−1.00.52.0
EMP_sd
s(EMP_sd,1)
20 40 60 80
−1.00.52.0
financ_freedom_m
s(financ_freedom_m,1) 2.5 3.0 3.5 4.0 4.5
−1.00.52.0
freedom_corruption_m_log
s(freedom_corruption_m_log,1)
0.05 0.10 0.15
−1.00.52.0
K_sd
s(K_sd,1.45)
2.5 3.0 3.5 4.0 4.5
−1.00.52.0
property_rights_m_log
s(property_rights_m_log,1.89)
Again, the edf values in table 4.4 point to linear inuences of nearly all variables except of Elec_-m_log. As can be seen in table 4.4 the nonlinear eect of Elec_m_log shows a degressive course, which indicates that countries with a better electricity supply tend to have a more developed ICT infrastructure.
Table 4.5: Regression Results for the bolasso Procedure (dependent variable is∆ lnIT)
OLS KS GAM
c 2.391
(0.000) 2.447 (0.000)
c 1.000
(0.000)
log_IT1 -0.636
(0.000) -0.630 (0.000)
s(log_IT1) 1.000
(0.000)
CSH_m_log -0.101
(0.021) -0.105 (0.021)
s(CSH_m_log) 1.995
(0.096)
Elec_m_log 0.171
(0.000) 0.150 (0.000)
s(Elec_m_log) 2.636
(0.000)
R2 0.951 0.955 R2 0.955
n 113 113 n 113
Note: Reported are the regression coecients or the equivalent degrees of freedom (edf) in the case of the spline variables indicated bys(·). Stated in parentheses arep-values of thet-statistics or theF-statistics for the signicance of the respective splines. In the case of OLS regressions the adjustedR2 is reported.
Renaud and Victoria-Feser (2010) explain theR2measure used in the case of the KS regressions.
The results of the bolasso procedure (see table 4.5) show a reduced set of (now) more robust explanatory variables to describe the average growth rate of ICT infrastructure during 2002-2012.
These arelnIT1(the log IT variable averaged over the previous period 2008-2012), again Elec_-m_log (access to electricity as percent of population) and (for the rst time) the share of gross capital formation (at current purchasing power parity), a variable of the category describing the economic status and structure. These three variables obtain an explanatory power of about 0.95, which is is (again) substantial.
While lnIT1 and Elec_m_log have already occurred in the preceding three-stage procedure, CSH_m_log (share of gross capital formation) was selected by bolasso as robust explanatory variable for the rst time. Like lnIT1, the regression coecient of CSH_m_log has a negative sign. Similar to the variable of property rights, we can suppose a potential indication of a catch-up eect. In general, the gross capital formation also includes investments in ICT infrastructure as well as investments in products requiring a certain degree of ICT infrastructure. Countries with a high share of gross capital formation have already realized complementary investments in ICT infrastructure. For countries with a comparatively low share of gross capital formation it means that a development of gross capital formation is associated partially with investments in ICT infrastructure. Hence, the growth rate of ICT infrastructure is comparatively higher for these countries.
Figure 4.5: GAM Results for the bolasso Procedure (dependent variable is∆ lnIT)
1 2 3 4 5
−1.00.01.02.0
log_IT1
s(log_IT1,1)
−3.0 −2.5 −2.0 −1.5 −1.0
−1.00.01.02.0
CSH_m_log
s(CSH_m_log,1.99)
1 2 3 4
−1.00.01.02.0
Elec_m_log
s(Elec_m_log,2.64)
The GAM regression results indicate only a nonlinear eect of Elec_m_log. As before, the curving of the spline in gure 4.5 has the same shape as in the preceding GAM regressions. It
should be recalled here that this eect is driven by the large heterogeneity of the electricity supply variable across countries (see the rugs at the bottom of the right-hand panel of the gure). The association is weaker (the curve atter) for the more highly developed countries with a better electricity support system clustered at the upper end of the scale.
In the variable selection explaining ICT infrastructure growth during 2002-2012, variables con-cerning the extent of regulation play a dominant role. This in in contrast to the rst two subsections, where variables explaining national economic wealth and structure as well as ge-ographical/regional variables are selected by Lasso. Similar to the preceding regressions, the access to electricity is selected in both the three-stage procedure and bolasso to explain ICT infrastructure growth during the decade. Once more, the importance of the infrastructural pre-requisites of ICT infrastructure is pointed out. As in all other regressions, no human capital describing variables are selected by one of the variable selection methods.