5.3 Empirical Framework
5.3.2 Validation of the Estimation Framework
ln(YY2010
1980)i =α+β1ln(Y1980)i+β2ln(n+g+δ)i+β3ln(I/GDP)i (29) +β4ln(HC)i+β5ln(ICT)i+ui.
We use the model equation, as given in equation (29), in the context of our empirical analyses in this chapter to assess the relationship between ICT and economic growth. In order to obtain robust results, we will also include other known growth determinants as control variables in the model. This serves to examine whether ICT inuences growth only under particular economic, nancial, institutional and/or policy environments. We will explain the variable sources in section 5.4.
As previously mentioned, this analysis suggests an endogeneity problem due to reverse causality between GDP and ICT. To prevent the suspected endogeneity problem, we will apply an instru-mental variable approach, as will be explained in subsection 5.3.3. However, a form of reverse causality can also be assumed from the explanatory variables of capital investment and human capital. For instance, Hanushek and Woessmann (2012) suggest that growth provides added resources that can be used to improve schools. Hence, this could lead to higher human capital.
For that reason, we use the values of human capital and the investment ratio from the initial year 1980. This serves to avoid further endogeneity problems and to prevent biased results of the (IV) estimates.70
Given the data-set in MRW (1992, pp. 434-436), we reestimate the regression results of the three samples. Table 5.1 presents the regression results from the orginial MRW estimation (see MRW 1992, p. 426, table V). In comparison to their result table, the reconstructed results dier minimally. Based on the available data-set, we also include a column with the regression results from all 104 available countries. In all four cases the GDP of the initial year 1960 signicantly explains the per-worker growth in the period 1960-1985. The negative sign indicates a catch-up process of poorer, less developed countries. Also, the term of ln(n+g+δ) and the investment ratio have signicant explanatory power for GDP growth. The explanatory variable School explains the per-worker income growth in three of four cases, except for the OECD sample. The models explain between 43.5% and 65.1% of the dependent variable variance. In the following we will focus on the fourth sample, which contains all available countries.71
Table 5.1: Regression Results of the Original MRW Model (reconstruction)
Non-Oil Intermediate OECD all
dependent variable log dierence GDP per working-age persons 1960-1985
c 3.022
(0.000) 3.709
(0.000) 2.755
(0.035) 3.113 (0.002)
ln(Y1960) -0.288
(0.000) -0.366
(0.000) -0.398
(0.000) -0.297 (0.000) ln(n+g+δ) -0.506
(0.083) -0.545
(0.063) -0.863
(0.020) -0.507 (0.047)
ln(I/GDP) 0.524
(0.000) 0.538
(0.000) 0.332
(0.073) 0.553 (0.000) ln(School) 0.231
(0.000) 0.270
(0.001) 0.228
(0.135) 0.216 (0.000)
N 98 75 22 104
R¯2 0.463 0.435 0.651 0.496
Note: Reported are the regression coecients and thep-values. The investment and population growth rates are averaged over the period 1960-1985. VariableSchool denotes the average percentage of working-age population in secondary school for the period 1960-1985.
We now gradually transfer the initial MRW model to the 1980-2010 period and specify the variables we use. The estimation results of this stepwise modied MRW model are shown in table 5.2. In the rst step (column (2)), we use variables from Penn World Table 8.0 (PWT) to replace the dependent variable the logarithmic dierence of the GDP per working-age persons with the logarithmic dierence of the GDP (series RGDPO)72per engaged persons (series emp), the initial GDP level per working-age persons of 1960 and the population growthnof the MRW data-set. The PWT contains national-accounts data of 167 countries. Up to 1950, data on GDP, capital, employment and population are available. The PWT data-set does not contain data per working-age persons. We also use data from the World Bank to replace the investment ratio by the gross capital formation as percentage of GDP. Merely 90 of the 104 countries in the MRW
71 The above-mentioned argumentation of MRW to exclude oil-producing countries from the investigation seems plausible to us. Nevertheless, we will also include this group of countries in our analysis, because we want to investigate the impact of ICT on economic growth using the broadest possible sample of countries.
72 The PWT 8.0 distinguishes between expenditure-side and output-side real GDP. In this paper, we use the output-side real GDP at chained PPPs.
data-set could be matched with the data-sets of the World Bank and PWT. Unfortunately, the number of engaged persons in 1960 is missing for several countries in the PWT data-set. For that reasons, the dependent variable can not be determined for 14 countries. As expected, the regression coecients in column (2) dier from the coecients in column (1).
In the next step, we use data from the Barro and Lee (2013) database in addition to the data of PWT 8.0. The database contains data of the country population by age groups. Through the use of these data from Barro and Lee, the dependent variable, the initial income and ncan be calculated as in the MRW by working-age persons in the age between 15-64. The estimation results are shown in column (3). The coecients of the initial income and human capital become smaller while the coecient of the intercept rises. The R¯2 slightly decreases to a level of 0.49.
The major dierence to model (1) is that the term ln(n+g+δ) no longer has a signicant explanatory power for this model and also the following models. This seems to be due to the operationalization of the variables. In several tests we have found that the original variables of MRW from Summers and Heston (1988) dier considerably from other data of commonly used databases (as the databases of Barro and Lee or World Bank). Sinceg+δis assumed to be 0.05, the values of ln(n+g+δ) across countries dier solely by population growth. Since population growth is not a source of long-term growth according to general growth theory, the insignicance of the term in the model is not of any further importance to us.73
To mitigate wide uctuations of the respective variables caused by economical, meteorological or political uctuations as well as armed conicts (e.g. civil wars) in several countries, we use the GDP per working-age persons values as average of the ve preceding years. Thus, the dependent variable describes the income growth in working-age persons between the average values for 1956-1960 and the average values for 2006-2010. In column (4), accordingly, the initial income YInitialdescribes the averaged GDP per working-age person for the years from 1956 to 1960. The regression results in column (4) are similar to these of column (3) with a higher of R¯2 of 0.529.
In the next step (column (5)), the observation period is changed to 1980-2010. As mentioned in the previous subsection, we replace the averaged percentage of working-age population in secondary school (as used in MRW 1992) by the human capital variable according to Hall and Jones (1999). Based on the total years of schooling, they calculate rates of return for dierent stages of education.74 According to the variable School, the data for human capital are averaged over the 1980-2010 observation period in model (5). The human capital variable according to Hall and Jones signicantly contributes to the model. The estimated coecient is remarkably higher as the correspondent value of the initial MRW estimation in column (1). The number of observations increases to 114, because of the higher country coverage of the variables in this observation period. The value of R¯2 rises through the use of the new human capital variable to a value of 0.634.
As explained in the previous subsection, we use the human capital and investment values of the initial year 1980 in order to avoid potential endogeneity problems. The results of this step are shown in column (6). We also averaged the human capital variable by average the values
73 Czernich et al. (2011), whose research is based on the MRW model, also nd no signicant inuence of the growth rate of the workforce.
74 The precise calculation is described in section 5.4.
from 1975 and 1980. The coecient value of the human capital variable dier markedly to the previous regression. The estimates coecient is more than twice as high as in the previous model (5) and almost more than 3.5 times as high as in the original MRW model in column (1). The estimate coecient of the investment variable only changes slightly. TheR¯2 decreases on a value of 0.578.
Table 5.2: Regression Results of the Model Validation
(1) (2) (3) (4) (5) (6)
dependent variable log di. GDP per working-age persons 1960-85 ...1980-2010
c 3.113
(0.000) 0.689
(0.411) 1.552
(0.040) 1.482
(0.040) 0.646
(0.306) 0.868 (0.178)
ln(YInitial) -0.297
(0.000) -0.213
(0.000) -0.184
(0.000) -0.166
(0.001) -0.208
(0.000) -0.196 (0.000) ln(n+g+δ) -0.507
(0.047) -0.663
(0.036) -0.236
(0.261) -0.245
(0.220) -0.059
(0.782) -0.064 (0.768) ln(I/GDP) 0.553
(0.000) 0.513
(0.000) 0.554
(0.000) 0.544
(0.000) 0.452
(0.000) 0.435 (0.000)
ln(HC) 0.216
(0.000) 0.201
(0.001) 0.171
(0.004) 0.195
(0.001) 0.304
(0.000) 0.752 (0.000)
N 104 90 90 90 114 114
R¯2 0.496 0.509 0.490 0.529 0.634 0.578
Note: Reported are the regression coecients and thep-values in parentheses. Explanatory variable YInitial denotes the GDP per worker value of 1960 in model (1)-(4) and the value of 1980 in the models (5)-(6). The operationalization of the variables is described in subsection 5.3.2.
We have now modied the original MRW model in ve steps for the use of growth regression in the period 1980-2010. The variables of the original model are specied with current data sources and transferred to the observation period 1980-2010. We further use levels of the investment ratio and human capital from the initial year 1980. Furthermore, we substitute the enrollment rates used in MRW by the human capital variable according to Hall and Jones (1999). The applied data, their sources and descriptive statistics are explained in section 5.4.
The estimation results of model (6) dier from that of the initial model (1) in two points.
First, the term ln(n+g+δ) does not explain the growth rate of GDP per working-age person signicantly. Second, the estimated coecient of the human capital variable is substantially higher due to the use of the human capital variable. Despite the dierences between the modied growth model and that of MRW, we nd it appropriate for further use. The dierences do not aect the basic pattern of results of growth regression. For this reason we will use the modied model in further analysis.