4.2.3 Infrastructure & Education

The next test will be an analysis the relationship between the remaining predictors and the outcome variables. But at first we shall eyeball the data and distributions for a while. The lowest aggregate score on the infrastructure index reached was 1 (by e.g. Telorman), the highest score was achieved by Bucharest (10). A simple regression indicates a considerable effect and fits the data adequately (R² = .6215, cf. Figure 4.3).

An interesting observation is that the ranking order of the Transylvanian counties becomes clearer. Cluj (CJ), Timișoara (TM) Arad (AR) or Sibiu (SB) exceed Hunedoara (HD), Harghita and Caraș-Severin (CS) not only with regard to per Capita GDP but also concerning infrastructure. Constanța (CT) on the other hand demonstrates that infrastructural well endowed counties must not be worse off just for belonging to the East of Romania, so do Bacau (BC), Suceava (SV) and Iasi (IS),[1] even though these better endowed counties in the East nevertheless seem to suffer from infrastructural shortages of their neighbor counties which of course have to be traversed when it comes to logistics (cf. Figure 4.4). For instance, travelling from Galați to checkpoint Nădlac (Arad) on European (and national) Roads would require the indirect route via Brăila, Ialomița, Bucharest and so on.

Image 4.3: Linear Regression – GDP per Capita and Infrastructure in Romania, by County 2005

Source: Own index based on ARIS 2008, INS 2008 for GDP data; own graphic, own calculations

GDP per capita in Romania follows a similar pattern on the map (cf. Figure 4.5). But the maps and the distribution in Figure 4.3 also clarify that infrastructure does of course not account for all the variance. With regard to FDI the slight nonlinear relation between GDP per capita and foreign economic activity – as observed in chapter 3.5 – becomes a real cubic function once Yi is replaced by IAi as predictor variable (cf. Figure 4.6). The effect can nonetheless be tracked sufficiently by linear regression, even though the linear model underestimates the role of infrastructure. The same is true for overall economic activity, TEAi, which follows the same pattern as FDI, though foreign firms seem to react even more sensitive to infrastructure than domestic firms.

Image 4.4: Map of Romania – Infrastructure and International Accessibility, by County

Source: Microsoft Map Point 2006 and own data [2]

Image 4.5: Map of Romania – GDP per Capita, by County 2005

Source: Microsoft Map Point 2006 and own data

Image 4.6: FDI, Infrastructure and International Accessibility in Romania

Source: Own index, ARIS 2008, ONCR 2008 a, INS 2008, own graphic, own calculations

The effect of human capital, Hi, can likewise be tracked well via linear models. Again, a simple regression analysis indicates a relatively strong effect and a well fit of the data, only diminished by one major outlier (county Ilfov without students but a very high per capita income). This outlier actually is none as students from Ilfov are obviously enrolled in Bucharest. For illustrational purposes, both Ilfov and Bucharest were removed from Figure 4.7.[3]

Image 4.7: Linear Regression – GDP per Capita and Students in Romania, by County, 2005

Source: INS 2007; own graphic, own calculations

After the removal of Ilfov and Bucharest three more outliers draw attention. These are Dolj (DJ), Galați (GL) and Iași (IS). All three perform better than their neighbors but worse than counties with a better infrastructural endowment or a better loca­tion. The relation between FDI and human capital (R² = .76) suffers from many outliers, which become especially visible if Bucharest and Ilfov are removed from the sample (R² = .35). But on the other hand, the relation is stronger and relatively linear with regard to total economic activity (R² = .642). Both predictor variables will be used now for multiple linear regressions in the form of

Yi= α + β₁IA_i + β₂H_i + e

A detailed overview on the results for GDP per capita (Yi) is presented in Table 4.4 to Table 4.8. The results suggest a strong and highly significant impact of both predictors while the data is fitted well.

Both independent variables correlate highly (infrastructure r = .789; qualified labor r = .764) with economic performance, captured here as GDP per capita by county at a highly significant level (> 99.9 %). With an R² = 0.76 the model fits the data well and the constant α of RON 7,408.72 is by far smaller than av­erage GDP per ca­pita on county level (RON 11,871.14). The standardized coefficients  for infrastructure of .518 and for human capital of .459 indicate both a strong, positive influence of the predictor variables on economic performance (GDP per capita). All parts of the model are also individ­ual highly significant at a > 99.9 % level. These results are in line with frequent complaints in the business press about the difficult connection to the European business network and short­ages on (qualified) labor markets. Hence, the impact of the two predictors on FDI and total economic activity remains to be tested next.

Again, the both predictors do a good job in tracking the location decision of foreign investors. The linear model underestimates the role of infrastructure (β = .3), which does not follow a linear trend but actually appears as a cubic function in the plot.  Nonetheless, the explained variance (R² = .821) indicates a well fit of the data and the individual significance levels are highly satisfying (>99.9). The results are similar for total economic activity.

Having in mind that the impact of infrastructure did not seem throughout linear with regard to FDI and total economic activity it becomes clear that the linear models above underestimate the role of infrastructure. Thus, a curve estimation shall show how much the fit of a simple re­gression could be improved if a nonlinear model would be applied.

A similar estimation for total economic activity leads to the conclusion that the linear model is more appropriate as the individual significance of the equation parts gets lost in the cubic model.

Footnotes

[1] This might also apply to Dolj (DJ) in the South. The county is infrastructural relatively well endowed, but surrounded by less well endowed neighbors and thus seems like an isolated island on the map in Figure 4.4.

[2] Note: not all roads visible on this map are European Roads. Some of them are normal national roads.

[3] The regression result including Bucharest and Ilfov is even better, despite the considerable negative effect of Ilfov for the regression: y = 10517 + 0.0794x with R² = 0.584 (cf. Appendix 5 for the figure).