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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
Infrastructure index for Romania and GDP per capita in a linear regression

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
Map of Romania and Romanian counties - Infrastructure index map

Source: Microsoft Map Point 2006 and own data [2]
Image 4.5: Map of Romania – GDP per Capita, by County 2005
Map of Romania and Romanian counties - GDP per Capita - GDP Map

Source: Microsoft Map Point 2006 and own data
Image 4.6: FDI, Infrastructure and International Accessibility in Romania
Regression Analysis for FDI and Infrastructure 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
Regression analysis for the impact of education on GDP in Romania

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 + β1IAi + β2Hi + 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.

Table 4.4 : Descriptive Statistics (SPSS) – GDP (Romania)

Variable

Mean

Std. Deviation

N

Yi  (GDP per Capita)

11,871.14

4,219.73

42

IAi (Infrastructure and International Accessibility)

3.92

2.35

42

Hi (Human Capital or Availability of Qualified Labor)

17,058.66

40,631.11

42

Source: Own table, own calculations

Table 4.5: Correlations (SPSS) – GDP (Romania)

Yi

IAi

Hi

Pearson Correlation Yi

1.000

.789

.764

IAi

.789

1.000

.588

Hi

.764

.588

1.000

Sig. (1-tailed) Yi

.

.000

.000

IAi

.000

.

.000

Hi

.000

.000

.

Source: Own table, own calculations
Table 4.6: Model Summary (b) (SPSS) – GDP (Romania)

Model

R

R Square

Adjusted R Square

Std. Error of Estimate

Change Statistics

Durbin-Watson

R Square Change

F Change

df1

df2

Sig. F Change

2.117

1

.872(a)

.760

.747

2,121.41

0.760

61.610

2

39

.000

a. Predictors: (Constant), Hi, IAi;b. Dependent Variable: Yi
Source: Own table, own calculations
Table 4.7: ANOVA (SPSS) (b) – GDP (Romania)
Model

Sum of Squares

df

Mean Square

F

Sig.

1 Regression

554,536,196.94

2

277,268,098.47

61.610

.000(a)

Residual

175,514,798.65

39

4,500,379.45

Total

730,050,995.59

41

a. Predictors: (Constant), Hi, IAi ;  b. Dependent Variable: Yi
Source: Own table, own calculations
Table 4.8 : Coefficients (SPSS) (a) – GDP (Romania)
Model

Unstandardized
Coefficients

Standardized
Coefficients

t

Sig.

95 % Confidence
Interval for B

B

Std. Error

Beta

Lower B. Upper B.
1 (Constant)

7,408.715

682.431

10.856

.000

6,028.38

8,789.06
IAi

931.650

174.552

.518

5.337

.000

578.585

1,284.716

Hi

.048

.010

.459

4.728

.000

.027

.068

a. Dependent Variable: Yi
Source: own table, own calculations

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.

Table 4.9: Descriptive Statistics (SPSS) – FDI (Romania)

Variable

Mean

Std. Deviation

N

FDIi  (Foreign Firms per capita)

0.0045

0.0062

42

IAi (Infrastructure and International Accessibility)

3.92

2.35

42

Hi (Human Capital or Availability of Qualified Labor)

17,058.66

40,631.11

42

Source: Own table, own calculations
Table 4.10: Correlations (SPSS) – FDI (Romania)

FDIi

IAi

Hi

Pearson Correlation FDIi

1.000

.710

.873

IAi

.710

1.000

.588

Hi

.873

.588

1.000

Sig. (1-tailed) FDIi

.

.000

.000

IAi

.000

.

.000

Hi

.000

.000

.

Source: Own table, own calculations
Table 4.11: Model Summary (b) (SPSS) – FDI (Romania)

Model

R

R Square

Adjusted R Square

Std. Error of Estimate

Change Statistics

Durbin-Watson

R Square Change

F Change

df1

df2

Sig. F Change

1.801

1

.906(a)

.821

.812

0.0027

0.821

89.589

2

39

.000

a. Predictors: (Constant), Hi, IAi ; b. Dependent Variable: FDIi
Source: Own table, own calculations
Table 4.12: ANOVA (SPSS) (b) – FDI (Romania)
Model

Sum of Squares

df

Mean Square

F

Sig.

1 Regression

.001

2

.001

89.589

.000(a)

Residual

.000

39

.000

Total

.002

41

a. Predictors: (Constant), Hi, IAi ; b. Dependent Variable: FDIi
Source: Own table, own calculations

Table 4.13: Coefficients (SPSS) (a) – FDI (Romania)
Model

Unstandardized
Coefficients

Standardized
Coefficients

t

Sig.

95 % Confidence
Interval for B

B

Std. Error

Beta

Lower B. Upper B.
1 (Constant)

.000

.001

–.489

0.628

–.002

.001
IAi

.001

.000

.30

3.583

0.001

.000

.001

Hi

1.06E-007

.000

.697

8.319

0.000

.000

.000

a. Dependent Variable: FDIi
Source: own table, own calculations

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.

Table 4.14: Descriptive Statistics (SPSS) – TEA (Romania)

Variable

Mean

Std. Deviation

N

TEAi  (Total Economic Activity)

.071

.022

42

IAi (Infrastructure and International Accessibility)

3.92

2.35

42

Hi (Human Capital or Availability of Qualified Labor)

17,058.66

40,631.11

42

Source: Own table, own calculations
Table 4.15: Correlations (SPSS) – TEA (Romania)

TEAi

IAi

Hi

Pearson Correlation TEAi

1.000

.764

.801

IAi

.764

1.000

.588

Hi

.801

.588

1.000

Sig. (1-tailed) TEAi

.

.000

.000

IAi

.000

.

.000

Hi

.000

.000

.

Source: Own table, own calculations
Table 4.16: Model Summary (b) (SPSS) – TEA (Romania)

Model

R

R Square

Adjusted R Square

Std. Error of Estimate

Change Statistics

Durbin-Watson

R Square Change

F Change

df1

df2

Sig. F Change

1.537

1

.879(a)

.772

.761

.011

.772

66.189

2

39

.000

a. Predictors: (Constant), Hi, IAi ; b. Dependent Variable: TEAi
Source: Own table, own calculations
Table 4.17: ANOVA (SPSS) (b) – TEA (Romania)
Model

Sum of Squares

df

Mean Square

F

Sig.

1 Regression

.015

2

.008

66.189

.000(a)

Residual

.004

39

.000

Total

.020

41

a. Predictors: (Constant), Hi, IAi ; b. Dependent Variable: TEAi
Source: Own table, own calculations
Table 4.18: Coefficients (SPSS) (a) – TEA (Romania)
Model

Unstandardized
Coefficients

Standardized
Coefficients

t

Sig.

95 % Confidence
Interval for B

B

Std. Error

Beta

Lower B. Upper B.
1 (Constant)

.050

.003

14.399

0.000

043

.057
IAi

.004

.001

.447

4.728

0.000

.002

.006

Hi

2.00E-007

.000

.538

5.699

0.000

.000

.000

a. Dependent Variable: TEAi
Source: own table, own calculations

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.

Table 4.19 : Curve Estimation for IAi  and FDI (Romania)
Model Predictor

Regression Fit F

Constant

B

Beta

T-value

Sig.

Multiple, linear, with Hi IAi

.821

89.589

.000

.001

.3

3.583

.001

Simple, linear IAi

.504

40.654

–.003

.002

.710

6.376

.000

Simple, cubic IAi

.852

72.809

–.001

.001

3 .358a

3.890

.000

a. whole function: FDIi = –.001 + 1.187 IAi – 3.612 IAi + 3.358 IAi (with the positive terms significant)
Source: own table, own calculations
Academic Research paper and Study of the Economy of Romania and Romanian Business

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).