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The Expected Inflow of Foreign Direct Investments in Poland: Focus on Regions

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Book cover New Cohesion Policy of the European Union in Poland

Part of the book series: Contributions to Economics ((CE))

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Abstract

The key aims of this work are: assess the future inflow of foreign direct investment (FDI) to Poland as an aggregate of its regions and to the regions themselves, both have not yet been seen in the literature on the topic.

To achieve the first goal, an Autoregressive Integrated Moving Average (ARIMA) model is built using Ordinary Least Squares (OLS) method of estimation of data on FDI inflows to Poland for years 1990–2004. After being positively evaluated with an ex post forecast (2005–2011), the model is then used for an ex ante forecast for years 2012–2020. Due to a significantly shorter time period (2005–2012), for which data on the number of foreign investors in each of the regions was available, a much simpler statistical approach had to be applied, that is a naïve forecast with a linear trend line where period is the only independent variable. Due to the wide variance of the number of investors, four determinants of FDI (i.e., gross domestic product with its per capita permutation, unit labor cost and unemployment) were correlated with the dependent variable of interest in order to assess the strength, direction and statistical validity of a possible linear relationship.

Results show that as an aggregate, Poland can expect an increasing inward activity from foreign investors, both in their number and in the inflow of money. This conclusion carries over to most of the regions as the number of foreign investors in them is forecasted to increase.

He is a recipient of the ‘Stypendia—dla nauki, dla rozwoju, dla Mazowsza’ scholarship instituted by the Warsaw School of Economics and financed by the European Union (European Social Fund).

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Authors and Affiliations

  1. Doctoral Programme in Management and Economics, Collegium of World Economy, Warsaw School of Economics, Warsaw, Poland

    Tomasz M. Napiórkowski

Authors
  1. Tomasz M. Napiórkowski
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Corresponding author

Correspondence to Tomasz M. Napiórkowski .

Editor information

Editors and Affiliations

  1. Jean Monnet Chair of European Integration Collegium of World Economy, Warsaw School of Economics, Warsaw, Poland

    Adam A. Ambroziak

Appendices

Appendix 1: Correlogram of FDI Inflows to Poland in Levels, 1990–2004

Date: 07/02/13 Time: 20:10

Sample: 1990 2004

Included observations: 15

Autocorrelation

Partial correlation

 

AC

PAC

Q-stat

Prob

. |***. |

. |***. |

1

0.461

0.461

3.8747

0.049

. |** . |

. |* . |

2

0.283

0.089

5.4474

0.066

. |** . |

. | . |

3

0.214

0.069

6.4177

0.093

. |** . |

. |* . |

4

0.274

0.176

8.1591

0.086

. | . |

. *| . |

5

0.049

−0.202

8.2192

0.145

. *| . |

. *| . |

6

−0.085

−0.139

8.4245

0.209

. *| . |

. *| . |

7

−0.192

−0.163

9.6020

0.212

. **| . |

. *| . |

8

−0.237

−0.158

11.640

0.168

. **| . |

. | . |

9

−0.249

−0.028

14.282

0.113

. **| . |

. | . |

10

−0.272

−0.063

18.061

0.054

. *| . |

. |* . |

11

−0.156

0.140

19.606

0.051

. *| . |

. | . |

12

−0.175

−0.046

22.214

0.035

Source: Author’s own calculation with the use of EViews 7 software

Appendix 2: Results of the Augmented Dickey–Fuller Test for Unit Root for FDI Inflows to Poland in Levels, 1990–2004

 

t-statistic

Prob.*

Augmented Dickey–Fuller test statistic

−0.751596

0.7925

Test critical values:

1 % level

−4.200056

 

5 % level

−3.175352

 

10 % level

−2.728985

 

Source: Author’s own calculation with the use of EViews 7 software

Appendix 3: Correlogram of FDI Inflows to Poland, Differenced 1990–2004

Date: 07/02/13 Time: 20:10

Sample: 1990 2004

Included observations: 14

Autocorrelation

Partial correlation

 

AC 

PAC

Q-stat

Prob

. | . |

. | . |

1

0.031

0.031

0.0168

0.897

. **| . |

. **| . |

2

−0.213

−0.214

0.8598

0.651

.***| . |

.***| . |

3

−0.430

−0.435

4.6207

0.202

. |* . |

. | . |

4

0.105

0.068

4.8662

0.301

. | . |

. *| . |

5

0.030

−0.182

4.8884

0.430

. | . |

. **| . |

6

−0.004

−0.211

4.8888

0.558

. | . |

. | . |

7

−0.029

0.011

4.9160

0.670

. | . |

. *| . |

8

−0.004

−0.162

4.9167

0.766

. |* . |

. | . |

9

0.097

0.018

5.3374

0.804

. | . |

. | . |

10

−0.013

−0.036

5.3470

0.867

. | . |

. | . |

11

0.032

−0.006

5.4236

0.909

. | . |

. | . |

12

−0.047

0.022

5.6730

0.932

Source: Author’s own calculation with the use of EViews 7 software

Appendix 4: Results of the Augmented Dickey–Fuller Test for Unit Root for FDI Inflows to Poland, Differenced 1990–2004

 

t-statistic

Prob.*

Augmented Dickey–Fuller test statistic

−5.002680

0.0030

Test critical values:

1 % level

−4.200056

 

5 % level

−3.175352

 

10 % level

−2.728985

 

Source: Author’s own calculation with the use of EViews 7 software

Appendix 5: Results of the Breusch-Godfrey Serial Correlation Lagrange Multiplier Test for the Presence of Autocorrelation in Residuals

Breusch-Godfrey serial correlation LM Test:

F-statistic

2.886332

Prob. F(2,6)

0.1324

Obs*R-squared

5.755586

Prob. Chi-Square(2)

0.0563

Source: Author’s own calculation with the use of EViews 7 software

Appendix 6: Number of Investors in Each of Polish 16 Regions, 2005–2012 (Percentage of Total, Column Sum = 100 %)

 

2005

2006

2007

2008

2009

2010

2011

2012

Dolnośląskie

19.08

11.05

8.46

8.87

9.04

9.00

8.97

9.30

Kujawsko-Pomorskie

2.65

2.81

2.26

4.17

5.58

5.56

5.54

4.72

Łódzkie

6.33

6.88

5.48

5.08

5.07

4.96

4.86

5.19

Lubelskie

2.35

1.55

1.13

0.78

0.71

0.54

0.37

1.30

Lubuskie

3.27

2.81

2.18

3.39

2.89

3.22

3.55

3.28

Małopolskie

4.80

4.75

5.56

4.69

4.68

4.90

5.11

4.92

Mazowieckie

19.18

36.14

42.47

37.03

36.37

36.02

35.68

37.59

Opolskie

1.84

1.55

1.77

3.19

3.21

3.03

2.86

2.26

Podkarpackie

3.67

2.81

1.85

4.63

4.68

4.45

4.23

2.39

Podlaskie

0.92

0.97

0.97

0.72

0.77

0.76

0.75

0.48

Pomorskie

7.65

4.75

4.75

3.85

3.72

3.95

4.17

3.62

Śląskie

9.49

7.85

8.70

7.69

7.83

7.90

7.97

8.75

Świętokrzyskie

1.02

1.45

1.37

1.50

1.35

1.48

1.62

1.37

Warmińsko-Mazurskie

2.04

1.36

0.73

1.50

1.54

1.58

1.62

1.71

Wielkopolskie

10.82

10.37

9.19

7.56

7.57

7.52

7.47

7.79

Zachodniopomorskie

4.90

2.91

3.14

5.35

5.00

5.12

5.23

5.33

Source: Author’s own graph based on data from PAIiIZ

Appendix 7: Pearson Coefficient Correlation Matrix Between the Number of Foreign Investors and Selected Macroeconomic Variables

Region

GDP

GDP/POP

ULC

Unemployment

Dolnośląskie

Pearson correlation

−0.246

−0.248

−0.175

0.518

Sig. (2-tailed)

0.639

0.636

0.740

0.293

Kujawsko-Pomorskie

Pearson correlation

0.909

0.907

0.951

−0.696

Sig. (2-tailed)

0.012

0.013

0.004

0.125

Łódzkie

Pearson correlation

0.899

0.899

0.896

−0.848

Sig. (2-tailed)

0.015

0.015

0.016

0.033

Lubelskie

Pearson correlation

−0.940

−0.940

−0.920

0.865

Sig. (2-tailed)

0.005

0.005

0.009

0.026

Lubuskie

Pearson correlation

0.776

0.775

0.835

−0.586

Sig. (2-tailed)

0.069

0.070

0.038

0.222

Małopolskie

Pearson correlation

0.950

0.952

0.944

−0.890

Sig. (2-tailed)

0.004

0.003

0.005

0.017

Mazowieckie

Pearson correlation

0.885

0.889

0.892

−0.962

Sig. (2-tailed)

0.019

0.018

0.017

0.002

Opolskie

Pearson correlation

0.942

0.942

0.933

−0.718

Sig. (2-tailed)

0.005

0.005

0.007

0.108

Podkarpackie

Pearson correlation

0.845

0.845

0.852

−0.461

Sig. (2-tailed)

0.034

0.034

0.031

0.357

Podlaskie

Pearson correlation

0.878

0.878

0.832

−0.741

Sig. (2-tailed)

0.021

0.021

0.040

0.092

Pomorskie

Pearson correlation

−0.230

−0.238

−0.208

0.495

Sig. (2-tailed)

0.661

0.650

0.692

0.318

Śląskie

Pearson correlation

0.932

0.931

0.920

−0.784

Sig. (2-tailed)

0.007

0.007

0.009

0.065

Świętokrzyskie

Pearson correlation

0.973

0.973

0.944

−0.916

Sig. (2-tailed)

0.001

0.001

0.005

0.010

Warmińsko-Mazurskie

Pearson correlation

0.587

0.585

0.620

−0.285

Sig. (2-tailed)

0.220

0.222

0.189

0.584

Wielkopolskie

Pearson correlation

0.975

0.976

0.975

−0.870

Sig. (2-tailed)

0.001

0.001

0.001

0.024

Zachodniopomorskie

Pearson correlation

0.852

0.851

0.870

−0.583

Sig. (2-tailed)

0.031

0.032

0.024

0.224

Source: Author’s own calculation with the use of SPSS 19 software

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Napiórkowski, T.M. (2014). The Expected Inflow of Foreign Direct Investments in Poland: Focus on Regions. In: Ambroziak, A. (eds) New Cohesion Policy of the European Union in Poland. Contributions to Economics. Springer, Cham. https://doi.org/10.1007/978-3-319-05335-6_8

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