Impact of computers and Internet access on economic development in Poland

Jan Grzegorek Impact of computers and Internet access on economic development in Poland Jan.grzegorek@telekomunikacja.pl Warszawa 1

The purpose and objective of the work Investigated the relationship between the statistics of the households equipped with computers and computers with Internet access and various economic indicators. It is the first of this type of disertation in the country. 2

Justification for the choice of the topic The increase of the knowledge value The demand for IT education egovernment - Access to the electronic public administration ecommerce - shopping and other services on the Internet Teleworking Hence the resulting research questions 3

Research Questions What are the characteristics of households using the computer and a computer with Internet access in Poland? Does the equipment of households in the computer and a computer with Internet access affect the economy in Poland? What is the delay between the equipment of households in the computer and a computer with Internet access in Poland? What is the Polish comparison with the world? 4

Data Source for analysing Research CBOS, GUS and other achieved in recent years among residents of Poland. The statistics available on the website http://www.stat.gov.pl/bdr_n/app Statistical Yearbooks 1998-2007 Data available on the Internet 5

Investigate of statistics dependences Households using personal computers and computers with access to the Internet Number of Enterprises of the Economy Scope of Gross Domestic Product Scope of Fixed Assets Population size 6

How investigated Analysis of the size of their economic characteristics. Gather data to study the statistical series. The estimation of the size of the test for the period 1995-2020 using statistics. The study of correlation and regression between the data. 7

Territorial range Voivodships DOLNOŚLĄSKIE KUJAWSKO-POMORSKIE LUBELSKIE LUBUSKIE ŁÓDZKIE MAZOWIECKIE MAŁOPOLSKIE OPOLSKIE PODKARPACKIE PODLASKIE POMORSKIE ŚWIĘTOKRZYSKIE ŚLĄSKIE WARMIŃSKO-MAZURSKIE WIELKOPOLSKIE ZACHODNIOPOMORSKIE Regions Region centralny * Region południowy * Region wschodni * Region północno-zachodni * Region południowo-zachodni * Region północny * Polska World - ITU * * In preparation 8

Statistical Tables containing statistical series 9

Raw data for Poland No Year Population Households using a personal computer Households using an Internet Economic enterprises Gross Domestic Product [mln zl] Fixed Assets [thousand zl] 1 1995 38 609 399 2 112 704 337 222 2 1996 38 639 341 2 414 182 422 436 3 1997 38 659 979 2 599 039 515 353 4 1998 38 666 983 2 844 256 600 902 5 1999 38 263 303 3 041 403 665 688 1 346 008 483 6 2000 38 253 955 3 186 704 744 378 1 444 803 734 7 2001 38 242 197 3 325 539 779 564 1 523 153 801 8 2002 38 218 531 3 468 218 808 578 1 605 823 560 9 2003 38 190 608 26,70% 12,80% 3 581 593 843 156 1 675 322 276 10 2004 38 173 835 32,90% 16,90% 3 576 830 924 538 1 747 888 672 11 2005 38 157 055 38,60% 22,50% 3 615 621 983 302 1 826 906 734 12 2006 38 125 479 43,70% 28,40% 3 636 039 1 913 333 226 13 2007 38 115 641 3 685 608 10

Raw data Central Region No Year Population Households using a personal computer Households using an Internet Economic enterprises Gross Domestic Product [mln zl] Fixed Assets [thousand zl] 1 1995 7 745 985 485 637 77 904-2 1996 7 739 379 585 523 101 889-3 1997 7 733 575 607 879 127 470-4 1998 7 725 205 662 457 153 944-5 1999 7 741 825 704 988 176 208 346 233 377,3 6 2000 7 734 591 732 088 196 275 384 230 416,8 7 2001 7 730 102 749 991 211 498 416 517 465,9 8 2002 7 727 827 786 429 217 794 436 901 517,0 9 2003 7 725 500 30,90% 15,60% 817 038 229 050 461 732 068,0 10 2004 7 726 596 33,90% 18,20% 830 244 247 277 482 117 278,0 11 2005 7 729 078 39,60% 23,90% 851 180 271 329 507 141 718,0 12 2006 7 734 361 45,70% 30,10% 850 822 523 364 131,0 13 2007 7 741 236 868 139 11

Raw data Mazowieckie Voivodships No Year Population Households using a personal computer Households using an Internet Economic enterprises Gross Domestic Product [mln zl] Fixed Assets [thousand zl] 1 1995 5 052 370 337 932 56 507-2 1996 5 053 982 419 178 76 695-3 1997 5 055 768 427 237 95 965-4 1998 5 057 134 468 577 117 401-5 1999 5 100 237 502 589 134 783 264 839 676,3 6 2000 5 102 790 520 646 150 182 298 304 579,2 7 2001 5 108 735 527 626 163 262 325 354 940,1 8 2002 5 116 506 554 112 167 333 342 305 513,0 9 2003 5 124 358 30,80% 16,00% 575 598 176 073 361 999 387,0 10 2004 5 134 952 35,80% 20,20% 585 529 189 565 378 207 329,0 11 2005 5 147 868 42,00% 26,90% 601 721 210 219 396 704 400,0 12 2006 5 164 008 48,00% 32,50% 609 601 407 013 232,0 13 2007 5 181 142 627 277 12

The estimation of the variables tested 13

Introduction Statistical Tables containing statistical series are incomplete. They contain fragmentary data. To complement the estimation we made to regard to linear and non-linear nature of the models and test size. 14

Using Mathematic In economics as in most natural scientific disciplines observed mathematization description of phenomena. This is a result of a certain level of development of the theory of this discipline, allowing formalization considerations. 15

Mathematical models used in disertation Linear function is used to describe Population Gros Domestic Product Fixed Assets Sigmoidal Personal computers (used in households share in % of them) Internet (used in households share in % of them) Log Enterprises It means that the type of curve give the best estimatation of presented data. 16

A sigmoid model A sigmoidal function is a mathematical function that produce sigmoid curve havig an S shape define by the formula. It is used to explain process which changing first slowly next grow up rapidly to duration and then changing slowly. 17

A linear model A sigmoidal function is a mathematical function that produce linear curve define by the formula. It is used to explain process which changing first slowly next grow up rapidly to duration and then changing slowly. Y=a*x+b 18

A log model A log function is a mathematical function that produce log curve havig shape define by the formula. It is used to explain process which changing slowly then linear Y= a + b*ln(x) 19

The population graph Population 38 800 000 38 600 000 38 400 000 38 200 000 Estimation y = - 19850*x + 38372058,8 R = 0,99 38 000 000 37 800 000 37 600 000 37 400 000 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Raw data Estimated data 20

Households using personal computer graph Estimation y = 1/(1+25,2*exp(-0,25*x)) alpha=0.050 21

Hoseholds using personal computers with access to the Internet graph Estimation y = 1/(1+135,4*exp(-0,33*x)) alpha=0.050 22

Delay between using coputers and Internet graph 1,5 years 4 years 2 years 23

Enterprises of the Economy graph Enterprises of the Economy 4 500 000 4 000 000 3 500 000 3 000 000 2 500 000 2 000 000 1 500 000 Estimation y=674940,9*log(x)+1989719,8 alpha=0.050 1 000 000 500 000 0 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Raw Data Estimated Data 24

Gross Domestic Product graph Gross Domestic Product 2 500 000 2 000 000 1 500 000 Estimation y = 61 377*x + 324 932 R = 0,98 1 000 000 500 000 0 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Raw Data Estimated Data 25

Fixed Assets graph Estimation y = 78874899*x + 964 968 418 R = 0,99 26

The study of correlation and regression between the data 27

Correlation between Enterprises of Economy and Computers graph Correlation between Enterprises of Economy and computers 4 500 000 4 000 000 3 500 000 3 000 000 2 500 000 2 000 000 1 500 000 1 000 000 500 000 Correlation y = 4150600+1335900*log10(x) R=0,96 Correlation y=1530500*x+2784537 R= 0,90 0 4,85% 6,14% 7,74% 9,72% 12,14% 15,05% 18,52% 22,58% 27,23% 32,44% 38,12% 44,14% 50,35% 56,54% 62,53% 68,17% 73,31% 77,90% 81,89% 85,30% 88,16% 90,52% 92,45% 94,02% 95,28% 96,28% Estimated Enterprises of the Economy Non Linear Regression Linear Regression 28

Summary Based on analysis of the issues related to the use of computers and the use of the Internet a logistic model was chosen as well fitting to even scarce data and thus most suitable for forecasting of these events. Analyses include diverse models of computer use and Internet access The the impact of computer use and Internet access on economic indicators in Poland is strong and important statically The analysis was performed using the program Statistica version 7.0 using various statistical models. 29

Internet users per 100 inhabitants in years 1994-2006 (source ITU) ITU 100,0% 90,0% 80,0% 70,0% 60,0% 50,0% 40,0% 30,0% 20,0% 10,0% 0,0% 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 World Developed Developing 30

Conclusions A study of time series of selected indicators shows good approximation with logistic or linear models, even if the data is scarce A study of statistical dependence between households equipped with computers or computers with Internet access and selected economic indicators shows very strong correlation between them. This is the first such survey in Poland. 31