5th International Conference on New Trends in Econometrics ... · 13:20 – 13:40 multi-criteria...

PROCEEDINGS BOOK

5th International Conference on New Trends

in Econometrics & Finance

2

APRIL 22-24 2019

Greece/Turkey

http://www.icntefconference.com/

3

ICNTEF’2019

5th

International Conference on New Trends in Econometrics &Finance

Athens/Greece

Published by the ICNTEF Secretariat

Editors:

Prof. Dr. Pınar AKKOYUNLU

ICNTEF Secretariat Büyükdere Cad. Ecza sok. Pol Center 4/1 Levent-İstanbul

E-mail: [email protected] http://www.icntefconference.com

Conference organised in collaboration with Smolny Institute of the

Russian Academy of Education

Copyright @ 2019 ICNTEF and Authors All Rights Reserved

No part of the material protected by this copyright may be reproduced or utilized in any form or by any means electronic or mechanical, including

photocopying , recording or by any storage or retrieval system, without written permission from the copyrights owners

http://www.icntefconference.com/

4

SCIENTIFIC COMMITTEE

Prof. Dr. Antonio Rodriguez ANDRES

Camilo Jose Cale University, Spain

Prof. Dr. Mehmet BALCILAR Eastern Mediterranean University, North Cyprus

Prof. Dr. Glenn Paul JENKINS

Eastern Mediterranean University, North Cyprus

Prof. Dr. Mehmet IVRENDİ Eastern Mediterranean University, North Cyprus

Prof. Dr. Agamirza BASHIROV Eastern Mediterranean University, North Cyprus

Prof. Dr. Rangan GUPTA

University of Pretoria, South Africa

Asst. Prof. Dr. Danbala DANJU Bank of Agriculture, Nigeria

5

ORGANIZATION COMMITTEE


Istanbul University, Turkey Conference Co Chair

6

Dear Colleagues,

We are proud to announce 5th International Conference on New Trends in Econometrics and

Finance (ICNTEF’19 ) which will be held at Athens, Greece between April 22 – 24, 2019.

Like the previous three conferences, this conference serves as a forum for academics,

practitioners, and central bank and government officials in Europe and all over the world to

present and discuss research results about the evolution of the international economics and of

the global financial system.

In the conference emphasis will be placed on the developments in emerging market

economies, on the fate of the recent trends and of the impact of these developments on

international trade, finance and regulation as well as on national economies and financial

systems. Theoretical, empirical and policy-oriented papers are all welcome.

The organizers encourage submissions of papers and posters on any topic within the overall

theme of the conference and in the following areas in particular:

Econometrics

Economics

EconomicPolicies

InternationalEconomics

Macroeconomics

Microeconomics

Industrial economics and regional

economic issues

Finance

Risk Management

Financial Markets

Financial Crises

Quantitative Finance

We also would like to inform you that our official Airline Partner, Turkish Airlines will

provide discounted flight tickets. details will be available shortly.

Abstract submission deadline is on the March 15th, 2019(extended)

All papers will be published in Conference Proceedings Book

We kindly wait for your participation in our conference in Athens to be held in April 22 – 24,

2019, with the expectations to realize a fruitful discussion ground together with enjoying its

social activities and hoping to leaving a trace on your memories.

Hoping to seeing you all in Athens

With my kindest regards

Organising Committee

This Conference is organized in cooperation with Smolny Institute of the Russian Academy

of Education, St. Petersburg.

7

22 APRIL 2019 MONDAY

08:30-17:00 : REGISTRATION

MAIN HALL : OPENING CEREMONY

09:40 – 10:00

Welcome Speech : Prof. Dr. Pınar AKKOYUNLU / Istanbul University

Conference Co Chair

HALL 1/ KEYNOTE SPEECH A

10:00 – 10:40 Keynote Speech: Prof. Dr. Ismihan BAYRAMOGLU Speech Title: On Some New Results on Order Statistics and Applications in Reliability Analysis

10:40 – 11:00 C O F F E E / T E A B R E AK

HALL 1 / SESSION A

SESSION CHAIR

Prof. Dr. Ismihan BAYRAMOGLU

TIME PAPER TITLE PRESENTER / CO AUTHOR

11:00 – 11:20 QUANTILE TRANSFORMATION BASED BAYES CLASSIFICATION AT GENE EXPRESSION LEVEL

Necla KOCHAN, Yazgı G. TÜTÜNCÜ, Göknur GINER, Luke GANDOLFO

11:20 – 11:40 A NOTE ON BIVARIATE RECORDS Gülder KEMALBAY

11:40 – 12:00 SMOOTH NONPARAMETRIC REGRESSION UNDER SHAPE RESTRICTIONS

Hongbin GUO, Yong Wang

12:00 – 12:20 COHERENT SYSTEMS UNDER MARSHALL-OLKIN RUN SHOCK MODEL

Murat OZKUT

12:20 – 13:20 LUNCH

HALL 1 / SESSION B

SESSION CHAIR



13:20 – 13:40 A SEARCH FOR A BETTER HEDONIC OFFICE RENT MODEL FOR ISTANBUL: INSIGHTS FROM PARAMETRIC VS. SEMIPARAMETRIC APPROACHES

Sinem Guler KANGALLI UYAR

13:40 – 14:00 CONSTRUCTING LOCATION-SPECIFIC PRICE INDEXES FROM SCANNER DATA

Lun LI

14:00 – 14:20 PARAMETER ESTIMATION WITH JACKKNIFE AND WEIGHTED MEDIAN IN

Necati Alp ERILLI

8

NON-PARAMETRIC REGRESSION ANALYSIS

14:20 – 14:40 FORECASTING FINANCIAL TIME-SERIES USING DATA MINING MODELS: A SIMULATION STUDY

Imad Bou-HAMAD, Ibrahim Jamali

14:40 – 15:00 THE IMPACT OF TWEET SENTIMENTS ON TECH STOCK RETURNS: AN APPLICATION OF ASYMMETRIC GRANGER CAUSALITY

Umut UYAR, Melike YAVUZ

15:00 – 15:20 C O F F E E / T E A B R E AK

HALL 1 / SESSION C

SESSION CHAIR

Phd. Huan Yang


15:20 – 15:40 CROSS-BORDER M&A AND THE PERFORMANCE OF ACQUIRER: IN THE PRESENCE OF THE ORIGIN EFFECT AND HETEROGENEOUS TREATMENT UNDER MULTI-REGION CONTEXT

Huan YANG

15:40 – 16:00 AN EMPIRICAL ANALYSIS OF PRODUCTIVITY AND INDUSTRIAL CONCENTRATION IN TURKISH MANUFACTURİNG INDUSTRIES

Aytekin GUVEN, Cevsen CIFTCI

16:00 – 16:20 MULTIVARIATE ANALYSIS BETWEEN WEB-BASED HOMEWORK AND ACHIEVEMENT AT STATISTICAL COURSE

MELTEM UCAL

16:20 – 16:40 SME FINANCE: IMPACT ON GROWTH AND DEVELOPMENT

Edna Stan-MADUKA, Sonny Nwankwo

23 APRIL 2019 TUESDAY


HALL 1/ KEYNOTE SPEECH B

09:40 – 10:40 Keynote Speech: Prof. Dr. MIKE TSIONAS Speech Title: Bayesian analysis of multi-objective portfolio problems

10:40 – 11:00 C O F F E E / T E A B R E AK

HALL 1 / SESSION D

SESSION CHAIR



9

11:20 – 11:40 THE NEW GENERATION AND THE WORLD OF WORK

Regina Zsuzsánna REICHER

11:40 – 12:00 NOTION OF SUBJECTIVE WELLBEING IN BULGARIA: MICROECONOMETRIC ANALYSIS USING CATEGORICAL RESPONSE MODELS BASED ON ESS DATA

VENELİN BOSHNAKOV

12:00 – 12:20 THE DYNAMICS OF INCREASING LAND PRICES IN THE PERI-URBAN LAND MARKETS OF DEVELOPING COUNTRIES : A CASE STUDY OF BENGALURU METROPOLITAN CITY, INDIA

Amrutha Mary VARKEY

12:20 – 12:40 FORECASTING REGIONAL INFLATION AND UNEMPLOYMENT: THE ROLE OF SPATIAL SPILLOVERS

Casto Martin Montero KUSCEVIC

12:40 – 13:20 LUNCH

HALL 1 / SESSION E

SESSION CHAIR

Prof. Dr. Gulhayat GOLBASI SIMSEK


13:20 – 13:40 MULTI-CRITERIA DECISION MAKING METHODS BASED ON INTUITIONISTIC FUZZY SETS

Nimet Yapıcı PEHLİVAN, Yasemin GÜNTER

13:40 – 14:00 DATA CLEANING: BIG DATA ANALYTICS FOR SMART CITIES

Carla Susete FRANCISCO, Ana Raquel CASTANHO, Tiago FONSECA

14:00 – 14:20 PHASE I DISTRIBUTION-FREE CONTROL CHARTING METHODS BASED ON CHANGE-POINT ANALYSIS FOR OUTBREAK DETECTION

Christina PARPOULA, Alex KARAGRIGORIOU

14:20 – 14:40 MODELLING OF MULTI-STATE SYSTEMS VIA A MARKOV SWITCHING APPROACH

Emmanouil-Nektarios KALLIGERIS, Alex KARAGRIGORIOU, Christina PARPOULA

14:40 – 15:00 SURVEYING THE RATE OF RETURN OF ASSETS OF TURKISH BANKS WITH INDEPENDENT COMPONENT ANALYSIS

GÜLHAYAT GÖLBAŞI ŞİMŞEK Zehra CİVAN, UTKU KUBİLAY ÇINAR

15:00 – 15:20 INFERENCES OF FIRTH LR, FLIC AND FLAC IN TERMS OF BIAS IN RARE EVENT CASE

Ezgi NAZMAN, Hülya OLMUŞ, Semra ERBAŞ

10

POSTER PRESENTATION

15:20 – 15:40 EXPLORING STATISTICAL MODELS: HUMAN RESPONSE TIME DISTRIBUTIONS ON PSYCHOLOGICAL EXPERIMENTS THEORY

Carla Susete G. FRANCISCO, Filipa RIBEIRO, José António S. MACIAS,

APPLICATION OF TIME SERIES MODELING FOR TOURISM : A CASE OF TURKEY

Özlem BERAK KORKMAZOĞLU

15:40 – 16:00 C O F F E E / T E A B R E AK

HALL 1 / SESSION F

SESSION CHAIR

Prof. Dr. Alex KARAGRIGORIOU


16:00 – 16:20 MODELLING OF FACTORS INFLUENCING THE CITATION COUNTS IN STATISTICS

Olcay ALPAY, Nazan DANACIOĞLU, Emel ÇANKAYA

16:20 – 16:40 THE PROBABILITY OF GIVING BIRTH TO A GIRL BETWEEN PROBABILISTIC AND DETERMINISTIC REASONING “A CASE STUDY OF STUDENTS TEACHERS IN EGYPT”

Samah Gamal Ahmed ELBEHARY

16:40 – 17:00 A CREDIT DEFAULT SWAP APPLICATION BY USING QUANTILE REGRESSION TECHNIQUE

Yuksel Akay UNVAN, Hüseyin Tatlıdil

17:00 – 17:20 MARKOV-MODULATED LINEAR REGRESSION AS ALTERNATING ONE

Nadezda SPIRIDOVSKA, Alexander ANDRONOV

17:20 – 17:40 APPLICATION OF MACHINE LEARNING FOR THE VALIDATION OF BEHAVIOURS OF SPRING CALVING DAIRY COWS AS INDICATIVE OF INSUFFICIENT GRASS ALLOCATION

Abu SHAFIULLAH, Jessica WERNER, Christina UMSTATTER, Emer KENNEDY, Lorenzo LESO, Bernadette O‘BRIEN

17:40 – 18:00 BETA-TRUNCATED-GEOMETRIC DISTRIBUTION WITH APPLICATION IN MODELING COUNT DATA WITH APPLICATIONS

Zainab All balushi

11

24 APRIL 2019 WEDNESDAY


HALL 1 / SESSION G

SESSION CHAIR

Prof. Dr. FATMA NOYAN TEKELİ


09:40 – 10:00 EFFECT OF JOB STRESS ON JOB SATISFACTION IN WHITE COLLAR-WORKERS: AN APPLICATION OF STRUCTURAL EQUATION MODELLING

Batuhan ÖZKAN , Fatma NOYAN TEKELI

10:00 – 10:20 TURKEY LABOR MARKET FOR THE EFFECT OF REGULATION OF THE STATE UNEMPLOYMENT: 1988-2018 PERIODS OF INTERVENTION ANALYSIS

Zeynep KARACOR , Burcu GUVENEK, Asiye KAYHAN

10:20 – 10:40 ANALYSIS OF AIR QUALITY WITH TIME SERIES ANALYSIS AND ARTIFICIAL NEURAL NETWORKS

Fadime AKSOY, Derya TOPDAG

11:00 – 11:30 CLOSING CEREMONY

12

5th

International Conference on New Trends in Econometrics & Finance

IN TURKISH MANUFACTURING INDUSTRIES .........................................................................................

Aytekin GÜVEN Cevsen ÇİFTÇİ ........................................................................................................... 13

PARAMETER ESTIMATION WITH JACKKNIFE AND WEIGHTED MEDIAN IN NON-PARAMETRIC

REGRESSION ANALYSIS.........................................................................................................................

Necati Alp ERILLI ............................................................................................................................... 19

THE NEW GENERATION AND THE WORLD OF WORK ............................................................................

Regina Zsuzsánna Reicher ................................................................................................................. 28

13

An empirical analysis of productivity and industrial concentration in

Turkish Manufacturing Industries

Aytekin GÜVEN1, Cevsen ÇİFTÇİ

2

Abstract

The negative effects of imperfect competition on productivity are criticized by many economists. According to the economists monopoly causes welfare losses. On the other hand “creative destruction theory” suggests that the large firms operate in imperfect competition create innovation. Hence, imperfect competition increases productivity. This paper searches for an answer to the debate. We investigate the total factor productivity and industrial concentration relationship for four digit Turkish manufacturing industries using different panel data techniques. The total factor productivity is used as a dependent variable, and concentration ratio, export, import wages and crises are independent variable in our empirical model. Results suggest that there is a negative relationship between concentration ratio and productivity. While concentration ratio increases, productivity decreases. The results implies that imperfect competition decreases productivity.

Key Words: Market Structure, Productivity, Panel Data Methodology, Turkish Manufacturing Industry, Nonlinearity.

1- Introduction

Imperfect competition causes many economic problems such as welfare lose, high price, low quantity, resource

allocation inefficiency and etc. Structure-Conduct-Performance (SCP) approach notes that market structures

affect economic performance of the firms. There are two conflicting ideas about market structure and

technological improvement and productivity. Schumpeter, (1942) highlights the importance of a “dynamic”

problem. According to the Schumpeter’s “creative destruction theory” large firms operate in imperfect

competition create innovation, technological developments and new products. Less competitive markets can

better afford to innovate and they are more productive. On the other hand Arrow (1962) suggests that the firms

has to be more effective under perfect competition conditions since there is normal profits. They should create

technological improvements in order to get more profits. This idea is known as “static” efficiency of perfect

competitive markets (Masahito, 2013).

There are different findings in the market structure and productivity literature. Some of them finds the positive

relationship, which means as industrial concentration increases (competition decreases), productivity increases

(Cr´epon et al. 1998; Blundell et al. 1999 and Taymaz, 2000). This results support the Schumpeter’s theory. On

the other hand some reveals negative relationship between two: whereas industrial concentration decreases

(competition increases), productivity increases (Geroski, 1990; Nickell, 1996 and Masahito (2013). This results

confirm the Arrow’s theory. Few studies indicate an inverse U shaped relationship between concentration and

productivity (Gopinath et al. 2004 and Aghion et al. 2005). According to their finding there is a nonlinear

relationship between two and a critical threshold level which the positive relationship turns to be negative.

The aim of this study is to investigate the total factor productivity and market structure (industrial concentration)

relationship considering above two conflicting ideas. We use different panel data techniques for Turkish

manufacturing industries to research the relationship. Our contribution is that this is the first study for Turkish

manufacturing industry applying current data and complex panel data techniques.

This paper is organized as follows. Part 2 provides a brief information on the market structure and productivity

in the Turkish manufacturing industry. Empirical literature about market structure and productivity is briefly

evaluated in Part 3. Data, methodology and the empirical model of the study are presented in Part 4. The last

section concludes the analysis and evaluates our empirical results from a policy perspective.

1 Associate Professor, Abant Izzet Baysal University, Turkey, [email protected] 2 Master Student,Abant Izzet Baysal University, Turkey, [email protected]

14

2- Market Structure and Productivity in The Turkish Manufacturing Industry

In this section we evaluate the market structure and productivity for the Turkish manufacturing industry. CR4,

CR8, Herfidahl-Hirschman Index, mark-up, are used to measure market structure. We use four firm

concentration ratio CR4; the sum of the market shares of the largest 4 firms in the market.

As far as the market structure of the manufacturing industry is concerned, one can say that the Turkish

manufacturing industry exhibits an imperfectly competitive market structure. The share of very high (CR4≥70)

and high concentrated (50≤CR4<70) industries is almost 43% in the whole manufacturing inustry. In year 2015

the concentration ratio is very high in 103 manufacturing industry sectors (CR4≥70%), 72 sectors had a high

concentration ratio (50% ≤CR4≤ 70%), 118 sectors were characterized by a medium concentration ratio (30%

≤CR4 ≤ 50%) and 234 sectors had low concentration ratios (CR4 <%30). The average concentration ratio (CR4)

was 51% percent in 1981, 55% in 1985, 56 % in 1990, 57% in 2001, 54 % in 2006 and 48 % 2015 (TurkStat,

2010). There is slightly decrease in the CR4 between 1981-2015 period (TurkStat, Turkish Statistical Institute)

2018).

Very high concentrated industries in the Turkish manufacture industry are, electronics, computers, optical

instruments, petroleum, air and spacecraft industries, and beer, as expected. Common features of these industries

are high barriers to entry, high capital, advanced technology. For these reasons there are few firms in these

industries. On the other hand, textile, wear, furniture and bakery products are very competitive industries with

high number of firms.

The other important issue is productivity. It is important for economic growth and technological developments.

The productivity is very substantial for Turkey, as a developing country. Productivity is commonly defined as a

ratio between the output and the inputs. In other words, it measures how efficiently production inputs, such as

labour and capital, are being used in an economy to produce a given level of output. There are different measures

of productivity. One of the most widely used measures of productivity is Gross Domestic Product (GDP)

(OECD, 2019). Graph 1 shows the GDP and TFP for Turkey, between 1980-2014. There is close relationship

bwteen GDP and TFP. GDP and TFP are very instable for Turkey, after 1980, for the outward looking and

export oriented strategy period.

Graph 1. GDP and TFP for Turkey 1980-2014

Source: TurkStat, 2018

3- Literature Review

We can sum up the literature findings three groups. The first group finds positive relationship between market

structure and productivity, that is imperfect markets are more productive. (Schumpetrian hypothesis). For

example Cr´epon, B., Duget, E., and Mairesse J. ((1998) for French manufacturing industries), Blundell, R.,

-15

-10

-5

0

5

10

15

GDP TFP

15

Griffith, R., and Van Reenen ((1999) for UK manufacturing industries found evidence that supporting

Schumpeter. Taymaz (2000) the most known study for Turkey, found that imperfect markets are more create

technology.

The second group findings support the Arrow Hypothesis. Geroski (1990) and Nickell (1996) for England,

Masahito (2013) for Japon show the negative relationship. That is perfectly markets are more productive.

The third group studies remarked the nonlinear relationship between two. So there is an inverse U shaped

relationship. Gopinath, Pick and Li (2004) for US indicate a threshold level. Until the critical level of

concentration ratio, there is a positive relation and beyond this point the relation turns negative. Similarly,

Aghion et al (2005) argue this inverse U shaped relationship for UK.

Table 1. Literature Review briefly

Positive relationship

(Schumpeter Hypothesis)

• Cr´epon et al. (1998), -market share and R&D- French manufacturing industries in 1990.

• Blundell et al. (1999), -product market competition and innovative activity - 340 UK manufacturing firms gathered between 1972 and

1982.

• Taymaz (2000) for Turkey- imperfect markets create more technology.

Negative relationship

(Arrow Hypothesis)

• Geroski (1990), -market concentration and innovation- UK data of 73 industrial sectors from 1970 to 1979.

• Nickell (1996) -market share and TFP growth- 700 UK manufacturing firms between 1972 and 1986.

• Masahito (2013), -competition and the total factor productivity- Japanese industry

inverse U shaped

relationship • Gopinath et al (2004), - productivity-industrial concentration- 4 digit

SIC level data for US, 1958-96.

• Aghion et al. (2005), -inverted-U relation between market competition and innovation- 311 UK firms from 1973 to 1994.

Source: Masahito (2013).

4- Empirical Formulation

The analyses in this study are based on the data from the Annual Industry and Services Statistics and Annual

Foreign Trade Statistics collected by TurkStat. Our analysis is based on four digit industry level (199 industries)

covering the years 2003-2014. Our sample is unbalanced panel. We follow two-step econometric methods: In the

first step, we calculate a productivity variable, Total Factor Productivity (TFP) as a dependent variable and then

we estimate productivity- market structure relationship applying different panel data techniques such as pooled

OLS, fixed effects, random effects and dynamic panel data in the second step.

Although there are many alternative methods to calculate TFP, we use Levinsohn and Petrin (2003) methodology applying firm level data employing Cobb-Douglas production function. Levinsohn and Petrin

(2003) suggest semi-parametric production function estimators to overcome potential simultaneity and/or

selection biases from the OLS estimation of production functions. This methodology assumes that production

technology is in the form of Cobb-Douglas. In Levinsohn and Petrin (2003), output of the firm is proxied by

value added or gross revenue Namely, the logarithm of a firm’s output is explained by the logarithm of the freely

variable inputs – labour (lit), material inputs (mit), and energy input (eit) and the logarithm of the state variable-

capital (kit).

(1)

In this Cobb- Douglas production function, the error term is disaggregated into productivity shocks known to the

producer (vit) and unobservable shocks (εit).

We also calculate CR4 using firm level data fort he period 2003-2014. Graph 2 shows that CR4 decreases

moderately and TFP increases reasonably in the Turkish manufacturing industry in this period. There is a

negative correlation between CR4 and TFP. The coefficient is -0.74. It means while market concentration

decreases, productivity increases. The other words as competition increases, productivity increment, too.

16

Graph 2. CR4 and TFP for The Turkish Manufacturing Industry (2003-2014)

Though we observe such a negative raletionship between market concantration and productivity, we need more

detailed econometric estimations. We estimate above TFP equation applying different panel data techniques.

(2)

In equation (2), CR4 represents four-firm concentration ratio as the market structure, M shows import, X

symbolize export, W shows real hourly wages and kr is crisis dummy variable takes 2009=1, otherwise=0. CR42

is used to capture nonlinearity. e is error term.

Table2 presents the the estimation results. There are four different models in the Table 2. In the first column the

TFP equation is estimated pooled OLS. According to the model 1, there is a significant and negative relation

between TFP and CR4. This result is important to figüre out the market structure and productivity. As market

concentration decreases, productivity increases. This result supports Arrow’s theory: More competition, more

productivity. This result is not change the other three different models. For all four models, the coefficient of

CR4 is negative (almost 1) and significant. Similarly, the square of CR4 is positive and significant in the all four

models. This emphasis a nonlinear relationship between productivity and market structure. Real wages affects

TFP positively. This effect is significant and dramatically. It can be explained efficiency wage theory: higher

wages increase productivity. Export and import influence TFP moderately and significantly. Their coefficient are

smaller than we expect. Developing countries could transfer technology through foreign trade and this transfer

could help their productivity. Economic crisis affect the productivity negatively, as expected. However this

relationship is significant only in the dynamic model. We use lagged value of dependent variable (TFPt-1) to

estimate dynamic model. Its coefficient positive and insignificant.

All four models are significant according to Wald statistics. Since TurkStat does not collect the price indexed in

the four digit industry level, we could not use price indexes in the models. For this reason the coefficient of

determination R2 is small for first 3 models.

8,30

8,50

8,70

8,90

9,10

9,30

9,50

9,70

9,90

0,30

0,40

0,50

0,60

0,70

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

CR4 TFP

17

Table 2. Estimation Results, Dependent Variable TFP

Model 1

Pooled

OLS

Model 2

Fixed

Effects

Model 3

Random

Effects

Model 4

Dynamic

Model

TFPt-1

--- ---- ---- 0.0059

( 0.0172)

CR4 -0.9579***

(0.2885)

-1.0736***

(0.3106)

-0.9579***

(0.2885)

-1.0829***

(0.3925)

CR4

2

0.5820**

(0.2321)

0.5678**

(0.2471)

0.5820**

(0.2321)

0.6520**

(0.3159)

Lnw 0.2599***

(0.0203)

0.2568***

(0.0220)

0.2599***

(0.0203)

0.1796***

(0.0229)

Lnx 0.0232*

(0.0132)

0.0437***

(0.0141)

0.0232*

(0.0132)

0.0466***

(0.0166)

Lnm 0.0329***

(0.0125)

0.0139

(0.0143)

0.0329***

(0.0125)

0.0860***

(0.0166)

kr -0.0358

(0.0218)

- 0.0345

(0.0218)

-0.0358

(0.0218)

-0.0311**

(0.0157)

Wald st (prob) 0.0000 0.0000 0.0000 0.0000

R2

Instrument

number

0.1414 0.1110 0.1414

73

No. of obs. 2386 2386 2386 2187

*, ** and ***, %10, %5 and %1 significancy respectively. (Standard errors in the paranthesis)

5- Conclusion

This study investigates productivity (TFP) and market structure (industrial concentration, CR4) relationship

considering two conflicting ideas. We use different panel data techniques for Turkish manufacturing industries.

To the best of our knowledge this study represents the first to examine the relationship between market structure

and productivity for Turkish manufacturing industry applying current data and complex panel data techniques.

The results from this study support Arrow’s theory: more competition more productivity. While concentration

ratio decreases, productivity increases. The other results from the study; this negative relation is nonlinear. Real

18

wages, export and import affect TFP positively. Real wages’ effect is very substantial comparing with export and

import. Economic crisis affect the productivity negatively, as expected.

According to this results, competition is important for productivity. The role of Competition Authority is

important. Turkish Competition Authority is so new comparing US and Europe. It is established in 1994. It

should develop more effective policies and controls for competition. The role of technology policies are critical

for productivity and development. Many industries in Turkish manufacturing industry operate in low tecnology.

The large part of Turkish export is stand on low or middle technology products. In order to improve productivity,

Turkish government could invest the new technics and machines. Finally, industry policies also are prominent.

The government might implement selective industry policies that notice international competition.

References

Aghion, P., Bloom N., Blundell, R., Griffith, R., and Howitt, P. (2005), “Competition and Innovation: An

Inverted-U Relationship”, Quarterly Journal of Economics, Vol. 120 (2), pp. 701-728.

Arrow, K. J. (1962), “Economic Welfare and the Allocation of Resources for Innovation”, in Nelson, R. R. ed.,

The Rate and Direction of Innovative Activity, Princeton University Press, pp. 609-625.

Blundell, R., Griffith, R., and Van Reenen, J. (1999), “Market Share, Market Value and Innovation in a Panel of

British Manufacturing Firms”, Review of Economic Studies, Vol. 66 (3), pp. 529-554.

Cr´epon, B., Duget, E., and Mairesse J. (1998), “Research, Innovation, and Productivity: An Econometric

Analysis at the Firm Level”, NBER Working Paper No. 6696.

Geroski, P. A. (1990), “Innovation, Technological Opportunity, and Market Structure”, Oxford Economic

Papers, Vol. 42 (3), pp. 586-602.

Gopinath, M., Pick, D. and Li, Y. (2004) An empirical analysis of productivity growth and industrial

concentration in us manufacturing, Applied Economics, 2004, vol. 36, issue 1, pp. 1-7. Masahito, A. (2013) Does Competition Improve Industrial Productivity? An analysis of Japanese industries on

the basis of the industry-level panel data, RIETI Discussion Paper Series 13-E-098.

Nickell, S. J. (1996), “Competition and Corporate Performances”, Journal of Political Economy, Vol. 104 (4),

pp. 724-746.

Schumpeter, J. A. (1942), Capitalism, Socialism, and Democracy, New York, NY: Harper.

Taymaz, E. (2000), “Teknolojik Gelişme ve Piyasa Yapısı, İmalat Sanayii Üzerine Bir İnceleme”, (der.),

Rekabet Kurumu Perşembe Konferansları.

TurkStat, (2018) Sanayi ve Hizmet Sektörlerinde Yoğunlaşma İstatistikleri,

http://tuik.gov.tr/PreTablo.do?alt_id=1036

OECD, (2019) “Defining And Measuring Productivity” http://www.oecd.org/sdd/productivitystats/40526851.pdf

https://econpapers.repec.org/article/tafapplec/

http://tuik.gov.tr/PreTablo.do?alt_id=1036

http://www.oecd.org/sdd/productivitystats/40526851.pdf

19

PARAMETER ESTIMATION WITH JACKKNIFE AND WEIGHTED

MEDIAN IN NON-PARAMETRIC REGRESSION ANALYSIS*

Necati Alp ERİLLİ1

1Sivas Cumhuriyet University, Dept. of Econometrics, Sivas, Turkey, [email protected]

Abstract

Regression analysis is one of the most commonly used estimation methods in statistical area. However, many assumptions

are required for a successful regression analysis. Non-parametric regression analysis methods provide better results when

these assumptions cannot be obtained. One of these methods, Theil-Sen method, is based on the median parameter. In this

study, the weighted median parameter was used instead of median parameter in Theil-Sen method and the success of the

proposed method was tested by Jackknife resampling method. It is observed that the proposed method gives better results

compared to the classical nonparametric methods, especially in the data with outliers.

Key Words: Theil-Sen Regression Analysis, Jackknife Method, Weighted-Median, Optimum, Hodges-Lehmann.

1. Introduction

Regression analysis; is one of the frequently used techniques in statistical estimation studies. The purpose

is; to investigate the relation between dependent and independent variable(s) estimated to be cause - effect

relation between them, to describe the assumed relation between variables as a function and to define this

relationship with a model. It is important to know the mathematical form of the relationship between dependent

and independent variables is known in advance. In such cases, there is a need for regression methods that allow

the assumption of linearity in the parametric regression to be stretched in order to make better predictions. These

methods are regression models known as nonparametric and semi-parametric regression methods.

Nonparametric regression analysis is a method that is used in situations where some assumptions that are

valid for parametric regression methods cannot be achieved and which give successful results. It is used median parameter in nonparametric analysis instead of arithmetic mean. Weighted median is a type of median used with

weights of samples in the data. It is useful as an estimator of central tendency, robust against outliers. It allows

for non-uniform statistical weights related to, e.g., varying precision measurements in the sample.

In previous works, weighted median is used as an alternative to Theil-Sen method [1,2,3]. In this study, the

Theil-Sen method, which is frequently used in nonparametric regression analysis [4,5], is proposed to use the

Jackknife method and Weighted Median methods together. The median parameter does not usually reflect the

effect of outliers. The weighted median calculates the model contribution if the outliers are small, since it gives

weight to each observation value. Optimum and Hodges-Lehmann calculations which can be used in constant

parameter calculations in Theil-Sen method were also investigated by Jackknife method. In different model

constructions, the advantages and weaknesses of Jackknife and Weighted Median methods are determined and

interpreted according to classical nonparametric regression methods.

1.1.Short Literature View

Theil-Sen regression method has been known and used for many years. Different applications in the

literature are being tried and the effectiveness of the method in different structures is being investigated. Fernandes ve Leblanc, investigated for Parametric (Modified Least Squares) and non-parametric (Theil-Sen)

consistent predictors are given for linear regression in the presence of measurement errors together with

analytical approximations of their prediction confidence intervals. Lavagnini et al. [6] reports the combined use

of the nonparametric Theil–Sen (TS) regression technique and of the statistics of Lancaster–Quade (LQ)

concerning the linear regression parameters to solve typical analytical problems, like method comparison,

calculation of the uncertainty in the inverse regression, determination of the detection limit. Wilcox [7] compares

the small-sample efficiency of the extended Theil-Sen estimator to the modified Buckley-James estimator when

the predictor is random. Zhou and Serfling [8] introduce multivariate spatial U-quantiles and develop a

corresponding Bahadur–Kiefer representation extending the classical Theil–Sen nonparametric simple linear

regression slope estimator, and for robust estimation of multivariate dispersion. Peng et al. [9] obtain the strong

consistency and asymptotic distribution of the Theil–Sen estimator in simple linear regression models with arbitrary error distributions. Siegel [10] used the repeated median algorithm is a robustified U-statistic in which

nested medians replace the single mean. Dang et al. [11] introduced Theil-Sen estimators in multiple linear

regression analysis.

* This study was supported with the project numbered CUBAP İKT-120.

http://www.wikizeroo.net/index.php?q=aHR0cHM6Ly9lbi53aWtpcGVkaWEub3JnL3dpa2kvQ2VudHJhbF90ZW5kZW5jeQ

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2. Theil-Sen Regression Method

This method was first proposed by Theil [4] and the procedure is firstly known as Theil’s Method. After

Sen [5] highlighted the relationship to Kendall’s tau it is named as the Theil–Kendall or Theil-Sen method. Theil

proposed estimating the slope of a regression line as the median of the slopes of all lines joining pairs of points

with different x values [4]. For a pair ,i ix y and ,j jx y the relevant slope is

j i

ij

j i

y yS

x x

. There will

be ( 1)

2

n n slopes for any data. The

1̂ statistic, which is the estimator of the parameter1 in simple

regression analysis, is calculated as the median of the slope values: 1ˆ

ijMedian S . Theil suggested for the

estimation of the intercept as 0 1ˆ ˆ

i iMedian y Median x [13].

2.1. Alternative Intercept Parameter Calculations with Theil-Sen Slopes

Some alternative calculations have been proposed in comparison to Theil's idea of finding the intercept

parameter. Let’s define 1ˆ

i i id y x calculated for all observations where 1̂ is calculated with the Theil-

Sen method. Hodges-Lehmann method for 0̂ is defined as the mean value of 0

ˆi id Mean d and

optimum method for 0̂ is defined as the median value of 0

ˆi id Median d .

The optimum approach does not require the assumption of symmetrically distributed id . It is better suited

especially for data with outliers. On the other hand, Hodges-Lehmann method may not be viable for data with

outliers [13,14,15].

2.2. Test of significance of slope parameter

To test 1ˆ 0 hypothesis, we can use the test statistics given in Equation.2 and 3:

( )

Ut

SD U (1)

where

1( )

2i i

nU rank y x

and

21

12i

n nSD U x x

(2)

The approximate p-value of the test is calculated to be Prob Z t , where Z is a random variable

having a standart normal distribution [1]. 2.3. Weighted Median

Median of a list of number is obtained by putting the numbers in increasing order and selecting the number

in the middle of the ordered list. The weighted median of a list of numbers ix with weights iw is obtained as

follows:

First, put the numbers ix in increasing order. By changing the indices, we can arrange so that

1 2 1 0.5kx x x L . The weights iw should be nonnegative and should add to 1.

Each weights proportional to the x-distance between the pair of points, that is given in Equation.3:

i j

ij

i j

x xw

x x

(3)

Find the index k such that;

1 2 1

1 2 1

0.5

0.5

k

k k

w w w

w w w w

L

L (4)

Then kx is the weighted median. Sometimes it happens that there is an index k such that

1 2 1... 0.5kw w w . Then 1 / 2k kx x is the weighted median [1].

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2.4. Jackknife Method

Jackknife method is a resampling technique especially useful for variance and bias estimation. [16]

introduce a technique for reducing the bias of a serial correlation estimator based on splitting the sample into two

half-samples. In his 1956 paper, he generalized this idea for testing hypotheses and finding confidence intervals

where traditional methods are not applicable or not well suited. The name “Jackknife” was given by Tukey in

1958 so sometimes it is also known as Quenouille-Tukey Jackknife method [17-18].

The jackknife procedure is very useful when outliers are present in the data or the dispersion of the

distribution is wide. In the jackknife method, it systematically recomputes the statistic, leaving out one

observation at a time from the observed sample. This is used to estimate the variability of statistic from the variability of that statistic between subsamples. This avoids the parametric assumptions that generally used in

obtaining the sampling distribution of the statistic to calculate standard error. Thus, this can be considered as a

nonparametric estimate of the parameter.

The jackknife estimator of a parameter is found by systematically leaving out each observation from a

dataset and calculating the estimate and then finding the average of statistics of these calculations. Let us assume

that we have a sample size n. The jackknife estimate is found by aggregating the estimates of each (n−1) -sized

sub-sample.

The method has two main goals; reducing the bias of point estimators and generating broadly applicable

and reasonably powerful test producers for problems where classical test producers are sensitive to nonnormality

of the underlying populations.

Jackknife method is used for bias removal. As we know that mean-square error for an estimator is equal to the square of bias plus variance of the estimator. If bias is much higher than variance then under some

circumstances Jackknife could be used.

3. Application

In application part Theil-Sen regression is performed with the Jackknife method and Weighted Median

methods together. The proposed method is tested in 3 different model constructions where the number of outlier

values is increased by 25% and 50% or some observations were written wrongly in the original data set. The

advantages and weaknesses of Jackknife and Weighted Median methods are determined and interpreted

according to classical nonparametric regression methods with Mean Absolute Error (MAE):

1

1ˆ

n

i j

j

MAE y yn

(5)

Although these two criteria were calculated for all data sets, the method selection was made according to

the MAE value in this study. This is because MAE is more robust to outliers since it does not make use of

square. On the other hand, MSE is more useful if we are concerned about large errors whose consequences are

much bigger than equivalent smaller ones.

First data set is given in Table.1 which has 30 samples and taken from [19]. Sample data consist of age

(independent variable) and systolic blood pressure (dependent variable) values for 30 individuals aged 17 to 69

years.

Table.1 Data Set.1

Y 144 220 138 145 162 142 170 124 158 154 162 150 140 110 128

X 39 47 45 47 65 46 67 42 67 56 64 56 59 34 42

Y 130 135 114 116 124 136 142 120 120 160 158 144 130 125 175

X 48 45 17 20 19 36 50 39 21 44 53 63 29 25 69

The scatterplot of the variables is given in the Figure.1.

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Figure.1 Scatterplot for Data.1

The estimation model for the sample data was obtained by 14 different methods. These methods are;

Ordinary Least Squares, Mood-Brown, Theil-Sen, Hodges-Lehmann, Optimum, Theil-Sen with Weighted

Median and Jackknife alternatives (Slope with median and mean, separately) [1, 4, 5,13, 21]. Parameters which

obtained from these methods and MAE values are given in Table.2. Table.2. Parameter Estimations and MAE Results for Data.1

Method Intercept Slope MAE

OLS 98.71472 0.97087 9.541101333

Mood-Brown 72.8 1.45 12.20666667

Theil-Sen 95.5 1 9.7

Theil-Sen Hodges-Lehmann 97.4 1 9.56

Theil-Sen Optimum 97 1 9.533333333

Theil-Sen W. Median 97.83333 0.948717949 9.584615829

Theil-Sen W. Median - Hodges-Lehmann 99.71452991 0.948717949 9.547008547

Theil-Sen W. Median - Optimum 99.21794872 0.948717949 9.547008547

Jackknife Theil-Sen W.Median (Slope with Median) 97.83333 0.948717949 9.584615829

Jackknife Theil-Sen W.Median Hodges-Lehmann (Slope with Median) 99.21794871 0.948717949 9.547008547

Jackknife Theil-Sen W. Median Optimum (Slope with Median) 102.4881721 0.948717949 10.30138409

Jackknife Theil-Sen W. Median (Slope with Mean) 102.1806241 0.853173097 9,619977511

Jackknife Theil-Sen W. Median Hodges-Lehmann (Slope with Mean) 99.7145299 0.853173097 10.0171643

Jackknife Theil-Sen W. Median Optimum (Slope with Mean) 104.0267876 0.853173097 9.725789685

As fort the results given in Table.2, we can clearly say that Theil-Sen with Optimum method has the best

results for MAE.

Secondly, the above calculations were repeated by creating an outlier of 25% and 50% of the original data.

The aim is to investigate the effect of the proposed method on deviating values in the data. Figure.2 shows the scatterplot for the data with 25% and 50% outliers.

Figure.2 Scatterplot with 25% and 50% for Data.1

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Results for Data.1 with 25% outliers is given in Table.3. Minimum MAE value belongs to Theil-Sen

Weighted Median with Optimum method.

Table.3. Results for Data.1 with 25% Outliers


OLS 94.886 1.08 14.5036

Mood-Brown 83.910365 1.141379 15.47561

Theil-Sen 95.556674 0.992395 13.90863

Theil-Sen Hodges-Lehmann 99.3487547 0.992395 14.22021

Theil-Sen Optimum 96.4425995 0.992395 13.88207







Jackknife Theil-Sen W. Median (Slope with Mean) 95.41420478 0.995432734 13.91983



Results for Data.1 with 50% outliers is also given in Table.4. Minimum MAE value belongs to Jackknife

Theil-Sen W. Median Optimum (Slope with Mean) method.

Table.4. Results for Data.1 with 50% Outliers


OLS 94.886 1.08 28.27093

Mood-Brown 64.72727273 1.727272727 27.64848

Theil-Sen 52.87426557 1.902467685 26.64896












Second data set is Pilot-Plant data and given in Table.5 which has 20 samples [19]. Dependent variable is

named as Titration and independent variable named as Sampling. Scatterplot of Data.2 also given in Figure.3. Table.5. Pilot-Plant Data

Y 76 70 55 71 55 48 50 66 41 43 82 68 88 58 64 88 89 88 84 88

X 123 109 62 104 57 37 44 100 16 28 138 105 159 75 88 164 169 167 149 167

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Figure.3 Scatterplot of Data.2

Parameters which obtained from 14 methods for Data.2. estimation and MAE values are given in Table.6.

Table.6. Parameter Estimations and MAE Results for Data.2


OLS 35.45827 0.321608 1.0145724

Mood-Brown 35.4814815 0.328042328 1.15978836

Theil-Sen 35.56 0.32 0.996












As for the results in Table.6. it is easily seen that minimum MAE value belongs to Theil-Sen Hodges-

Lehmann method.

In the next application. let's assume that the last observation of Data.2 (88) is entered incorrectly as 8.8 and

re-analyze the data. Table.7. Parameter Estimations and MAE Results for Data.2-1


OLS 42.14278 0.2183136 8.48560296

Mood-Brown 37.3703704 0.296296296 5.374814815

Theil-Sen 33.6489362 0.319148936 5.917446809






Theil-Sen W. Median - TriMean 35.6527778 0.319444 4.9495782


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Jackknife Theil-Sen W. Median (Slope with Median) 33.609671 0.31952468 5.920340033



As we look at Table.7. it is easily seen that minimum MAE value belongs to Theil-Sen Optimum method.

Last application is Forearm data taken from [1] and given in Table.8. Scatterplot of variables also given in

Figure.4.

Table.8. Forearm Data

Y 165.8 169.8 170.7 170.9 157.5 165.9 158.7 166 158.7 161.5 167.3 167.4 159.2 170 166.3 169 156.2

X 28.1 29.1 29.5 28.2 27.3 29 27.8 26.9 27.1 27.8 27.3 30.1 27.3 30.9 28.8 28.8 25.6

Y 159.6 155 161.1 170.3 167.8 163.1 165.8 175 159.8 166 161.2 160.4 164.3 166 167.2 167.2

X 25.4 26.6 26.6 29.3 28.6 26.9 26.3 30.1 27.1 28.1 29.2 27.8 27.8 28.6 27.1 29.7

Figure.4 Scatterplot of Data.3

Estimation results for parameters and MAE values for Forearm data is given in Table.9. Table.9. Parameter Estimations and MAE Results for Forearm Data


OLS 92.20928 2.58185 2.938649

Mood-Brown 55.8692308 3.846153846 3.256643

Theil-Sen 91.4406504 2.674796748 3.188618












Jackknife Theil-Sen W. Median Optimum (Slope with Mean) has minimum MAE value.

In the next application study. let's assume that the last observation is written incorrectly as 16.72. If we

apply 14 regression analysis to this new data structure we will get results as given in Table.10.

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Table.10. Parameter Estimations and MAE Results for Forearm Data-1


OLS 214.84042 -1.95676 10.70436

Mood-Brown 139.23048 0.97657553 8.1661

Theil-Sen 95.9342105 2.513157895 7.744131







Jackknife Theil-Sen W. Median Hodges-Lehmann (Slope with Median) 90.2544108 2.488888889 9.217566





As for Table.10; Jackknife Theil-Sen W. Median (Slope with Median) has the minmum MAE value for the

Forearm data.

For all data sets which analyzes performed above. significance tests are tested for the slope coefficients

separately. As an example for Data.1; 1 is found significant at 0.05 and 0.01 level with values of

32134.4313

( ) 75.0646

Ut

SD U . Only slope parameters calculated by OLS method for Table.7, Table.9

and Table.10 were found insignificant and all other results were found significant under 0.01 confidence level. 4. Conclusion

Model prediction studies have always been one of the most preferred subjects of sciences such as statistics,

economics and econometrics. There are many different methods defined by different model structures and almost

all of them have the same object: To make strong predictions with significant parameters. The OLS method is

the most powerful estimation method when assumptions are achieved. In cases where assumptions are not

provided, strong estimations can be obtained with non-parametric or semi-parametric methods.

In this study, Theil-Sen method, which is frequently used in nonparametric regression analysis, is discussed.

Theil-Sen method was used to calculate the median parameter. With this study, weighted median is used instead

of median parameter and with Jackknife method was used together in Theil-Sen method.

In this study, 7 data structures with 3 different data were studied to investigate the effect of weighted

median parameter with Jackknife on Theil-Sen method. Jackknife Theil-Sen W. Median Optimum (Slope with Mean) method was found to have less MAE values than other Jackknife methods. When we look at the overall

study, Theil-Sen Optimum method was found to give better results. However, this method has been replaced by

weighted median studies in data with outliers. If we look back Jackknife results, it can be sait that optimum

method with mean and median slopes has showed that they will be good alternatives for nonparametric

regression analysis.

Instead of the median parameter, it is used the weighted median to give weights to the observations. Thus,

the contribution of observations to data is fully reflected. With Jackknife method, smaller confidence intervals

were obtained by minimizing standard errors. The use of weighted median parameters and Jackknife method in

multiple regression analysis is also considered as further studies.

References

[1] Birkes D., Dodge Y. Alternative Methods of Regression. John Wiley & Sons Inc.. NY. USA; 1993.

[2] Adichie JN., 1967. Estimates of Regression Parameters Based on Rank Tests. Annals of Mathematical Statistics. 38. p. 894-904.

[3] Kraft CH., Van Eeden C., 1972. Linearized Rank Estimates and Signed-Rank Estimates for the General Linear Hypothesis. Annals of

Mathematical Statistics. 43. p. 42-57.

[4] Theil H., 1950. A Rank Invariant Method of Linear and Polynomial Regression Analysis. III. Nederl. Akad. Wetensch.Proc., Series A.

53. 1397‐ 1412.

[5] Sen PK., 1968. Estimates of the regression coefficient based on Kendall's tau. Journal of the American Statistical Association. 63: 1379–

1389.

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[6] Lavagnini I., Badocco D., Pastore P., Magno F., 2011. Theil–Sen nonparametric regression technique on univariate calibration, inverse

regression and detection limits. Talanta. 87. 180– 188.

[7] Wilcox RR., 1998. Simulations on the Theil-Sen regression estimator with right-censored data. Statistics & Probability Letters. 39. 43-

47.

[8] Zhou W., Serfling R., 2008. Multivariate spatial U-quantiles: A Bahadur–Kiefer representation, a Theil–Sen estimator for multiple

regression. and a robust dispersion estimator. Journal of Statistical Planning and Inference. 138.1660 – 1678.

[9] Peng H., Wang S., Wang X., 2008. Consistency and asymptotic distribution of the Theil–Sen estimator. Journal of Statistical Planning

and Inference. 138. 1836 – 1850.

[10] Siegel AF., 1982. Robust regression using repeated medians. Biometrika. 69. 1. pp. 242-4.

[11] Dang X., Peng H., Wang X., Zhang H., 2008. Theil-Sen Estimators in a Multiple Linear Regression Model. Olemiss Edu. 2008.

[12] Sprent P. Applied Nonparametric Statistical Methods. Chapman and Hall Pub.. London. UK; 1989.

[13] Hodges. JL., Lehmann. EL., 1963. Estimates of location based on rank tests. Ann. Math. Statist. 34. 598–611.

[14] Lehmann. EL., Dabrera HJM. Nonparametrics: Statistical Methods Based on Ranks. Holden-Day Inc.. SF. USA; 1975.

[15] Erilli NA., Alakuş K.. 2016. Parameter Estimation In Theil-Sen Regression Analysis with Jackknife Method. Eurasian Econometrics.

Statistics & Empirical Economics Journal. v.5. p:28-41.

[16] Quenouille. MH., 1949. Approximate tests of correlation in time series. Journal of the Royal Statistical Society. Series B. 11. 68-84.

[17] Quenouille. MH., 1956. Notes on bias estimation. Biometrika 43. 353-360.

[18] Tukey. JW., 1958. Bias and confidence in not quite large samples. Annals of Mathematical Statistics. 29.614.

[19] Spath H. Mathematical Algorithms for Linear Regression. London: Academic Press. pp 304.; 1992.

[20] Daniel. C., Wood. FS. Fitting Equations to Data. John Wiley&Sons. New-York;1971.

[21] Mood AM., Brown GW., 1951. On Median Tests for Linear Hypotheses. Proceedings of the Second Berkeley Symposium On

Mathematical Statistics and Probability. Berkeley and LA: The Univ. of California Pres.. USA.

28

THE NEW GENERATION AND THE WORLD OF WORK Regina Zsuzsánna Reicher, PhD

Óbuda University Keleti Faculty of Business and Management, Institute of Enterprise

Management Tavaszmező str. 17., 1084, Budapest, Hungary

[email protected]

Abstract:

Europe is facing a severe demographic crisis. Advanced societies can be characterized by aging. The declining number of

births has now resulted in a radical decrease in the number of young workers in Hungary. This problem is further exacerbated

by the strengthening of structural unemployment. Currently, employers are fighting for the Y and Z generations on the labour

market. However, this young generation changes jobs 3-4 times more often than the older generation. Typically, they are not

loyal and it is difficult to retain them in a job for more than 2-3 years. The current study briefly introduces each generation,

then discusses the differences between generations. The research is trying to explore under what conditions would a fresh

graduate from a university undertake a job and what circumstances would make an employee satisfied and content.

Keywords: Generation Z, CSR activity, structured unemployment, working habits

JEL Classification: J11 Demographic Trends, Macroeconomic Effects, and Forecasts

M 51 Firm Employment Decisions, Promotions

M14Corporate Culture Diversity Social Responsibility

O15 Human Resources, Human Development, Income Distribution, Migration

1 Generations – is each of them a different world?

First of all, let us discuss what generation means for us. The definition we chose and approved is the

concept used by KSH (Hungarian Central Statistical Office) since 1970, according to which:

“Generation is a specific type of cohort population definition: it refers to all of the people born and

living at about the same time, regarded collectively. The members of the generation should experience

the same significant events in terms of the status and issues of population synchronously (e.g.

obtaining some level of school qualification, marriage, child birth, employment, death, etc.) thus the

date and frequency of these events can be compared to the time factors.” (KSH, 2015) Reeves and Oh

(2008) have also reviewed the generational differences and summarised the findings from the

references. They also make suggestions for the definition of generation. In their opinion, generation

means the people born in the same period of time (this period is becoming shorter and shorter); that is

the members of a generation have similar features on the basis of historical experiences, economic and

social conditions, technological development and other societal changes. They note, however, that

these conclusions were reached on the basis of limited data, therefore these should be used by also

considering the reliability and validity of these data. The researchers do not agree on exactly what

criteria must or should be involved in the exploration of generational differences – different

geographical location, different technical/ecological/business innovation, cultural differences, etc.

Many of our previous articles discuss in detail that the basis of generation research was provided by

William Strauss and Neil Howe American historians with their publication Generations published in

1991 (Kolnhofer-Derecskei & Reicher 2017a). In addition to the introduction to generational features

in their paper, they also reveal that from geographical aspect there might be differences in the

borderlines that can be observed in some areas. For instance, the generations growing up in the Eastern

European and Central European region met the same change-inducing factors at different stages than

their Western European or American counterparts.

On the basis of sociological studies, currently the following generation ranges can be observed (Table

1)

mailto:[email protected]

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Table 1. Generations in Hungary

By Strauss By Róbert and Valuch By Kolnhofer-Derecskei

and Reicher

Veterans Born before 1945 Born before 1949 These generations were

not discussed in their

earlier papers Baby boomers 1945-1964 1949-1962

Generation X 1965-1980 1963-1979 Born before 1982

Generation Y 1981-1995 1980- 1995 1983-1996

Generation Z 1996- 1996- 1997-2010

Alfa Generation Born after 2010

Source: own edition.

Strauss and his co-author took the American processes of socialization as a basis while Róbert and his

co-author analysed the Hungarian historical conditions. Due to the frequent political and economic

changes, they often distinguished several separate stages within a generational range.

According to our research (Kolnhofer-Derecskei & Reicher 2017b), the borders slightly shifted, and

our results show that these are not very sharp. On the basis of our surveys, a so-called Cuspers cohort

could be identified, who were born close to the point of generation shift and their behaviour and

attitudes show the features of both generations, therefore they cannot be classified as belonging clearly

to one generation.

The differences among the conclusions of Hungarian research projects, of course, can be due to the

fact that the researchers approached the topic from different aspects. Valuch et al examined the

generations from the aspect of information technology and its impact on generations (Róbert & Valuch

2013). Annamária Tari analysed the subject from sociological and mostly psychological angle (Tari

2010). All the researchers agreed, however, that the communication of new generations is very

different from the previous generations and the HR experts and psychologists have drawn the attention

of employers to the fact that this would lead to serious consequences among employees, managers and

staff. Moreover, the behaviour of the members of generations may also diverge in many areas. They

have dissimilar ideas about the employment opportunities, working hours, family, possible leisure

time activities, immobility and mobility. Several researchers reported that during the same period of

time the members of generation Y change jobs four times more on average than employees belonging

to generation X. As a consequence, the fluctuation – high staff turnover - can make the everyday

operation of companies rather difficult (hrportal.hu). This process can be a very serious cost factor in

the life of a company because every company invests in the employee in the new starter phase and the

invested resources will never return if the employee cannot be retained. Moreover, the new labour

force requires again time, energy and money from recruitment to training (Dajnoki & Fenyves, 2014).

The Hungarian references discuss the examination of cooperation among generations in the workplace

environment. The research by Bencsik and Eisingerné reveals that the different generations face

significant challenges during their everyday interactions. It is a serious task for the management of

companies to handle the conflicts between generations and to sort out the workplace challenges of

each generation, as well as the everyday problems of employees living in different forms of family and

different cycles of family life (Bencsik & Eisingerné, 2013).

According to Dale Carnegie’s research, almost three-fourth of employees are not committed fully to

their job. Another, even bigger problem is that major part of middle managers have low professional

as well as emotional commitment (dalecarnegietraining.hu). The discontentment of middle managers

may also be due to the fact that the companies in Hungary still use promotion as the only reward for

good work. Therefore, if somebody is successful in an area can move up on the career ladder. As a

consequence, after some time they will naturally reach a position where they are unable to perform

well and it leads to frustration and dissatisfaction (Laurence, 1989).

30

As a result of all the above detailed, the labour market will probably be totally transformed in the

future. The development of robotization is accelerating and in those areas, where the human resource

can be replaced, the robot technology will take over the role of people (vg.hu). Parallel with this, of

course, the HR profession is also going through enormous changes because handling fluctuation

imposes great burden on the experts working in this field. Accordingly, it can be expected in this area,

too, that new technologies will be implemented in the future (g7.hu). Such new technology is, for

example, the network-based approach to the replacement system, which would enable to explore the

hidden replacement potential of the organisations (Szilágyi, 2015).

The role of network capital seems to be strengthening, especially on the employers’ side in case of

skills shortages, but the employees also use their network of connections when looking for jobs

(old.tarki.hu). Kádár’ research confirms that those who carefully plan their career and look for jobs,

give high priority to their network of connection built during their higher education studies (Kádár,

2017).

2 Empirical research

Our research consists of two complex parts. The first survey was carried out in 2015, its objective was

specifically the analysis of job search habits of generation Y. The classification of generations was

also compiled during this on the basis of a focus-group examination performed in the frames of a

qualitative research. Our aim was to find out how the members of generation Y and X can characterize

themselves and how they describe the other generation.

As heads of research we found the summary sheet compiled by Patterson (2005) to be the most

informative. This table summarizes the different features of generations – in terms of work

characteristics – as follows:

31

Figure 1. Generation stereotypes

Source (Patterson, 2005.)

The table was left in its original (English) language on purpose because later the Hungarian

equivalents were defined with the help of students. The parameters listed in the table were mixed and

the students were asked to select from them those features they regard as typical generation Y features,

and those, which are totally the opposite, in other words: not generation Y feature at all.

Finally, after multiple pilot rounds, the following features were kept, which clearly distinguish the

members of generation X and Y:

workaholic

family-oriented

conservative/old-

fashioned

conscious consumer

respecting diversity

permanent sensation

seeking

technology

addiction

environmental

protection

risk avoiding

creative

lack of personal

relations

complaining

urban citizen

optimistic

trend-follower

use of foreign

languages

immobilized

loyalty

global citizen

insensitive

knowledge of rights

conformist

novelty seeking

assertiveness

prolonged higher

education studies

self-consciousness

receptive to

innovation

force of habit

social media

presence

self-confidence

looking for peace

and quiet

The questionnaire for the quantitative research was compiled on the basis of this selection. The

questionnaire was sent out to all the students (approx. 12 thousand people) and the respondents had to

assess their own generation on the basis of the selected features. Later the respondents were classified

into generations according to their age. After checking the answers from almost 800 respondents, there

were 783 evaluable responses. Out of the 783 respondents 662 persons belonged to generation Y. Both

full-time and part-time students participated in the survey, therefore some respondents had had

substantial work experiences and some had just started to look for jobs or worked as interns at the time

of the survey.

32

Figure 2 Distribution of respondents by generation

Source: own edition

Comparing the five features regarded by the respondents as the most important for their own

generation, the differences originating not only from the generation gap, but also from the age

characteristics become clearly obvious. Comparing the relative frequency of responses from students

belonging to generation X and Y, the following differences can be seen:

Figure 3 Word frequency analysis of generational features

Source: own edition

The results regarding the examined features were summarised in a table, in order to introduce those

which show significant differences.

11%

85%

5%

X Y Cuspars

33

Table 2. Significant differences between the features

Significantly different: Not significantly different:

- Workaholic

- Family-oriented - Conservative

- Conscious consumer

- Technology-addicted (savvy)

- Risk-avoiding

- Trend follower

- Use of foreign languages

- Immobilized

- Loyal

- Insensitive

- Knowledge of rights

- Rule follower - Prolonged higher education studies

- Receptive to innovation

- Force of habit

- Social media presence

- Looking for peace and quite

- Embracing diversity and changed

valued - Permanent sensation seeking

- Environmental protection

- Creative

- Lack of personal relations

- Complaining

- Urban citizen

- Optimistic

- Global citizen

- Novelty seeking

- Assertiveness

- Self-consciousness - Self-confidence

Source: Kolnhofer-Derecskei et al 2017a

Among the examined concepts there are, of course, age characteristics (marked in blue), furthermore

there are features originating in personality differences (in green) and finally there are some, which are

related to the issues of our days (in purple) regardless of age. Several references and a number of

researchers discuss, which features can be regarded as generation traits and which features are specific

rather to the particular stage of life.

It is indisputable that one of the most typical characteristics of generation Y – who are also referred to

as young, digital natives – and the following generation, generation Z is their internet use habits and

their relation to digital devices. Therefore, our research covered, among other things, the

consciousness regarding the internet use and the efforts these groups make in terms of safe internet

use.

Figure 4. Security settings

Source: own edition

It was presumed that those who use the different internet platforms more frequently would be more

careful when setting up their profile. The significance test, however, did not prove our hypothesis,

although it deviated from the 95% reliability belonging to the hypothesis analysis only with 2

thousandths (p=0,07)

0,00%

10,00%

20,00%

30,00%

40,00%

50,00%

60,00%

C X Y

Not at all

Part

very fully

34

Typically, however, those, who would search for the job applicant on the Internet, would also manage

their profile in case of applying for a job (p=0,0022). It means that the respondents are fully aware that

the settings of their profiles can be an important factor during the assessment of their job applications.

Our research also explored what websites they typically used for job search and what sites they

planned to use in the future. In this regard there were no big surprises in the findings, because almost

95% of respondents were registered on Facebook. Therefore it was understandable that major

proportion of respondents mentioned this platform in connection with job search. There was, however,

a significant difference in the use of the two extremely important job search websites (Facebook vs

LinkedIn). While Facebook was preferred mainly by those with lower school qualifications, the

website of LinkedIn was used to a greater extent by people with master degrees.

Figure 5. Job search on the Internet

Source: own edition

It was a surprise, though, that people think about job search as an activity without any costs. They do

not calculate with transport costs or other additional costs. Another interesting thing is that the

different genders think about salaries differently. Women would ask for significantly lower wages at a

job interview than their male counterparts, even if they have the same qualifications in the same or

similar professional field.

The research was continued with interviews based on qualitative methodology conducted with

employers and employees, as well as with focus group examinations. The aim of the second

examination was to map the job search and working habits of generation Z. Generation Z has just

entered the labour market, therefore exploring their behaviour patterns would help to draft job

descriptions, job advertisements and salary bands, as well as developing a range of fringe benefits. The

aim of our questionnaire survey was to analyse these areas. The questionnaire is being compiled at the

moment.

Three areas were discussed with the participants during the focus group examinations. The groups

consisted of 10-12 young people, students of Óbuda University, belonging to generation Z. Altogether

57 students participated in the survey, including full-time students as well as part-time students who

were already in employment.

The group tried to identify the concept of a good manager, then they tried to define and describe the

bad manager.

In the third question, they had to collect what aspects they regarded important in a job advertisement;

on what basis they would decide if they could apply for similar jobs, offering different conditions.

35

Figure 6. Job selection criteria

Source: own edition

It is clearly obvious from the word cloud what the most important job selection criteria are. Payment is

in the first place, but the participants gave high priority to the brand value of the workplace, to the

different company programmes in connection with CSR or the activities of the company regarding

social responsibility. They liked flexible working hours, possibilities of working from their home

office and they also regarded the professional development opportunities important.

3 Summary

The structural skill shortage is increasingly obvious in Hungary. While there is labour surplus in some

areas and those people, who intend to work in these jobs often have to face unemployment for several

months, there is serious labour force shortage in other areas. Of course, the reason for this is not only

the changing population pyramid of the declining population – according to KSH data – but also the

increasing ratio of older generation. The reason is not only the high number of emigrants, typically

highly qualified professionals leaving Hungary in the recent years. It may also contribute to this

phenomenon, if the company reacts to the external factors too late, the human resource management is

not functioning well, the age distribution of employees, the expected date of retirements, their

payment grade, position, their satisfaction is not examined when the fluctuation starts to become a

serious issue and no preventive measures are taken against resignations. The aim of our research is to

highlight the most important generation stereotypes and reveal the attitudes of young employees in the

area of job search and working habits. It is examined where and how they are looking for jobs, how

they select the job, where they are happy to work. Our results show that - just like in their everyday

life - they rely on the online space even regarding their job search,. The use of these platforms,

however, is mostly not conscious. They do not update their resumes, do not attach great importance to

adjusting their introductory documents to the expectations of the company. They often have unrealistic

expectations regarding their managers, but when they choose a workplace, the brand value of the

company and the area of social responsibility are also important for them, in addition to the payment.

It is a question, of course, how much they will live with these possibilities later in their workplaces.

The interviewees working in executive positions, were a bit sceptical about this.

4 References

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10-25.

36

[2] Dajnoki K. – Fenyves V. (2014): Fluktuációs sajátosságok feltárása egy multinacionális szervezet példáján

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Universitatis Selye, International scientific journal, Vol. 6, No. 2., Komarno, pp. 89-100.

[5] Kolnhofer-Derecskei, A. – Reicher, R. – Szeghegyi, Á. (2017a): The X and Y Generations’ Characteristics

Comparison. Acta Polytechnica Hungarica. Vol. 14, No. 8, 107-125. oldal

[6] Kolnhofer-Derecskei, A. – Reicher, R. Zs (2017b) Hungarian students’ job-seeking behaviors ISMA University, pp. 69-77.

[7] Laurence J. P. (1989) A Peter-elv, avagy miért fordulnak mindig rosszra a dolgok? Kossuth Könyvkiadó,

Budapest

[8] Patterson, C. (2005): Generation stereotypes, APA.Org

[9] http://www.apa.org/monitor/jun05/stereotypes.aspx (Letöltés dátuma: 2015. április)

[10] Reeves, T. C. – Oh, E., 2008. Generational Differences. In: Handbook of Research on Educational

Communications. Springer, pp. 295-303.

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attitűdök és részvételi minták történetiszociológiai megközelítésben. Politikatudományi Szemle XXII/4.

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[12] Szilágyi, Gy. (2015): Reveal the Organizational Substitution Anomalies Using Network Analysis Methods. In: Szakál, Anikó (szerk.) 10th Jubilee IEEE International Symposium on Applied Computational

Intelligence and Informatics (SACI 2015) Budapest, Magyarország : Óbudai Egyetem, (2015) pp. 341-344.,

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most-sem-mindegy-hogy-mit-irsz-az-

oneletrajzodba/?fbclid=IwAR04jEkY9n9b4o2YXCyAruSdT87TMjxwn9Hh1IEaeAeC9MepY_KFIwLFpL

4

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https://www.dalecarnegietraining.hu/

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https://www.hrportal.hu/hr/a-fluktuaciokezeles-harom-szintje-20171115.html/2

https://www.vg.hu/velemeny/elemzes/az-ssc-k-robotizacioja-1008562/

http://old.tarki.hu/adatbank-h/kutjel/pdf/a761.pdf

https://g7.hu/fizetett-hirdetes/20190322/a-digitalizacio-miatt-teljesen-atalakul-a-munkaeropiac-de-mar-most-sem-mindegy-hogy-mit-irsz-az-oneletrajzodba/?fbclid=IwAR04jEkY9n9b4o2YXCyAruSdT87TMjxwn9Hh1IEaeAeC9MepY_KFIwLFpL4




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