MEASURING CHANGE BY USING CROSS

SECTIONAL STUDIES: HOW TURKISH SOCIETY

BECAME RIGHTIST?

EMRE ERDOĞAN, PHD.

e m r e . e r d o g a n @ ı n f a k t o . c o m . t r

P l e a s e d o n ’ t q u o t e - ı t ’ s D r a f t

D R A F T

2

INTRODUCTION

We are living in a rightist society. All available data sets show that average self-

replacement of Turkish voters is located towards the right end of political spectrum and

Turkey is the most rightist country in the Europe. Same figures also show that Turkish

electorate significantly shifted to right during last 20 years (Esmer, 2012, p.52).

Reasons of this shift are innumerable and worth to analyze. However, in this short paper

we are interesting in the quantity and nature of changing ideological orientations of

Turkish voters; than reasons and consequences of this shift.

Change is an inevitable component of social life and discontinuities are much more

visible than continuities in societies. However, there is a confusion in discussing change,

since we tend to accept societies as individuals and are equating societal change as

change of an individual in his/her life course. But, we know that societies are at least

summation of its individuals living in these societies and change may be a result of

changing individuals’ position in a given parameter or change of individuals composing

the society. Undermining this difference may lead to overestimate or underestimate the

quantity of change. Moreover, accepting the society as the same entity overtime creates

an important illusion of continuity.

Ideological shift of Turkish voters is a good case for understanding change and

decomposing it to its components. In the following parts of the paper, we will use data

provided by the World Values Survey Research Program and our own proprietary data to

decompose ideological shift in Turkish society.

Two different approaches will be used, first one is developed by Firebauh (1992 and

1997) to understand change in population figures and second one is a merely new method

developed by Yang and Land (2004 with Fu, 2006, 2008, 2013) and uses more

sophisticated statistical techniques.

These approaches are complementary than competitive and using both of them can give

important information about the scope of the change in our society.

3

MEANING OF THE LEFT -RIGHT SEMANTICS

Positioning political parties and voters on a scale from left to right is among the oldest

traditions of political science. Laponce (1981) defined this left-right cleavage as a

“political Esperanto” as a schema representing ideological differences: Parties and

candidates position themselves on this scale, policy proposals are analyzed from this

perspective and it helps voters’ to cope with the complexities of the political universe

(p.56).

According to Lipset and Rokkan (1967) left-right cleavage is a product of historical

developments of the Western Europe such as the French and Industrial Revolutions and

modern political parties have their roots on these old conflicts and coalitions. This old

fashioned cleavage does not only help voters to position parties and candidates, it also

works as a good measurement of utility for voters; as Downs stated in 1957, voters make

their decisions according to relative positions of parties and candidates vis-à-vis their

own positioning. Consequently parties/candidates can move across this scale and change

their positions to attract maximum number of voters (p.142). Political parties are using

this spatial metaphor in forming strategic alliances and government coalitions and they

tend to cooperate with the closest one among many (Swann, 1973; Taylor and Laver,

1973).

A significant number of empirical works showed that self-placement of voters act as

good indicator of their prospective voting behavior. According to a group of scholars,

self-placement in the left-right scale is an indicator of party identification and voters

position themselves according to the well-known positions of their preferred parties

meaning that partisans’ positioning is a directly with how they perceive their preferred

part’s position on the left-right continuum (Fuchs and Klingemann, 1989, Lupia and

McCubbins, 1998 and Knutsen, 1997).

Meanwhile, some other scholars argues that the individual left-right self-placement

reflect issue preferences and/or value orientations. From this point of view, this metaphor

acts as a meta-issue, an “over-arching spatial dimensions” or as a vector representing

D R A F T

4

different solutions to problems or communalities in values (Ingehart and Klingemann,

1976; Huber, 1989; Knutsen, 1995, 1997).

Validity of the left-right continuum has been questioned with the emergence of new

issues. Dalton (1996) showed that while left-right distinction is still valid, meaning

attributed to this dimension changed with the rise of new left/new right framework.

Left/right attitudes have strong correlations with post-materialist values while economic

issues are still effective (p.334).

Although the left-right spectrum as a metaphor is widely accepted in the study of Turkish

politics, there is always a criticism for directly importing this schema from the

“developed” world (Baydur, 2009, Küçükömer, 1969), or the enduring center-periphery

cleavage dominates the political scene (Mardin, 1973; Kalaycıoğlu, 1994; Kalaycıoğlu,

1999:64-66; Çarkoğlu and Toprak, 2000) several empirical works showed that this

metaphor is an effective indicator of party choice.

In his early piece, Yılmaz Esmer (1995) showed that there is a correlation between self-

placement of voters and their voting preferences. There was a significant difference

between voters of the leftist parties (SHP and DSP) with voters of the right wing (ANAP,

DYP, RP) and voters of the RP were positioned at the outmost point of the spectrum with

an average of 7,29 over 10 (p.84).

Findings of Esmer, following the critical election of 1999 (2002) shows that self-

placement in the left-right scale is an important explanatory variable of party choice.

Voters positioning themselves towards the right end of the spectrum tends to vote for the

rightist parties (ANAP, DYP, FP and MHP) while leftist voters vote for the leftist parties

(CHP and DSP) (pp.106-107). Following these early works, Çarkoğlu and Toprak (2000)

used spatial distance between self-placement of voters and perceived positions of

political parties as an explanatory variable (p.115).

By the general elections of 2002, self-placement of voters is widely used to explain party

preference by different scholars. According to Çarkoğlu and Kalaycıoğlu (2007) “as

respondents move one notch from left to right in their self placement scores… they tend

to be less likely to be a CHP voters as opposed to an AKP voter (p.181). Çarkoğlu (2008)

shows that ideological positions of voters is one the most important determinants of party

5

choice as well as economic evaluations of voters (p.333). Kalaycıoğlu (2008) using same

data set with Çarkoğlu, built a set of simultaneous equations to estimate party preference

of voters. While he concludes that party identification is the most important determinant

of voters’ choice in Turkey, self-placement in left-right scale has a significant effect on

party preference (p.13).

Çarkoğlu, in a recent article (2012) summarized his findings for the last three elections

and stated that left-right self-placement had always a statistically significant effect on

party choice of voters while economic evaluations –especially negative ones- lost their

explanatory powers (p.517). All these multivariate analyses showed that left-right self-

placement of voters cannot be excluded from the analysis of voting behavior in Turkey.

As self-placement of voters has statistically meaningful role in explaining party choice,

distribution of voters across left-right spectrum attracts special attention.

Figure 1. Change in Left Right Self Placement1

1 Data is obtained from Çarkoğlu-Kalaycıoğlu (2007), p.116 while 2010 and 2012 data is drawn from our proprietary data set.

%0

%20

%40

%60

Left Centre Right

1990 1996 2002 2004 2007 2010 2012

D R A F T

6

Different surveys shows that Turkish electorate shifted towards the right end of the

political spectrum. In 1990, when the first wave of WVS has been conducted, 50 percent

of voters were locating at the center of the political spectrum whereas remaining 50

percent was almost equally distributed between left and right parts. In 1996, share of the

political left declined to 22 percent, then 17 percent in 2002 and remained around 16 to

20 percent. In the meantime, percentage of those locating themselves at the right half of

the spectrum increased to 42 percent in 1996 and 51 percent in 2012. These scores clearly

present that average positioning of Turkish voters shifted towards to the right-most end of

the spectrum.

Figure 2. Change in Average Left Right Self Placement2

Data which we will use in our analyses also confirm these findings: average self-

placement of voters shifted towards the right pole between 1990 and 2012. In 1990,

average self-placement of voters was 5,38 in a 1-10 scale where 10 stands for the outmost

2 Data from 1990 to 2007 is obtained from World Values Survey, while 2010 and 2012 data is drawn from our proprietary data set. Since sample sizes of dataset differs, we preferred to weight them giving equal representation in the pooled data. All samples are assumed to represent Turkish population above 18 years old.

5,38

6,15 5,82

6,17 6,07

6,49

1,00

2,00

3,00

4,00

5,00

6,00

7,00

8,00

9,00

10,00

1990 1996 2001 2007 2010 2012

Lef

t-R

igh

t S

elf

Pla

cem

ent

7

right position. According to same data set, this score increased to 6,2 in 1996 followed by

a declined in 2001. By 2007, average score continued to shift towards the right pole and

the last survey showed that average self-placement of Turkish voters is 6,5 over 10;

placing Turkey as a “rightist” society. According to the findings of the last wave of the

World Values Survey, Turkey is located at the outmost right point in in Europe (Esmer,

2012, p.52).

Reasons of this shift are innumerable and needs further attention. Some economic factors

such as subsequent economic crises in 1994, 1999 and 2001 might create a suitable

environment for rightist tendencies. Kim and Fording’s (2001) detailed analysis of the

Western democracies showed that macroeconomic developments such as inflation,

economic growth and unemployment led to ideological shift in voters’ ideological

positions. According to them inflation created a suitable environment for a rightist shift

(p.64). Same analysis also showed that governments’ ideological positioning played a

role as an attractor and government’s shift has been echoed in ideological shift of voters,

which may be an acceptable argument for the Turkish case since the AKP as the

hegemonic actor of political scene is highly characterized with its conservative

orientation. Finally as Kim and Fording’s stated, the external environment may be

decisive in ideological shifts of voters. Current hegemony of the Neoliberalism may

attract Turkish voters to right (Öniş, 2010).

All these questions are worth to answer but they are beyond the limits of this paper. We

will concentrate on the quantity of change rather than reasons of this shift.

Above description of changes in distribution of voters across the left-right spectrum,

emphasizing on the shift of Turkish electorate towards the right end undermines a simple

fact. Since all of supporting data are obtained through cross-sectional surveys, observed

change is between different samples collected at different time points. Hence, change

does not only stem from changes within samples assumed representing same population;

it may be a result of differences between samples. This difference may be something

more than a shift due to sampling errors and it may be attributed to changes between

populations at different time points.

D R A F T

8

In short, shift towards to right may be a result of changing structure of populations as

well as individuals’ movements. Generational replacement and aging are two potential

explanations of this change in the population. People can die (mortality), move

(migration) and be older (age) between two time points and any change can be related

with these factors. Consequently, in order to talk about a change in a given societal

parameter, we need to decompose it to its components and differentiate individual

change.

In the following parts of the paper, we will try to make a decomposition exercise and to

observe the quantity of shift of individuals towards to the right-end of spectrum.

9

METHODS.

FIREBAUGH’S DECOMPOSITION METHOD

ALGEBRAIC METHOD

Glenn Firebaugh’s (1992) approach to measuring change in societies is based on

decomposing observed change into its components. According to him, observed change

may be a result of :

a) individuals’ changing position in the issue,

b) population turnover,

c) both individual change and population turnover.

Any observed change in the societal parameters –in opinions, attitudes or statistics- is

composed of three components. As it is so far argued, interpreting trend statistics

obtained by cross sectional studies as ones produced by panel studies leads

underestimation of component B and C; and attributing all change to A. A clever analysis

should take all these components into account.

Firebaugh formulates its approach as follows:

= microchange effect+turnover effect + joint contribution of individual

change and turnover; where μ2 and μ1 stand for overall mean of Y at time 2 and 1,

respectively.

Firebaugh proposes to use cohorts as keys for following individual change in cross-

sectional surveys. According to him:

“the key is whether population turnover within birth cohorts is independent of Y.

With repeated cross-section data we can follow birth cohorts over time even

though we cannot literally follow individuals . If turnover is independent of Y

within cohorts, then change in μ is entirely individual change, none is due to

population turnover” (p.2)

2 1 

D R A F T

10

It is clear that turnover within birth cohorts include mortality (deaths) and migration. In

our example, migration rates is relatively small, hence migration effect on turnover

within birth cohorts is ignorable. But mortality is a significant factor which may hinder

change within birth cohorts. Hence overall change is also affected by this mortality

effect. It may be a result of replacement of old cohorts with new cohorts and changes in

relative sizes of cohorts may be echoed in the changing overall mean.

Firebaugh produced a series of equations to decompose group differences.

2 1

2 2 1 1

1 1 1

 

 

 

j j j j

j j

j j j j j j

j j j

p p

p p p

Where ∆μj is change in the mean for subgroup j, and ∆pj change in j’s population share.

After decomposition, 1st component in equation 3 is micro-change –a weighted sum of

within cohort change-. If there is no population turnover, ∆pj=0 meaning that 2nd

and 3rd

components will be dropped out. If social change is totally composed of individual

changes, 1st component reflects all change, as it is assumed. When there is no individual

change -∆µj=0- , 1st and 3

rd components drop out and social change stems from

turnover/cohort replacement and 2nd

component presents all change.

Third component is somewhat residual it represents joint effect of individual change and

generational replacement. It is generally small and as Firebaugh argued it is possible to

distribute it to other components and this strategic choice is dependent to structure of

available data and assumptions of the researcher. In our case, we prefer to keep the 3rd

component to show how it could be interpreted.

Firebaugh’s methodology is based on a series of calculations and a sample SPSS syntax

is presented in the annex of his paper (1992) . In this part of the paper we will use this

algorithm to calculate above stated parameters.

11

Figure 3. Change in Left Right Self Placement

Above figure shows the change of cohort averages over 7 different waves of data and

helps us to visualize our discussion. Differences between diamonds and circles show

ideological shift within cohorts during the last 20 years while the horizontal axis indicates

differences between cohorts. Range within cohorts also stems from changing size of the

cohort in the population. For example, percentage of the four oldest cohorts declined to 8

percent in 2012 from 29 percent in 1990. As number of respondents within cohorts

decreases, within cohort variance increases. Firebaugh’s approach takes this change of

relative sizes of cohorts into account.

4

5

6

7

8

<=

1935

1936 -

1940

1941 -

1945

1946 -

1950

1951 -

1955

1956 -

1960

1961 -

1965

1966 -

1970

1971 -

1975

1976 -

1980

1981 -

1985

1986 -

1990

1991+

1990

1996

2001

2007

2010

2012

D R A F T

12

Figure 4. Change in Left Right Self Placement: Algebraic Decomposition

Firebaugh’s methodology helped us to distinguish contributions of individual and cohort

turnover change to average shift of Turkish society. According to this graph average self-

placement on the left-right scale increased by 0,77 points between 1990 and 1996. Our

calculations showed that individuals shifted by 1 points towards to the right end, however

this shift compensated by the generational replacement towards to the left by 0,29 points.

Same calculations also show that there has been no individual change between 1996 and

2001 while generational replacement attracted the average placement to the left end by

0,30 points.

Available trend data shows that average placement of Turkish voters shifted towards the

right by 0,34 points. This shift is highly the result of changing individual positions by

0,79 points despite the opposite effect of generational replacement. By 2007, this

smoothing effect of generational replacement lost its momentum and individual shifts

towards the right end dominated the political scene. Especially generational replacement

had no effect between 2010 and 2012, consequently average placement of Turkish voters

shifted towards the right end of the spectrum as a result of individual change.

0,77

-0,33

0,34

-0,09

0,41

-0,29 -0,30

-0,45

-0,23

0,01

1,06

-0,03

0,79

0,13

0,40

-0,60

-0,40

-0,20

0,00

0,20

0,40

0,60

0,80

1,00

1,20

96-90 01-96 07-01 10-07 12-10

TOTAL TURNOVER IND

13

LINEAR DECOMPOSITION

Another method proposed by Firebaugh (1997) is linear decomposition, as an alternative

to the algebraic method. This approach assumes linear and additive within cohort change.

This method also aims to divide social change into two components, one part resulting

from cohort replacement and second part resulting from change in individual values.

Linear decomposition method is based on a regression analysis.

In the first step, change in Y within cohort is estimated through a linear regression

analysis. Assuming that within cohort slopes are linear and parallel, basic regression

equation is modeled as follows:

0 1 2 it itY b bYEAR b COHORT e

Where Yit is the value of Y for the ith

respondent in the ith

survey, b0 estimated intercept,

b1 is the estimated within cohort slope b2 is the estimated cross-cohort slope. Yearit is the

year of measurement of the ith

respondent in the ith

survey, and Cohortit is birth year for

the ith

respondent in the tth

survey. (p.24)

In the second step, the contributions of intracohort change and cohort replacement to

overall social change are estimated by using above calculated slope coefficients:

Estimated contribution of intracohort change = b1 (YRT-YR1)

Where YRT is the year of final survey and YR1 is the year of the first survey.

Estimated contribution of cohort replacement=b2 (CT-C1),

Where CT is average year of birth for the sample in the last survey and C1 is

average year of birth for the sample in the first survey.

We conducted linear decomposition method to above described data set. Results are as

follows:

D R A F T

14

Figure 5. Change in Left Right Self Placement: Linear Decomposition

Scores calculated by using the linear decomposition method shows that change in the

average self-placement is highly due to change in the individual positions of Turkish

voters. Effects of cohort replacement fluctuated between 0,11 to 0,06 points to the left

and it disappeared by 2010. As new cohorts have almost equal rightist positions in the

political spectrum; this generational replacement lost its function as a barrier to

radicalization of Turkish society.

0,83

-0,27

0,45

-0,03

0,40

-0,06 -0,06 -0,11

-0,05

0,01

0,77

-0,33

0,34

-0,09

0,41

-0,40

-0,20

0,00

0,20

0,40

0,60

0,80

1,00

96-90 01-96 07-01 10-07 12-10

INTRACOHORT COHORT REPLACEMENT CALCULATED

15

Figure 6. Change in Left Right Self Placement: Comparison of Linear and Algebraic Decomposition Methods

Above figure presents a comparison of two different approaches proposed by Firebaugh.

LD stands for linear decomposition while AD indicates algebraic decomposition.

Although that two different approaches calculated different turnover and individual

change scores, there is a strong correlation between estimations, about 0,98. Independent

from quantitative differences, both methods tell same story: Observed shift towards the

right end of the political spectrum is highly a result of changes in individuals’

orientations, meanwhile generational replacement acted as a barrier against the rising the

rightist orientation until 2007. Especially, between 2007-2011 when individual shift

reached to a peak, generational replacement played a counterbalancing role.

Despite differences in estimations, Firebaugh’s approach is very useful to differentiate

change at individual level and change as a result of generational replacement. By using

this methodology, it is possible to learn about historical factors affecting people’s

attitudes without being hindered by the generational replacement, as we observed in the

above case.

-0,06 -0,06 -0,11

-0,05 0,01

0,83

-0,27

0,45

-0,03

0,40

-0,29 -0,30

-0,45

-0,23

0,01

1,06

-0,03

0,79

0,13

0,40

-0,60

-0,40

-0,20

0,00

0,20

0,40

0,60

0,80

1,00

1,20

96-90 01-96 07-01 10-07 12-10

LD-Turnover LD-Individual AD-Turnover AD-Individual

D R A F T

16

YANG AND LAND’S HIERARCHICAL AGE-PERIOD-COHORT (HAPC)

MODEL

Another approach to decompose different components is developed by Yang and Land

(2004 with Fu, 2006, 2008, 2013) is called “Hierarchical Age-Period –Cohort Models”

(HAPC). Yang’s approach is to estimate distinct age, period and cohort effects by using

repeated cross-sectional surveys. In this situation indiviudals are nested within cells

created by the cross-classification of two types of social context: birth cohorts and survey

years. This approach is to estimate distinct age, period and cohort effects by using cross-

classified random effects model (CCRM). These models allow researchers to discover

the contextual effects of period –or the history- and cohort –or collective history-.

Yang and Land (2008) propose to develop a mixed effects model, where age of the

respondent is an individual level covariate with fixed effects , and period and cohort are

random contextual effects. This model also open to include a group of covariates at

individual level or at higher levels -at cohort or period levels-.

Model is described as follows:

Level-1 or “Within-Cell” Model:

0 1 ijk jk l ijk oj ok itkY b b AGE b x u v e

Level 2- or “Between Cell” Model

0 0 ojk m oj kB u v

Combined Model:

0 1 ijk l ijk m itkY b AGE b x e

where, within each birth cohort j and survey year k, respondent i’s score is modeled as a

function of her age and other individual characteristics.

In this model b0jk is the intercept or “cell mean”, average Y value of individuals

belonging birth cohort j and are surveyed in year k , keeping all other variables (Age and

covariates) at the Level-1 fixed effects. ejk is random individual effect –deviation of

individual ijk’s score from the mean for cohort-period jk-. γo the model intercept, or grand

17

mean score of all sampled individuals; ∑γ are level-2 covariates, u0j is the random effect

of cohort j averaged over all periods and v0k is random effect of period k.

It’s possible to estimate parameters by using a mixed level model, where period and

cohort variables are defined as second and third random effects, while individual

characteristics are defined as fixed effects at first level.

Yang and Land used this approach to explain verbal capacity of respondents by using the

verbal test score data of the General Social Survey. Their findings show that variance in

the verbal test score of participants is highly due to individual level differences such as

age, gender, race and level of education. Meanwhile their analyses also showed that inter-

cohort differences are statistically significant whereas variation by time periods was not

significant controlling for other variables. (Yang and Land 2006, p.92). They concluded

that vocabulary knowledge declined across cohorts (p.93)

Yang, Frenk and Land (2009) used same methodology to analyze birth cohort and time

period effects on the likelihood of voting in U.S. presidential elections. By using data

provided the American National Election Surveys for 14 different elections between

1952-2004, they found that period effects are significant as determinants of voting

tendency, meanwhile inter-cohort variance is relatively small after controlling individual

level variables. Meanwhile same analysis showed that some cohorts have more tendency

to vote (especially those born between 1940-1950) and birth cohorts between 1965 and

1970 tend not to vote compared to other cohorts, after controlling for individual variables

(p.28).

This approach is merely new and not used yet in different contexts. A good example is

provided by Wilkes and Corrigal-Brown (2011) in their analysis of changing attitudes

towards immigrants in Canada. Authors preferred to employ a different strategy of model

building. They first included period and cohort variables; then they discussed their

findings after introducing individual and second level variables. They also included

macro level variables such as unemployment rate for cohorts and annual unemployment

rate for periods. Their analysis showed that adding up individual variables such as

education hindered inter-cohort differences meanwhile since random effects of period

D R A F T

18

variable remained statistically significant, leading authors to conclude that changing

attitudes over time are result of an ideological shift within time (p.88).

The HAPC approach contributes to our attempt to differentiate sources of overall

attitudinal change. If random effects of cohort and period are statistically significant after

controlling for other covariates, it is possible to show how history affects this change.

19

By using above defined dataset, we conducted the HAPC analysis by using self-

placement of respondents as dependent variable. Our first model only includes age as

independent variable at the first level and random effects of cohort and period.

Table 1. Results of the HAPC Analysis, Age is Covariate

Estimate S.Error Pr > |t|

Constant 5,497 0,162 <,0001

Age 0,012 0,002 <,0001

Fixed Effects Estimate S.Error Z

Variance(COHORT) 0,006 0,006 1,172

Variance(PERIOD) 0,105 0,064 1,629

Random Effects

COHORT Estimate S.Error Pr > |t|

<= 1935 0,01 0,07 0,94

1936 - 1940 -0,01 0,07 0,85

1941 - 1945 0,04 0,08 0,57

1946 - 1950 0,00 0,07 0,98

1951 - 1955 -0,03 0,07 0,63

1956 - 1960 -0,06 0,07 0,34

1961 - 1965 -0,04 0,06 0,54

1966 - 1970 0,01 0,06 0,83

1971 - 1975 0,13 0,07 0,09

1976 - 1980 0,04 0,06 0,56

1981 - 1985 -0,06 0,07 0,38

1986 - 1990 -0,03 0,07 0,63

1991+ 0,02 0,07 0,83

PERIOD Estimate S.Error Pr > |t|

1990 0,131 0,144 0,363

1996 0,479 0,149 0,001

2001 -0,185 0,140 0,187

2007 0,190 0,148 0,198

2010 0,195 0,173 0,261

2012 0,570 0,177 0,001

According to above table presenting our findings, age of respondents has a positive effect

on ideological self-placement. As age increases, one’s self-placement shifts towards the

right end of the political spectrum. Analysis of fixed effects shows that there is a

D R A F T

20

significant difference between periods of surveys; while inter-cohort differences are

statistically insignificant. If we analyse random effects of each level two variables, we

observe that only one birth cohort has a significant difference compared to other birth

cohorts: those have born between 1971 and 1975 tends to be slightly rightist by 0,13

points. Meanwhile, differences between periods are more significant. 1996 and 2012

surveys provided the most rightist averages by 0,50 points, after controlling all other

variables constant. These differences may be attributed to historical developments rather

than cohort replacement.

21

Table 2. Results of the HAPC Analysis, Age, Education and Gender are Covariate

Estimate S.Error Pr > |t|

Constant 6,727 0,193 <,0001

Age 0,001 0,002 0,701

Education -0,282 0,017 <,0001

Gender=Female 0,184 0,052 0,0004

Fixed Effects Estimate S.Error Z

Variance(COHORT) 0,008 0,006 1,277

Variance(PERIOD) 0,132 0,080 1,642

Random Effects

COHORT Estimate S.Error Pr > |t|

<= 1935 0,037 0,078 0,640

1936 - 1940 -0,020 0,078 0,799

1941 - 1945 0,032 0,077 0,674

1946 - 1950 -0,011 0,072 0,877

1951 - 1955 -0,052 0,071 0,464

1956 - 1960 -0,069 0,067 0,302

1961 - 1965 -0,049 0,063 0,436

1966 - 1970 -0,002 0,061 0,979

1971 - 1975 0,141 0,076 0,063

1976 - 1980 0,069 0,068 0,316

1981 - 1985 -0,043 0,070 0,538

1986 - 1990 -0,033 0,073 0,652

1991+ 0,000 0,079 0,997

PERIOD Estimate S.Error Pr > |t|

1990 -0,654 0,168 0,000

1996 -0,040 0,159 0,799

2001 -0,125 0,155 0,420

2007 0,250 0,162 0,123

2010 0,075 0,159 0,635

2012 0,494 0,163 0,003

Our second model includes some covariates at individual level: Education and female

dummy. Analysis of first level variables shows that including new variables diminished

effect of age and it became statistically insignificant. Education has a negative effect on

D R A F T

22

rightist views. As education increases by one points, one’s self-placement in the spectrum

shifts towards the left end by 0,28 points. Gender has also a significant effect. Table

shows that women tends to be locate themselves towards to the right end of the spectrum

by 0,20 points.

Adding these individual level variables didn’t change the overall picture. Analysis shows

that inter-cohort differences are insignificant, while there is significant difference

between periods, after controlling for education, gender and age. There is significant

difference between 1990 and other survey years, 1990 presents a significantly more leftist

position, while the last survey indicates a shift about 0,5 points. Since new individual

level variable didn’t affect these differences, it means that there is some room for further

explanation these differences, including using historical events such as economic crisis so

on.

23

DISCUSSION

In this short paper we tried to use different methods of decomposition to understand the

nature and quantity of change in the Turkish electorate in terms of ideological shift

towards right, presented by all available data sets. There are different explanations of this

shift, from economy to external environment; from political culture to the leadership

effect; but quantity of this change is still under suspect.

Generally, change in a parameter is accepted as difference between to data sets collected

in two different time points. It is assumed that comparison of these data gives an idea

about the magnitude of change. Challenging point here is that observed change has

different components. First component is individual change, shift of individual voters

towards right in our case. It is worth to discuss why individuals became more rightist

within 10 to 20 years. Secondly, this shift may be a result birth cohort replacement, in

other words change in the structure of society when old birth cohorts are replaced with

new ones; hypothetically having different values set or socialization process. Finally,

third component is the history. Historical events may affect a society and push all

individuals towards right. Classical interpretations of sample averages underestimate

second and third components and tended to attribute this change to first and sometimes

third components.

We proposed two different approaches to address this problem. First approach is

developed by Firebaugh (1992, 1997) and it aims to decompose change into two

components: individual change and turnover –cohort replacement-. When we used to

alternative methods of Firebaugh decomposition we observed that major portion of

rightist shift in Turkey is due to shift in individuals. However same analyses showed that

turnover played an important role during 1990s, as a barrier to shift. For example, if

turnover effect would be 0, meaning that old and new birth cohorts would have same

ideological orientations, average positioning of Turkish voters in 2010 would be 6,61

over 10 instead of 6,16, with a difference of 5 percent created by cohort replacement.

Same data shows that this effect disappeared by 2010

Two alternative methods of Firebaugh, algebraic decomposition and linear decomposition

estimated different quantities for both components, especially lower scores for turnover

D R A F T

24

effect. But, estimations are strongly correlated (r=0,98) meaning that different methods

told same story.

Second approach, developed by Yang and Land (2004, 2013) is more complicated and

attempts to differentiate age, period (history) and cohort effects on any societal change.

Their method is based on analyzing pooled cross-sectional data in a hierarchical structure

–period*cohort*individuals- and estimating fixed effects of covariates and random

effects of contextual variables, period and cohort. If contextual variables are sufficient to

explain variance between individuals, random effects of period and cohort would be

disappeared. Or, the existence of random contextual effects indicates existence of

historical events.

Our first model which only incorporates age as an independent variable showed that

change in individuals’ self-placement on left-right spectrum is a result of periodic shift,

than differences between cohorts. When we included education and gender as individual

level covariates, overall picture didn’t change. Education is the most important factor

affecting individuals’ ideological orientation, one point increase in education attracts

individual voter to left by 0,3 points, keeping all other variables constant. It means that

education matters, within same cohort at same period. Women locate themselves in a

more rightist position than men, even they were born in the same cohort and measured in

same survey. In this model birth cohort effect is insignificant, meaning that there is no

intercohort differences –after controlling for education, age and gender- however period

has an statistically significant random effect which leaves a role for history.

All of our analyses didn’t produce information to challenge our conventional wisdom.

Turkish society became more rightist within 20 years because individuals became more

rightist. However, by using different decomposition methods we showed that

generational replacement played a barrier to overall shift, because new generations were

more centrist than older generations they replaced. Secondly, this barrier effect

disappeared by 2010 meaning that newcomers are rightist as they parents. Thirdly, as the

HAPC analyses showed, individual factors such as education or gender matters; but there

is still some random effect stemming from periodic differences, and historical factors are

waiting for being discovered.

25

APPENDIX

Algebraic Decomposition Example

Proportion of

Cohort

Mean Individual

Change

Turnover Interaction

A B C D =(D-C)*A =(B-

A)*C

=(B-

A)*(D-C)

COHORT 1990 1996 1990 1996 96-90 96-90 96-90

<= 1935 0,13 0,08 5,82 6,43 0,08 -0,26 -0,03

1936 - 1940 0,09 0,04 5,73 6,76 0,10 -0,33 -0,06

1941 - 1945 0,05 0,06 5,44 6,38 0,05 0,02 0,00

1946 - 1950 0,09 0,08 5,41 6,64 0,11 -0,07 -0,02

1951 - 1955 0,12 0,11 5,40 6,29 0,10 -0,05 -0,01

1956 - 1960 0,13 0,13 5,09 5,85 0,10 -0,02 0,00

1961 - 1965 0,14 0,13 5,40 6,02 0,09 -0,07 -0,01

1966 - 1970 0,16 0,15 4,89 5,49 0,10 -0,07 -0,01

1971 - 1975 0,09 0,14 5,65 6,39 0,06 0,28 0,04

1976 - 1980 0,10 6,23 0,00 0,00 0,63

1981 - 1985 0,00 0,00 0,00

1986 - 1990 0,00 0,00 0,00

1991+ 0,00 0,00 0,00

Total 0,79 -0,57 0,54

Interaction

Distributed

1,06 -0,29

Observed Change 0,77

D R A F T

26

Linear Decomposition Results:

B Std.

Error

Beta Sig Difference Average Birth

1996-

1990

Constant -248,75 38,51 0,00 1953,59 1990

Year 0,14 0,02 0,17 0,00 6,00 1958,96 1996

Cohort -0,01 0,00 -0,07 0,00

2001-

1996

Constant 136,82 48,95 0,01 5,00 1958,96 1996

Year -0,05 0,02 -0,05 0,03 1964,10 2001

Cohort -0,01 0,00 -0,06 0,01

2007-

1996

Constant -110,82 41,46 0,01 6,00 1964,10 2001

Year 0,08 0,02 0,09 0,00 1970,51 2007

Cohort -0,02 0,00 -0,09 0,00

2010-

2007

Constant 61,41 81,95 0,45 3,00 1970,51 2007

Year -0,01 0,04 -0,01 0,79 1973,70 2010

Cohort -0,02 0,00 -0,09 0,00

2012-

2010

Constant -373,52 118,35 0,00 2,00 1973,70 2010

Year 0,20 0,06 0,08 0,00 1975,00 2012

Cohort -0,01 0,00 -0,07 0,00

27

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