By Alisha Armas
In this post, I examine the effect of Ukrainian as the
native language of voters on differences between voter turnout in the November
and December rounds of the 2004 Ukrainian presidential election. Language and
politics in Ukraine are deeply divided, especially between its western and
eastern/southern regions. In recent elections, this divide has become visible
in the support of political parties. In 2004, the Our Ukraine candidate, Viktor
Yushchenko, won the northern and western regions, while support for the Party
of Regions candidate, Viktor Yanukovych, came from the East and South (see
Figure 1). Furthermore, as Figure 2 shows, native Ukrainian speakers are
concentrated in the West and North, and fades as one moves southward and
eastward. More than reflecting a regional divide, language influences the media
that Ukrainians consume, which can shape their political motivations.
Western and Eastern
Ukrainian media provided different coverage between the two rounds of the 2004
elections that I examine. After Yanukovych claimed victory in the fraudulent
November runoff against Yushchenko, hundreds of thousands of Yushchenko’s
supporters protested in Kyiv’s central square. This “Orange Revolution” ended
on December 3 when the Supreme Court of Ukraine ordered a revote to occur on
December 26, which Yushchenko won. Throughout these events, the Ukrainian media
provided fair coverage of the protests and the candidates prior to the December
revote while non-Ukrainian speaking regions tend to get their news from Russian
sources, which had a negative impact on the Eastern regions. [1] Because of
these factors, I hypothesize that electoral districts with higher percentages
of native Ukrainian speakers will have greater increases in voter turnout
between the November runoff and December revote. It is important to look at the
effect of language on changes in voter turnout to help understand if the Orange
Revolution was a one-time surge of political participation that was confined to
the Kyiv area.
The literature identifies
several factors that influences voter turnout. For example, Wolfinger and
Rosenstone find that education in particular, and age and income to a lesser
extent, affect whether or not individuals vote. [2] Most literature on the
Orange Revolution focuses on the outcome of the election, and may only deal
tangentially with voter turnout. For example, Clem and Craumer examine voter
turnout to explain Yanukovych’s victory in the November election, arguing that
Donetsk and Luhansk oblasts had large increases in turnout that may have been
the result of ballot box stuffing or false reporting. [3]
Table 1. Summary Statistics
Variable
|
Mean
|
Std. Dev.
|
Min
|
Max
|
Description
|
Change in
Voter Turnout
|
-3.36
|
5.17
|
-21.61
|
19.44
|
December
Revote Voter Turnout – November Voter Turnout
|
Native
Language: Ukrainian (%)
|
66.37
|
31.33
|
6.35
|
99.58
|
%
of population whose native language is Ukrainian
|
Disposable
Income per Capita
|
4394.85
|
892.56
|
380.20
|
7623.90
|
Ukrainian
Hryvnia
|
Average Age
|
38.88
|
2.05
|
33.50
|
45.10
|
Average
age of the population
|
Higher
Education (%)
|
31.18
|
5.65
|
21.30
|
47.70
|
%
of the population with higher education
|
Fraud
|
0.14
|
0.34
|
0.00
|
1.00
|
1
if the region was subject to fraud in the November runoff (Donetsk and
Luhansk regions)
|
Observations
|
207
|
I look directly at the differences
between voter turnout in the November runoff and the December revote. This will
provide a more detailed look than analysis at the regional level while still
using politically significant units. I calculate the change in voter turnout from
turnout data reported by the Ukrainian Central Election Commission for each of
Ukraine’s 225 electoral districts. [4] I use voter turnout data from the
November runoff rather than first round data, because both the November and December
elections were between Yanukovych and Yushchenko, while there were over 20
candidates in the first round. However, accusations of fraud plagued the
November election. Reports indicate that fraud was particularly prevalent in
the Donetsk and Luhansk regions. [5] Because international observers consider
the December fair, fraud in the November elections likely drives much of the
decreases in voter turnout in Donetsk and Luhansk. As Figure 3 shows, many of
the greatest decreases in turnout are in Donetsk and Luhansk electoral
districts. Therefore, I include a dummy variable that is 1 for electoral
districts in Donetsk and Luhansk, and zero for all other districts. For native
Ukrainian speakers, I use data from the 2001 All Ukrainian Census, which is
Ukraine’s most recent census. [6] While I prefer more recent data, the State
Statistics Service of Ukraine reports low levels of migration at the the oblast
level between 2001 and 2004, indicating little change in Ukrainian
demographics. Therefore, the use of the 2001 data should not cause drastic problems.
Additionally, to correct for negative skewness, I change Ukrainian language to the
percent of the population whose native language is not Ukrainian (100 – native
Ukrainian speakers (%)) and log this new variable. Therefore, for my hypothesis
to be true, electoral districts with higher percentages of non-native Ukrainian
speakers should have greater decreases in voter turnout between the November
and December elections.
I additionally control for income, age, and
education. Income data at the subregional level is not available. Therefore, I apply
2004 regional data on per capita disposable income from the State Statistics
Service of Ukraine to all districts that fall within a given region. Because
most electoral districts consist of both urban and rural areas, the differences
in income between electoral districts should be minimal. For age, I use the
average age as reported in the 2001 census. [6] Finally, for education, I use
data from the 2001 census at the regional level, since data at smaller units are
not available. I choose to measure education as the percent of people who
possess higher education because differences between regions are more
pronounced for higher education than secondary education. While it may seem
prudent to control to political affiliation, language is high correlated with
support for either Yanukovych’s Party of Regions or Yushchenko’s Our Ukraine.
Since I believe that language, partially through the media, influences both
voter turnout and political ideology, I do not include a variable for political
affiliation or ideology.
Table 2. Determinants of Changes
in Voter Turnout in 2004 Ukrainian Presidential Elections
DV: Changes in Voter Turnout [7]
|
(1)
|
(2)
|
(3)
|
(4)
|
(5)
|
Native
Language Not Ukrainian(log) [7]
|
-0.076***
|
-0.073***
|
-0.076***
|
-0.078***
|
-0.015
|
(-4.00)
|
(-3.73)
|
(-4.02)
|
(-3.65)
|
(-0.68)
|
|
Income(log) [7]
|
-0.059
|
0.024
|
0.004
|
0.101
|
|
(-0.49)
|
(0.20)
|
(0.02)
|
(0.76)
|
||
Average Age
|
-0.054***
|
-0.055***
|
-0.036**
|
||
(-4.18)
|
(-4.18)
|
(-2.85)
|
|||
Higher
Education(log) [7]
|
0.054
|
-0.266
|
|||
(0.25)
|
(-1.26)
|
||||
Fraud
|
-0.486***
|
||||
(-5.84)
|
|||||
Constant
|
3.132***
|
3.617***
|
5.051***
|
5.048***
|
4.480***
|
(52.84)
|
(3.64)
|
(4.98)
|
(4.96)
|
(4.73)
|
|
R-Squared
|
0.07
|
0.07
|
0.15
|
0.15
|
0.27
|
Observations
|
207
|
207
|
207
|
207
|
207
|
Note:
OLS estimates with t-stats in parentheses. * p < 0.05, ** p < 0.01, *** p
< 0.001
Source:
Ukrainian Central Election Commission, 2001 All Ukrainian Census, and State
Statistics Service of Ukraine
For a 1% increase in the percent of non-native
Ukrainian speakers, the change in voter turnout will, on average, decrease by
0.02%, all else constant, which supports my hypothesis. However, this is not
proof of causality since changes in voter turnout may cause reported native
language. After all, Ukrainians may vote out of patriotism, and so, also out of
patriotism, report their native language as Ukrainian, the only official
language of Ukraine. However, high voter turnout is not confined to
Ukrainian-speaking districts. In the next presidential election in 2010,
districts with few native Ukrainian speakers also had high turnout rates (see
Figure 4). In fact, districts where native Ukrainian speakers composed less
than 50% of the population had an average turnout rate of
70.34%, while districts where 50% or more of the population were native
Ukrainian speakers had an average turnout of only 68.65%.
Furthermore, this relationship lacks
statistical significance, meaning that language may have no effect on the
change in voter turnout. This lack of statistical significance only occurs once
the fraud variable, which is based on region, is introduced. It is possible
that region has a powerful effect on voter turnout. However, in 2004, language,
region, and fraud were all closely related: regions that experienced fraud in
the November elections were also in the East and had few native-Ukrainian
speakers. Yet, even in the absence of the fraud variable, the effect of
language on voter turnout is small. It seems unlikely that language was a
powerful motivator in the differences in voter turnout. While this does not
support my hypothesis, the small effect of language may mean that increased
voter turnout and the political participation that occurred during the Orange
Revolution may occur among non-native Ukrainian speakers as well.
Bibliography
Blais, Andre. "What Affects Voter
Turnout?" Annual Review of Political Science
9(2006): 111-25. doi:10.1146/annurev.polisci.9.070204.105121.
Clem, Ralph S., and Peter R. Craumer.
"Shades of Orange: The Electoral
Geography of Ukraine's 2004 Presidential Elections." Eurasian
Geography and Economics 46, no. 5 (2005): 364-85.
doi:10.2747/1538-7216.46.5.364.
"Course of Voting in Oblasts of Ukraine." Central Election Commission of
Ukraine - "The Elections of the President of Ukraine" 2004.
Accessed February 26, 2015. http://www.cvk.gov.ua/pls/vp2004/wp0011e.
"Databank." 2001 All Ukrainian Census. 2015. Accessed
February 26, 2015.
http://database.ukrcensus.gov.ua/MULT/Database/Census/databasetree_en.asp.
Karatnycky, Adrian. "Ukraine's Orange Revolution." Foreign Affairs, March/April 2005. Accessed March 13, 2015. www.foreignaffairs.com/articles/60620/adrian-karatnycky/Ukraines-orange-revolution.
"Рорulation Income in Region of Ukraine." State Statistics Service of
Ukraine. January 21, 2015. Accessed February 26, 2015.
http://ukrstat.gov.ua/.
Tora, Alex. "Population of those whose mother language is Ukrainian in Ukraine (2001)." WikiMedia Commons. December 12, 2008. Accessed February 26, 2015. http://commons.wikimedia.org/wiki/File:Ukraine_census_2001_Ukrainian.svg.
"Ukraine Elections Map Nov2004." Wikipedia. December 4, 2004. Accessed February 26, 2015. http://en.wikipedia.org/wiki/File:Ukraine_ElectionsMap_Nov2004.png.
Wolfinger, Raymond E. and Steven J. Rosenstone. Who Votes?. New Haven: Yale University Press, 1980.
Wolfinger, Raymond E. and Steven J. Rosenstone. Who Votes?. New Haven: Yale University Press, 1980.
[1] Adrian Karatnycky, "Ukraine's Orange Revolution," Foreign Affairs, March/April 2005, accessed March 13, 2015. www.foreignaffairs.com/articles/60620/adrian-karatnycky/Ukraines-orange-revolution.
[2] Raymond E. Wolfinger and Steven J. Rosenstone, Who Votes? (New Haven: Yale University Press, 1980).
[3] Ralph S. Clem and Peter R. Craumer, "Shades of Orange: The Electoral Geography of Ukraine's 2004 Presidential Elections." Eurasian Geography and Economics 46, no. 5 (2005). doi:10.2747/1538-7216.46.5.364.
[4] For a list of electoral districts by region, see Appendix A.
[3] Ralph S. Clem and Peter R. Craumer, "Shades of Orange: The Electoral Geography of Ukraine's 2004 Presidential Elections." Eurasian Geography and Economics 46, no. 5 (2005). doi:10.2747/1538-7216.46.5.364.
[4] For a list of electoral districts by region, see Appendix A.
[7] Because change in voter turnout, income, and higher
education are significantly positively skewed, I log these variables.
APPENDIX
A. Electoral Districts by Region
Region
|
District Numbers
|
Crimea
|
1-10
|
Vinnytsia
|
11-18
|
Volyn
|
19-23
|
Dnipropetrovsk
|
24-40
|
Donetsk
|
41-63
|
Zhytomyr
|
64-69
|
Zakarpatska
|
70-84
|
Ivano-Frankivsk
|
85-90
|
Kyiv
|
91-99
|
Kirovohrad
|
100-104
|
Luhansk
|
105-116
|
Lviv
|
117-128
|
Mikolaiv
|
129-134
|
Odesa
|
135-146
|
Poltava
|
147-154
|
Rivne
|
155-159
|
Sumy
|
160-165
|
Ternopil
|
166-170
|
Kharkiv
|
171-184
|
Kherson
|
185-189
|
Khmelnyts
|
190-196
|
Cherkasy
|
197-203
|
Chernivtsi
|
204-207
|
Chernihiv
|
208-213
|
Kyiv (City)
|
214-223
|
Sevastopol
|
223-225
|
APPENDIX
B. Calculating the Native Language: Ukrainian and Average Age Variables
The 2001 All Ukrainian
Census reports information on native language and average age at the raion and
major city level, while electoral districts are composed of one or more of
these areas. In the case of some of the larger cities, electoral districts may
be composed of a portion of the city. In the cases where only a portion of the
city composes an electoral district, census data for the entire city are used for the electoral district. In cases where electoral districts are
composed of several raions and/or cities, the language or age is weighted by
the population, and I add the weighted values together and divide by the total
population. For Kyiv (city), the electoral districts are the same as the
districts for the census, so no changes were made.
Example:
Electoral District 180 (Kharkiv Oblast)
The Ukrainian Central Election Commission
states that Electoral District 180 consists of the cities of Izium and
Kupiansk, and Borivskyi, Iziumskyi, Kupianskyyi and Shevchenkivskyi raions. The
2001 census reports the following information for native Ukrainian speakers and
population for each of these areas as follows:
City
or Raion
|
Native Language: Ukrainian (%)
|
Population
|
IZIUM
|
74.22
|
56,114
|
KUPIANSK
|
69.28
|
62,620
|
BORIVSKYI RAION
|
91.52
|
21,128
|
IZIUMSKYI RAION
|
91.67
|
22,002
|
KUPIANSKYI RAION
|
88.94
|
29,581
|
SHEVCHENKIVSKYI RAION
|
91.23
|
23,213
|
TOTAL:
|
214,658
|
Weighting the language by population:
City
or Raion
|
Weighted Language
(Ukrainian Language * Population)
|
IZIUM
|
4164781.08
|
KUPIANSK
|
4338313.6
|
BORIVSKYI RAION
|
1933634.56
|
IZIUMSKYI RAION
|
2016923.34
|
KUPIANSKYI RAION
|
2630934.14
|
SHEVCHENKIVSKYI RAION
|
2117721.99
|
TOTAL:
|
17202308
|
Finally, the total weighted language is
divided by the total population and the resulting value is used as the percent
of the population for the electoral district who are native Ukrainian speakers.
In this case, 17202308 ÷ 214658 = 80.14%.
1. Yes, the question is clearly stated. Native Language is the IV, change in voter turnout is the DV. It might be helpful to mention the language demographics in the first paragraph when you introduce the two candidates.
ReplyDelete2. Clearly states why this question is important for understanding the 2004 election, but I think there is an opportunity to point out broader trends as well. Maybe about understanding divisions in Ukraine or understanding how political identity motivates voters?
3. The end result is that further research is needed. This isn’t a bad result.
4. Yes, bivariate graph with parametric and non-parametric fit.
5. Yes, summary and regression table.
6. The maps are great, it would be interesting to have a map showing changes in voter turnout by region below the map showing native language.
Tables are clear, professionally formatted, and pretty BUT the Appendix tables are formatted differently from the regression table. I would consider making them match.
7. I do not see evidence that the author questioned the assumption of linearity. The graph does look fairly linear, with the exception of an uptick towards the higher end of the x-axis.
8. Does a good job controlling for relevant confounds, the only thing I would question would be the dummy variable controlling for the regions of Donetsk, Luhansk, and Dnipropetrovsk. These are regions that have seen a high degree of violence and separatism recently. They’re also closer to Russia; occupants may feel less “Ukranian” and have chosen to vote for the other candidate or abstained in either round of voting. The author could cite a source on the claim that these regions had high degrees of voter fraud.
9. The author does not offer a strong argument against reverse causality. In this case, it’s very hard to see how having a high voter turnout could possibly be causing people to speak Ukranian, so I’m not sure how a reverse causality argument would be framed. I suppose reverse causality could be an issue if ballots were offered only in Ukranian and non-speakers were unable to vote, but that doesn't seem to have been the case based on the information presented.
10. The interpretation of the results is pretty clear, but I might place more emphasis on the fact that the correlation is not statistically significant. I might rephrase this section a little bit to emphasize that although the relationship shown is in the expected direction, there is a decent probability that the correlation here is observed due to chance.
Overall, I thought this was a very smart, interesting post. Although I’m not familiar with the literature on Ukranian voter turnout, it seems that the author chose an innovative way to inspect a highly relevant question. I think the visuals are particularly great. Without the maps it might be pretty hard for a layman to follow the discussion on certain regions.
In terms of organization, the lead is a bit buried. The first paragraph gives great background information that is vital to understanding the analysis, but the central question is not explained until paragraph two. Just something to think about.
Format looks very nice at first glance.
ReplyDelete"I examine the effect of Ukrainian as the native language on the change of voter turnout between the December revote and the second round turnout at the electoral district level"
A few things are unclear in the main research question: the native language of who? the voters? the candidates? This becomes clear later on but it would be good to identify up front. I also think what you are assessing would be clearer if 'change of' is modified to 'difference between'
Good discussion on confound variables from the literature. Also a good discussion on why this is important. It is an interesting topic that I wouldn't have otherwise considered.
Table 1 is clear and concise.
It is not clear (without having more background information) why a dummy variable for Donetsk (etc.) controls for fraud. Maybe a bit more explanation there?
What distribution/linearity problems are the logged variables addressing? The distribution in the graph appears (at least without looking at the data) to still be fairly skewed.
Reverse Causality seems unlikely to be a problem here...I don't see voter turnout rates impacting language percentages...
Good historical discussion of other problematic factors.
Figure 4 is well formatted but is a bit confusing when compared to your tables. In the summary you are examining % Ukrainian. In the regression table you have ln(%not Ukrainian). I think keeping the variables consistent so that the reader knows the graphs and tables all refer to the variable at the same scale is important to reduce confusion.
The maps are very useful to help the reader understand the discussion. I'm not sure the third map is strictly necessary though. It doesn't seem to convey any more relevant information than the others.
Interpretation and conclusions seem good. The graph seems to show that language has basically no effect until it approaches the 90th percentile. Maybe a bit of discussion about why that may be?
Overall, very well written and intelligent post covering an interesting topic. The post was generally well organized and, as someone with very little background knowledge on Ukraine, I was able to understand the issues that were being discuessed and why they are important. I think the first paragraph (which contains good and important information) could be tweaked a little to better identify the question up front.
I should say that I liked the blog because it is original and interesting. It addressed a relevant topic that wasn't familiar to me. The blog developed clearly all the criteria proposed for the peer evaluation; graph, tables and maps are clear and useful. The hypothesis is also clear and it is easy to identify the dependent and independent variables.
ReplyDeleteI would suggest that following the line of having a strong introduction to put the reader in context, it would be also useful to have a stronger conclusion where you develop more the interpretation and explanation of your results clearly. In addition, I did not see any reference for figure 3 within the blog. I think in paragraph 4 you can make the reference when you are talking about the districts with fraud. When you control for fraud, you use a dummy variable that is 1 for electoral districts in Donetsk, Dnipropetrovsk, and Luhansk, and zero for all other districts. According to figure 3 some of those are districts that share border with Russia, thus, it wasn’t clear to me why use just 3 districts instead of all districts with high Russian influence due to common border such as Sunny, Khankiv, etc. Probably you should explain more about this variable (fraud) because I think it could have a problem of content validity that might affect your model. I personal believe that you can explain more about the importance of fraud as a control variable in order to give context about the influence of Russia, corruption, etc. Therefore, it would make more sense for someone without background in Ukrainian history, to understand why you included fraud as an alternative to explain changes in voter turnout.
Additionally, I would suggest including a control variable related to political party to control for an historical representation (e.g. conservatives, liberals, progressives). This variable also explains changes in voter turnout. also, you should explaining in detail the graph and the relation there.
In summary it is a friendly blog that I would love to read again.