ROLE OF AGRICULTURE ON REGIONAL VARIATION OF PARLIAMENTARY ELECTION RESULTS IN LATVIA

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1 ECONOMICS University of Latvia Abstract The main objective of this research is to find out if quantitative factors describing agriculture have a statistically significant role on the parliamentary election results in Latvia. If this statistical relationship can be proven, then it is important to interpret the causality behind it. 18 agricultural indicators from the 2001 Census of Agriculture were selected to be analyzed with the 8 th Saeima political party election results by using multiple linear regression analysis. These 18 parameters are different in their character, and they describe the size of farms, level of education for farmers, land usage statistics, proportion and productivity of certain crops, livestock and the usage of farm machinery. The main hypothesis of this research was that the rural civil parishes with a high intensity of agriculture have a statistically significant difference in election results when compared to the rest of the election results in Latvia. Initial results showed a strong correlation between election results and agricultural indicators, but when the ethnic factor was taken into account in the linear regression model the role of these agricultural indicators was greatly diminished. Key words: electoral geography, Census of Agriculture, multiple regression analysis. Introduction Spatial variation of parliamentary election results in Latvia has been influenced by a variety of factors. These factors differ from each other in the etent of their influence, where some factors have a much greater role than others. For eample, ethnic factor is the most important factor that influences election results (Paiders and Paiders, 2011) and it is more important than urban rural differences that influence the election results in Latvia to a much lesser amount (Paiders and Paiders, 2013). These factors that influence the results of political parties also can be categorized by their impact on the course of many elections. Most of the factors have a steady influence with a relatively small change amongst different elections, but there are some factors whose influence varies over time. For eample, the role of regional candidates has increased after the 10 th Saeima elections, because after that it became impossible to be a candidate in multiple electoral districts and it increased the role of local candidates with limited geographical area of influence. Quantitative research in finding out the full variety of factors that influence Saeima election results in Latvia can be problematic, because of the huge role of the ethnic factor in Latvian politics. The eplained proportion of the dispersion of political party results this factor eplains is so large that it is complicated to gain statistically significant results about other factors that influence the election results. The main objective of this research is to find out if quantitative factors describing agriculture have a statistically significant role on the parliamentary election results in Latvia and if this statistical relationship can be proven, then it is important to interpret the causality behind it. Agricultural indicators were used from the 2001 Census of Agriculture (Centrālā Statistikas Pārvalde, 2003) where a total of 176 parameters were obtained. These results were analyzed on a local level (civil parishes) comparing them with the 8 th Saeima election results (atsauce) because these elections were the closest in terms of time to the 2001 Census of Agriculture. The reason why newer agricultural data was not used is because 2001 was the last year of agricultural census when the results were published on a civil parish level. The 2010 Census of Agriculture was published on a county level which vastly reduces the size of the analyzed data. The main hypothesis of the research was that rural civil parishes with a high intensity of agriculture have a statistically significant difference in election results when compared to the rest of the election results in Latvia. Materials and Methods The study analyzes the officially approved results of the elections of the 8 th, 9 th, 10 th and 11 th Saeima (parliament) of the Republic of Latvia (Centrālā Vēlēšanu Komisija, 2011). The results of all political parties participating in the parliamentary elections were acquired and analyzed, but for parties having overcome the 5% threshold the level of analysis was more detailed. In the 8 th Saeima elections 6 political parties overcame this threshold: For Fatherland and Freedom/LNNK (TBLNNK), For Human Rights in United Latvia (PCTVL), New Era Party (JL), People s Party (TP), Latvia s First Party (LPP) and Union of Greens and Farmers (ZZS). In this paper abbreviations for these parties were used. Only the rural parishes where there was agricultural census data available were included in the research with a total of 481 analyzed civil parishes. From the 176 obtained parameters describing agriculture only 171

2 18 were selected to be analyzed with the political party election results in order to reduce the collinearity and similarity of the agricultural parameters. These 18 parameters differ in their character and they describe the size of farms, level of education for farmers, land usage statistics, proportion and productivity of certain crops, livestock and the usage of farm machinery. In this research, the result of every political party who overcame the 5% threshold in the 8 th Saeima elections was analyzed with those 18 parameters by using multiple linear regression analysis. This analysis allows to find the part of the dispersion (R 2 ) of the results of a political party that can be eplained by these 18 parameters only factoring in the ones with a statistically significant correlation. This analysis also weighs in the collinearity of between these 18 factors and ecludes it from the model. After the initial analysis, the results of 6 political parties in the 8 th Saeima elections were obtained, and a new model was tested which used 18 initial agricultural parameters and the proportion of Latvians in rural parishes from the 2000 census data (Centrālā Statistikas Pārvalde, 2010). Therefore, this analysis allowed to test whether the previously obtained results showed a believable and statistically significant relation between election results and agricultural factors (this model ecludes collinearity with the ethnic factor) or this relationship just eplains that both the election results and agricultural factors are related to the ethnic composition of Latvia. Results and Discussion The obtained results showed that 18 agricultural parameters have a varying degree of influence on the Table 1 Summary of multiple linear regression analysis results of political parties in the 8 th Saeima elections Analyzed parameter Analyzed political party in 8 th Saeima elections TBLNNK PCTVL JL TP LPP ZZS average farm size (ha) % of farms producing agricultural products % of farm owners with a higher education in agriculture % of farm owners with a practical eperience in agriculture Average size of forest land (ha) in farms Meliorated farmland (ha) per 1 farm % of meadows from total used land % of pastures from total used land 5 10 ha sized farm % of total used land ha sized farm % of total used land % of potatoes from total harvested land % of harvested land from total used land % of industrial plants from total harvested land Cattle per 1 farm Pigs per 1 farm Sheep per 1 farm Poultry per 1 farm Motor mowers per 1 farm Total number of factors Dispersion eplained by the model (R 2 ) 4.1% 48.7% 53.0% 53.4% 17.5% 14.5% 172

3 results of political parties (Table 1). For eample, TBLNNK election was not at all influenced by these factors and only showed a minor positive correlation between their election result and the usage of farming machinery (described with the number of motor mowers per 1 farm). From all political parties in the 8 th Saeima, TBLNNK relationship with agricultural factors is the smallest, accounting only for 4.1% dispersion of their results. For PCTVL, their election results were statistically significantly related to 7 of the analyzed parameters. The main factor that influenced their election results in 2002 was 5 10 ha sized farm percentage of total used land (Figure 1). This parameter eplains the role of small farms in the rural parish and these kinds of farms have the highest role in Latgale electoral district. In Latgale 5 10 ha sized farms compose about 25% of total used land, which is more than 2 times larger than in the rest of rural Latvia. It also eplains why the correlation for this parameter with PCTVL election results is positive because PCTVL had their largest support (more than 27%) in rural parishes in Latgale, at the same time they had less than 5% of the vote in the rest of rural Latvia. This linear regression with PCTVL alone is quite significant (R 2 = 36%). JL election results in the 8 th Saeima election had the largest number of agricultural parameters (13) with whom it had a statistically significant relationship, accounting for about 53% dispersion of their results. The main agricultural parameter that influenced JL election results is the same as PCTVL, 5 10 ha sized farm percentage of total used land, alone eplaining more than 25% dispersion of the parties results. The main difference between JL and PCTVL election results lies in the fact that the JL correlation with this parameter is positive (JL had less than 10% of the vote in rural Latgale while 25% in the rest of rural parishes in Latvia). Most of the 13 agricultural parameters that influenced JL election results are similar in a way that they have a contrast in their values between Latgale and the rest of Latvia. In comparison, TP election result is only significantly influenced by 6 agricultural factors, while the multiple regression model is almost equally strong as for JL, eplaining 53.4% dispersion of the results. The main parameter that influences TP election results also is the same as for JL and PCTVL. The main difference for JL in the number of significant factors can perhaps be eplained by the fact that even though the Latgale and the rest of Latvia contrast is strong for TP 8 th Saeima election results, they also have a noticeable variation of their results outside Latgale (which is not that noticeable in many analyzed agricultural parameters), obtaining their highest result in Zemgale electoral district. LPP and ZZS 8 th Saeima election results were to a lesser etent influenced by the analyzed agricultural factors eplaining 17.5% dispersion of LPP election results and 14.5% of ZZS dispersion. For LPP the main parameter that influenced their results also were 5 10 ha sized farm % of total used land showing a small but statistically significant positive correlation. ZZS election result was mostly influenced by parameter describing average farm size (ha) showing a small positive correlation (R 2 =10%). This relation can be eplained with higher ZZS election results in Kurzeme rural parishes (especially around Ventspils city) where this political party had their strongest election results. This factor also increased its role in later elections (in 2010 and 2011) when their election result around Ventspils area was even higher. The reason why ZZS and LPP are less influenced by these agricultural factors than JL, TP and PCTVL is probably because ZZS and LPP have a much smaller contrast in their election results between Latgale and the rest of Latvia. For eample, ZZS managed to gain 16% of the vote in rural Latgale which is only a fraction smaller than their result in the rest of rural Latvia (17.5%). The obtained results for analyzed parties (Table 1.) show that the highest influence on election results have those parameters that are describing the farm structure in a parish, not the ones describing the agricultural specialization in the farms. These highly influential parameters are more related to the wealth and standard of living in a parish. That can be described with parameters related to intensity of farm machinery or role of small (5 10 ha) farms. It turns out that the type of crop grown in the farms (agricultural specialization) or the type of livestock mostly used in farms has almost no noticeable effect on election results. It must be noted that this multiple regression analysis for agricultural parameters also produces largely similar results if used on political party results in recent elections. Election results of Harmony Centre in the 9 th Saeima election are equally influenced by these 18 agricultural parameters, as are PCTVL in the 8 th Saeima elections, eplaining 44% of dispersion and having almost the same parameters with a statistically significant correlation. The same is true for other analyzed political parties and also for the 10 th and 11 th Saeima elections. The reason this model works for latter elections is probably related to the fact that political parties largely keep their voters and spatial characteristics of their results in many elections with a relatively slow change over time. The rural Latgale and rest of rural Latvia contrast is also present in other elections. Of course, it would be more relevant to compare the 10 th and 11 th Saeima elections with the 2010 Census of Agriculture, 173

4 but these results have not been obtained in the parish level of detail. If the parameters in multiple linear regression model also include the percentage of Latvians in 2000 then the obtained results have a completely different meaning. For all political parties in the 8 th Saeima elections, the eplained dispersion increased, but it was because of the inclusion of proportion of Latvians in the model. Meanwhile, the role of agricultural factors in the model was greatly reduced. For eample, 18 analyzed agricultural factors account for 49% of the PCTVL 8 th Saeima election results and with the proportion of Latvians (Figure 2.) included in the model this new model now eplained almost 81% dispersion of the party results. But in the new model, the proportion of Latvians alone eplained 74% of the PCTVL election results which left only 7% of the eplained dispersion for the analyzed agricultural factors. Smaller but also similar differences between these models are true for the rest of analyzed 8 th Saeima political parties. This means that ethnic factor not only has a large correlation with the election results but also with many agricultural parameters. Therefore, most of the relation election results have to agricultural parameters is indirect. The reason why a proportion of Latvians has a relation to certain agricultural parameters is probably caused by historical development of land use characteristics in Latvia. It also must be noted that even though agricultural factors are related to election results to a much lesser etent than the first model showed, it still is a statistically significant factor that has an influence over the election results and it must be included into a more detailed quantitative about election results. The most important factor, that has a relation to the election results, is the parameter describing the role of 5 10 ha sized farms (Figure 1). If compared to the proportion of Latvians in 2000, the correlation between them is relatively strong, eplaining a statistically significant part of the dispersion. The main similarities are in the Latgale and the rest of Latvia contrast that persists in both parameters. This contrast is so severe that even though these parameters have a different composition in the rest of Latvia, this contrast makes their correlation with each other strong. By conducting cartographic analysis of the Irish parliamentary election of 2002, a significant difference was found in the election activity between urban and rural areas, with city dwellers showing a lower activity in the parliamentary election. It has been eplained both by stronger sense of community in rural populations, and by the high proportion of Figure ha sized farm % of total used land. Data: Central Statistics Bureau of Latvia 174

5 Figure 2. Proportion of Latvians in 2000 census. Data: Central Statistics Bureau of Latvia senior citizens in the rural regions of Ireland, with this group of population being more politically-active (Kavanagh et al., 2004). In the eample of Italian election results, a major role in their geographical dispersion is played by the uneven spatial distribution of various developmental factors on a regional level in the country. The results of the Italian Christian Democracy party in the period from 1953 to 1987 show that the standard deviation of the party s results is much greater between regions on a countrywide scale than between provinces on a regional scale (Agnew, 1996). However, the author of this study notes at the end of the article that there are several limitations in applying the neighborhood effect to interpretation of the results. Geographical research with the application of scalar field properties is limited by several factors. One of them is the modifiable areal unit problem (MAUP). Gehlke and Biehl (1934) discovered that the correlation coefficient is sensitive to scale changes of the eamined territories. Openshaw and Taylor (1979) began using the term MAUP for the investigation and assessment of this problem. The focus has largely been placed on research of how spatial models are affected by scale changes (Fotheringham and Wong, 1991; Briant et al., 2010, etc.). This work is a part of electoral geography, but the geographical factors which have an impact on election results are different between countries. In case of Turkey parliamentary elections, it was found that four major divisions are shaping the electoral geography: religion, ethnicity, regional economic prosperity, and previous state association (West, 2005). In Latvia, out of three possible levels on which electoral geography can be researched local, regional, and national (Krampe, 2005) election results have most often been eamined at the national (electoral district) level. Electoral geography has been relatively little researched at the academic level in Latvia, with studies that deal with the spatial distribution of parliamentary election results giving it very little attention (How Democratic Is Latvia, 2005). Studies of electoral geography have also eamined the behavior of specific voter groups in relation to geographical factors, with groups being formed by ethnicity, race, income level, etc. (Groffman and Handley, 1989; McLaughlin, 2008). The influence of the ethnic composition on parliamentary election results in Latvia has already been discussed in previous publications of the authors (Paiders, 2012; Paiders and Paiders, 2011). Often, studies in the field of electoral geography focus specifically on 175

6 eamining the electorate of radical political forces, including research of its spatial dispersion (Aleseev, 2006; Stefanova, 2009; O Loughlin et al., 1994). When evaluating the results of other countries, the differences in the political systems of these countries in comparison to Latvia must be taken into account. Conclusions Multiple linear regression analysis allows to measure the impact on election results for many potentially influential parameters allow to create a model that also takes into account the collinearity of these analyzed parameters; and the relationship and collinearity between many parameters from the agricultural census is too large not be noticed. 18 agricultural parameters that were used in the multiple regression model allowed to eplain a considerable part of the dispersion of the 8 th Saeima election results. For TBLNNK, LPP and ZZS their relation to the analyzed parameters in the model can be considered small, accounting for less than 20 % of dispersion of their results, while for PCTVL, JL and TP the model was more relevant, accounting for more than 48% of their results. The main reason why certain political party results in the 8 th Saeima had more significant results in their relation to the analyzed agricultural parameters are perhaps connected with the gap in the election results between rural Latgale and the rest of rural Latvia. If this gap is large then the connection with the agricultural parameters is strong but if the election results are more equally distributed amongst the electoral districts then these 18 parameters have a small influence. The 2001 agricultural census data can be used to analyze later elections (even in 2010 and 2011) mostly because the political parties tend to keep their voters and spatial characteristics in many elections. The importance of agricultural factors is greatly reduced if the proportion of Latvians is included in the model. For PCTVL in 2002, agricultural factors together with the ethnic factor eplained almost 81% dispersion of the party results. But in the new model the proportion of Latvians alone accounted for 74% of the PCTVL election results which left only 7% of dispersion eplained by agricultural factors. Before the ethnic factor was included in the parameters, agricultural factors accounted for 48% on PCTVL results. For other political parties this tendency is also similar. References 1. Agnew J. (1996) Mapping politics: how contet counts in electoral geography. Political Geography, 15 (2), pp Aleseev M. (2006) Ballot-Bo Vigilantism. Ethnic Population Shifts and Xenophobic Voting in Post- Soviet Russia. Political Behavior. 28 (3), pp Briant A., Combes P.P., Lafourcade M. (2010) Dots to boes: Do the size and shape of spatial units jeopardize economic geography estimations? Journal of Urban Economics Vol 67, (3), May 2010, pp Centrālā Statistikas Pārvalde (2003) Latvijas gada lauksaimniecības skaitīšanas rezultāti. (Central Statistics Bureau of Latvia, Results of the 2001 Agricultural Census in Latvia). Riga (in Latvian). 5. Centrālā Statistikas Pārvalde (2010) gada tautas skaitīšana. (Central Statistics Bureau of Latvia, 2000 Population Census). Available at: html, 23 January (in Latvian). 6. Centrālā vēlēšanu komisija (2011) Saeimas vēlēšanas (Central Election Commission, Saeima elections). Available at: 27 October (in Latvian). 7. Fotheringham A.S., Wong D.W.S. (1991) The modifiable areal unit problem in multivariate statistical analysis. Environment and Planning A. Vol. 23(7) pp Grofman B., Handley L. (1989) Black Representation: Making Sense of Electoral Geography at Different Levels of Government. Legislative Studies Quarterly, 14 (2), pp Stratēģiskās analīzes komisija (How Democratic is Latvia: Audit of Democracy). (2005) LU Akadēmiskais apgāds, Rīga, 309 lpp. (in Latvian). 10. Kavanagh A., Mills G., Sinnott R. (2004) The geography of Irish voter turnout: A case study of the 2002 General Election. Irish Geography, 37 (2), pp Krampe A. (2005) Latvijas elektorālie procesi politiskajā ģeogrāfijā (Electoral proceses in political Geography in Latvia). Ģeogrāfija. Ģeoloģija. Vides zinātne: Referātu tēzes, Latvijas Universitāte, Rīga, lpp. (in Latvian). 12. McLaughlin E. (2009) Racial, Ethnic or Rational Voters. Splitting Tickets in South Africa. Politikon. 35 (1), pp Paiders J., Paiders J. (2011) Nacionālā sastāva ietekme uz 10. saeimas vēlēšanu rezultātu ģeogrāfisko sadalījumu (Ethnic composition influence on 10 th Saeima Election results). Ģeogrāfija. Ģeoloģija. Vides zinātne: Referātu tēzes, Latvijas Universitāte, Rīga, lpp. (in Latvian). 176

7 14. Paiders J., Paiders J. (2013) EU Border Proimity Effect On Political Choice In Parliamentary Election Of Latvia. Regional formation and development studies. 1 (9) Klaipeda: Faculty of Social Sciences Klaipėda University. pp O Loughlin J., Flint C., Anselin L. (1994) The Geography of the Nazi Vote: Contet, Confession and Class in the Reichstag Election of Annals of the Association of American Geographers, 84 (3), pp Stefanova B. (2009) Ethnic nationalism, social structure, and political agency: eplaining electoral support for the radical right in Bulgaria. Ethnic and Racial Studies. 32 (9) pp West J.W. (2005) Regional cleavages in Turkish politics: An electoral geography of the 1999 and 2002 national elections Political Geography Volume 24, Issue 4, pp