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Inequality, transaction costs and voter turnout: evidence from Canadian provinces and Indian states

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Abstract

This article tests for the presence of a regularity in the relationship between income inequality and voter turnout in two countries with common Westminster parliamentary systems. We begin by using data from a panel of Canadian provinces to assess two contrasting monotonic hypotheses: conflict theory that predicts a positive monotonic relationship (inequality promoting conflict and greater electoral participation) against relative power theory that predicts a negative monotonic relationship (inequality leading to political alienation and electoral disengagement). Nesting these hypotheses within a rational choice model of voter turnout, we find that neither hypothesis explains the data convincingly while a search across fractional polynomials finds that the relationship is better described as non-monotonic with an inverted U shape. The generality of this finding is assessed by rerunning the analysis on a panel of 14 Indian states. The commonality of results across countries with similar political structures but widely different demographics and stages of development is striking and consistent with the hypothesis that conflict theory operates at low levels of income inequality before growing inequality leads to voter alienation and lower voter turnout. In the Canadian case the tipping point arises at an income Gini of 0.32 while the Indian case peaks at consumption Gini of 0.34.

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Notes

  1. While recent work most often finds a negative relationship arising between turnout and inequality (Ritter and Solt, 2019), it remains unclear whether that finding is specific to the time period chosen and/or the methods used to test the hypotheses. Filette’s overview (2016, p. 72) is that “empirical indications diverge and give rise to competing theoretical arguments to be tested.”

  2. Voter turnout is sometimes defined as the proportion of the eligible population who vote rather than the proportion of those registered who vote in part because there is often more reliable historical data on the population proportion eligible to vote than on voter registrations (Vowles, 2010). When enrollment is not automatic, unlike the Canadian and Indian cases, an explanation of voter turnout needs to deal with the two-stage choice of whether to register and then whether to vote given registration.

  3. In many statements of the turnout problem only the second part of this decision is given serious consideration. Posed this way, the problem of voter turnout encounters the Downsian (1957) voting paradox; why would any individual choose to vote when the likelihood of influencing the election outcome in their favor is close to zero?

  4. The literature has used reasons such as civic duty, expressive voting, and simply the enjoyment of election participation to supplement instrumental voting.

  5. The time period was determined by the availability of annual observations on Gini coefficients in Canada (1976 to 2019). The ten Canadian provinces (from west to east) are British Columbia, Alberta, Saskatchewan, Manitoba, Ontario, Quebec, New Brunswick, Nova Scotia and Newfoundland and Labrador.

  6. The 14 states in our empirical analysis are Andhra Pradesh, Bihar, Gujarat, Haryana, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Odisha, Punjab, Rajasthan, Tamil Nadu, Uttar Pradesh and West Bengal.

  7. For other challenges to monotonicity, see Leonida et al. (2013) who find non-monotonicity (U-shaped) in the relationship between political competition and economic freedom and Leonida et al. (2015) who find the same relationship between political competition and economic growth.

  8. An earlier version of this paper based solely on Canadian federal elections had too few observations to do more than suggest the likelihood of an inverted U shape for the voter turnout/income inequality relationship. The use of annual provincial data here has increased the number of observations tenfold. We note that the panel is unbalanced because the last provincial election is often prior to our ending date and data is sometimes missing on elections prior to 1976.

  9. The time coverage of the panel reflects the current availability of annual observations on the Gini coefficient. The Gini coefficient used in the Canadian tests is based on adjusted total income (as opposed to market or after-tax income).

  10. That is, we treat election values as providing point estimates of an ongoing continuous process of political change.

  11. The simple correlation between voter turnout and Gini coefficient is −0.5848.

  12. In Canada the minimum voting age was lowered from 21 to 18 in 1970 (prior to our starting date) and has since remained at that level.

  13. A number of writers have found that voters respond more to bad versus good economic outcomes. See, for example, Dassonneville and Lewis-Beck (2014) and Dash and Ferris (2021).

  14. The former was found more effective in Canada (Nadeau and Blais 1993 and Ferris and Voia 2015), whereas the absence of unemployment data for Indian states led us to use the growth rate of state per capita income. In response to a question from one of our reviewers, we note that the level of real income and real income per capita are both non-stationary. This implies that their inclusion in our estimated relationship would produce a spurious outcome. More appropriate would be its inclusion in first difference form. This is the way real GDP per capita has been included in the case of Indian states. As mentioned above, unemployment rates are found to be a better indicator of the business cycle in Canada (and are used as such rather than the growth rate of real GDP per capita).

  15. The panel datasets used in these tests are available online at Carleton University’s Dataverse site (Ferris, 2021).

  16. We also explored other nonlinear representations. In the case of Indian states, the inclusion of a cubic term reduces the explanatory power of both inequality and the equation and fp model with three terms indicates that the quadratic representation produces the better fit. In the Canadian case, we used the data to generate a nonparametric fit and found that a nonlinear quadratic fits the data best with no evidence of there being a cubic effect. The nonparametric representation reinforces the presence of an asymmetry in the nonlinearity.

  17. There is a large body of literature that emphasizes the importance of differences in electoral institutions (parliamentary versus presidential; majority versus proportional voting rules etc.) for voter turnout (see writers such as Jackman, 1987, and Blais, 2006). In the case of Canada’s provinces and India’s states, a common election and governance framework—Westminster parliamentary, majority voting, maximum 5-year governing terms—allows the results not to be biased by important institutional differences. In other Westminster governments, such as the UK, Denmark and New Zealand, the absence of subnational provinces or states (at least until recently in the UK) limits the time frame available (and hence the number of useable observations). In Australia, which does have multiple subnational states, compulsory voting makes the comparison of voter turnout numbers problematic.

  18. On the use of consumption versus income inequality, see Attanasio and Pistaferri (2016).

  19. These 14 states cover about 85% of the Indian population and exclude states such as Assam, and Jammu and Kashmir that were subject to subdivision, forms of insurgency and other forms of electoral violence. See Diwakar (2008) for a complementary analysis covering all Indian states using macro-level data and Panda (2019) who uses political-economic data to analyze voter turnout using individual level data.

  20. Dash and Ferris (2021) document a negative relationship between income growth and electoral volatility in Indian states over the 1957–2013 period. Dash et al. (2019) discuss different measures of competitiveness in relation to Indian states.

  21. See footnote 24 and the Data Appendix for detail on how the Gini coefficients were constructed from consumption data.

  22. Data on the state percentage of older population is provided in the Census of India. To extend the data from 2011 to 2018, we used data provided by the World Bank available only at the national level. By assuming that the difference between national and state figures in the 2011 census persists, we generated figures for each state in 2018. Figures for non-census years were generated by interpolating between census years.

  23. There is one important data outlier corresponding to the 1992 election in the Punjab. This election was the first following the imposition of president’s rule in the Punjab to counter a Sikh extremist insurgency. The voter turnout in this election was exceptionally low (24% versus an average of about 65%). A dummy variable for this election was used to minimize this election’s effect on the other results.

  24. Household consumption expenditure survey data in India are available only until 2011–2012. To extend the data to 2018–2019, we used household consumption expenditure collected in the Periodic Labour Force Survey (PLFS) in 2018–2019. Since the survey questionnaire and methodology of collecting consumption expenditure information of PLFS survey is different from that of consumption expenditure of earlier surveys, household consumption expenditures from these surveys are not strictly comparable. Jajoria and Jatav (2020) discuss these differences. However, if we restrict our analysis for the period through 2011–2012, we find results very similar to those in Tables 3 and 4. These results are available on request.

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Acknowledgements

The Canadian and Indian data were collected under 2007 and 2013 grants from the Social Sciences and Humanities Research Council of Canada and form part of a larger project on political competition in Canada and India undertaken with Stanley L Winer. On this and other projects we have benefited from research assistance by Haizhen Mou, Alexandre Couture-Gagnon, Derek Olmstead, Sarah Mohan, Beatriz Peraza, Samira Hasanzadeh and Jerome Archambault. Our thanks go to two referees whose comments structured the revision of this paper.

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Authors and Affiliations

  1. School of Economics, XIM University, Bhubaneswar, India

    Bharatee Bhusana Dash

  2. Department of Economics, Carleton University, Ottawa, Canada

    J. Stephen Ferris

  3. Department of Economics, University of Orleans, Orléans, France

    Marcel-Cristian Voia

  4. Department of Economics, University of Bucharest, Bucharest, Romania

    Marcel-Cristian Voia

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  1. Bharatee Bhusana Dash
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Correspondence to J. Stephen Ferris.

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Appendix on data sources and derivations

Appendix on data sources and derivations

Canadian provinces:

Provincial Gini coefficients of adjusted total income. Statistics Canada Table 11100134. Cansim II v96439638 online https://doi.org/10.25318/1110013401-eng.

Unemployment rates by province Cansim II v2062815, monthly average.

Percentage of population young (20–24) Cansim Table: 17100005 (formerly CANSIM 051-0001).

Online election data by province: Ontario (elections.on.ca); Quebec (electionsquebec.qc.ca); British Columbia (elections.bc.ca); Alberta (elections.ab.ca); Manitoba (electionsmanitoba.ca); Saskatchewan (elections.sk.ca); New Brunswick (elections.nb.ca); PEI (electionspei.ca); Nova Scotia (electionsnovascotia.ca); Newfoundland and Labrador (elections.gov.nl.ca).

Indian states:

State-level Gini coefficients were estimated from household consumption expenditure data collected by The National Sample Survey Office (NSSO) of the Ministry of Statistics and Programme Implementation through periodic nationally representative surveys. Two types of surveys are conducted: quinquennial (or “thick”) rounds done at 5-year intervals on a large sample of households and annual/semi-annual (or “thin”) rounds undertaken during intervening periods on smaller samples. The government has made this household level data available for public use, retrospectively from the 38th round in 1983 (Tables 5, 6). Expenditure information from both thick and thin annual rounds were used to estimate Gini coefficients for the Indian states. The last survey in the public domain is for 2011–2012 (68th round) so to extend the data series after 2011–2012, we have used the monthly per capita consumption expenditure reported in the Periodic Labour Force Survey (PLFS) in 2018–2019. To extend the dataset before 1983, we used the Gini coefficients estimated and provided by Ozler et al. (1996). Two adjustments were made to make their Gini coefficients compatible with ours: (1) Ozler, Datt, and Ravallion estimated Gini coefficients for rural and urban areas separately, whereas we used rural and urban population weights to generate aggregate Gini coefficients; and (2) official reports were used to provide the number of households in different consumption intervals. There are some disparities between the Gini coefficients estimated from household consumption expenditures and consumption expenditure intervals for the common survey rounds. For most of the states, the pattern of disparity goes in the same direction. We used the disparity ratio to revise the Gini coefficients backward and for years when consumption expenditure information is unavailable, Gini coefficients were generated by interpolating between survey rounds.

Election data: Election commission of India and calculation of authors.

Economic data: Central Statistical Organisation (CSO) and Census of India.

The datasets used in the tests are available online at Carleton’s Dataverse site (see Ferris, 2021).

Table 5 Descriptive statistics: Canadian provinces, 1976–2019
Full size table
Table 6 Descriptive statistics: Indian states, 1957–2018
Full size table

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Dash, B.B., Ferris, J.S. & Voia, MC. Inequality, transaction costs and voter turnout: evidence from Canadian provinces and Indian states. Public Choice 194, 325–346 (2023). https://doi.org/10.1007/s11127-022-01035-8

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